ESPÉRANCE DE VIE ET ASSURANCE-VIE GÉNÉTIQUEMENT PRÉVUES\nÉVALUATION\nCONTEXTE DE L'INVENTION\n[0001] Traditionnellement, le marché de l’assurance vie offrait des alternatives limitées à un preneur d’assurance qui souhaitait se départir de ses polices actuelles. En règle générale, le titulaire de la police cède la police et reçoit les liquidités énumérées dans les valeurs de déchéance de la police ou laisse celle-ci expirer et reçoit une couverture d'assurance supplémentaire sous la forme d'une assurance temporaire supplémentaire, aussi longtemps que les valeurs de rachat le permettent. Ces valeurs de non-confiscation sont au mieux minimales. Avant les lois types sur la non-confiscation, qui prévoient désormais le calcul de valeurs minimales, l’absence de péremption empêchait l’assuré de ne rien recevoir du tout. Cette forme classique du marché de l’assurance est un monopsone avec la dynamique de marché d’un acheteur, la compagnie d’assurance, qui fait face à de nombreux vendeurs, les preneurs d’assurance, ce qui entraîne un pouvoir de fixation des prix considérable pour les compagnies d’assurance. Cette situation s'apparente à un monopole dans lequel un seul vendeur est confronté à de nombreux acheteurs. Les assureurs en place appliquent une tarification à la monopsone à celle des assurés. Toutefois, la valeur intrinsèque d’un contrat d’assurance-vie dépasse toujours la valeur de rachat offerte à l’assuré. En raison de cette dynamique de marché, un marché secondaire a évolué, appelé marché de règlement à vie.\n[0002] Dans le marché de règlement à vie, un tiers soumissionnaire achète la police auprès du titulaire de la police et en devient le titulaire remplaçant, avec les mêmes droits de propriété que le titulaire initial. Les tiers propriétaires sont généralement disposés à payer beaucoup plus au titulaire initial du contrat qu'à la compagnie d'assurance monopsone. Le marché de l'assurance secondaire, cependant, est extrêmement inefficace pour évaluer les transactions sur polices. Les propriétaires remplaçants sont des acheteurs financiers qui versent au propriétaire initial davantage que les autres soumissionnaires et qui perçoivent les indemnités de décès sous forme de rendement financier.\n[0003] Il est utile de comprendre le rôle des participants dans le processus de transaction de stratégie. La personne assurée est la personne dont la vie est couverte par la police considérée et qui est généralement le titulaire initial de la police. Habituellement, le\n La personne assurée est le vendeur de la police dans la transaction, bien que, après la transaction de règlement initial, le vendeur puisse alors être tout titulaire de police successif. Un conseiller, tel qu'un conseiller financier ou un agent d'assurance, agit généralement en tant que consultant pour conseiller le vendeur sur les solutions de remplacement disponibles. Les offres générées pour les contrats d'assurance vie peuvent être appelées offres de règlement en vie. Un courtier est la personne responsable des achats, sollicite plusieurs soumissionnaires et travaille de préférence avec quatre à cinq soumissionnaires, appelés fournisseurs de règlement à vie. Un fournisseur de vie-règlement est l'entité qui formule l'offre d'achat et la transmet aux courtiers. Les fournisseurs de colonies de vie peuvent souscrire des polices pour leur propre compte ou pour d'éventuels investisseurs économiques en aval. Un fournisseur d'espérance de vie est une société de services spécialisée qui examine les dossiers médicaux afin de fournir des estimations de souscription de l'espérance de vie de l'assuré au fournisseur de règlement vie pour la formulation de l'offre. Les investisseurs financent généralement les prestataires de règlement vie (par exemple, par l’intermédiaire de fonds de couverture, de banques d’investissement). Dans certains cas, les investisseurs peuvent créer leur propre fournisseur interne. Parfois, les investisseurs peuvent être des fiducies qui émettent des obligations (à leurs détenteurs) sous forme de titres dérivés. Ces obligations financent les acquisitions de polices et sont remboursées par le règlement des polices acquises.\n[0004] Initialement, le titulaire de la police ou le client peut consulter un conseiller afin de décider de la vente de sa police. Le client et le conseiller peuvent travailler ensemble pour décider si un courtier sera impliqué dans la transaction ou s'ils iront directement aux fournisseurs. Le client et le conseiller peuvent soumettre la police pour évaluation et le propriétaire de la police publie des informations médicales. Les fournisseurs de colonies de vie ordonnent ensuite un rapport d’espérance de vie auprès des fournisseurs d’espérance de vie afin d’accéder au risque associé à la transaction proposée. Ce rapport examinera les antécédents médicaux de l'assuré pour voir si la police répond aux critères de soumission. Si la politique répond aux critères d'un règlement viager, le fournisseur peut ensuite envoyer des offres directement au client ou au client par l'intermédiaire d'un courtier. Voici quelques exemples de critères pour un règlement viager: 1) si la personne assurée a une espérance de vie limitée en raison d'un âge avancé ou de problèmes de santé, 2) la police est transférable et est en vigueur pour une période allant au-delà de la contestabilité\n période, 3) la police est émise par une compagnie d’assurance américaine, et 4) un capital-décès d’au moins 50 000 $ est associé à la police. À ce stade, le client et le conseiller peuvent examiner les offres et le client peut accepter une offre préférentielle. Le client et le conseiller peuvent compléter le dossier de clôture du fournisseur et renvoyer les documents essentiels. Le fournisseur peut placer en encaissement le paiement en espèces pour la police et soumettre des formulaires de changement de propriété à la compagnie d’assurance. Les documents peuvent être vérifiés et les fonds transférés au vendeur de police.\n[0005] Tout type de police d'assurance-vie peut être acheté lors d'une transaction, telle que la vie universelle, la vie temporaire, la vie entière ou la vie de survie. Le titulaire du contrat peut être un ou plusieurs particuliers, une fiducie, une société ou une organisation à but non lucratif, une banque ou une autre institution financière, une société à responsabilité limitée, une société de personnes ou une autre entité commerciale. La valeur nominale d'une police d'assurance fournit une valeur maximale à partir de laquelle la valeur de rachat est déterminée. Pour une personne de santé normale, une courbe de survie est générée par l'analyse de l'âge par rapport à la valeur de la politique, le point de départ étant l'âge de l'achat de la police et le résultat final étant prédite par l'espérance de vie estimée d'un individu de «santé normale». et se situe à l'âge de décès prédit, où la valeur économique de la politique est égale à la valeur nominale réelle de la politique. Cette courbe de survie fournit une représentation graphique de la valeur économique de la police d'assurance sur le marché secondaire de l'assurance. La connaissance supplémentaire des conditions médicales d'un individu permet une plus grande précision dans la prévision de l'espérance de vie, mais à ce jour, les applications générales reposent uniquement sur les dossiers médicaux et les antécédents familiaux. Lors de l'examen des dossiers médicaux, la valeur d'une politique individuelle sur le marché secondaire peut se situer en dehors de la courbe de survie «santé normale» si cette personne est en bonne santé ou en mauvaise santé.\n[0006] La valeur de rachat d'une police d'assurance-vie est déterminée au moment de l'émission et est basée sur des données de mortalité standard entièrement souscrites. Ces valeurs sont définies et ne changent pas lorsque l'état de santé du titulaire de la police change. La valeur des règlements viagers est déterminée au moment du règlement et est basée sur les\n la mortalité altérée au règlement, l'espérance de vie, selon l'estimation du fournisseur d'espérance de vie, et le taux de rendement, l'horizon temporel et la tolérance au risque requis des acquéreurs financiers successifs. Ces valeurs sont définies par les sociétés de règlement à vie et varient en fonction du niveau de dépréciation du titulaire de la police. L’espérance de vie de l’assuré est cruciale pour la formation d’une offre d’entreprise de règlement à vie. À ce jour, ces offres de règlement vie sont basées sur une souscription vie conventionnelle et utilisent des dossiers médicaux.\n[0007] L'évaluation traditionnelle des polices d'assurance-vie n'a pas de valeur prédictive et, comme indiqué ci-dessus, repose sur des informations historiques (par exemple, dossiers médicaux, antécédents médicaux familiaux et habitudes de vie). Les méthodes décrites dans le présent document tiennent compte des raisons sous-jacentes ayant une incidence sur l'espérance de vie et non prises en compte actuellement par les acheteurs, les vendeurs et les investisseurs Il existe un marché et un besoin d'amélioration de la précision d'évaluation des polices d'assurance-vie.\n[0008] Le séquençage du génome humain a permis de mieux comprendre les bases génétiques de la maladie et de la mortalité humaines, deux facteurs importants de l'espérance de vie. Cela a également permis de mieux comprendre les causes génomiques sous-jacentes des différences qui surviennent entre les personnes en réponse à leur environnement. Plusieurs modifications génomiques (telles que les variations du nombre de copies) et des modifications structurelles à petite échelle (telles que les inversions et les délétions) ont été impliquées dans la pathologie de la maladie. Par exemple, les modifications d'un seul nucléotide dans des positions spécifiques du génome humain, appelées polymorphismes d'un nucléotide simple (SNP), ont un effet sur les différences phénotypiques observées entre les individus. Les différences entre les SNP peuvent influer sur la vulnérabilité des individus aux facteurs environnementaux, tels que le tabagisme, et sur leur probabilité de réagir aux interventions médicales. Les SNP sont l'un des facteurs qui affectent la prédisposition génétique d'un individu à développer une certaine maladie et peuvent également être prédictifs de la mortalité d'un individu due à une maladie. \n [0009] Les progrès récents en matière de technologie de génotypage à grande vitesse ont permis à la communauté scientifique de progresser dans l'identification et la validation de nombreux polymorphismes génétiques courants associés au risque de maladie.\n[00010] Depuis 1977, la méthode de Sanger est la méthode choisie pour les études de séquençage de l’ADN, y compris le projet du génome humain. Cependant, au cours des dernières années, un certain nombre de technologies de séquençage ne s'appuyant plus sur la méthode de Sanger et présentant des améliorations dans les domaines fondamentaux de longueur, de débit et de coût de lecture (Chan. 2005. Mutation Research. 573: 12-40 Lander et al., 2001. Nature 409: 860-921, Shaffer, 2007. Nature Biotechnology 25 (2): 149; Nature Methods, janvier 2008. 5 (1)). Des exemples de ces techniques incluent: la technologie de pyroséquençage de 454 Sciences de la vie; technologie de polymérisation-colonies développée par Solexa, Inc. et actuellement détenue et commercialisée par Illumina, Inc .; et séquençage par ligature, développé par Agencourt Bioscience Corp., qui constitue désormais la base des séquenceurs du système SoLID d’Applied Biosystems; et le séquençage d'une molécule, tel que celui développé et commercialisé par Helicos Biosciences.\n[00011] Par rapport au coût du projet du génome humain, les technologies ci-dessus peuvent séquencer le génome humain pour beaucoup moins cher. Des technologies (telles que celles proposées par Helicos Biosciences, Pacific Biosciences et Oxford Nanopore Technologies) ont démontré la capacité de réduire davantage ce coût.\n[00012] Les matrices SNP peuvent être utilisées pour profiler plusieurs centaines de milliers à un million de marqueurs SNP pour un individu donné à un coût raisonnable. Ces tableaux sont utilisés pour étudier la variation génétique dans l'ensemble du génome. Une société de génétique personnelle, 23andMe, a dévoilé un tableau qui génotypera près de 600 000 SNPs pour 399 $. Les coûts de séquençage diminuent considérablement chaque année, ce qui diminue le coût du séquençage du génome.\n[00013] Plusieurs approches ont été proposées pour caractériser la contribution de la génétique à la susceptibilité aux maladies et à la longévité ou à la durée de vie.\n Kenedy et al., (2008/0228818), décrit dans son intégralité ici une méthode, un logiciel, une base de données et un système de bioinformatique dans lesquels les profils d'attribut d'individus positifs d'attribut requête et d'attributs négatifs sont comparés. Voir également les demandes de brevet US n ° 2008/0076120, 2007/0259351, 2007/0042369, 2008/0228772, 2008/0187483, 2003/0040002, 2006/0068432, 2008/0131887, 2008/0195327, les brevets américains n ° 7 406 453 et 6 653 073. , Publication internationale n ° WO 2004/048591, WO 2004/050898, WO 2006/138696, WO 2006121558, WO 2007127490. Ces sources n'expliquent pas la capacité de préparer une méta-analyse des données disponibles sur une multitude de gènes et variantes génétiques et corréler ces données collectives pour déterminer une espérance de vie en relation avec l’évaluation des polices d’assurance vie.\n[00014] La contribution génétique à l'espérance de vie est multiplicative sur l'échelle de risque, comme l'attend le nombre important de traits héréditaires transmis de génération en génération (Risch. 2001. Cancer Epidemiology Biomarkers & Prevention. 10: 733-741). Cependant, la capacité de détecter les interactions entre les allèles à risque est limitée en raison de la taille des échantillons des études épidémiologiques en cours. Par conséquent, la présente invention propose une nouvelle approche pour intégrer les données d'études épidémiologiques de manière utile, par rapport à la prédiction personnalisée du risque génétique et à la prédiction personnalisée de l'espérance de vie. Cette approche est démontrée dans des modes de réalisation de la présente invention.\nRésumé de l'invention\n[00015] La présente invention concerne un procédé d'utilisation d'un appareil de base de données central pour évaluer une police d'assurance-vie pour un membre d'une population. L'appareil de base de données central contient une base de données génétique et une base de données sur l'espérance de vie. Le procédé d'évaluation de politique comprend: a) l'identification d'au moins un gène candidat; b) utiliser un appareil de récupération adapté pour récupérer de la littérature afin de collecter une littérature contenant des données de risque relatives aux données du gène candidat et de l'espérance de vie; d) télécharger les données de risque de la littérature recueillie dans la base de données génétiques; e) télécharger les données d'espérance de vie tirées de la littérature recueillie dans la base de données sur l'espérance de vie; g) en utilisant un ordinateur pour calculer un\n indice de risque collectif basé sur les données de risque téléchargées et les données d'espérance de vie téléchargées; h) collecte des données d'entrée du membre de la population; i) en utilisant les données d'entrée collectées et l'indice de risque collectif calculé pour déterminer une espérance de vie génétiquement prédite (GPLE) pour le membre; et j) évaluer la police d'assurance-vie sur la base du GPLE.\n[00016] Dans un autre mode de réalisation, la présente invention fournit un procédé pour évaluer les niveaux de prime de police d'assurance-vie pour une population dans un appareil de base de données central, comprenant les étapes consistant à: a) identifier au moins un gène candidat; b) utiliser un appareil de récupération adapté pour récupérer de la littérature afin de collecter une littérature contenant des données de risque relatives aux données du gène candidat et de l'espérance de vie; d) télécharger les données de risque de la littérature recueillie dans la base de données génétiques; e) télécharger les données d'espérance de vie tirées de la littérature recueillie dans la base de données sur l'espérance de vie; g) utiliser un ordinateur pour calculer un indice de risque collectif basé sur les données de risque téléchargées et les données d'espérance de vie téléchargées; h) collecte des données d'entrée du membre de la population; i) en utilisant les données d'entrée collectées et l'indice de risque collectif calculé pour déterminer un GPLE pour le membre; et j) évaluer la valeur de la prime de la police d’assurance vie sur la base du GPLE.\n[00017] La présente invention concerne également un système d'évaluation d'une police d'assurance-vie pour un membre d'une population. Dans ce mode de réalisation, le système comprend un serveur informatique et un appareil de base de données central, cet appareil comprenant une base de données génétique et une base de données d'espérance de vie, et le serveur étant configuré pour: a) inviter un utilisateur à identifier au moins un gène candidat; ; b) invite l'utilisateur à rassembler des ouvrages contenant des données de risque relatives à au moins un gène candidat et des données d'espérance de vie; c) télécharger les données de risque de la littérature recueillie dans la base de données génétiques; d) télécharger les données d'espérance de vie tirées de la littérature recueillie dans la base de données sur l'espérance de vie; e) calculer un indice de risque collectif basé sur les données de risque téléchargées et les données d'espérance de vie téléchargées; f) invite l'utilisateur à fournir des données d'entrée relatives au membre de la population; g) utiliser les données d'entrée fournies et le collectif calculé\n indice de risque pour déterminer un GPLE pour le membre; et h) évaluer la police d'assurance-vie en fonction du GPLE déterminé.\n[00018] Dans un autre mode de réalisation, les données d'entrée comprennent un échantillon biologique collecté à partir de l'élément. Dans ce mode de réalisation, l'échantillon biologique contient de l'ADN génomique.\n[00019] Dans un autre mode de réalisation, une séquence d'ADN génomique est isolée de l'échantillon biologique du membre. Dans encore un autre mode de réalisation, un gène candidat est contenu dans la séquence d'ADN génomique isolée.\n[00020] La présente invention concerne en outre un procédé permettant d’utiliser le profil génomique d’un individu pour évaluer sa police d’assurance vie en 1) obtenant un échantillon biologique de l’individu, 2) déterminant la séquence génomique à partir de l’échantillon biologique, 3) mettant en corrélation la séquence génomique avec la base de données centrale contenant les données de risque génétique et d'espérance de vie, 4) le calcul d'un GPLE pour l'individu et 5) l'évaluation de la police d'assurance-vie pour l'individu sur la base de la GPLE ou la détermination des niveaux de prime d'un contrat d'assurance-vie pour l'individu sur la base de la GPLE.\n[00021] Dans un autre mode de réalisation, la police d'assurance-vie est catégorisée sur la base du GPLE.\n[00022] Dans d’autres modes de réalisation de la présente invention, des facteurs supplémentaires peuvent être utilisés pour évaluer la valeur d’une police d’assurance vie, tels que des marqueurs génétiques, des antécédents médicaux, des habitudes personnelles, des habitudes d’exercice, des habitudes alimentaires, des habitudes de santé, des habitudes sociales, des expositions professionnelles, des expositions environnementales. le même. Dans un mode de réalisation, les marqueurs génétiques peuvent être choisis parmi des mutations ponctuelles d'ADN, des mutations de décalage de cadre d'ADN, des délétions d'ADN, des insertions d'ADN, des inversions d'ADN, des mutations d'expression de l'ADN, des modifications chimiques de l'ADN, etc. Dans un autre mode de réalisation, les marqueurs génétiques peuvent être des polymorphismes mononucléotidiques (SNP). \n [00023] Dans un autre mode de réalisation, les antécédents médicaux comprennent des informations relatives à une maladie manifestée, un trouble, une condition pathologique et / ou une séquence d'ADN génomique.\n[00024] Dans un autre mode de réalisation de la présente invention, l'indice de risque collectif peut être un risque relatif, un rapport de risque ou un rapport de cotes. Dans un mode de réalisation préféré, l'indice de risque collectif est un rapport de cotes de méta-analyse.\n[00025] Dans encore un autre mode de réalisation, l'appareil de base de données central est mis à jour de manière itérative avec des données de risque et des données d'espérance de vie supplémentaires.\nDESCRIPTION BRÈVE DES DESSINS\n[00026] FIGUE. 1 est un exemple d'interface de fenêtre d'affichage permettant de rechercher de la littérature dans une base de données.\n[00027] FIGUE. 2 est un exemple d'interface de fenêtre d'affichage permettant de rechercher des résumés dans une base de données.\n[00028] FIGUE. 3 est un organigramme illustrant des aspects des procédés décrits ici.\n[00029] FIGUE. 4 est un exemple de champs de données liés aux gènes candidats et à la maladie.\n[00030] FIGUE. 5 est un organigramme illustrant des aspects des procédés décrits ici.\n[00031] FIGUE. 6 est un organigramme illustrant des aspects des procédés décrits ici.\n[00032] FIGUE. 7 est un exemple de courbe de survie calculée en relation avec l'exemple 4.\nDESCRIPTION DÉTAILLÉE\n[00033] La présente invention concerne des procédés, des systèmes informatiques et des bases de données permettant d’évaluer et d’évaluer les polices d’assurance vie d’une population en fonction de facteurs tels que l’information génétique, les antécédents médicaux, les habitudes personnelles, les habitudes d’exercice, les habitudes alimentaires, les habitudes sociales et les habitudes. Divulgué ici sont\n bases de données, ainsi que des systèmes permettant de créer des bases de données et d’y accéder, décrivant ces facteurs pour les populations et permettant d’effectuer des analyses en fonction de ces facteurs. Les méthodes, systèmes informatiques et logiciels peuvent être utiles pour identifier des combinaisons complexes de facteurs pouvant être mis en corrélation avec des calculs d'espérance de vie et des prévisions de survie. Les méthodes, systèmes informatiques et bases de données peuvent également être utilisés pour analyser la valeur des polices d’assurance vie en fonction de la présence de ces facteurs et de leur influence sur les taux d’espérance de vie et de survie calculés. Les méthodes, systèmes informatiques et bases de données peuvent également être utilisés pour déterminer la valeur marchande des polices d’assurance vie pour le marché de l’assurance secondaire.\n[00034] La présente invention concerne des procédés améliorés d'évaluation de polices d'assurance-vie. Plus spécifiquement, la présente invention fournit de nouveaux procédés pour incorporer des informations génétiques dans la détermination de l'espérance de vie et de la valeur de la police d'assurance économique ou marchande. Cette information génétique procure des avantages directs en permettant aux acheteurs de polices d'accéder à de nouveaux segments de marché. À l’heure actuelle, les méthodes disponibles permettent d’évaluer la politique de la personne présentant une déficience médicale sur la base des antécédents médicaux et familiaux et à l’aide de tables d’espérance de vie. En utilisant les procédés de la présente invention, des polices d'assurance-vie pour des personnes possédant une information génétique altérée dans des gènes candidats ou des gènes associés à une espérance de vie améliorée ou diminuée deviennent des atouts précieux. En outre, les nouveaux procédés de la présente invention fournissent des avantages et des améliorations directs par rapport aux procédés de l’état de la technique en ce qu’ils identifient une population de personnes qui seraient sinon négligées sur le marché de l’assurance secondaire (par exemple, des individus en bonne santé présentant des mutations génétiques à haut risque).\n[00035] L'arrivée prochaine de réseaux de SNP plus complets et moins chers permettra le génotypage rapide d'individus à travers le spectre économique. En tant que tels, les modèles qui intègrent les résultats des dernières études d'association génétique pour prédire le risque de maladie et de mortalité deviendront très importants. Par conséquent, avec une compréhension croissante des causes génétiques de la\n maladies polygéniques, un mode de réalisation de la présente invention démontre la capacité de prédire le risque de maladie, la GPLE et l'évaluation de la politique d'assurance-vie en tenant compte de la présence de marqueurs génétiques spécifiques.\n[00036] Ces marqueurs génétiques peuvent être n’importe quel génome, génotype, haplotype, chromatine, chromosome, locus chromosomique, matériel chromosomique, acide désoxyribonucléique (ADN), allèle, gène, grappe de gène, locus de gène, polymorphisme de gène, mutation génique, marqueur de gène, nucléotide, simple nucléotide polymorphisme (SNP), polymorphisme de longueur de fragment de restriction (RPLP), répétition tandem à nombre variable (VNTR), variation du nombre de copies (CNV), marqueur de séquence, site de marqueurs de séquence (STS), plasmide, unité de transcription, produit de transcription, acide ribonucléique ( ARN), micro-ARN, ADN de copie (ADNc) et séquence d’ADN contenant des mutations ponctuelles, des mutations de décalage de cadre, des délétions, des insertions, des inversions, des mutations d’expression et des modifications chimiques (par exemple, méthylation de l’ADN). Les marqueurs génétiques comprennent la séquence nucléotidique et, le cas échéant, la séquence d'acides aminés codée de l'un quelconque des marqueurs ci-dessus ou de tout autre marqueur génétique connu de l'homme du métier.\n[00037] Des modes de réalisation de la présente invention concernent des procédés permettant de déterminer le GPLE associé à la valeur d'un contrat d'assurance-vie en utilisant des associations génétiques pour la susceptibilité aux maladies et la longévité. La présente invention concerne également des procédés permettant d’identifier la contribution d’une information génétique à la prédiction de son état de santé médical et de son espérance de vie et de l’effet de cette information sur les courbes de survie utilisées pour évaluer les polices d’assurance vie.\n[00038] La présente invention concerne un procédé permettant de déterminer le GPLE selon trois perspectives: 1) l’identification d’informations génétiques ou d’associations gène / maladie et l’utilisation des odds ratios (OR) associés pour construire des courbes de survie modifiées pour la population de génotypes donnée; 2) identification des gènes candidats impliqués dans la détermination de la durée de vie (longévité) ou des probabilités d'espérance de vie et utilisation des variations au niveau des locus génétiques associés pour calculer\n évolution positive ou négative des probabilités d'espérance de vie; 3) l'identification des changements dans les probabilités d'espérance de vie pour évaluer les polices d'assurance-vie.\n[00039] Bien qu'ils soient applicables à n'importe quel gène, les gènes candidats préférés de la présente invention peuvent être ceux impliqués dans une maladie, des maladies liées au vieillissement, et des gènes impliqués dans le maintien et la réparation du génome. Le vieillissement est un phénomène biologique complexe, susceptible d'être contrôlé par de multiples mécanismes et processus, génétiques et épigénétiques. Grâce à l'interaction et à l'interdépendance des systèmes biologiques, il est possible de déterminer la survie ou la durée de vie d'un organisme. Le rôle des gènes sur la survie ou la durée de vie a été étudié chez des jumeaux, des mutants génétiques humains du vieillissement prématuré, des études de liaison génétique pour la transmission de la durée de vie et des études sur des marqueurs génétiques de longévité exceptionnelle. Les gènes impliqués dans le processus de vieillissement, tels que les gènes d'assurance de la longévité, les gènes associés à la longévité, les vitagènes et les gérontogènes, sont des exemples de gènes candidats. Les gènes d'assurance de la longévité peuvent être des variants (ou des allèles) de certains gènes qui permettent à un organisme de vivre plus longtemps. Des mutations dans ces gènes peuvent modifier la pente des courbes de mortalité en fonction de l'âge. Sans se limiter à aucune théorie, certains gérontogènes peuvent réduire la durée de vie en bloquant l’expression des gènes d’assurance de la longévité.\n[00040] Les études d'association pangénomique (GWAS) montrent que la majorité des variants génétiques de la population ne présentent qu'un risque légèrement accru de maladie (Wray et al. 2007. Genome Research. 17 (10): 1520-1528; Wray et al. 2008 Opinion actuelle en génétique et développement, 18: 1-7; Consortium de contrôle des cas Wellcome Trust, 2007. Nature 447 (7145): 661-78). Wray et al. 2007, Wray et al. 2008 et Wellcome Trust Case Control Consortium 2007 sont incorporés dans leur intégralité par référence. Ce risque est reflété dans les OR numériques, on observe généralement un OR inférieur à 1,5, avec de nombreux OR autour de 1,1 à 1,2, avec un effet neutre pour un variant génétique ayant un odds ratio égal à 1. Les variants génétiques présentant des effets plus significatifs sur les risques de maladie possèdent généralement des rapports de cotes supérieurs à 2. \n [00041] Une simulation de GWAS par Wray et al. montre que, pour une étude cas-témoins portant sur 10 000 cas et contrôles, il sera possible d'identifier les plus gros loci (~ 75) expliquant plus de 50% de la variance génétique dans la population (Wray et al. 2007. Genome Research. 17 (10): 1520-1528). En outre, un regroupement des données permet de prédire un pourcentage élevé du risque génétique, même lorsque des mutations avec des RUP relativement faibles constituent la base de cette prédiction. Par exemple, Wray et al. ont identifié une corrélation> 0,7 entre le risque génétique prédit et le risque génétique réel (expliquant> 50% de la variance génétique) même pour les maladies contrôlées par 1 000 locus avec un risque relatif moyen de seulement 1,04.\n[00042] Les procédés de la présente invention offrent de nombreux avantages. Premièrement, la puissance statistique des données d'association génétique peut être augmentée en regroupant les résultats en utilisant des modes de réalisation de la présente invention provenant de plusieurs GWAS, ce qui peut aider à identifier de nombreux autres variants à risque avec des effets de petite taille. En outre, ces variantes de risque peuvent être utilisées pour expliquer un pourcentage plus élevé de variance génétique.\n[00043] Deuxièmement, des méthodes statistiques optimales peuvent être utilisées pour sélectionner et combiner plusieurs risques génétiques (tels que les SNP) dans une équation de prédiction du risque. C'est un défi commun à la plupart des études de génomique car le nombre de variables mesurées est beaucoup plus grand que le nombre d'échantillons. Dans la présente invention, plusieurs techniques d'apprentissage automatique, telles que les machines à vecteurs de support et les forêts à décision aléatoire, peuvent être appliquées aux données d'expression génétique de micropuces pour améliorer le diagnostic et la stratification du risque dans les études cliniques. Ces méthodes et un certain nombre d’autres méthodes qui ont été appliquées à la sélection des PNS peuvent être utiles pour la construction d’une équation de prédiction du risque.\n[00044] Des modes de réalisation de la présente invention prévoient l’intégration de données provenant d’un large éventail d’études d’associations génétiques afin d’améliorer efficacement la probabilité de prédiction de contracter une maladie donnée (par exemple, risque relatif, rapport de cotes, rapport de risque, etc.) et la mortalité due à cette maladie pendant trois mois. une personne compte tenu de son profil génomique. Dans certains modes de réalisation, la génomique d’un individu\n Le profil peut être combiné à des informations médicales et démographiques supplémentaires pour améliorer encore la probabilité de prédiction. En outre, les prédictions d'espérance de vie générées par les modes de réalisation de l'invention peuvent être utilisées pour évaluer les polices d'assurance-vie détenues par ces personnes.\n[00045] La présente invention fournit un procédé par lequel des données de risque de susceptibilité génétique peuvent être extraites de la littérature et compilées dans un appareil de base de données central. Les données de risque peuvent être des données contenant des contributions statistiques d'attributs génétiques liés à une maladie (par exemple, risque relatif, rapports de cotes, rapports de risque, valeurs prédictives, etc.). Dans la première phase de la collecte de données (curation primaire), des études ayant été effectuées sur un grand nombre de sujets tels que la méta-analyse, l'analyse groupée, des articles de synthèse et des études d'association pangénomique (GWAS) peuvent être incluses. La présente invention prévoit des cycles ultérieurs de collecte et de curation de données. Les phases ultérieures de la collecte de données (par exemple, la curation secondaire et la curation finale) peuvent utiliser des études d'association génétique à plus petite échelle pour affiner ces résultats. Un procédé selon cette invention est décrit ci-dessous:\n[00046] identifier les maladies à haute mortalité et leurs associations génétiques pertinentes (gènes candidats);\n[00047] rechercher, récupérer et filtrer la littérature pertinente;\n[00048] conservation des données de la littérature;\n[00049] déposer les données pertinentes dans la base de données centrale;\n[00050] construire un cadre statistique pour intégrer les données;\n[00051] recevoir des données d'entrée (par exemple, profil génomique de gènes candidats);\n[00052] calculer un score de susceptibilité à la maladie ou de mortalité, et un GPLE basé sur le profil génétique de l'individu (séquence génomique); et\n[00053] corréler le score GPLE à une valeur ou à un niveau de prime d'assurance vie prédit.\n Identifier les maladies à haute mortalité et leurs associations génétiques pertinentes\n[00054] Des maladies spécifiques à mortalité élevée ont été identifiées sur la base d'une enquête sur les données de mortalité provenant de diverses ressources publiques. Lors de l'identification d'une maladie particulière, toutes les associations d'intérêt génétiques et environnementales peuvent être explorées par des équipes scientifiques composées d'individus désignés pour examiner la littérature identifiée (l'équipe scientifique comprend par exemple un responsable de projet, un conservateur principal, un conservateur secondaire et un gestionnaire de base de données). La liste des associations peut être revue et modifiée sur une base continue, ce qui donne une liste de plus en plus longue, en termes de nombre de maladies incluses et de nombre de gènes candidats (déterminants génétiques) ayant un effet établi sur les taux de mortalité de ces maladies. déjà répertorié et sous enquête.\n[00055] Des exemples de maladies abordées par les procédés de la présente invention comprennent: polypose coli adénomateuse, maladie d'Alzheimer, sclérose latérale amyotrophique, tumeur cérébrale, bronchite chronique, carcinome, cancer de l'endomètre, carcinome hépatocellulaire, carcinome du poumon non à petites cellules, carcinome canalaire pancréatique, cancer le carcinome cellulaire, le carcinome à petites cellules, la thrombose de l'artère carotide, l'infarctus cérébral, les troubles cérébrovasculaires, le néoplasie intraépithéliale cervicale, les néoplasmes coliques, le syndrome de Mellitus , néoplasmes œsophagiens, syndrome de Gardner, néoplasmes gastriques, néoplasmes de la tête et du cou, thrombose de la veine hépatique, néoplasmes colorectaux héréditaires, anévrisme intracrânien, embolie intracrânienne, embolie intracrânienne et thrombose, thrombose, voie respiratoire. LEOPARD syndrome, leukemia, T-cell leukemia-lymphoma, acute B-cell leukemia, chronic B-cell leukemia, lymphocytic leukemia, acute lymphocytic leukemia, acute Ll lymphocytic leukemia, acute L2 lymphocytic leukemia, chronic lymphocytic leukemia, lymphocytic, acute megakaryocytic leukemia, acute myelocytic leukemia, myeloid leukemia, chronic myeloid leukemia, chronic myelomonocytic leukemia, acute nonlymphocytic leukemia, pre B-cell leukemia,\n acute promyelocyte leukemia, acute T-cell leukemia, liver disease, liver neoplasms, long QT syndrome, longevity, lung neoplasms, mammary neoplasms, Marfan syndrome, microvascular angina, mitral valve insufficiency, mitral valve prolapse, mitral valve stenosis, myocardial infarction, myocardial ischemia, myocardial reperfusion injury, myocardial stunning, myocarditis, nephritis, hereditary nephritis, ovarian neoplasms, pancreatic neoplasms, prostate neoplasm, chronic obstructive pulmonary disease, pulmonary embolism, pulmonary emphysema, pulmonary heart disease, pulmonary valve stenosis, rectal neoplasms, retinal vein occlusion, rheumatic heart disease, Romano-Ward syndrome, cardiogenic shock, sick sinus syndrome, sigmoid neoplasms, intracranial sinus thrombosis, tachycardia, supraventricular tachycardia, ventricular tachycardia, thromboembolism, thrombophlebitis, thrombosis, torsades de pointes, tricuspid atresia, tricuspid valve insufficiency, and other diseases known to one of ordinary skill in the art. In preferred embodiments, the disease(s) is bladder cancer, lung cancer, breast cancer, and/or pancreatic cancer.\n[00056] Exemplary candidate genes are those involved in disease, aging- associated diseases, and genes that are involved in genome maintenance and repair. Some examples of candidate genes are apoliprotein E, apolipoprotein C3, microsomal triglyceride transfer protein, cholesteryl ester transfer protein, angiotensin I-converting enzyme, insulin-like growth factor 1 receptor, growth hormone 1, glutathione- S -transferase Ml (GSTMl), catalase, superoxide dismutases 1 and 2, heat shock proteins, paraoxonase 1 , interleukin 6, hereditary haemochromatosis, methyenetetrahydrofolate reductase, sirtuin 3, tumor protein p53, transforming growth factor βl, klotho, werner syndrome, mutL homologue 1, mitochondrial mutations (Mt5178A, Mt8414T, Mt3010A and J haplotype), cardiac myosin binding protein C (MYBPC3) as well as other candidate genes involved in longevity known to one of ordinary skill in the art. In preferred embodiments, the candidate gene is glutathione-S-transferase Ml (GSTMl) or cardiac myosin binding protein C (MYBPC3). \n Searching, retrieving and filtering of relevant literature\n[00057] Embodiments of the present invention provide tools for automated searching, retrieval and filtering of results from databases, such as PubMed and HuGE. PubMed is an online database of indexed articles, citations and abstracts from medical and life sciences journals maintained by the National Library of Medicine. HuGE (Human Genome Epidemiology) is a searchable knowledge base of genetic associations. HuGE Literature Finder is a continuously updated literature information system that systematically curates and annotates publications on human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene-environment interactions, and evaluation of genetic tests. In addition to PubMed and HuGE, databases and sources known to one of ordinary skill in the art that contain the appropriate information could also be used.\n[00058] The present invention provides a computer system wherein databases are searched and desired information is collected based on the search parameters entered by the user through an interface. The present invention provides a code for searching the database and selecting relevant articles based on search criteria (e.g., Appendix A illustrates computer system coding for the HuGE metasearch – Advanced software). A user interface as an exemplary search related to GSTMl is shown in FIG. 1. The additional filters for searching provided in the code and on the interface can allow the user to limit searching to articles that contain or do not contain specific words. For example, Appendix B illustrates the first five results of the search hits identified from running the criteria presented in FIG. 1 through the code in Appendix A.\n[00059] The present invention also provides a computer system wherein abstracts are searched and desired information is collected based on the search parameters entered by the user through an interface. The present invention provides a search code for identifying and parsing the relevant information from abstracts in the literature (e.g., Appendix C illustrates computer system coding for the abstract fetcher – parser software). A user interface as an exemplary search \n related to bladder cancer with five identified studies (PubMed IDs entered) is shown in FIG. 2. For example, Appendix D shows the results of the search run through the interface of FIG. 2, utilizing the coding of Appendix C.\n[00060] Embodiments of the present invention also provide search and retrieval tools that permit searching a combination of generic or specific disease terms (e.g., heart disease) and gene symbol (e.g., APOE) on a public resource of choice in an automated fashion. These tools take into account the various ontologically associated disease terms from UMLS (Unified Medical Language System) and MeSH (Medical Subject Headings) vocabulary. For example, the associated terms with "heart disease" can include "coronary aneurysm" and "myocardial stunning". The search tool can also take into account gene name synonyms or sub-types (e.g., "apolipoprotein E2" and "apolipoprotein E3" as subtypes for the gene symbol "APOE"). This preferred comprehensive approach ensures retrieval of an extensive literature set for the particular disease-gene combination of interest.\n[00061] Embodiments of the present invention also provide search and retrieval tools that can be used to limit the culled results based on a variety of factors. These factors can include: country or region in which the study was performed or type of study (e.g., genetic association, gene-environment interactions, clinical trial, genome-wide association study and the like). Several publication parameters for each document (such as the title, abstract, PubMed ID, journal, author list and year of publication) can be automatically parsed by these tools. All of this information can be uploaded into the central database apparatus.\n[00062] Embodiments of the present invention provide a filtering tool that enables searching the titles and abstracts of the retrieved records based on any combination of terms. Several types of terms can be supported by the tool. Exemplary terms are: statistical terms (e.g., odds ratio (OR), hazard ratio (HR), relative risk (RR), p-values, primary statistic, number of cases and controls, adjusting variable, confidence intervals and the like); environmental effect terms \n (e.g., smoking, exercise, geographic location, language, temperature, altitude, and the like); personal terms (e.g., ethnicity, gender, age distribution of the study population); interaction terms (e.g., gene/gene interaction terms, gene/environment interaction terms); and other general terms (e.g., statistical significance, phenotype description, time of onset, study model used, study approach (classical or Bayesian), endpoints and outcomes such as, accelerated disease progression or sudden death). The filtering tool can also provide for the use of markers such as binary data fields to enter review status information (e.g., indication as to whether the article and the electronic record have been marked for additional review, whether the electronic record of data collected is ready to proceed to upload into the genetic database, and the like)\n[00063] Boolean logic can be implemented, which allows the user to enter any combination of the above described terms or additional terms known to one of ordinary skill in the art. Case-sensitive searches can be preformed to aid in narrowing the results. The methods of the present invention can be created by systems using a variety of programming languages including but not limited to C, Java, PHP, C++, Perl, Visual Basic, sql and other languages which can be used to cause the computing system of the present invention to perform the steps of the methods described herein.\nCurating data from literature\n[00064] A preferred embodiment of the present invention is shown in FIG. 3, the scientific articles and literature containing risk data (e.g., statistical contributions of genetic attributes related to disease) identified by the exemplary search methods of the present invention (11) can be passed through a primary curation phase (12) where the articles can be retrieved using a retrieval apparatus and filtered by article content prior to collecting the first set of data in an electronic record (13). Upon initiation of primary curation (12), the curation fields can be mapped to the data fields (18) in the genetic database (20). This process can be done iteratively as additional curation fields could be entered into the electronic record of data collected (13, 15, 17). The scientific articles and \n literature containing risk data can be subject to additional review. A review mechanism can be utilized that marks the article of concern for additional review [shown as secondary curation (14) or final curation (16)]. Without being limited to a specific number of review/curation rounds, the present invention provides for single or multiple rounds of article searching and curation of data. The publications identified and curated can be archived in the genetic database and/or central database apparatus to facilitate quick referencing.\n[00065] A secondary curation phase (14) can follow the primary curation phase (12) where additional literature and experimental results can be retrieved and the appropriate risk data can be obtained and collected in an electronic record (15). A final curation phase (16) can also follow the secondary curation phase (14) where additional literature and experimental results can be retrieved or the collected data can be reviewed to produce an electronic record of data collected (17) that can be uploaded into the genetic database (19). The genetic database (20) can serve as a central repository for the risk data associated with gene/gene interactions and/or gene/environment interactions.\nDeposition of relevant data into the central database apparatus\n[00066] The central database apparatus can be the central location of all the automatically searched, retrieved and filtered literature as well as curated literature. Curated literature and electronic records pending final curation can also be stored in the central database apparatus. A secondary set of tables can store pending results and final results in order to preserve the quality of the final statistical model.\n[00067] The electronic record of data collected can be stored in tables comprising fields of information related to the genetic markers identified. As shown by example in FIG. 4, the data fields can include various information related to the candidate gene [e.g. synonym names for the candidate genes or disease (33), information related to the disease (34), information related to candidate gene (35), information related to the article/literature searched (36), \n statistical information (37) and information related to the genetic marker (38)]. The electronic record of data can be stored in a master file after population of the data in the designated fields. For exemplary purposes, a representative GSTMl field database can be created using the code of Appendix E.\n[00068] The central database apparatus can also be used to log information associated with the curation process, such as identification of the user, date and time of data upload, and curation status of the publication and electronic record. For security purposes, users of the central database apparatus can be granted different access privileges to the tables and database.\n[00069] A number of interfaces to the database can be developed by one of ordinary skill in the art to enable easy and intuitive access to the data set of interest. Interfaces can also be developed for direct entry of curation results into the database or uploading of the full text of the article from which the data was collected.\n[00070] Due to the evolving process of scientific research, newly determined studies in genetic association are being conducted on a regular basis. To address this, the database can have a field that specifies the date when the database was last updated. At periodic intervals, the database can be queried for literature resources for all curated diseases in the database, and new references can be identified that have not been curated and deposited into the electronic record or the central database apparatus. The central database apparatus can then be augmented by these references through the curation process. The new date when this comparative search is performed can be recorded, and all records in the database can be updated to reflect the new curation date.\nBuilding a statistical framework to integrate the data (risk data)\n[00071] Hazard ratio (HR), relative risk (RR) and odds ratio (OR) calculations can be used as risk data to determine the statistical contribution of genetic attributes to occurrence of an event (such as disease). In a prospective study, RR is the ratio of the proportion of cases having a pre-defined disease in \n the exposed group (e.g., those with the genetic variant of interest) over that in the control group (e.g., those without the genetic variant of interest). In a case- control retrospective study, such as GWAS, calculation of the OR is preferred and can be estimated as the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or the ratio of being exposed to an event for the case group (e.g., those with allele of interest) over that in the control group (e.g., those without the allele of interest).\n[00072] In one embodiment of the present invention, the relative risk is used. For example, if the number of observations in each exposure/outcome combination is labeled as those shown in Table 1, the calculation of RR is A/(A+B)/C/(C+D). In a rare disease/outcome with incidence < 10%, A (C) is much smaller than B (D). Therefore, RR can be approximated by A/B/C/D, which is equal to A/C/B/D, the OR. However, for more common outcomes, the OR always overstates the RR, sometimes dramatically. Alternative statistical methods can be used for estimating an adjusted RR when the outcome is common (Localio et al. 2007. J Clin Epidemiol. 60(9):874-882; McNutt et al. Am J 2003. Epidemiol. 157(10):940-943; Zhang et al. 1998. Jama. 280(19):1690-1691).
"},{"id":"text-2","type":"text","heading":"","plain_text":"[00073] In another embodiment, the hazard ratio is used. The hazard ratio (HR) is the ratio of the hazards of the treatment and control groups at a particular point in time. There is no direct mathematical relationship between the OR and the HR. However, the HR can be approximated by the odds ratio (OR) using a Taylor series expansion assuming disease prevalence is small (Walker. 1985. Appl Statist. 34(l):42-48). \n [00074] Since the sample size of most genetic-association studies is small to moderate leading to inconsistent results, meta-analysis, that combine multiple studies with similar measures are warranted to evaluate the significance of the genetic associations. Meta-analysis permits the calculation of summary ORs, which are weighted averages of ORs from individual studies. Both Mantel Haenszel and Peto's methods are commonly used by one of skill in the art to estimate such summary ORs in meta-analysis. These methods require 2 x 2 tables that cannot control for confounding factors.\n[00075] In addition, it is preferred to select an effect model. Usually the choice is between a fixed effects model, which indicates that the conclusions derived in the meta-analysis are valid for the studies included in the analysis, and a random effects model, which assumes that the studies included in the metaanalysis belong to a random sample of a universe of such studies. When the studies are found to be homogeneous, random and fixed effects models are indistinguishable.\n[00076] Engels et al. systematically evaluated 125 meta-analysis studies, and concluded that random effects estimates, which incorporate heterogeneity, tended to be less precisely estimated than fixed effects estimates (Stat Med. 2000 JuI 15;19(13):1707-28). Furthermore, summary odds ratios and risk differences agreed in statistical significance, leading to similar conclusions about whether treatments affected the outcome. Heterogeneity was common regardless of whether treatment effects were measured by odds ratios or risk differences. However, risk differences usually displayed more heterogeneity than odds ratios.\n[00077] Meta analysis techniques have been implemented in several statistical software packages, including R (The R Project for Statistical Computing; http://www.r-project.org/). Most of these packages also allow investigators to test studies for heterogeneity and publication bias, which refers to the greater likelihood of research with statistically significant results to be reported in comparison to those with null or non significant results. \n [00078] In still another embodiment of the present invention, an odds ratio (OR) is used. The OR is the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or to a sample-based estimate of that ratio. These groups might be men and women, an experimental group and a control group, or any other dichotomous classification (e.g., with and without a specific risk allele). If the probabilities of the event in each of two groups are p (first group) and q (second group), then the OR is expressed by the following formula:\nWO – p ) _ p Q – g ) q /( l – q ) q (l ~ p )\n[00079] An OR = 1 indicates that the condition or event under study is equally likely in both groups. An OR > 1 indicates that the condition or event is more likely in the first group.\n[00080] In another embodiment, the central database apparatus contains a panel of risk SNPs (SNPs located in risk alleles of candidate genes) with their corresponding ORs for each disease. In an additional embodiment, the central database apparatus also contains a list of ORs for implicated environmental factors and optionally ORs for interactions between SNPs and environmental factors. These ORs can be indicative of how likely a person is to develop a disease given his genetic makeup and environmental factors. The ORs for SNPs and environmental factors can be assumed to be additive within a particular disease.\nReceiving input data (e.g. genomic sequence including sequence of candidate genes) from an individual\n[00081] Genetic information can be collected from an individual by a variety of methods known in the art. In one embodiment collection involves the contribution by the individual of a buccal swab (i.e., inside the cheek), a blood sample, or a contribution of other biological materials containing genetic information for that individual. The genetic sequence can be determined by \n known methods such as that disclosed in Stephan et al, US 2008/0131887, incorporated in its entirety by reference, as well as methods employed by companies such as Seq Wright, GenScript, GenoMex, Illumina, ABI, 454 Life Sciences, Helicos and additional methods known to persons of ordinary skill in the art.\nCalculation of disease susceptibility, fatality scores and GPLE\n[00082] From the central database apparatus, data can be extracted to calculate statistical parameters such as an individual's ORs of disease susceptibility based on the specific SNPs that individual possesses. These ORs can be used to calculate fatality scores. Curated ORs from a wide range of high mortality diseases along with fatality scores for the diseases can be generated in the central database apparatus. The fatality score can qualitatively take into account several relevant factors such as mortality, average age of disease manifestation and prevalence within the population. The list of fatality scores can be customizable based on user or external third party databases results and preferences, and can reflect results from external databases results about the relative importance of the diseases in predicting mortality.\n[00083] The ORs calculated by the meta-analysis approach of the method provided by the present invention can be used as weights for the fatality scores to calculate an overall life expectancy for an individual given his/her genotype (i.e. GPLE). The GPLE is an individual age-specific probability for living an additional number of years given that individuals genetic profile (i.e. genomic DNA sequence) for the candidate genes of interest. This GPLE will be strongly indicative of mortality, with higher values corresponding to individuals at greater risk of contracting or succumbing to a high mortality disease. As more GWAS are completed, more gene/gene and gene/environment interaction ORs can be reported and calculated and as next-generation sequencing technologies are widely adapted these calculations will increase in precision. \n [00084] In one embodiment, the methods of the present invention can be utilized to provide survivorship data for people with specific risk genotype patterns. For these individuals, a panel of risk alleles in candidate genes can be identified in the electronic record of data collected. Individuals with a specific combination of these risk alleles can be monitored until their death in order to provide actual mortality data for the particular risk alleles of these candidate genes and more accurately determine life expectancy. Many GWAS are based on case-control design to identify risk alleles associated with certain diseases or traits. With actual mortality data for individuals with known genetic profiles, the methods of the present invention provide a database that can be populated with actual mortality data, resulting in an additional sample population to utilize in calculating probabilities and predicted genetic life expectancy for individuals with these risk alleles. This can provide more precise estimates and life tables (also called mortality tables or actuarial tables) based on genetic profiles.\n[00085] In another embodiment, the genetic information from the deceased individuals can be used to calculate mortality rates and/or life expectancies for those carrying specific risk alleles of candidate genes. Life tables show the probability of surviving until the next year for someone of a given age. Classification of the data in life tables is subdivided by gender, personal habits, economic condition, ethnicity, medical conditions and other factors attributable to life expectancy. There are multiple sources for mortality tables, such as The Society of Actuaries, National Center for Health Statistics (NCHS), CDC, and others known to a person of ordinary skill in the art. Life tables can provide basic statistical data for deaths and diagnosed cause of death correlated with personal factors (e.g., sex, race, lifestyle habits, social habits, education, and the like) and mortality. See National Vital Statistics Report. CDC. 56(10): 1-124.\n[00086] Life expectancy is the average number of years of life remaining at a given age. The starting point for calculating life expectancies is the age- specific death rates of the population members. For example, if 10% of a group of \n people alive at their 90th birthday die before their 91st birthday, then the age- specific death rate at age 90 would be 10%.\n[00087] These values can be used to calculate a life table, which can be used to calculate the probability of surviving to each age. In actuarial notation, the probability of surviving from age x to age x+n is denoted nPχ and the probability of dying during age x (i.e. between ages x and x+1) is denoted Qx.\n[00088] The life expectancy at age x, denoted e* , is then calculated by adding up the probabilities to survive to every age. This is the expected number of complete years lived:\nOO OO","html":"[00073] In another embodiment, the hazard ratio is used. The hazard ratio (HR) is the ratio of the hazards of the treatment and control groups at a particular point in time. There is no direct mathematical relationship between the OR and the HR. However, the HR can be approximated by the odds ratio (OR) using a Taylor series expansion assuming disease prevalence is small (Walker. 1985. Appl Statist. 34(l):42-48). \n [00074] Since the sample size of most genetic-association studies is small to moderate leading to inconsistent results, meta-analysis, that combine multiple studies with similar measures are warranted to evaluate the significance of the genetic associations. Meta-analysis permits the calculation of summary ORs, which are weighted averages of ORs from individual studies. Both Mantel Haenszel and Peto's methods are commonly used by one of skill in the art to estimate such summary ORs in meta-analysis. These methods require 2 x 2 tables that cannot control for confounding factors.\n[00075] In addition, it is preferred to select an effect model. Usually the choice is between a fixed effects model, which indicates that the conclusions derived in the meta-analysis are valid for the studies included in the analysis, and a random effects model, which assumes that the studies included in the metaanalysis belong to a random sample of a universe of such studies. When the studies are found to be homogeneous, random and fixed effects models are indistinguishable.\n[00076] Engels et al. systematically evaluated 125 meta-analysis studies, and concluded that random effects estimates, which incorporate heterogeneity, tended to be less precisely estimated than fixed effects estimates (Stat Med. 2000 JuI 15;19(13):1707-28). Furthermore, summary odds ratios and risk differences agreed in statistical significance, leading to similar conclusions about whether treatments affected the outcome. Heterogeneity was common regardless of whether treatment effects were measured by odds ratios or risk differences. However, risk differences usually displayed more heterogeneity than odds ratios.\n[00077] Meta analysis techniques have been implemented in several statistical software packages, including R (The R Project for Statistical Computing; http://www.r-project.org/). Most of these packages also allow investigators to test studies for heterogeneity and publication bias, which refers to the greater likelihood of research with statistically significant results to be reported in comparison to those with null or non significant results. \n [00078] In still another embodiment of the present invention, an odds ratio (OR) is used. The OR is the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or to a sample-based estimate of that ratio. These groups might be men and women, an experimental group and a control group, or any other dichotomous classification (e.g., with and without a specific risk allele). If the probabilities of the event in each of two groups are p (first group) and q (second group), then the OR is expressed by the following formula:\nWO – p ) _ p Q – g ) q /( l – q ) q (l ~ p )\n[00079] An OR = 1 indicates that the condition or event under study is equally likely in both groups. An OR > 1 indicates that the condition or event is more likely in the first group.\n[00080] In another embodiment, the central database apparatus contains a panel of risk SNPs (SNPs located in risk alleles of candidate genes) with their corresponding ORs for each disease. In an additional embodiment, the central database apparatus also contains a list of ORs for implicated environmental factors and optionally ORs for interactions between SNPs and environmental factors. These ORs can be indicative of how likely a person is to develop a disease given his genetic makeup and environmental factors. The ORs for SNPs and environmental factors can be assumed to be additive within a particular disease.\nReceiving input data (e.g. genomic sequence including sequence of candidate genes) from an individual\n[00081] Genetic information can be collected from an individual by a variety of methods known in the art. In one embodiment collection involves the contribution by the individual of a buccal swab (i.e., inside the cheek), a blood sample, or a contribution of other biological materials containing genetic information for that individual. The genetic sequence can be determined by \n known methods such as that disclosed in Stephan et al, US 2008/0131887, incorporated in its entirety by reference, as well as methods employed by companies such as Seq Wright, GenScript, GenoMex, Illumina, ABI, 454 Life Sciences, Helicos and additional methods known to persons of ordinary skill in the art.\nCalculation of disease susceptibility, fatality scores and GPLE\n[00082] From the central database apparatus, data can be extracted to calculate statistical parameters such as an individual's ORs of disease susceptibility based on the specific SNPs that individual possesses. These ORs can be used to calculate fatality scores. Curated ORs from a wide range of high mortality diseases along with fatality scores for the diseases can be generated in the central database apparatus. The fatality score can qualitatively take into account several relevant factors such as mortality, average age of disease manifestation and prevalence within the population. The list of fatality scores can be customizable based on user or external third party databases results and preferences, and can reflect results from external databases results about the relative importance of the diseases in predicting mortality.\n[00083] The ORs calculated by the meta-analysis approach of the method provided by the present invention can be used as weights for the fatality scores to calculate an overall life expectancy for an individual given his/her genotype (i.e. GPLE). The GPLE is an individual age-specific probability for living an additional number of years given that individuals genetic profile (i.e. genomic DNA sequence) for the candidate genes of interest. This GPLE will be strongly indicative of mortality, with higher values corresponding to individuals at greater risk of contracting or succumbing to a high mortality disease. As more GWAS are completed, more gene/gene and gene/environment interaction ORs can be reported and calculated and as next-generation sequencing technologies are widely adapted these calculations will increase in precision. \n [00084] In one embodiment, the methods of the present invention can be utilized to provide survivorship data for people with specific risk genotype patterns. For these individuals, a panel of risk alleles in candidate genes can be identified in the electronic record of data collected. Individuals with a specific combination of these risk alleles can be monitored until their death in order to provide actual mortality data for the particular risk alleles of these candidate genes and more accurately determine life expectancy. Many GWAS are based on case-control design to identify risk alleles associated with certain diseases or traits. With actual mortality data for individuals with known genetic profiles, the methods of the present invention provide a database that can be populated with actual mortality data, resulting in an additional sample population to utilize in calculating probabilities and predicted genetic life expectancy for individuals with these risk alleles. This can provide more precise estimates and life tables (also called mortality tables or actuarial tables) based on genetic profiles.\n[00085] In another embodiment, the genetic information from the deceased individuals can be used to calculate mortality rates and/or life expectancies for those carrying specific risk alleles of candidate genes. Life tables show the probability of surviving until the next year for someone of a given age. Classification of the data in life tables is subdivided by gender, personal habits, economic condition, ethnicity, medical conditions and other factors attributable to life expectancy. There are multiple sources for mortality tables, such as The Society of Actuaries, National Center for Health Statistics (NCHS), CDC, and others known to a person of ordinary skill in the art. Life tables can provide basic statistical data for deaths and diagnosed cause of death correlated with personal factors (e.g., sex, race, lifestyle habits, social habits, education, and the like) and mortality. See National Vital Statistics Report. CDC. 56(10): 1-124.\n[00086] Life expectancy is the average number of years of life remaining at a given age. The starting point for calculating life expectancies is the age- specific death rates of the population members. For example, if 10% of a group of \n people alive at their 90th birthday die before their 91st birthday, then the age- specific death rate at age 90 would be 10%.\n[00087] These values can be used to calculate a life table, which can be used to calculate the probability of surviving to each age. In actuarial notation, the probability of surviving from age x to age x+n is denoted nPχ and the probability of dying during age x (i.e. between ages x and x+1) is denoted Qx.\n[00088] The life expectancy at age x, denoted e* , is then calculated by adding up the probabilities to survive to every age. This is the expected number of complete years lived:\nOO OO
"},{"id":"text-3","type":"text","heading":"","plain_text":"[00089] Because age is rounded down to the last birthday, on average people live half a year beyond their final birthday, so half a year is added to the life expectancy to calculate the full life expectancy.\n[00090] Life expectancy is by definition an arithmetic mean. It can be calculated also by integrating the survival curve from ages 0 to positive infinity. For an extinct population of individuals, life expectancy can be calculated by averaging the ages at death. For a population of individuals with some survivors it is estimated by using mortality experience in recent years.\n[00091] Using this life expectancy calculation, no allowance has been made for expected changes in life expectancy in the future. Usually when life expectancy figures are quoted, they have been calculated in this manner with no allowance for expected future changes. This means that quoted life expectancy figures are not generally appropriate for calculating how long any given individual of a particular age is expected to live, as they effectively assume that current death rates will be "frozen" and not change in the future. Instead, life expectancy figures can be thought of as a useful statistic to summarize the current health status of a population. Some models do exist to account for the evolution of \n mortality (e.g., the Lee-Carter model) (R.D. Lee and L.Carter 1992. J. Amer. Stat. Assoc. 87:659-671) and can be used in the embodiments of the invention.\n[00092] Given the age, gender, race (AGR) of a person, the median life expectancy of the person can be calculated from mortality tables. Life expectancy calculations, in general, are heavily dependent on the criteria used to select the members of the population from which it is calculated. The baseline life expectancy (BLE) can be defined as the median life expectancy of individuals with matched AGR parameters.\n[00093] The inclusion of information on additional parameters such as medical factors (e.g., disease, stage of disease, treatment regimen, medical history and the like), environmental factors (e.g., exercise, smoking, occupational exposure and the like) and extended demographic information (e.g., geographical region, socioeconomic status and the like) can substantially enhance the life expectancy estimate for an individual. The specific life expectancy (SLE) of an individual for a given disease can be defined as the median life expectancy of individuals affected with that disease, with matched demographic, medical and environmental parameters. The specificity of the SLE for an individual for a given disease can depend on the availability of detail in the literature.\n[00094] The present invention provides a method for improved calculation of life expectancy based on genetic profiles, resulting in a GPLE. The inclusion of genetic information for an individual, such as SNPs, can increase the accuracy of life expectancy estimates. The GPLE is the median life expectancy of individuals with matched genetic profiles for individual candidate genes. In addition, calculation of GPLE by the methods herein, utilizes a central database apparatus under constant evolvement, continually factoring in the newest developments in genetic association scientific research reported in the literature.\n[00095] In preferred embodiments, the GPLE for an individual can be calculated from a blended approach, a minimum approach or any other approach known to one of ordinary skill in the art (in cases where the SLEs are not \n available, BLEs can be used). An example of a blended approach for three diseases is shown below. This approach calculates GPLE based on a combination of SLEs for three diseases (ij, i2, je3), where all the corresponding OR(i) values contribute to the GPLE:\n_ ORQ1) * SLEQ1) + OR(J2) • SLE(J2) + OR(J3) • SLE(J3) OR(I1) + OR(i2) + ORQ3)\n[00096] An example of a minimum approach for three diseases is shown below. This approach calculates GPLE based on the minimum of scaled SLEs for the diseases, where the scale factor for a corresponding ORQ) value is dependent on age and gender:\nmm • SLE(h) SLEJi2) SLE(J3)[SlOR(JySlOR(I[SlOR(JySlOR(I[SlOR(JySlOR(I[SlOR(JySlOR(I2) ' φR(h)\n[00097] The advantages of the GPLE calculation methods of the present invention above are twofold: 1) they combine a measure of the likelihood of an individual developing a disease (ORQ)) with the life expectancy of the individual with the genetic markers for that disease (reflected in the GPLE) and 2) a numerical value is provided that is indicative of the life expectancy of a person taking into account multiple input data or parameters, such as genetic, medical, environmental, demographic parameters.\n[00098] A preferred embodiment of the present invention is shown in FIG. 5. The determination of GPLE (28) can be based on information contained in a genetic database (20) and a life expectancy database (25). The genetic database can be comprised of information as discussed in FIG. 3. The life expectancy database (25) can contain information related to life expectancy data (21) and life table data (23). The retrieval of a specific life expectancy (22) from reported life expectancy data and the retrieval or construct of a baseline life expectancy (23) from reported life table data can be collectively housed in the life expectancy database (25). To determine GPLE, a user can calculate a collective risk index (26) based on multiple genetic factors and, along with the input data \n (27) from an individual, calculate a GPLE (28). The calculated GPLE can take into account individual or multiple genetic markers affiliated with disease susceptibility and longevity.\nDetermination of life insurance policy value based on GPLE\n[00099] The resultant GPLE can be utilized in the evaluation of life insurance policies. The GPLE can be inserted into standard time value of money equations, such as Present Value, Future Value, IRR and Net Present Value methods to calculate the theoretical value of a policy given the resultant life expectancy based on the genetic disposition of the insured. The GPLE can be used as a time interval in any standard financial valuation equation that calls for discounting or accruing in the analysis of life insurance products.\n[000100] Time value of money approaches can discount an amount of funds in the future to determine their worth at a prior period, generally the present. This technique is applied to both lump sums and streams of cash flow. Adjustments in the calculations can be made for whether the cash flow takes place at the beginning or the end of the period. Additional mathematical adjustments may also be made to adjust for certain policy features, such as minimum guaranteed returns, compounding periods and the like.\n[000101] The present value v'-" of a single payment made at n periods in the future is\n[000102] où n is the number of periods until payment, P is the payment amount, and r is the periodic discount rate. The present value v« of equal payments made each successive period in perpetuity (a.k.a. the present value of a perpetuity) is given by\nΣ J (l + ιT r ' (2) \n [000103] The present value v' of equal payments made each successive period for « periods (i.e. the present value of an annuity) is given by","html":"[00089] Because age is rounded down to the last birthday, on average people live half a year beyond their final birthday, so half a year is added to the life expectancy to calculate the full life expectancy.\n[00090] Life expectancy is by definition an arithmetic mean. It can be calculated also by integrating the survival curve from ages 0 to positive infinity. For an extinct population of individuals, life expectancy can be calculated by averaging the ages at death. For a population of individuals with some survivors it is estimated by using mortality experience in recent years.\n[00091] Using this life expectancy calculation, no allowance has been made for expected changes in life expectancy in the future. Usually when life expectancy figures are quoted, they have been calculated in this manner with no allowance for expected future changes. This means that quoted life expectancy figures are not generally appropriate for calculating how long any given individual of a particular age is expected to live, as they effectively assume that current death rates will be "frozen" and not change in the future. Instead, life expectancy figures can be thought of as a useful statistic to summarize the current health status of a population. Some models do exist to account for the evolution of \n mortality (e.g., the Lee-Carter model) (R.D. Lee and L.Carter 1992. J. Amer. Stat. Assoc. 87:659-671) and can be used in the embodiments of the invention.\n[00092] Given the age, gender, race (AGR) of a person, the median life expectancy of the person can be calculated from mortality tables. Life expectancy calculations, in general, are heavily dependent on the criteria used to select the members of the population from which it is calculated. The baseline life expectancy (BLE) can be defined as the median life expectancy of individuals with matched AGR parameters.\n[00093] The inclusion of information on additional parameters such as medical factors (e.g., disease, stage of disease, treatment regimen, medical history and the like), environmental factors (e.g., exercise, smoking, occupational exposure and the like) and extended demographic information (e.g., geographical region, socioeconomic status and the like) can substantially enhance the life expectancy estimate for an individual. The specific life expectancy (SLE) of an individual for a given disease can be defined as the median life expectancy of individuals affected with that disease, with matched demographic, medical and environmental parameters. The specificity of the SLE for an individual for a given disease can depend on the availability of detail in the literature.\n[00094] The present invention provides a method for improved calculation of life expectancy based on genetic profiles, resulting in a GPLE. The inclusion of genetic information for an individual, such as SNPs, can increase the accuracy of life expectancy estimates. The GPLE is the median life expectancy of individuals with matched genetic profiles for individual candidate genes. In addition, calculation of GPLE by the methods herein, utilizes a central database apparatus under constant evolvement, continually factoring in the newest developments in genetic association scientific research reported in the literature.\n[00095] In preferred embodiments, the GPLE for an individual can be calculated from a blended approach, a minimum approach or any other approach known to one of ordinary skill in the art (in cases where the SLEs are not \n available, BLEs can be used). An example of a blended approach for three diseases is shown below. This approach calculates GPLE based on a combination of SLEs for three diseases (ij, i2, je3), where all the corresponding OR(i) values contribute to the GPLE:\n_ ORQ1) * SLEQ1) + OR(J2) • SLE(J2) + OR(J3) • SLE(J3) OR(I1) + OR(i2) + ORQ3)\n[00096] An example of a minimum approach for three diseases is shown below. This approach calculates GPLE based on the minimum of scaled SLEs for the diseases, where the scale factor for a corresponding ORQ) value is dependent on age and gender:\nmm • SLE(h) SLEJi2) SLE(J3)[SlOR(JySlOR(I[SlOR(JySlOR(I[SlOR(JySlOR(I[SlOR(JySlOR(I2) ' φR(h)\n[00097] The advantages of the GPLE calculation methods of the present invention above are twofold: 1) they combine a measure of the likelihood of an individual developing a disease (ORQ)) with the life expectancy of the individual with the genetic markers for that disease (reflected in the GPLE) and 2) a numerical value is provided that is indicative of the life expectancy of a person taking into account multiple input data or parameters, such as genetic, medical, environmental, demographic parameters.\n[00098] A preferred embodiment of the present invention is shown in FIG. 5. The determination of GPLE (28) can be based on information contained in a genetic database (20) and a life expectancy database (25). The genetic database can be comprised of information as discussed in FIG. 3. The life expectancy database (25) can contain information related to life expectancy data (21) and life table data (23). The retrieval of a specific life expectancy (22) from reported life expectancy data and the retrieval or construct of a baseline life expectancy (23) from reported life table data can be collectively housed in the life expectancy database (25). To determine GPLE, a user can calculate a collective risk index (26) based on multiple genetic factors and, along with the input data \n (27) from an individual, calculate a GPLE (28). The calculated GPLE can take into account individual or multiple genetic markers affiliated with disease susceptibility and longevity.\nDetermination of life insurance policy value based on GPLE\n[00099] The resultant GPLE can be utilized in the evaluation of life insurance policies. The GPLE can be inserted into standard time value of money equations, such as Present Value, Future Value, IRR and Net Present Value methods to calculate the theoretical value of a policy given the resultant life expectancy based on the genetic disposition of the insured. The GPLE can be used as a time interval in any standard financial valuation equation that calls for discounting or accruing in the analysis of life insurance products.\n[000100] Time value of money approaches can discount an amount of funds in the future to determine their worth at a prior period, generally the present. This technique is applied to both lump sums and streams of cash flow. Adjustments in the calculations can be made for whether the cash flow takes place at the beginning or the end of the period. Additional mathematical adjustments may also be made to adjust for certain policy features, such as minimum guaranteed returns, compounding periods and the like.\n[000101] The present value v'-" of a single payment made at n periods in the future is\n[000102] où n is the number of periods until payment, P is the payment amount, and r is the periodic discount rate. The present value v« of equal payments made each successive period in perpetuity (a.k.a. the present value of a perpetuity) is given by\nΣ J (l + ιT r ' (2) \n [000103] The present value v' of equal payments made each successive period for « periods (i.e. the present value of an annuity) is given by
"},{"id":"text-4","type":"text","heading":"","plain_text":"[000104] where P is the periodic payment amount.\n[000105] In applying the GPLE to value a policy, the GPLE can be used to project the date of death by adding the GPLE, which is essentially a time interval to the current date. The GPLE would represent the time interval in the future that the insured would be projected to expire, thereby generating a payment inflow of the face value of the policy at that date in the future. In order to calculate the theoretical value of the policy, the life insurance face value or policy proceeds would be discounted back from that projected future date to the present using either a market or required interest rate. In addition, the present value of the future stream of cash outlays representing the periodic premium payments required to keep the policy in force would be deducted from the present value of the policy proceeds received.\n[000106] A preferred embodiment of the present invention is shown in FIG. 6. The evaluation of a life insurance policy can be conducted using input from the GPLE (28) and from external input variables (e.g., interest rates, expenses, investments, returns, and the like) (29). The input conditions (27 and 28) can be used in actuarial calculations to determine a value for the life insurance policy as an asset (32) or to determine the value for the policy premium of a life insurance policy for an individual (31).\nExample 1: Calculation of OR(disease) for an individual with GSTMl null genotype\n[000107] For example, an OR for bladder cancer can be determined. To calculate the odds ratio, thirty-one population-based case-control studies were curated from PubMed to investigate the risk of bladder cancer associated with glutathione-S-transferase Ml (GSTMl) null genotype. To avoid confounding by \n ethnicity, five Caucasian-based studies were used, which included 896 cases and 1,241 controls. Odds ratios from these five individual studies range from 1.15 to 2.2 (Arch. Toxicol. 2000 74(9):521-6, Cytogen. Cell. Gen. 2000 91(l-4):234-8, Int. J. Cancer 2004 110(4):598-604, Cancer Lett. 2005 219(l):63-9, Carcinogenesis 2005 26(7): 1263-71.). The summary OR calculated using the Mantel-Haenszel method was 1.37 (95% CI [1.15, 1.64]) for the fixed effect model and 1.56 (95% CI [1.12, 1.91]) for the random effect model. This result also showed no significant heterogeneity in study outcomes among these five studies (p=0.08). The OR estimate from this analysis is similar to the summary OR from a meta-analysis conducted by Engel et al. that included seventeen individual studies (OR=I.44; 95% CI [1.23, 1.68]; 2,149 cases and 3,646 controls).\nExample 2: Calculation of OR(disease) for lung cancer, breast cancer and pancreatic cancer\n[000108] Assuming a list of three diseases (wherein for disease i, let OR(i) represent the cumulative additive effect of all relevant ORs for a given person): lung cancer (lung), breast cancer (breast) and pancreatic cancer (pancreatic), and each with ten known SNPs. For the example below, the following assumptions can be made; each SNP has an OR of 1.2. Environmental effect of smoking has an OR of 1.5 for lung cancer in general, and 1.6 when found in combination with SNP 1 for lung cancer. The OR of smoking for breast and pancreatic cancer is not known.\n[000109] For a given person, their SLE can be estimated for lung, breast and pancreatic cancer from the best matched life expectancy or life table data from literature, for example:\n[000110] SLE(lung) = 1.5 years, SLE(breast) = 10 years, SLE(pancreatic) = 1 year\n[000111] The OR(lung) for a given person can be calculated as follows based on the different scenarios: \n [000112] If an individual has SNPs 2-10, but not SNP 1, and is a non- smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)*9 + 1 = 2.8\n[000113] If an individual has SNPs 1-10, and is a non-smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2- 1)* 10 + 1 = 3\n[000114] If an individual has SNPs 1-10, and is a smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)* 10 + (0.6) + 1 = 3.6\n[000115] If an individual has SNPs 2-10, and is a smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)*9 + (0.5) + 1 = 3.3\n[000116] Similar to the OR(lung) calculations above, the OR(breast) and OR(pancreatic) can be similarly calculated to be OR(breast) = 0.5 and OR(pancreatic) = 1.2\nExample 3: Calculation of GPLE for an individual with SNPs 1-10 who is a smoker using a blended approach.\n[000117] The GPLE for the individual in Example 2 can be calculated using a blended approach that does not prioritize one disease over another. This type of approach evaluates the diseases in combination and provides for an overall perspective. The blended approach can be calculated as follows:\n_ OR(lung) • SLE(lung) + OR(breast) • SLE(breast) + OR(pancreatic) • SLE(pancreatic)\nOR(lung) + OR(breast) + OR(pancreatic) _ 3.4«1.5 + 0.5 «10 + 1.2«l 3.4 + 0.5 + 1.2\n= 2.22\nExample 4: Calculation of GPLE for an individual with SNPs 1-10 who is a smoker using a minimum approach.\n[000118] The GPLE for the individual in Example 2 can also be calculated using a minimum approach that factors in age and sex, resulting in a \n GPLE generated by the disease with the greatest contribution. The minimum approach can be calculated as follows:\n.[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—j– >\n[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J\n[000119] where p is a function of age and sex. Specifically, p = 1 + a ■ exp(-/? • age I λsexe), a,β > 0. Note that p is a monotonic decrease function of age, and α and β are two tuning parameters that can be determined by the mortality table. λsexe is a constant factor for sex, which is also determined by mortality table. λsexe=l for female if OR(disease)>l; otherwise, λsexe=l for male. If α=4, β=l/25, and λseχ=0.94, using the equation above, a GPLE minimum of (3.97, 17.50, 6.13), which is 3.97 for a male and min (4.12, 17.62, 6.16) = 4.12 for a female is generated. FIGUE. 7 illustrates a survival curve representing the relation between ξJθR(lung) and age/sex.\nExample 5: Calculation of GPLE for an individual with a high risk genetic mutation\n[000120] A high prevalence of mutation (4%, deletion of 25 bp) in the gene encoding cardiac myosin binding protein C (MYBPC3) is associated with high risk of heart failure (OR=7) [Dhandapany PS et al. (2009). A common MYBPC3 (cardiac myosin binding protein C) variant associated with cardiomyopathies in South Asia. Nat Genet. 41(2):187-91.]. Assuming SLE is 15 for individuals at age 55. If α=8, β=l/30, and λsexe=0.9, applying the minimum approach for life expectancy calculation, the GPLE is 5.8 for men and 6.4 for women with this gene mutation, e.g, 38% or 42% of SLE. Similarly, if SLE is 25 for individuals at age 45, the GPLE is 11.5 for men and 12.4 for women (46% or 50% of SLE). \n Example 6: Determination of life insurance policy value based on fatality score\n[000121] In continuation of the individual presented in Example 4 (the male, age 55 who has a mutation for the gene encoding cardiac myosin binding protein C (MYBPC3) and has a fatality score of 5.8), the calculations below assume the insured has a policy that has a face value of $1,000,000 and has monthly premiums due of $1000 a month to keep the policy in force. In addition, annual interest rate of 6% is assumed.\n[000122] The life expectancy fatality score of 5.8 can be converted into 69.6 months.\n[000123] Applying the formula for Present Value results in the present value of the policy proceeds would be $706,711.41.\n[000124] From this we must subtract the Present Value of the 69.6 payments which equals -$58,657.72 as the total cost in present value terms of the 69.6 payments.\n[000125] Therefore the theoretical value of this policy assuming an interest rate of 6% is $706,711.41- $58,657.72= $648,053.69. \nAPPENDICE","html":"[000104] where P is the periodic payment amount.\n[000105] In applying the GPLE to value a policy, the GPLE can be used to project the date of death by adding the GPLE, which is essentially a time interval to the current date. The GPLE would represent the time interval in the future that the insured would be projected to expire, thereby generating a payment inflow of the face value of the policy at that date in the future. In order to calculate the theoretical value of the policy, the life insurance face value or policy proceeds would be discounted back from that projected future date to the present using either a market or required interest rate. In addition, the present value of the future stream of cash outlays representing the periodic premium payments required to keep the policy in force would be deducted from the present value of the policy proceeds received.\n[000106] A preferred embodiment of the present invention is shown in FIG. 6. The evaluation of a life insurance policy can be conducted using input from the GPLE (28) and from external input variables (e.g., interest rates, expenses, investments, returns, and the like) (29). The input conditions (27 and 28) can be used in actuarial calculations to determine a value for the life insurance policy as an asset (32) or to determine the value for the policy premium of a life insurance policy for an individual (31).\nExample 1: Calculation of OR(disease) for an individual with GSTMl null genotype\n[000107] For example, an OR for bladder cancer can be determined. To calculate the odds ratio, thirty-one population-based case-control studies were curated from PubMed to investigate the risk of bladder cancer associated with glutathione-S-transferase Ml (GSTMl) null genotype. To avoid confounding by \n ethnicity, five Caucasian-based studies were used, which included 896 cases and 1,241 controls. Odds ratios from these five individual studies range from 1.15 to 2.2 (Arch. Toxicol. 2000 74(9):521-6, Cytogen. Cell. Gen. 2000 91(l-4):234-8, Int. J. Cancer 2004 110(4):598-604, Cancer Lett. 2005 219(l):63-9, Carcinogenesis 2005 26(7): 1263-71.). The summary OR calculated using the Mantel-Haenszel method was 1.37 (95% CI [1.15, 1.64]) for the fixed effect model and 1.56 (95% CI [1.12, 1.91]) for the random effect model. This result also showed no significant heterogeneity in study outcomes among these five studies (p=0.08). The OR estimate from this analysis is similar to the summary OR from a meta-analysis conducted by Engel et al. that included seventeen individual studies (OR=I.44; 95% CI [1.23, 1.68]; 2,149 cases and 3,646 controls).\nExample 2: Calculation of OR(disease) for lung cancer, breast cancer and pancreatic cancer\n[000108] Assuming a list of three diseases (wherein for disease i, let OR(i) represent the cumulative additive effect of all relevant ORs for a given person): lung cancer (lung), breast cancer (breast) and pancreatic cancer (pancreatic), and each with ten known SNPs. For the example below, the following assumptions can be made; each SNP has an OR of 1.2. Environmental effect of smoking has an OR of 1.5 for lung cancer in general, and 1.6 when found in combination with SNP 1 for lung cancer. The OR of smoking for breast and pancreatic cancer is not known.\n[000109] For a given person, their SLE can be estimated for lung, breast and pancreatic cancer from the best matched life expectancy or life table data from literature, for example:\n[000110] SLE(lung) = 1.5 years, SLE(breast) = 10 years, SLE(pancreatic) = 1 year\n[000111] The OR(lung) for a given person can be calculated as follows based on the different scenarios: \n [000112] If an individual has SNPs 2-10, but not SNP 1, and is a non- smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)*9 + 1 = 2.8\n[000113] If an individual has SNPs 1-10, and is a non-smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2- 1)* 10 + 1 = 3\n[000114] If an individual has SNPs 1-10, and is a smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)* 10 + (0.6) + 1 = 3.6\n[000115] If an individual has SNPs 2-10, and is a smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)*9 + (0.5) + 1 = 3.3\n[000116] Similar to the OR(lung) calculations above, the OR(breast) and OR(pancreatic) can be similarly calculated to be OR(breast) = 0.5 and OR(pancreatic) = 1.2\nExample 3: Calculation of GPLE for an individual with SNPs 1-10 who is a smoker using a blended approach.\n[000117] The GPLE for the individual in Example 2 can be calculated using a blended approach that does not prioritize one disease over another. This type of approach evaluates the diseases in combination and provides for an overall perspective. The blended approach can be calculated as follows:\n_ OR(lung) • SLE(lung) + OR(breast) • SLE(breast) + OR(pancreatic) • SLE(pancreatic)\nOR(lung) + OR(breast) + OR(pancreatic) _ 3.4«1.5 + 0.5 «10 + 1.2«l 3.4 + 0.5 + 1.2\n= 2.22\nExample 4: Calculation of GPLE for an individual with SNPs 1-10 who is a smoker using a minimum approach.\n[000118] The GPLE for the individual in Example 2 can also be calculated using a minimum approach that factors in age and sex, resulting in a \n GPLE generated by the disease with the greatest contribution. The minimum approach can be calculated as follows:\n.[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—j– >\n[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J\n[000119] where p is a function of age and sex. Specifically, p = 1 + a ■ exp(-/? • age I λsexe), a,β > 0. Note that p is a monotonic decrease function of age, and α and β are two tuning parameters that can be determined by the mortality table. λsexe is a constant factor for sex, which is also determined by mortality table. λsexe=l for female if OR(disease)>l; otherwise, λsexe=l for male. If α=4, β=l/25, and λseχ=0.94, using the equation above, a GPLE minimum of (3.97, 17.50, 6.13), which is 3.97 for a male and min (4.12, 17.62, 6.16) = 4.12 for a female is generated. FIGUE. 7 illustrates a survival curve representing the relation between ξJθR(lung) and age/sex.\nExample 5: Calculation of GPLE for an individual with a high risk genetic mutation\n[000120] A high prevalence of mutation (4%, deletion of 25 bp) in the gene encoding cardiac myosin binding protein C (MYBPC3) is associated with high risk of heart failure (OR=7) [Dhandapany PS et al. (2009). A common MYBPC3 (cardiac myosin binding protein C) variant associated with cardiomyopathies in South Asia. Nat Genet. 41(2):187-91.]. Assuming SLE is 15 for individuals at age 55. If α=8, β=l/30, and λsexe=0.9, applying the minimum approach for life expectancy calculation, the GPLE is 5.8 for men and 6.4 for women with this gene mutation, e.g, 38% or 42% of SLE. Similarly, if SLE is 25 for individuals at age 45, the GPLE is 11.5 for men and 12.4 for women (46% or 50% of SLE). \n Example 6: Determination of life insurance policy value based on fatality score\n[000121] In continuation of the individual presented in Example 4 (the male, age 55 who has a mutation for the gene encoding cardiac myosin binding protein C (MYBPC3) and has a fatality score of 5.8), the calculations below assume the insured has a policy that has a face value of $1,000,000 and has monthly premiums due of $1000 a month to keep the policy in force. In addition, annual interest rate of 6% is assumed.\n[000122] The life expectancy fatality score of 5.8 can be converted into 69.6 months.\n[000123] Applying the formula for Present Value results in the present value of the policy proceeds would be $706,711.41.\n[000124] From this we must subtract the Present Value of the 69.6 payments which equals -$58,657.72 as the total cost in present value terms of the 69.6 payments.\n[000125] Therefore the theoretical value of this policy assuming an interest rate of 6% is $706,711.41- $58,657.72= $648,053.69. \nAPPENDICE
"},{"id":"text-5","type":"text","heading":"","plain_text":"# ! /usr/bin/perl use strict; use warnings ; use LWP 5.64; # Loads all important LWP classes, and makes sure your version is reasonably recent. use Data : : Dumper ; use CGI ' : standard ' ; use CGI :: Carp qw(fatalsToBrowser) ; use File:: Temp qw/ tempfile tempdir /; my $num_of_terms = 5; # Change this setting to change the number of search terms allowed for each filtering level.\n#my $adminemail = ; print header; if ( !param)\n print << 'EOF' ;","html":"# ! /usr/bin/perl use strict; use warnings ; use LWP 5.64; # Loads all important LWP classes, and makes sure your version is reasonably recent. use Data : : Dumper ; use CGI ' : standard ' ; use CGI :: Carp qw(fatalsToBrowser) ; use File:: Temp qw/ tempfile tempdir /; my $num_of_terms = 5; # Change this setting to change the number of search terms allowed for each filtering level.\n#my $adminemail = ; print header; if ( !param)\n print << 'EOF' ;
"},{"id":"text-6","type":"text","heading":"","plain_text":"EOF print "", start_html ( 'HuGE meta- search' ) , "" , hi ( ' HuGE metasearch – Advanced ' ) , "" ; print "This is a powerful yet convenient and simple front end to the HuGE Literature Finder tool.",br,\n' Important : You will need to read the Très bref ' ,\n 1 Documentation ' , ' in order to use it correctly . ' ,p, start_multipart_form; print "Enter search terms for HuGE navigator database: ",br, textfield(-name=> ' condition' , -size=>40) ; print " (Do Not enter boolean queries into this box.)"; print "Enter search tags to further filter context by and highlight or eliminate: ",p; print 'Must contain tout of these words ' ,br,- \n foreach my $i (1.. $num_of_terms)\n my $paramname = "and_searchterm" . $i; print textfield(-name=>$paramname, – size=>15) , '  ,- ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "and_casesensitive" . $i; print checkbox ( -name=>$paramname ,\n-selected => 0,\n-value=> 1Y ',\n-label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p; print 'Must contain tout of these words ' , br; foreach my $i (1.. $num_of_terms)\n my $paramname = "or_searchterm" . $i; print textfield ( -name=>$paramname, – size=>15) , '   ;   ;   ; ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "or_casesensitive" . $i; print checkbox (-name=>$paramname, -selected => 0, -value=>'Y' ; -label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p ; print 'Must ne pas contain any of these words ',br; foreach my $i (1.. $num_of_terms)\n my $paramname = "not_searchterm" . $i; print textfield (-name=>$paramname, – size=>15) , '   ;   ;   ; ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "not_casesensitive" . $i; print checkbox (-name=>$paramname, -selected => 0, -value=> ' Y' , -label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p; print "All filter terms are assumed to be exact phrases. Non\n wild cards . " ; print br , checkbox ( -name=> ' showabstract ' , – selected => 1 ,\n-value=> ' Y ' ,\n-label=>' ■ ) , " Check here if you want to see full abstract ." ,hr; print "Use the engine that is " ; print ' ' , "n" ; print 'faster but cuts corners and can fail' , "n" ; print 'slower but rigorous and failsafe' , "n" ; print ' ' , "n" ; print '  ,-  ,- ' , submit (' SUBMIT '), " Scnbsp    " , reset, ' ' , end_form, hr;\n else\n{ my $dir = tempdir (DIR => " /var/www/vhosts/default/htdocs/tmpdir/ " ) ; if (! (-d $dir) ) system ("mkdir $dir"); \n# print ' ' ; print ' ' ; my $searchcondition = param ( "condition" ); my %searchterm = ( ) ; my %casesens = ( ) ; foreach my $lo ("and", "or", "not")\n{ foreach my $i (1.. $num_of_terms)\n my $paramtag = $lo. "_searchterm" . $i; if (param ($paramtag) && param ($paramtag) =~ /S/)","html":"EOF print "", start_html ( 'HuGE meta- search' ) , "" , hi ( ' HuGE metasearch – Advanced ' ) , "" ; print "This is a powerful yet convenient and simple front end to the HuGE Literature Finder tool.",br,\n' Important : You will need to read the Très bref ' ,\n 1 Documentation ' , ' in order to use it correctly . ' ,p, start_multipart_form; print "Enter search terms for HuGE navigator database: ",br, textfield(-name=> ' condition' , -size=>40) ; print " (Do Not enter boolean queries into this box.)"; print "Enter search tags to further filter context by and highlight or eliminate: ",p; print 'Must contain tout of these words ' ,br,- \n foreach my $i (1.. $num_of_terms)\n my $paramname = "and_searchterm" . $i; print textfield(-name=>$paramname, – size=>15) , '  ,- ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "and_casesensitive" . $i; print checkbox ( -name=>$paramname ,\n-selected => 0,\n-value=> 1Y ',\n-label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p; print 'Must contain tout of these words ' , br; foreach my $i (1.. $num_of_terms)\n my $paramname = "or_searchterm" . $i; print textfield ( -name=>$paramname, – size=>15) , '   ;   ;   ; ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "or_casesensitive" . $i; print checkbox (-name=>$paramname, -selected => 0, -value=>'Y' ; -label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p ; print 'Must ne pas contain any of these words ',br; foreach my $i (1.. $num_of_terms)\n my $paramname = "not_searchterm" . $i; print textfield (-name=>$paramname, – size=>15) , '   ;   ;   ; ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "not_casesensitive" . $i; print checkbox (-name=>$paramname, -selected => 0, -value=> ' Y' , -label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p; print "All filter terms are assumed to be exact phrases. Non\n wild cards . " ; print br , checkbox ( -name=> ' showabstract ' , – selected => 1 ,\n-value=> ' Y ' ,\n-label=>' ■ ) , " Check here if you want to see full abstract ." ,hr; print "Use the engine that is " ; print ' ' , "n" ; print 'faster but cuts corners and can fail' , "n" ; print 'slower but rigorous and failsafe' , "n" ; print ' ' , "n" ; print '  ,-  ,- ' , submit (' SUBMIT '), " Scnbsp    " , reset, ' ' , end_form, hr;\n else\n{ my $dir = tempdir (DIR => " /var/www/vhosts/default/htdocs/tmpdir/ " ) ; if (! (-d $dir) ) system ("mkdir $dir"); \n# print ' ' ; print ' ' ; my $searchcondition = param ( "condition" ); my %searchterm = ( ) ; my %casesens = ( ) ; foreach my $lo ("and", "or", "not")\n{ foreach my $i (1.. $num_of_terms)\n my $paramtag = $lo. "_searchterm" . $i; if (param ($paramtag) && param ($paramtag) =~ /S/)
"},{"id":"text-7","type":"text","heading":"","plain_text":"$searchterm$lo $i = param ($paramtag) ;\n$searchterm$lo $i =~ s/s+$//g;\n$searchterm$lo $i =~ s/UNEs+//g; \n$paramtag = $lo. "_casesensitive" . $i; if (param ($paramtag) && param ($paramtag) =~ /S/) $casesens$lo $i = param ($paramtag) ; my $showabstract = param ( "showabstract" ); my $outfile = "HuGE_fetched. csv" ; open (OUTCSV, ">$dir/$outfile") or die "Cannot open $dir/$outfile \nfor writingn" ; print OUTCSV "HuGE Query, $searchconditionn" ; print OUTCSV "Highlighting/Filtering Tag(s)n"; print OUTCSV "All these terms are required:";\n# Tagging all the required terms with the actual HuGE query is a good idea because it\n# will reduce the actual number of hits that need to be fetched. But the user better not enter\n# an OR into the HuGE query (because HuGE does not tolerate mixing logical operators) . my $full_hugestring = $searchcondition; if (param ( 'version' ) eq 'hardhack')\n{ foreach my $key (keys % $searchterm "and" )\n my $srchterm = $searchterm "and" $key ; if (defined $srchterm && $srchterm =~ /S/)\n print OUTCSV " , $srchterm" ;\n$full_hugestring .= " AND $srchterm" ; \n print OUTCSV "nAny of these terms are required:"; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (defined $srchterm && $srchterm =- /S/) print OUTCSV "$srchterm, " ; \n print OUTCSV "n"; print OUTCSV "All these terms are avoided:"; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (defined $srchterm && $srchterm =~ /S/) print OUTCSV "$srchterm, " ; \n print OUTCSV "nn"; my $browser = LWP: :UserAgent->new; my $url = "http: //hugenavigator .net/HuGENavigator/searchSummary .do" ; my $response = $browser->post ( $url,[[[[\n'User-Agent' => 'Mozilla/4.76 [en] (Win98; U) ', 'Accept' => 'image/gif, image/x-xbitmap, image/jpeg, image/pjpeg, image/png, */* ' ,\n 1 Accept -Charset' => ' iso-8859-1, * ,utf-8 ' , ' Accept -Language ' => 'en-US',\n'firstQuery' => $full_hugestring, 'publitSearchType ' => "now", \n 1 whichContinue ' => "firststart" , 'check' => "n", 'dbType' => "publit", 1Mysubmit' => "go" ], ); die "$url error: ", $response->status_line unless $response->is_success; die "Weird content type at $url — ", $response->content_type unless $response->content_type eq ' text/html ' ; my @pmids = ( ) ; if ( $response->content =~ /No articles found/)\n # print $response->content ; print "Couldn't find the match-string in the responsen" ; exit; open (TEMP, " >$dir/huge_metasearcher .html" ) or die "Cannot open huge_metasearcher.html for writingn" ; print TEMP $response->content ; close TEMP; my $startindex = index ($response->content, "fileDownloadForm" ) ; my $subtextl = substr ($response->content, $startindex) ; my $endindex = index ($subtextl, "Search Criteria: " ); my $subtext2 = substr ($subtextl, 0, $endindex) ;\n$subtext2 =~ s/ . *value="//g; $subtext2 =~ s/">.*//g; $subtext2 =~ s/file.*//g; $subtext2 =~ s/\n.*//g; $subtext2 =~ s/ . *Text. *//g; $subtext2 =~ s/s+//g; $subtext2 =~ s/pubmedid//g;\n@pmids = split (/,/, $subtext2) ; print 'Final HuGE query: ' . $full_hugestring. "n" ; print "Number of records hit from the HuGE database = " , scalar (Opmids) , "" ; open (LOG, " >$dir/huge_metasearcher . log" ) or die "Cannot open $dir/huge_metasearcher . log for writingn" ; print LOG "PMIDs are n" . join( "n" ,@pmids) . "nn" ;\n## It ' s faster to lump 12 PMIDs together and fetch at a time rather than sending an\n## HTTP request to pubmed for each one separately (try higher at own risk with lynx) . So.. my $i=0; my $lumpsize = 18; my @medline_articles = ( ) ; while ($i<scalar (Opmids) -1)","html":"$searchterm$lo $i = param ($paramtag) ;\n$searchterm$lo $i =~ s/s+$//g;\n$searchterm$lo $i =~ s/UNEs+//g; \n$paramtag = $lo. "_casesensitive" . $i; if (param ($paramtag) && param ($paramtag) =~ /S/) $casesens$lo $i = param ($paramtag) ; my $showabstract = param ( "showabstract" ); my $outfile = "HuGE_fetched. csv" ; open (OUTCSV, ">$dir/$outfile") or die "Cannot open $dir/$outfile \nfor writingn" ; print OUTCSV "HuGE Query, $searchconditionn" ; print OUTCSV "Highlighting/Filtering Tag(s)n"; print OUTCSV "All these terms are required:";\n# Tagging all the required terms with the actual HuGE query is a good idea because it\n# will reduce the actual number of hits that need to be fetched. But the user better not enter\n# an OR into the HuGE query (because HuGE does not tolerate mixing logical operators) . my $full_hugestring = $searchcondition; if (param ( 'version' ) eq 'hardhack')\n{ foreach my $key (keys % $searchterm "and" )\n my $srchterm = $searchterm "and" $key ; if (defined $srchterm && $srchterm =~ /S/)\n print OUTCSV " , $srchterm" ;\n$full_hugestring .= " AND $srchterm" ; \n print OUTCSV "nAny of these terms are required:"; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (defined $srchterm && $srchterm =- /S/) print OUTCSV "$srchterm, " ; \n print OUTCSV "n"; print OUTCSV "All these terms are avoided:"; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (defined $srchterm && $srchterm =~ /S/) print OUTCSV "$srchterm, " ; \n print OUTCSV "nn"; my $browser = LWP: :UserAgent->new; my $url = "http: //hugenavigator .net/HuGENavigator/searchSummary .do" ; my $response = $browser->post ( $url,[[[[\n'User-Agent' => 'Mozilla/4.76 [en] (Win98; U) ', 'Accept' => 'image/gif, image/x-xbitmap, image/jpeg, image/pjpeg, image/png, */* ' ,\n 1 Accept -Charset' => ' iso-8859-1, * ,utf-8 ' , ' Accept -Language ' => 'en-US',\n'firstQuery' => $full_hugestring, 'publitSearchType ' => "now", \n 1 whichContinue ' => "firststart" , 'check' => "n", 'dbType' => "publit", 1Mysubmit' => "go" ], ); die "$url error: ", $response->status_line unless $response->is_success; die "Weird content type at $url — ", $response->content_type unless $response->content_type eq ' text/html ' ; my @pmids = ( ) ; if ( $response->content =~ /No articles found/)\n # print $response->content ; print "Couldn't find the match-string in the responsen" ; exit; open (TEMP, " >$dir/huge_metasearcher .html" ) or die "Cannot open huge_metasearcher.html for writingn" ; print TEMP $response->content ; close TEMP; my $startindex = index ($response->content, "fileDownloadForm" ) ; my $subtextl = substr ($response->content, $startindex) ; my $endindex = index ($subtextl, "Search Criteria: " ); my $subtext2 = substr ($subtextl, 0, $endindex) ;\n$subtext2 =~ s/ . *value="//g; $subtext2 =~ s/">.*//g; $subtext2 =~ s/file.*//g; $subtext2 =~ s/\n.*//g; $subtext2 =~ s/ . *Text. *//g; $subtext2 =~ s/s+//g; $subtext2 =~ s/pubmedid//g;\n@pmids = split (/,/, $subtext2) ; print 'Final HuGE query: ' . $full_hugestring. "n" ; print "Number of records hit from the HuGE database = " , scalar (Opmids) , "" ; open (LOG, " >$dir/huge_metasearcher . log" ) or die "Cannot open $dir/huge_metasearcher . log for writingn" ; print LOG "PMIDs are n" . join( "n" ,@pmids) . "nn" ;\n## It ' s faster to lump 12 PMIDs together and fetch at a time rather than sending an\n## HTTP request to pubmed for each one separately (try higher at own risk with lynx) . So.. my $i=0; my $lumpsize = 18; my @medline_articles = ( ) ; while ($i<scalar (Opmids) -1)
"},{"id":"text-8","type":"text","heading":"","plain_text":"$i=$i+$lumpsize; \n if ( $i>=scalar (Θpmids) ) $i=scalar (@pmids) -1 ; my @current_pmids = @pmids [ ($i- $lumpsize) . . $i] ; my $url =\n 1 http : //www . ncbi . nlm . nih . gov/pubmed/ ' .joint", " , @current_j?mids ) . ' ?report =medline&format=text ' ; print LOG "Current URL: $urln" ; my $current_medline_articles_lumped = "lynx -dump – dont_wrap_jpre ' $url ' – ; my @current_medline_articles = split (/PMID-/, $current_medline_articles_lumped) ; shift (@current_medline_articles) ; push (@medline_articles, @current_medline_articles) ;","html":"$i=$i+$lumpsize; \n if ( $i>=scalar (Θpmids) ) $i=scalar (@pmids) -1 ; my @current_pmids = @pmids [ ($i- $lumpsize) . . $i] ; my $url =\n 1 http : //www . ncbi . nlm . nih . gov/pubmed/ ' .joint", " , @current_j?mids ) . ' ?report =medline&format=text ' ; print LOG "Current URL: $urln" ; my $current_medline_articles_lumped = "lynx -dump – dont_wrap_jpre ' $url ' – ; my @current_medline_articles = split (/PMID-/, $current_medline_articles_lumped) ; shift (@current_medline_articles) ; push (@medline_articles, @current_medline_articles) ;
"},{"id":"text-9","type":"text","heading":"","plain_text":"# End of lumped fetching procedure print LOG "nn"; my %Articles = () ; foreach my $medline_article (@medline_articles)\n{\n$medline_article = "PMID- ". $medline_article; my $pmid = 0 ; my @medline_lines = split (/n/, $medline_article) ; my %medline_hash = ( ) ; my $current_key = " " ; foreach my $line (@medline_lines)\n if ($line =~ /S/)\n if ($line =~ /ΛS/ && substr ($line, 4, 1) eq "-","html":"# End of lumped fetching procedure print LOG "nn"; my %Articles = () ; foreach my $medline_article (@medline_articles)\n{\n$medline_article = "PMID- ". $medline_article; my $pmid = 0 ; my @medline_lines = split (/n/, $medline_article) ; my %medline_hash = ( ) ; my $current_key = " " ; foreach my $line (@medline_lines)\n if ($line =~ /S/)\n if ($line =~ /ΛS/ && substr ($line, 4, 1) eq "-
"},{"id":"text-10","type":"text","heading":"","plain_text":"$current_key = substr ($line, 0, 4) ; $current_key =~ s/s+//g;\n my $current_value_line = substr ($line, 5) ;\n$current_value_line =~ s/UNE //g; chomp $current_value_line; if (defined $medline_hash$current_key )","html":"$current_key = substr ($line, 0, 4) ; $current_key =~ s/s+//g;\n my $current_value_line = substr ($line, 5) ;\n$current_value_line =~ s/UNE //g; chomp $current_value_line; if (defined $medline_hash$current_key )
"},{"id":"text-11","type":"text","heading":"","plain_text":"$medline_hash$current_key .=\n$current value line;\n else $medline_hash$current_key =\n$current_value_line,- if ($current_key eq "TI" $current_key .eq\n"AB")","html":"$medline_hash$current_key .=\n$current value line;\n else $medline_hash$current_key =\n$current_value_line,- if ($current_key eq "TI" $current_key .eq\n"AB")
"},{"id":"text-12","type":"text","heading":"","plain_text":"$medline_hash$current_key .= "n";\n elsif ($current_key eq "PMID")","html":"$medline_hash$current_key .= "n";\n elsif ($current_key eq "PMID")
"},{"id":"text-13","type":"text","heading":"","plain_text":"$pmid = $current_value_line,- $pmid =~ s/s+//g,- \n# print "Addingn $current_value_linen TOn€current_key";","html":"$pmid = $current_value_line,- $pmid =~ s/s+//g,- \n# print "Addingn $current_value_linen TOn€current_key";
"},{"id":"text-14","type":"text","heading":"","plain_text":"if ($pmid == 0) die "PMID is still unresolved for this article \n$medline_article\n" ; \n$medline_hash"PMID" =~ s/s+//g; $Articles$pmid = %medline_hash;\n# print "" , $Articles$pmid-> "AB" , "" ,- print LOG Dumper (%Articles) , "n======================================nnn" ; close LOG;\n# print join("", @pmids) , "\n" ; print "Highlighted tag (s) : " ; print "All-are-required terms: "; foreach my $key (keys %$searchterm "and" )\n my $srchterm = $searchterm "and" $key ,- if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "nAny-one- is-required terms: " ; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "nMust-be-absent terms: " ; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "n" ;\n##### FILTERING STEP BEGINS ##### my Ofiltered_pmidsl = (); if (scalar(keys %$searchterm "and" ) > 0 && parara ( 'version' ) eq ' rigorous ' )\n{ foreach my $pmid (@pmids)\n my ($ab, $ti) = ($Articles$pmid-> "AB" , $Articles$pmid->"TI") ; my $yes = 1; foreach my $key (keys %$searchterm "and" )\n my $srchterm = $searchterm "and" $key ; if (uc ($casesens"and" $key) =~ /Y/) \n if ( found ($srchterm, $ti) == 0 && found ($srchterm, $ab) == 0) $yes = 0; \n else\n if (found_i ($srchterm, $ti) == 0 && found_i ($srchterm, $ab) == 0) $yes = 0; \n if ($yes == 1) @filtered_jpmidsl = addtolist (@filtered_jpmidsl, $pmid) ; else @filtered_pmidsl = Opmids,- if (scalar (@filtered_pmidsl) == 0)\n print "No articles pass the ALL-ARE-REQUIRED search terms. Try altering the highlighting requirements . n" ; exit;\n else print scalar (Ofiltered_praidsl) ." articles passed the ALL- ARE-REQUIRED filtersn"; my @filtered_pmids2 = (); if (scalar(keys %$searchterm "or" ) > 0)\n{ foreach my $pmid (@filtered_jpmidsl)\n{ my ($ab, $ti) = ($Articles$praid -> "AB" , $Articles$pmid->"TI") ; my $yes = 0 ; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (uc($casesens"or"$key) =~ /Y/)\n if ( found ($srchterm, $ti) == 1 else\n if (found_i ($srchterm, $ti) == 1 if ($yes == 1) @filtered_pmids2 = addtolist (@filtered_jpmids2 , $pmid) ; else @filtered_jpmids2 = @filtered_pmidsl; if (scalar (@filtered_pmids2) == 0)\n print "No articles pass after the ANY-ARE-REQUIRED search terms. Try altering the highlighting requirements . n" ; exit; \n else print scalar (@filtered_j?mids2) ." articles passed the ANYONE- IS -REQUIRED filtersn" ; my @filtered_pmids3 = (); if (scalar (keys %$searchterm "not" ) > 0)\n{ foreach my $pmid (@filtered_jpmids2)\n my ($ab, $ti) = ($Articles$pmid -> "AB" , $Articles$pmid->"TI") ; my $yes = 1; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (uc($casesens"not"$key) =~ /Y/)\n found ($srchterm, $ab) == 1)\n print "\nSearch term $srchterm exists in title $ti or abstract $ab\nn" ; ; \n else\n found_i ($srchterm, $ab) == 1) $yes = 0; if (found_i ($srchterm, $ti) == 1 if ($yes == 1) push (@filtered_jpmids3 , $pmid) ; \n else @filtered_pmids3 = @filtered_j)mids2; if (scalar (@filtered_pmids3) == 0)\n print "No articles pass after the MUST-BE-ABSENT search terms. Try altering the highlighting requirements. n" ; exit;\n else print scalar (Ofiltered_jpmids3) ." articles passed the MUST- BE-ABSENT filtersn" ; my $webdir = $dir;\n$webdir =~ s//var/www/vhosts/default/htdocs//g; print 'Click ici to download output in CSV format\n'; print "\nn" ; , print "\nn"; \n if (uc ($showabstract) =~ /Y/)\n print "#"; print "PMID"; print "Titre" ; print "Context" ; print "Abstraitn" ; print OUTCSV " # , PMID, Title, Context ,Abstractn" ;\n else\n print "#"; print "PMID" ; print "Titre" ; print "Contextn" ,- print OUTCSV "#, PMID, Title, Contextn";\n print "\nn"; foreach my $i (1..scalar (Ofiltered_pmids3) )\n{ my $pmid = $filtered_jomids3 [$i-l] ; if (defined $Articles$pmid ) else die "No article for PMID $pmid or some other unknown error . n" ; # print "Currently processing PMID $pmidn" ; my %medline_hash = %$Articles$pmid ; print "\nn"; print '\n ' . "$i\n" ; my $pmid_link = "http: //www.ncbi .nlm.nih.gov/pubmed/" . $medline_hash "PMID" ; print '\n ' ; print ' ' . $medline_hash "PMID" . ""; my $modti = $medline_hash "TI" ; my $modab = $medline_hash "AB" ; foreach my $lo ("and", "or")\n{ foreach my $key (keys %$searchterm$lo )\n my $srchterm = $searchterm$lo $key ; if (uc ($casesens$lo $key ) =~ /Y/)","html":"if ($pmid == 0) die "PMID is still unresolved for this article \n$medline_article\n" ; \n$medline_hash"PMID" =~ s/s+//g; $Articles$pmid = %medline_hash;\n# print "" , $Articles$pmid-> "AB" , "" ,- print LOG Dumper (%Articles) , "n======================================nnn" ; close LOG;\n# print join("", @pmids) , "\n" ; print "Highlighted tag (s) : " ; print "All-are-required terms: "; foreach my $key (keys %$searchterm "and" )\n my $srchterm = $searchterm "and" $key ,- if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "nAny-one- is-required terms: " ; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "nMust-be-absent terms: " ; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "n" ;\n##### FILTERING STEP BEGINS ##### my Ofiltered_pmidsl = (); if (scalar(keys %$searchterm "and" ) > 0 && parara ( 'version' ) eq ' rigorous ' )\n{ foreach my $pmid (@pmids)\n my ($ab, $ti) = ($Articles$pmid-> "AB" , $Articles$pmid->"TI") ; my $yes = 1; foreach my $key (keys %$searchterm "and" )\n my $srchterm = $searchterm "and" $key ; if (uc ($casesens"and" $key) =~ /Y/) \n if ( found ($srchterm, $ti) == 0 && found ($srchterm, $ab) == 0) $yes = 0; \n else\n if (found_i ($srchterm, $ti) == 0 && found_i ($srchterm, $ab) == 0) $yes = 0; \n if ($yes == 1) @filtered_jpmidsl = addtolist (@filtered_jpmidsl, $pmid) ; else @filtered_pmidsl = Opmids,- if (scalar (@filtered_pmidsl) == 0)\n print "No articles pass the ALL-ARE-REQUIRED search terms. Try altering the highlighting requirements . n" ; exit;\n else print scalar (Ofiltered_praidsl) ." articles passed the ALL- ARE-REQUIRED filtersn"; my @filtered_pmids2 = (); if (scalar(keys %$searchterm "or" ) > 0)\n{ foreach my $pmid (@filtered_jpmidsl)\n{ my ($ab, $ti) = ($Articles$praid -> "AB" , $Articles$pmid->"TI") ; my $yes = 0 ; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (uc($casesens"or"$key) =~ /Y/)\n if ( found ($srchterm, $ti) == 1 else\n if (found_i ($srchterm, $ti) == 1 if ($yes == 1) @filtered_pmids2 = addtolist (@filtered_jpmids2 , $pmid) ; else @filtered_jpmids2 = @filtered_pmidsl; if (scalar (@filtered_pmids2) == 0)\n print "No articles pass after the ANY-ARE-REQUIRED search terms. Try altering the highlighting requirements . n" ; exit; \n else print scalar (@filtered_j?mids2) ." articles passed the ANYONE- IS -REQUIRED filtersn" ; my @filtered_pmids3 = (); if (scalar (keys %$searchterm "not" ) > 0)\n{ foreach my $pmid (@filtered_jpmids2)\n my ($ab, $ti) = ($Articles$pmid -> "AB" , $Articles$pmid->"TI") ; my $yes = 1; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (uc($casesens"not"$key) =~ /Y/)\n found ($srchterm, $ab) == 1)\n print "\nSearch term $srchterm exists in title $ti or abstract $ab\nn" ; ; \n else\n found_i ($srchterm, $ab) == 1) $yes = 0; if (found_i ($srchterm, $ti) == 1 if ($yes == 1) push (@filtered_jpmids3 , $pmid) ; \n else @filtered_pmids3 = @filtered_j)mids2; if (scalar (@filtered_pmids3) == 0)\n print "No articles pass after the MUST-BE-ABSENT search terms. Try altering the highlighting requirements. n" ; exit;\n else print scalar (Ofiltered_jpmids3) ." articles passed the MUST- BE-ABSENT filtersn" ; my $webdir = $dir;\n$webdir =~ s//var/www/vhosts/default/htdocs//g; print 'Click ici to download output in CSV format\n'; print "\nn" ; , print "\nn"; \n if (uc ($showabstract) =~ /Y/)\n print "#"; print "PMID"; print "Titre" ; print "Context" ; print "Abstraitn" ; print OUTCSV " # , PMID, Title, Context ,Abstractn" ;\n else\n print "#"; print "PMID" ; print "Titre" ; print "Contextn" ,- print OUTCSV "#, PMID, Title, Contextn";\n print "\nn"; foreach my $i (1..scalar (Ofiltered_pmids3) )\n{ my $pmid = $filtered_jomids3 [$i-l] ; if (defined $Articles$pmid ) else die "No article for PMID $pmid or some other unknown error . n" ; # print "Currently processing PMID $pmidn" ; my %medline_hash = %$Articles$pmid ; print "\nn"; print '\n ' . "$i\n" ; my $pmid_link = "http: //www.ncbi .nlm.nih.gov/pubmed/" . $medline_hash "PMID" ; print '\n ' ; print ' ' . $medline_hash "PMID" . ""; my $modti = $medline_hash "TI" ; my $modab = $medline_hash "AB" ; foreach my $lo ("and", "or")\n{ foreach my $key (keys %$searchterm$lo )\n my $srchterm = $searchterm$lo $key ; if (uc ($casesens$lo $key ) =~ /Y/)
"},{"id":"text-15","type":"text","heading":"","plain_text":"$modti = bolden($modti, $srchterm) ; $modab = bolden($modab, $srchterm) ;\n else","html":"$modti = bolden($modti, $srchterm) ; $modab = bolden($modab, $srchterm) ;\n else
"},{"id":"text-16","type":"text","heading":"","plain_text":"$modti = bolden_i ($modti, $srchterm) ;\n$modab = bolden_i ($modab, $srchterm) ; \n print ' \n ' . $modti . "\n";\n my ©sentences = split (/. /, $medline_hash "AB" ) ; print ' \n ' ; my $local_output = " " ; foreach my $sentence (©sentences)\n my $modsent = $sentence; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo )\n my $srchterm = $searchterm$lo $key ; if (uc($casesens$lo$key) =~ /Y/)","html":"$modti = bolden_i ($modti, $srchterm) ;\n$modab = bolden_i ($modab, $srchterm) ; \n print ' \n ' . $modti . "\n";\n my ©sentences = split (/. /, $medline_hash "AB" ) ; print ' \n ' ; my $local_output = " " ; foreach my $sentence (©sentences)\n my $modsent = $sentence; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo )\n my $srchterm = $searchterm$lo $key ; if (uc($casesens$lo$key) =~ /Y/)
"},{"id":"text-17","type":"text","heading":"","plain_text":"$modsent = bolden ($modsent, $srchterm) ;\n else","html":"$modsent = bolden ($modsent, $srchterm) ;\n else
"},{"id":"text-18","type":"text","heading":"","plain_text":"$modsent = bolden_i ( $modsent , $srchterm) ;","html":"$modsent = bolden_i ( $modsent , $srchterm) ;
"},{"id":"text-19","type":"text","heading":"","plain_text":"if ($modsent ne $sentence) $local_output .= $modsent . " . " ; print ""; if ($local_output =~ /S/) print $local_output; else print " – " ; print ""; print "\n"; if (uc ($showabstract) =~ /Y/)\n print ' \n'; print ""; if ($modab =~ /S/) print $modab; else print "-\n"; print " " ; print "\n"; print OUTCSV\n"$i, $pmid, " . $medline_hash "TI" . " , $local_output, " . $medline_hash "AB" . " n" ;\n else\n print OUTCSV "$i, $pmid, " . $medline_hash "TI" . " , $local__outputn" ;\n print "\nn";\n print "\nn" ; close OUTCSV; } \nsub found\n{ my ($searchterm, $text) = ($_[0] , $_[1]) if (defined $searchterm && $searchterm =~ /S/) else return (1) ; if (defined $text && $text =~ /S/) else { return (0) ; ) if ($text =~ / Q€searchtermEW/ | | $text =~ /UNEQ€searchtermEW/ | | $text =~ /WQ€searchtermEW/)","html":"if ($modsent ne $sentence) $local_output .= $modsent . " . " ; print ""; if ($local_output =~ /S/) print $local_output; else print " – " ; print ""; print "\n"; if (uc ($showabstract) =~ /Y/)\n print ' \n'; print ""; if ($modab =~ /S/) print $modab; else print "-\n"; print " " ; print "\n"; print OUTCSV\n"$i, $pmid, " . $medline_hash "TI" . " , $local_output, " . $medline_hash "AB" . " n" ;\n else\n print OUTCSV "$i, $pmid, " . $medline_hash "TI" . " , $local__outputn" ;\n print "\nn";\n print "\nn" ; close OUTCSV; } \nsub found\n{ my ($searchterm, $text) = ($_[0] , $_[1]) if (defined $searchterm && $searchterm =~ /S/) else return (1) ; if (defined $text && $text =~ /S/) else { return (0) ; ) if ($text =~ / Q€searchtermEW/ | | $text =~ /UNEQ€searchtermEW/ | | $text =~ /WQ€searchtermEW/)
"},{"id":"text-20","type":"text","heading":"","plain_text":"# print "$text\nA\n$searchterm\nn" ; return (1) ;\n else\n return ( 0 ) ;","html":"# print "$text\nA\n$searchterm\nn" ; return (1) ;\n else\n return ( 0 ) ;
"},{"id":"text-21","type":"text","heading":"","plain_text":"sub found__i\n{ my ($searchterm, $text) = ($_[0] , $_[1]) if (defined $searchterm && $searchterm =~ /S/) else return (1) ; if (defined $text && $text =~ /S/) else { return(O); ' if ($text =~ / Q€searchtermEW/i | | $text =~ /UNEQ€searchtermEW/i | | $text =~ /WQ€searchtermEW/i)","html":"sub found__i\n{ my ($searchterm, $text) = ($_[0] , $_[1]) if (defined $searchterm && $searchterm =~ /S/) else return (1) ; if (defined $text && $text =~ /S/) else { return(O); ' if ($text =~ / Q€searchtermEW/i | | $text =~ /UNEQ€searchtermEW/i | | $text =~ /WQ€searchtermEW/i)
"},{"id":"text-22","type":"text","heading":"","plain_text":"# print "$text\nA\n$searchterm\nn" ; return ( 1 ) ;\n else\n return ( 0 ) ;","html":"# print "$text\nA\n$searchterm\nn" ; return ( 1 ) ;\n else\n return ( 0 ) ;
"},{"id":"text-23","type":"text","heading":"","plain_text":"sub addtolist\n my ($array_ref, $element) = ($_[0] , $_[1]) my ©array = @$array_ref ,■ my $found = 0 ; foreach my $exel (©array)\n if ($exel == $element) $found = 1; \n if ($found == 0)\n push (©array, $element) ;\n return (©array) ; \n sub bolden\n my ($text, $string) = ($_[0] , $_[1]) if ($text =~ /ΛQ€stringEW/ sub bolden_i\n my ($text, $string) = ($_[0] , $_[1]) if ($text =~ /ΛQ€stringEW/i \nAPPENDICE","html":"sub addtolist\n my ($array_ref, $element) = ($_[0] , $_[1]) my ©array = @$array_ref ,■ my $found = 0 ; foreach my $exel (©array)\n if ($exel == $element) $found = 1; \n if ($found == 0)\n push (©array, $element) ;\n return (©array) ; \n sub bolden\n my ($text, $string) = ($_[0] , $_[1]) if ($text =~ /ΛQ€stringEW/ sub bolden_i\n my ($text, $string) = ($_[0] , $_[1]) if ($text =~ /ΛQ€stringEW/i \nAPPENDICE
"},{"id":"text-24","type":"text","heading":"","plain_text":"Below we show the results from querying the HuGE database using our *.cgi script (see Appendix A and Figure 1) and the search term, "GSTMl". To reduce the number of hits from 1132 to 480, we required that each abstract include "GSTMl" and any of the following terms: "OR", "Ratio", "Odds" (all case-sensitive). Below are the tabulated results showing 1) a running index of abstracts; 2) the PMID of the abstract; 3) the title of the abstract; 4) the context within which the additional query terms were found in the abstract (for example, "OR" in the first record retrieved); 5) the entire PubMed abstract corresponding to the PMID in the second column. The first five hits are shown.\nFinal HuGE query: GSTMl\nNumber of records hit from the HuGE database = 1132\nHighlighted tag(s):\nAll-are-required terms:\nAny-one-is-required terms: OR Ratio Odds\nMust-be-absent terms:\n1132 articles passed the ALL- ARE-REQUIRED filters 480 articles passed the ANY-ONE-IS-REQUIRED filters 480 articles passed the MUST-BE-ABSENT filters Click here to download output in CSV format \n#[PMIDTitleContextAbstract[PMIDTitleContextAbstract[PMIDTitleContextAbstract[PMIDTitleContextAbstract\n1 19338664 GSTMl and The results showed BACKGROUND: Previous GSTTl that the overall OR evidence implicates polymorphisms andlwas 1.42 (95%CI = polymorphisms of GSTMl and nasopharyngeal 1.21-1.66) for GSTMl GSTTl, candidates of phase II cancer risk: an polymorphism. While enzymes, as risk factors for vidence-based forGSTTl various cancers. A number of meta-analysis. polymorphism, the studies have conducted on the overall C > R was 1.12 association of GSTMl and (95% CI = 0.93-1.34). GSTTl polymorphismwith susceptibility to nasopharyngeal carcinoma (NPC). However, inconsistent and inconclusive results have been obtained. In the present study, we aimed to assess the possible associations of NPC risk with GSTMl and GSTMl null genotype, respectively. METHODS: The associated literature was acquired through deliberate searching and selected based on the established inclusion criteria for publications, then the extracted data were further analyzed using systematic metaanalyses. RESULTS: A total of 85 articles were identified, of which eight case-control studies concerning NPC were selected. The results showed that the overall OK was 1.42 (95%CI = 1.21-1.66) for GSTMl polymorphism. While forGSTTl polymorphism, the overall OR was 1.12 (95% CI = 0.93-1.34). CONCLUSION: The data were proven stable via sensitivity analyses. The results suggest GSTMl deletion as a risk factor for NPC and failed to suggest a marked correlation of GSTTl polymorphisms with NPC risk. \n #|PMID JTitϊe Context Abstract\n19347979 Evaluation of Patients carrying the GPXl- INTRODUCTION: We evaluated the role glutathione metabolic CC genotype had a of glutathionelrelated genotypes on genes on outcomes in clinically significant overall survival, time to progression, advanced non-small decline in the UNISCALE adverse events, and quality of life (QOL) cell lung cancer (odds ratio (OR) : 7.5; p = in stage IIIB/IV non-small cell lung patients after initial 0.04), total Functional cancer patients who were stable or treatment with Assessment of Cancer respondingfrom initial treatment with platinum-based Therapy-Lung score (OR: platinum-based chemotherapy and chemotherapy: an 11.0; p = 0.04), physical subsequently randomized to receive daily NCCTG-97-24-51 (OR: 7.1; p = 0.03), oral carboxyaminoimidazole or a placebo. based study. functional (OR: 5.2; p = METHODS: Of the 186 total patients, 113 0.04), and emotional well- had initial treatment with platinum being constructs (OR: 23.8; therapy and DNA samplesof whom 46 p = 0.01). also had QOL data. These samples were analyzed using six polymorphic DNA markers that encode five important enzymes in the glutathione metabolic pathway. Patient QOL was assessed using the Functional Assessment of Cancer Therapy-Lung and the UNISCALE QOL questionnaires. A clinically significant decline in QOL was defined as a 10% decrease from baseline to week-8. Multivariate analyses were used to evaluate the association of the genotypes on the four endpoints. RESULTS: Patients carrying a GCLC 77 genotype had a worse overall survival (hazardratio (HR) = 1.5, p = 0.05). Patients carrying the GPXl-CC genotype had a clinically significant decline in the UNISCALE (odds ratio (HH) : 7.5; p = 0.04), total Functional Assessment of Cancer Therapy-Lung score (OR: 11.0; p = 0.04), physical (OR: 7.1; p = 0.03), functional (OR: 5.2; p = 0.04), and emotional well- being constructs (OR: 23.8; p = 0.01). CONCLUSIONS: Genotypes of glutathione-related enzymes, especially GCLC, may be used as host factors in iredicting patients' survival after latinum-based chemotherapy. GPXl may e an inherited factor in predicting atients' QOL. Further investigation to define and measure theeffects of these genes in chemotherapeutic regimens, drug toxicities, disease progression, and QOL are critical.","html":"Below we show the results from querying the HuGE database using our *.cgi script (see Appendix A and Figure 1) and the search term, "GSTMl". To reduce the number of hits from 1132 to 480, we required that each abstract include "GSTMl" and any of the following terms: "OR", "Ratio", "Odds" (all case-sensitive). Below are the tabulated results showing 1) a running index of abstracts; 2) the PMID of the abstract; 3) the title of the abstract; 4) the context within which the additional query terms were found in the abstract (for example, "OR" in the first record retrieved); 5) the entire PubMed abstract corresponding to the PMID in the second column. The first five hits are shown.\nFinal HuGE query: GSTMl\nNumber of records hit from the HuGE database = 1132\nHighlighted tag(s):\nAll-are-required terms:\nAny-one-is-required terms: OR Ratio Odds\nMust-be-absent terms:\n1132 articles passed the ALL- ARE-REQUIRED filters 480 articles passed the ANY-ONE-IS-REQUIRED filters 480 articles passed the MUST-BE-ABSENT filters Click here to download output in CSV format \n#[PMIDTitleContextAbstract[PMIDTitleContextAbstract[PMIDTitleContextAbstract[PMIDTitleContextAbstract\n1 19338664 GSTMl and The results showed BACKGROUND: Previous GSTTl that the overall OR evidence implicates polymorphisms andlwas 1.42 (95%CI = polymorphisms of GSTMl and nasopharyngeal 1.21-1.66) for GSTMl GSTTl, candidates of phase II cancer risk: an polymorphism. While enzymes, as risk factors for vidence-based forGSTTl various cancers. A number of meta-analysis. polymorphism, the studies have conducted on the overall C > R was 1.12 association of GSTMl and (95% CI = 0.93-1.34). GSTTl polymorphismwith susceptibility to nasopharyngeal carcinoma (NPC). However, inconsistent and inconclusive results have been obtained. In the present study, we aimed to assess the possible associations of NPC risk with GSTMl and GSTMl null genotype, respectively. METHODS: The associated literature was acquired through deliberate searching and selected based on the established inclusion criteria for publications, then the extracted data were further analyzed using systematic metaanalyses. RESULTS: A total of 85 articles were identified, of which eight case-control studies concerning NPC were selected. The results showed that the overall OK was 1.42 (95%CI = 1.21-1.66) for GSTMl polymorphism. While forGSTTl polymorphism, the overall OR was 1.12 (95% CI = 0.93-1.34). CONCLUSION: The data were proven stable via sensitivity analyses. The results suggest GSTMl deletion as a risk factor for NPC and failed to suggest a marked correlation of GSTTl polymorphisms with NPC risk. \n #|PMID JTitϊe Context Abstract\n19347979 Evaluation of Patients carrying the GPXl- INTRODUCTION: We evaluated the role glutathione metabolic CC genotype had a of glutathionelrelated genotypes on genes on outcomes in clinically significant overall survival, time to progression, advanced non-small decline in the UNISCALE adverse events, and quality of life (QOL) cell lung cancer (odds ratio (OR) : 7.5; p = in stage IIIB/IV non-small cell lung patients after initial 0.04), total Functional cancer patients who were stable or treatment with Assessment of Cancer respondingfrom initial treatment with platinum-based Therapy-Lung score (OR: platinum-based chemotherapy and chemotherapy: an 11.0; p = 0.04), physical subsequently randomized to receive daily NCCTG-97-24-51 (OR: 7.1; p = 0.03), oral carboxyaminoimidazole or a placebo. based study. functional (OR: 5.2; p = METHODS: Of the 186 total patients, 113 0.04), and emotional well- had initial treatment with platinum being constructs (OR: 23.8; therapy and DNA samplesof whom 46 p = 0.01). also had QOL data. These samples were analyzed using six polymorphic DNA markers that encode five important enzymes in the glutathione metabolic pathway. Patient QOL was assessed using the Functional Assessment of Cancer Therapy-Lung and the UNISCALE QOL questionnaires. A clinically significant decline in QOL was defined as a 10% decrease from baseline to week-8. Multivariate analyses were used to evaluate the association of the genotypes on the four endpoints. RESULTS: Patients carrying a GCLC 77 genotype had a worse overall survival (hazardratio (HR) = 1.5, p = 0.05). Patients carrying the GPXl-CC genotype had a clinically significant decline in the UNISCALE (odds ratio (HH) : 7.5; p = 0.04), total Functional Assessment of Cancer Therapy-Lung score (OR: 11.0; p = 0.04), physical (OR: 7.1; p = 0.03), functional (OR: 5.2; p = 0.04), and emotional well- being constructs (OR: 23.8; p = 0.01). CONCLUSIONS: Genotypes of glutathione-related enzymes, especially GCLC, may be used as host factors in iredicting patients' survival after latinum-based chemotherapy. GPXl may e an inherited factor in predicting atients' QOL. Further investigation to define and measure theeffects of these genes in chemotherapeutic regimens, drug toxicities, disease progression, and QOL are critical.
"},{"id":"text-25","type":"text","heading":"","plain_text":"#PMID Title Context Abstract\n19303722 Association of NAT2, Results: It was found that Objective: To explore the\nGSTMl, GSTTl, significant associations of the association of polymorphisms in CYP2A6, and CYP2A13 NAT2 slow-acetylator genotype N-acetyltransferase 2 (NAT2), gene polymorphismswith (odds ratio, CM: 2.42; 95% glutathione S-transferase (GST), susceptibility and clinicopathologic •onfidence interval, CI: 1.47-3.99), cytochrome P450 (CYP) 2A6, and characteristics of bladder GSTMl null genotype (OR: 1.64; CYP 2A13 genes with cancer inCentral China. 95% CI: 1.11-2.42) and susceptibility and clinicopathologic GSTMl/GSTTl-double null characteristics of bladder cancer in genotype (OR: 1.72; 95% CI: 1.00- a Chinese population. Methods: In 2.95) with increased risk of a hospital-based case-control study bladder cancer. Conversely, of 208 cases and 212 controls carriers with at least one matched on age and gender, CYP2A6*4 allele showed lower genotypes were determined by risk than the non-carriers (OR: PCR-based methods. Risks were 0.47; 95% CI: 0.28-0.79). evaluated by unconditional logistic regression analysis. Results: It was found that significant associations of the NAT2 slow-acetylator genotype (odds ratio, C)H: 2.42; 95% confidence interval, CI: 1.47- 3.99), GSTMl null genotype (OR: 1.64; 95% CI: 1.11-2.42) and GSTMl/GSTTl-double null genotype (OR: 1.72; 95% CI: 1.00- 2.95) with increased risk of bladder cancer. Conversely, carriers with at least one CYP2A6*4 allele showed lower risk than the non-carriers (OR: 0.47; 95% CI: 0.28-0.79). The adjusted ORs (95% CI) for smokers with NAT2 slow- acetylator, GSTMl null, GSTMl/GSTTl-double null genotype, and variant CYP2A6 genotypes were 2.99 (1.44-6.25), 1.98 (1.13-3.48), 2.66 (1.22-5.81) and 0.41 (0.20-0.86), respectively. Furthermore, NAT2 slow- acetylator, GSTMl null, and GSTMl/GSTTl-double null genotypes were associated with higher tumor grade (P=0.001, 0.022, and 0.036, respectively), and only NAT2 slow-acetylator genotype was associated with higher tumor stage (P=0.007). CYP2A13 was not associated with risk or tumor characteristics. Conclusion: It is suggested that NAT2 slow-acetylator, GSTMl null, GSTMl/GSTTl-double null, and variant CYP2A6 genotypes may play important roles in the development of bladder cancer in Henan area, China. \n #1PMID ffϊtie Context Abstract\n5)19303595 Negative effects of The risk of low motility with high OBJECTIVE: Effects of ambient serum p,p'-DDE on DDE-DDT exposure was increased exposure to DDT and its metabolites sperm parameters in men with the GSTTl null (DDE-DDT) on human sperm and modification by genotype compared to those with parameters and the role of genetic genetic GSTTl intact (odds ratio (C)R) polymorphisms in modifyingthe polymorphisms. =4.19, 95% confidence interval association were investigated. (CI) 1.05-16.78 and OR=3.57, 1.43- METHODS: Demographics, 8.93, respectively). Risk for low medical history data, blood and morphology in men with high semen samples were obtained from DDE-DDT and one or both the first 336 male partners of CYPlAl *2A alleles was lower couples presenting to 2 infertility compared to men with the common clinics. Serum was analyzed for CYPlAl alleles ^GR- 2.18, 0.78- organochlorines (OC) and DNA for 6.07 vs. OR 3.45, 1.32-9.03, polymorphisms in GSTMl, GSTTl, respectively). Effects of high DDE- GSTPl and CYPlAl . Men with DDT on low sperm concentration each sperm parameter considered\n>R- 2.53, 1.0-6.31) was low by WHO criteria (concentration unaffected by the presence of the <20million/mL, motility <50%, polymorphisms. morphology <4%) were compared to men with all normal sperm parameters in logistic regression models, controlling for sum of other OC pesticides. RESULTS: High DDE-DDT level was associated with significantly increased odds for all 3 low sperm parameters. The risk of low motility with high DDE-DDT exposure was increased in men with the GSTTl null genotype compared to those with GSTTl intact (odds ratio","html":"#PMID Title Context Abstract\n19303722 Association of NAT2, Results: It was found that Objective: To explore the\nGSTMl, GSTTl, significant associations of the association of polymorphisms in CYP2A6, and CYP2A13 NAT2 slow-acetylator genotype N-acetyltransferase 2 (NAT2), gene polymorphismswith (odds ratio, CM: 2.42; 95% glutathione S-transferase (GST), susceptibility and clinicopathologic •onfidence interval, CI: 1.47-3.99), cytochrome P450 (CYP) 2A6, and characteristics of bladder GSTMl null genotype (OR: 1.64; CYP 2A13 genes with cancer inCentral China. 95% CI: 1.11-2.42) and susceptibility and clinicopathologic GSTMl/GSTTl-double null characteristics of bladder cancer in genotype (OR: 1.72; 95% CI: 1.00- a Chinese population. Methods: In 2.95) with increased risk of a hospital-based case-control study bladder cancer. Conversely, of 208 cases and 212 controls carriers with at least one matched on age and gender, CYP2A6*4 allele showed lower genotypes were determined by risk than the non-carriers (OR: PCR-based methods. Risks were 0.47; 95% CI: 0.28-0.79). evaluated by unconditional logistic regression analysis. Results: It was found that significant associations of the NAT2 slow-acetylator genotype (odds ratio, C)H: 2.42; 95% confidence interval, CI: 1.47- 3.99), GSTMl null genotype (OR: 1.64; 95% CI: 1.11-2.42) and GSTMl/GSTTl-double null genotype (OR: 1.72; 95% CI: 1.00- 2.95) with increased risk of bladder cancer. Conversely, carriers with at least one CYP2A6*4 allele showed lower risk than the non-carriers (OR: 0.47; 95% CI: 0.28-0.79). The adjusted ORs (95% CI) for smokers with NAT2 slow- acetylator, GSTMl null, GSTMl/GSTTl-double null genotype, and variant CYP2A6 genotypes were 2.99 (1.44-6.25), 1.98 (1.13-3.48), 2.66 (1.22-5.81) and 0.41 (0.20-0.86), respectively. Furthermore, NAT2 slow- acetylator, GSTMl null, and GSTMl/GSTTl-double null genotypes were associated with higher tumor grade (P=0.001, 0.022, and 0.036, respectively), and only NAT2 slow-acetylator genotype was associated with higher tumor stage (P=0.007). CYP2A13 was not associated with risk or tumor characteristics. Conclusion: It is suggested that NAT2 slow-acetylator, GSTMl null, GSTMl/GSTTl-double null, and variant CYP2A6 genotypes may play important roles in the development of bladder cancer in Henan area, China. \n #1PMID ffϊtie Context Abstract\n5)19303595 Negative effects of The risk of low motility with high OBJECTIVE: Effects of ambient serum p,p'-DDE on DDE-DDT exposure was increased exposure to DDT and its metabolites sperm parameters in men with the GSTTl null (DDE-DDT) on human sperm and modification by genotype compared to those with parameters and the role of genetic genetic GSTTl intact (odds ratio (C)R) polymorphisms in modifyingthe polymorphisms. =4.19, 95% confidence interval association were investigated. (CI) 1.05-16.78 and OR=3.57, 1.43- METHODS: Demographics, 8.93, respectively). Risk for low medical history data, blood and morphology in men with high semen samples were obtained from DDE-DDT and one or both the first 336 male partners of CYPlAl *2A alleles was lower couples presenting to 2 infertility compared to men with the common clinics. Serum was analyzed for CYPlAl alleles ^GR- 2.18, 0.78- organochlorines (OC) and DNA for 6.07 vs. OR 3.45, 1.32-9.03, polymorphisms in GSTMl, GSTTl, respectively). Effects of high DDE- GSTPl and CYPlAl . Men with DDT on low sperm concentration each sperm parameter considered\n>R- 2.53, 1.0-6.31) was low by WHO criteria (concentration unaffected by the presence of the <20million/mL, motility <50%, polymorphisms. morphology <4%) were compared to men with all normal sperm parameters in logistic regression models, controlling for sum of other OC pesticides. RESULTS: High DDE-DDT level was associated with significantly increased odds for all 3 low sperm parameters. The risk of low motility with high DDE-DDT exposure was increased in men with the GSTTl null genotype compared to those with GSTTl intact (odds ratio
"},{"id":"text-26","type":"text","heading":"","plain_text":"=4.19, 95% confidence interval (CI) 1.05-16.78 and OR=3.57, 1.43-8.93, respectively). Risk for low morphology in men with high DDE-DDT and one or both CYPlAl *2A alleles was lower compared to men with the common CYPlAl alleles (OR=2.18, 0.78- 6.07 vs. OR=3.45, 1.32-9.03, respectively). Similar results were obtained for men with low DDE- DDT exposure. Effects of high DDE-DDT on low sperm concentration (OR=2.53, 1.0-6.31) was unaffected by the presence of the polymorphisms. CONCLUSION: High DDE-DDT exposure adversely affected all 3 sperm parameters and its effects were exacerbated by the GSTTl null polymorphism and by the CYPlAl common alleles. \nAPPENDICE","html":"=4.19, 95% confidence interval (CI) 1.05-16.78 and OR=3.57, 1.43-8.93, respectively). Risk for low morphology in men with high DDE-DDT and one or both CYPlAl *2A alleles was lower compared to men with the common CYPlAl alleles (OR=2.18, 0.78- 6.07 vs. OR=3.45, 1.32-9.03, respectively). Similar results were obtained for men with low DDE- DDT exposure. Effects of high DDE-DDT on low sperm concentration (OR=2.53, 1.0-6.31) was unaffected by the presence of the polymorphisms. CONCLUSION: High DDE-DDT exposure adversely affected all 3 sperm parameters and its effects were exacerbated by the GSTTl null polymorphism and by the CYPlAl common alleles. \nAPPENDICE
"},{"id":"text-27","type":"text","heading":"","plain_text":"#!/usr/bin/perl\nuse strict; use warnings; use LWP 5.64; # Loads all important LWP classes, and makes sure your version is reasonably recent. use Data::Dumper; use CGI ':standard'; use CGI:: Carp qw(fatalsToBrowser); use File::Temp qw/ tempfile tempdir /; my $num_of_terms = 5; # Change this setting to change the number of search terms allowed for each filtering level.\n#my $adminemail = print header; if (! par am)\n print « 'EOF';","html":"#!/usr/bin/perl\nuse strict; use warnings; use LWP 5.64; # Loads all important LWP classes, and makes sure your version is reasonably recent. use Data::Dumper; use CGI ':standard'; use CGI:: Carp qw(fatalsToBrowser); use File::Temp qw/ tempfile tempdir /; my $num_of_terms = 5; # Change this setting to change the number of search terms allowed for each filtering level.\n#my $adminemail = print header; if (! par am)\n print « 'EOF';
"},{"id":"text-28","type":"text","heading":"","plain_text":"EOF\nprint "ici to download output in CSV format\n'; print "<table cellpadding = V1OV cellspacing = VOV border = V3V align =\n"left">n"; \n print "\nn"; if (uc($showabstract) =~ IYI)\n print "\n#\n"; print "\nPMID\n"; print "\nTitre\n"; print "\nContext\n"; print "\nAbstrait\nn"; print OUTCSV "#,PMID,Title,Context,Abstractn";\n else\n print "\n#\n"; print "\nPMID\n"; print "\nTitre"; print "\nContext\nn" ; print OUTCSV M#,PMID,Title,Contextn";\n print "\nn"; foreach my $i(l..scalar(@filtered_pmids3))\n{ my $pmid = $filtered_pmids3[$i-l];\n# print "Currently processing PMID $pmidn"; my %medline_hash = %$ Articles $pmid; print "\nn"; print '\n'."$i\n"; my $pmid_link =\n"http://www.ncbi.nlm.nih.gov/pubmed/".$medline_hashMPMIDM; print '\n'; print '<a href="l.$pmid_link.'">'.$medline_hash "PMID" . "\n" ; my $modti = $medline_hash"TI"; my $modab = $medline_hash"AB"; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo)\n my $srchterm = $searchterm$lo$key; if (uc($casesens$lo $key) =~ IYI)","html":"EOF\nprint "ici to download output in CSV format\n'; print "<table cellpadding = V1OV cellspacing = VOV border = V3V align =\n"left">n"; \n print "\nn"; if (uc($showabstract) =~ IYI)\n print "\n#\n"; print "\nPMID\n"; print "\nTitre\n"; print "\nContext\n"; print "\nAbstrait\nn"; print OUTCSV "#,PMID,Title,Context,Abstractn";\n else\n print "\n#\n"; print "\nPMID\n"; print "\nTitre"; print "\nContext\nn" ; print OUTCSV M#,PMID,Title,Contextn";\n print "\nn"; foreach my $i(l..scalar(@filtered_pmids3))\n{ my $pmid = $filtered_pmids3[$i-l];\n# print "Currently processing PMID $pmidn"; my %medline_hash = %$ Articles $pmid; print "\nn"; print '\n'."$i\n"; my $pmid_link =\n"http://www.ncbi.nlm.nih.gov/pubmed/".$medline_hashMPMIDM; print '\n'; print '<a href="l.$pmid_link.'">'.$medline_hash "PMID" . "\n" ; my $modti = $medline_hash"TI"; my $modab = $medline_hash"AB"; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo)\n my $srchterm = $searchterm$lo$key; if (uc($casesens$lo $key) =~ IYI)
"},{"id":"text-29","type":"text","heading":"","plain_text":"$modti = bolden($modti, $srchterm);\n$modab = bolden($modab, $srchterm); \n autre","html":"$modti = bolden($modti, $srchterm);\n$modab = bolden($modab, $srchterm); \n autre
"},{"id":"text-30","type":"text","heading":"","plain_text":"$modti = bolden_i($modti, $srchterm); $modab = bolden_i($modab, $srchterm); print '\n'.$modti."\n"; my @sentences = split (Λ. /, $medline_hash"AB"); print '\n'; my $local_output = ""; foreach my $sentence (@sentences)\n my $modsent = $sentence; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo)\n my $srchterm = $searchterm$lo $key; if (uc($casesens$lo $key) =~ IYI)","html":"$modti = bolden_i($modti, $srchterm); $modab = bolden_i($modab, $srchterm); print '\n'.$modti."\n"; my @sentences = split (Λ. /, $medline_hash"AB"); print '\n'; my $local_output = ""; foreach my $sentence (@sentences)\n my $modsent = $sentence; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo)\n my $srchterm = $searchterm$lo $key; if (uc($casesens$lo $key) =~ IYI)
"},{"id":"text-31","type":"text","heading":"","plain_text":"$modsent = bolden($modsent, $srchterm);\n else","html":"$modsent = bolden($modsent, $srchterm);\n else
"},{"id":"text-32","type":"text","heading":"","plain_text":"$modsent = bolden_i($modsent, $srchterm);\n if ($modsent ne $sentence) $local_output .= $modsent". "; print ""; if ($local_output =~ ASI) print $local_output; else print "-"; print ""; print "\n"; if (uc($showabstract) =~ IYI)\n print '\n'; print ""; if ($modab =~ ΛS/) print $modab; else print "-"; print ""; print "\n";\n print OUTCSV M€i,$pmid,".$medline_hash"Trje.",$local_output,M.$medline_hash"AB"."n";\n else\n print OUTCSV\n"$i,$pmid," .$medline_hash "TI" . ",$local_outputn" ;\n print "\nn";\n} print "\nn"; close OUTCSV; } sub found\n $text =~ /ΛQ€searchtermEW/ 1 sub found_i\n sub addtolist\n{ my ($array_ref, $element) = ($_[0], $_[1]) my @array = @$array_ref); my $found = 0; foreach my $exel(@array)\n if ($exel = $element) $found = 1; \n if ($found == 0)\n push (@array, $element);\n return (@array);\n sub bolden\n my ($text, $string) = ($_[0], $_[1]) if ($text =~ /ΛQ€stringEW/ sub bolden_i\n{ my ($text, $string) = ($_[0], $_[1]) if ($text =~ /ΛQ€stringEW/i || $text =~ ΛWQ€stringEW/i || $text =~ / Q€stringEW/i)","html":"$modsent = bolden_i($modsent, $srchterm);\n if ($modsent ne $sentence) $local_output .= $modsent". "; print ""; if ($local_output =~ ASI) print $local_output; else print "-"; print ""; print "\n"; if (uc($showabstract) =~ IYI)\n print '\n'; print ""; if ($modab =~ ΛS/) print $modab; else print "-"; print ""; print "\n";\n print OUTCSV M€i,$pmid,".$medline_hash"Trje.",$local_output,M.$medline_hash"AB"."n";\n else\n print OUTCSV\n"$i,$pmid," .$medline_hash "TI" . ",$local_outputn" ;\n print "\nn";\n} print "\nn"; close OUTCSV; } sub found\n $text =~ /ΛQ€searchtermEW/ 1 sub found_i\n sub addtolist\n{ my ($array_ref, $element) = ($_[0], $_[1]) my @array = @$array_ref); my $found = 0; foreach my $exel(@array)\n if ($exel = $element) $found = 1; \n if ($found == 0)\n push (@array, $element);\n return (@array);\n sub bolden\n my ($text, $string) = ($_[0], $_[1]) if ($text =~ /ΛQ€stringEW/ sub bolden_i\n{ my ($text, $string) = ($_[0], $_[1]) if ($text =~ /ΛQ€stringEW/i || $text =~ ΛWQ€stringEW/i || $text =~ / Q€stringEW/i)
"},{"id":"text-33","type":"text","heading":"","plain_text":"$text =~ sΛQ$&E/ $& /ig;","html":"$text =~ sΛQ$&E/ $& /ig;
"},{"id":"text-34","type":"text","heading":"","plain_text":"return ($text); \nAPPENDICE","html":"return ($text); \nAPPENDICE
"},{"id":"text-35","type":"text","heading":"","plain_text":"Five PMEDs were fetched and filtered using the word "Bladder" (see Appendix C which shows our *.cgi script and Figure 2 which shows the graphical interface for the Abstract Fetcher and Parser). The filtering process reduced the number of abstracts from five to four. Below are the tabulated results showing 1) a running index of abstracts; 2) the PMID of the abstract; 3) the title of the abstract; 4) the context within the abstract in which the word, "bladder", was found; 5) the entire PubMed abstract corresponding to the PMID in the second column.\nAbstract Fetcher and Parser\nHighlighted tag(s): All-are-required terms: 'bladder' Any-one-is-required terms: Must-be-absent terms:\n4 articles passed the ALL-ARE-REQUIRED filters 4 articles passed the ANY-ONE-IS-REQUIRED filters 4 articles passed the MUST-BE-ABSENT filters Click here to download output in CSV format \n#PMID JTitle Context (Abstract\n1 11131031-Glutathione Genotype distributions for Genotype distributions for transferase GSTPl, GSTMl, and GSTTl GSTPl, GSTMl, and GSTTl isozyme were determined in 91 patients were determined in 91 patients genotypes in with prostatic carcinoma and 135 with prostatic carcinoma and 135 patients with patients with Madder carcinoma patients with bladder carcinoma prostate and and compared with those in 127 and compared with those in 127 h I adder abdominal surgery patients abdominal surgery patients carcinoma. without malignancies. 3%, chi2 without malignancies. None of the P=0.02, Fisher P =0.03). genotypes differed significantly Homozygosity for the GSTMl with respect to age or sex among null allele was more frequent controls or cancer patients. In the among bladder carcinoma group of prostatic carcinoma patients (59% in bladder patients, GSTTl nullallele carcinoma patients vs 45% in homozygotes were more prevalent controls, Fisher P=0.03, chi2 (25% in carcinoma patients vs. P=0.02, OR=I .76, CI=I.08-2.88). 13% in controls, Fisher P =0.02, These findings suggest that chi2 P=0.02, OR=2.31, CI = 1.17- pecific single polymorphic GST ■4.59) and the combined M1-/T1 – genes, that is GSTMl in the case null genotype was also more of bladder cancer and GSTTl in frequent (9% vs. 3%, chi2 P=0.02, the case of prostatic carcinoma, Fisher P =0.03). Homozygosity are most relevant for the for the GSTMl null allele was development of these urological more frequent among lifecldei malignancies among the general carcinoma patients (59% in population in Central Europe. . bladder carcinoma patients vs 45% in controls, Fisher P=0.03, chi2 P=0.02, OR=I.76, CI=I.08- 2.88). In contrast to a previous report, no significant increase in the frequency of the GSTPIb allele was found in the tumor patients. Except for the combined GSTMl-/ Tl -null genotype in prostatic carcinoma, none of the combined genotypes showed a significant association with either of the cancers. These findings suggest that specific single polymorphic GST genes, that is GSTMl in the case of bladder cancer and GSTTl in the case of prostatic carcinoma, are most relevant for the development of these urological malignancies among the general population in Central Europe. \n #PMID Title Context Abstract\n211173863 Susceptibility As a result of this mutation, the Glutathione S-transferase (GST, E.C. genes: GSTMl :χpression of GSTM3 can be 2.5.1.18) comprises a family of and GSTM3 as influenced. The mutated GSTM3 isoenzymes that play a key role in the genetic risk gene has been reported to be involved detoxification of such exogenous factors in in increased susceptibility for the substrates as xenobiotics, bladder cancer. development of cancer, but no environmental substances, and information is available concerning carcinogenic compounds. At least five its role in bladder cancer. We have mammalian GST gene families have identified patients with a been identified to be polymorphic, and heterozygous GSTM3 geno- type mutations or deletions of these genes who carry a significantly increased contribute to the predisposition for risk for the development of bladder several diseases, including cancer. cancer. Here we report that the The gene cluster of GSTMl -GSTM5 mutation of intronβ of GSTM3 has been reported to be localized on increases the risk for bladder cancer chromosome Ip and spans a length of (odds ratio: 2.31; 95% confidence nearly 100 kb. One mutation of the interval [CI], 1.79-2.82). GSTM3 gene generates a recognition Heterozygous carriers of the GSTMl site for the transcription factor yin null genotype have a significantly yang 1. As a result of this mutation, levated risk of developing biaddcr the expression of GSTM3 can be ancer.We calculated an odds ratio of influenced. The mutated GSTM3 gene 3.54 (95% CI, 2.99-4.11) for this has been reported to be involved in ;enotype. These observations lead to increased susceptibility for the the assumption that the lack of development of cancer, but no detoxification by glutathione information is available concerning its conjugation predispose to bladder role in Maddfc"* cancer. We have cancer when at least one oftwo alleles identified patients with a heterozygous is affected. Furthermore, individuals GSTM3 geno- type who carry a presenting the homozygous wild type significantly increased risk for the of GSTMl and GSTM3 are development of hlmhhr cancer. Here significantly protected against we report that the mutation of intronδ hiaritlei cancer. . of GSTM3 increases the risk for Madder cancer (odds ratio: 2.31; 95% confidence interval [CI], 1.79-2.82). We developed a procedure to identify heterozygous or homozygous carriers of the GSTMl alleles. Heterozygous carriers of the GSTMl null genotype have a significantly elevated risk of developing iji<J!Jei cancer. We calculated an odds ratio of 3.54 (95% CI, 2.99-4.11) for this genotype. These observations lead to the assumption that the lack of detoxification by glutathione conjugation predispose to bladder cancer when at least one oftwo alleles is affected. Furthermore, individuals presenting the homozygous wild type of GSTMl and GSTM3 are significantly protected against bladder cancer. \n #|PMID Title Context JAbstract 3 11757669 Polymorphisms of We investigated the effect of We investigated the effect of glutathione S- the GSTMl and GSTTl null the GSTMl and GSTTl null transferase genes genotypes, and GSTPl 313 genotypes, and GSTPl 313 (GSTMl5 GSTPl andiA/G polymorphism on btotider A/G polymorphism on bladder GSTTl)and bhtddei cancer susceptibility in a case cancer susceptibility in a case cancer susceptibility control studyof 121 btatkk-i control studyof 121 O!add<τ in the Turkish cancer patients, and 121 age- cancer patients, and 121 age- population. and sex-matched controls of and sex-matched controls of the Turkish population. GSTTl the Turkish population. The was shown notto be associated adjusted odds ratio for age, sex, with bladder cancer. In and smoking status is 1.94 individuals with the combined [95% confidence intervals (CI) risk factors of cigarette 1.15-3.26] for the GSTMl null smoking and the GSTMl null genotype, and 1.75 (95% CT genotype, the risk of hladkUv 1.03-2.99) for the GSTPl 313 ancer is 2.81 times (95% CI A/G or G/G genotypes. GSTTl 1.23-6.35) that of persons who was shown notto be associated both carry the GSTMl -present with 1HHiUiCf cancer. genotype and do not smoke. Combination of the two high- Similarly, the risk is 2.38-fold risk genotypes. GSTMl null (95% CI 1.12-4.95) for the and GSTPl 313 A/G or G/G, combined GSTPl 313 A/G and revealed that the risk increases G/ G genotypes and smoking. to 3.91-fold (95% CI 1.88- These findings support the role 8.13) compared with the for the GSTMl null and the combination of the low-risk GSTPl 313 AG or GG genotypes of these loci. In genotypes in the development individuals with the combined of bladder cancer. risk factors of cigarette Furthermore, gene-gene smoking and the GSTMl null (GSTMl -GSTPl) and gene- genotype, the risk of bhuickr :nvironment (GSTMl- cancer is 2.81 times (95% CI moking, GSTPl -smoking) 1.23-6.35) that of persons who interactions increase this risk both carry the GSTMl -present substantially. . genotype and do not smoke. Similarly, the risk is 2.38-fold (95% CI 1.12-4.95) for the combined GSTPl 313 A/G and G/ G genotypes and smoking. These findings support the role for the GSTMl null and the GSTPl 313 AG or GG genotypes in the development of Madder cancer. Furthermore, gene-gene (GSTMl -GSTPl) and gene- environment (GSTMl- smoking, GSTPl -smoking) interactions increase this risk substantially.\n #[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract\n411825664 Combined To evaluate the association To evaluate the association effect of between genetic polymorphism of between genetic polymorphism of glutathione S- GSTMl, GSTTl and development STMl, GSTTl and development transferase Ml of Madder cancer, a hospital- of bladder cancer, a hospital- and Tl based case-control study was based case-control study was genotypes on conducted in South Korea. The conducted in South Korea. The bladder cancer study population consisted of 232 study population consisted of 232 risk. histologically confirmed male histologically confirmed male adder cancer cases and 165 adder cancer cases and 165 male controls enrolled from male controls enrolled from urology departments with no urology departments with no previous history of cancer or previous history of cancer or systemic diseases in Seoul during systemic diseases in Seoul during 1997-1999. The GSTMl null 1997-1999. The GSTMl null genotype was significantly genotype was significantly associated with bladder cancer associated with bladder cancer (OR: 1.6, 95% CI: 1.0-2.4), (OR: 1.6, 95% CI: 1.0-2.4), whereas the association observed whereas the association observed for GSTTl null genotype did not for GSTTl null genotype did not reach statistical significance (OR: reach statistical significance (OR: 1.3, 95% CI: 0.9-2.0). There was a 1.3, 95% CI: 0.9-2.0). There was a statistically significant multiple statistically significant multiple interaction between GSTMl and nteraction between GSTMl and GSTTl genotype for risk of GSTTl genotype for risk of bladder cancer (P=O.04); the risk bladder cancer (P=0.04); the risk associated with the concurrent associated with the concurrent lack of both of the genes (OR: 2.2, ack of both of the genes (OR: 2.2, 95% CI: 1.2-4.3) was greater than 95% CI: 1.2-4.3) was greater than the product of risk in men with the product of risk in men with GSTMl null/GSTTl present (OR: GSTMl null/GSTTl present (OR: 1.3, 95% CI: 0.7-2.5) or GSTMl 1.3, 95% CI: 0.7-2.5) or GSTMl present/GSTTl null (OR: 1.1, present/GSTTl null (OR: 1.1, 95% CI: 0.6-2.2) genotype 95% CI: 0.6-2.2) genotype combinations. . combinaisons. \nAPPENDICE","html":"Five PMEDs were fetched and filtered using the word "Bladder" (see Appendix C which shows our *.cgi script and Figure 2 which shows the graphical interface for the Abstract Fetcher and Parser). The filtering process reduced the number of abstracts from five to four. Below are the tabulated results showing 1) a running index of abstracts; 2) the PMID of the abstract; 3) the title of the abstract; 4) the context within the abstract in which the word, "bladder", was found; 5) the entire PubMed abstract corresponding to the PMID in the second column.\nAbstract Fetcher and Parser\nHighlighted tag(s): All-are-required terms: 'bladder' Any-one-is-required terms: Must-be-absent terms:\n4 articles passed the ALL-ARE-REQUIRED filters 4 articles passed the ANY-ONE-IS-REQUIRED filters 4 articles passed the MUST-BE-ABSENT filters Click here to download output in CSV format \n#PMID JTitle Context (Abstract\n1 11131031-Glutathione Genotype distributions for Genotype distributions for transferase GSTPl, GSTMl, and GSTTl GSTPl, GSTMl, and GSTTl isozyme were determined in 91 patients were determined in 91 patients genotypes in with prostatic carcinoma and 135 with prostatic carcinoma and 135 patients with patients with Madder carcinoma patients with bladder carcinoma prostate and and compared with those in 127 and compared with those in 127 h I adder abdominal surgery patients abdominal surgery patients carcinoma. without malignancies. 3%, chi2 without malignancies. None of the P=0.02, Fisher P =0.03). genotypes differed significantly Homozygosity for the GSTMl with respect to age or sex among null allele was more frequent controls or cancer patients. In the among bladder carcinoma group of prostatic carcinoma patients (59% in bladder patients, GSTTl nullallele carcinoma patients vs 45% in homozygotes were more prevalent controls, Fisher P=0.03, chi2 (25% in carcinoma patients vs. P=0.02, OR=I .76, CI=I.08-2.88). 13% in controls, Fisher P =0.02, These findings suggest that chi2 P=0.02, OR=2.31, CI = 1.17- pecific single polymorphic GST ■4.59) and the combined M1-/T1 – genes, that is GSTMl in the case null genotype was also more of bladder cancer and GSTTl in frequent (9% vs. 3%, chi2 P=0.02, the case of prostatic carcinoma, Fisher P =0.03). Homozygosity are most relevant for the for the GSTMl null allele was development of these urological more frequent among lifecldei malignancies among the general carcinoma patients (59% in population in Central Europe. . bladder carcinoma patients vs 45% in controls, Fisher P=0.03, chi2 P=0.02, OR=I.76, CI=I.08- 2.88). In contrast to a previous report, no significant increase in the frequency of the GSTPIb allele was found in the tumor patients. Except for the combined GSTMl-/ Tl -null genotype in prostatic carcinoma, none of the combined genotypes showed a significant association with either of the cancers. These findings suggest that specific single polymorphic GST genes, that is GSTMl in the case of bladder cancer and GSTTl in the case of prostatic carcinoma, are most relevant for the development of these urological malignancies among the general population in Central Europe. \n #PMID Title Context Abstract\n211173863 Susceptibility As a result of this mutation, the Glutathione S-transferase (GST, E.C. genes: GSTMl :χpression of GSTM3 can be 2.5.1.18) comprises a family of and GSTM3 as influenced. The mutated GSTM3 isoenzymes that play a key role in the genetic risk gene has been reported to be involved detoxification of such exogenous factors in in increased susceptibility for the substrates as xenobiotics, bladder cancer. development of cancer, but no environmental substances, and information is available concerning carcinogenic compounds. At least five its role in bladder cancer. We have mammalian GST gene families have identified patients with a been identified to be polymorphic, and heterozygous GSTM3 geno- type mutations or deletions of these genes who carry a significantly increased contribute to the predisposition for risk for the development of bladder several diseases, including cancer. cancer. Here we report that the The gene cluster of GSTMl -GSTM5 mutation of intronβ of GSTM3 has been reported to be localized on increases the risk for bladder cancer chromosome Ip and spans a length of (odds ratio: 2.31; 95% confidence nearly 100 kb. One mutation of the interval [CI], 1.79-2.82). GSTM3 gene generates a recognition Heterozygous carriers of the GSTMl site for the transcription factor yin null genotype have a significantly yang 1. As a result of this mutation, levated risk of developing biaddcr the expression of GSTM3 can be ancer.We calculated an odds ratio of influenced. The mutated GSTM3 gene 3.54 (95% CI, 2.99-4.11) for this has been reported to be involved in ;enotype. These observations lead to increased susceptibility for the the assumption that the lack of development of cancer, but no detoxification by glutathione information is available concerning its conjugation predispose to bladder role in Maddfc"* cancer. We have cancer when at least one oftwo alleles identified patients with a heterozygous is affected. Furthermore, individuals GSTM3 geno- type who carry a presenting the homozygous wild type significantly increased risk for the of GSTMl and GSTM3 are development of hlmhhr cancer. Here significantly protected against we report that the mutation of intronδ hiaritlei cancer. . of GSTM3 increases the risk for Madder cancer (odds ratio: 2.31; 95% confidence interval [CI], 1.79-2.82). We developed a procedure to identify heterozygous or homozygous carriers of the GSTMl alleles. Heterozygous carriers of the GSTMl null genotype have a significantly elevated risk of developing iji<J!Jei cancer. We calculated an odds ratio of 3.54 (95% CI, 2.99-4.11) for this genotype. These observations lead to the assumption that the lack of detoxification by glutathione conjugation predispose to bladder cancer when at least one oftwo alleles is affected. Furthermore, individuals presenting the homozygous wild type of GSTMl and GSTM3 are significantly protected against bladder cancer. \n #|PMID Title Context JAbstract 3 11757669 Polymorphisms of We investigated the effect of We investigated the effect of glutathione S- the GSTMl and GSTTl null the GSTMl and GSTTl null transferase genes genotypes, and GSTPl 313 genotypes, and GSTPl 313 (GSTMl5 GSTPl andiA/G polymorphism on btotider A/G polymorphism on bladder GSTTl)and bhtddei cancer susceptibility in a case cancer susceptibility in a case cancer susceptibility control studyof 121 btatkk-i control studyof 121 O!add<τ in the Turkish cancer patients, and 121 age- cancer patients, and 121 age- population. and sex-matched controls of and sex-matched controls of the Turkish population. GSTTl the Turkish population. The was shown notto be associated adjusted odds ratio for age, sex, with bladder cancer. In and smoking status is 1.94 individuals with the combined [95% confidence intervals (CI) risk factors of cigarette 1.15-3.26] for the GSTMl null smoking and the GSTMl null genotype, and 1.75 (95% CT genotype, the risk of hladkUv 1.03-2.99) for the GSTPl 313 ancer is 2.81 times (95% CI A/G or G/G genotypes. GSTTl 1.23-6.35) that of persons who was shown notto be associated both carry the GSTMl -present with 1HHiUiCf cancer. genotype and do not smoke. Combination of the two high- Similarly, the risk is 2.38-fold risk genotypes. GSTMl null (95% CI 1.12-4.95) for the and GSTPl 313 A/G or G/G, combined GSTPl 313 A/G and revealed that the risk increases G/ G genotypes and smoking. to 3.91-fold (95% CI 1.88- These findings support the role 8.13) compared with the for the GSTMl null and the combination of the low-risk GSTPl 313 AG or GG genotypes of these loci. In genotypes in the development individuals with the combined of bladder cancer. risk factors of cigarette Furthermore, gene-gene smoking and the GSTMl null (GSTMl -GSTPl) and gene- genotype, the risk of bhuickr :nvironment (GSTMl- cancer is 2.81 times (95% CI moking, GSTPl -smoking) 1.23-6.35) that of persons who interactions increase this risk both carry the GSTMl -present substantially. . genotype and do not smoke. Similarly, the risk is 2.38-fold (95% CI 1.12-4.95) for the combined GSTPl 313 A/G and G/ G genotypes and smoking. These findings support the role for the GSTMl null and the GSTPl 313 AG or GG genotypes in the development of Madder cancer. Furthermore, gene-gene (GSTMl -GSTPl) and gene- environment (GSTMl- smoking, GSTPl -smoking) interactions increase this risk substantially.\n #[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract\n411825664 Combined To evaluate the association To evaluate the association effect of between genetic polymorphism of between genetic polymorphism of glutathione S- GSTMl, GSTTl and development STMl, GSTTl and development transferase Ml of Madder cancer, a hospital- of bladder cancer, a hospital- and Tl based case-control study was based case-control study was genotypes on conducted in South Korea. The conducted in South Korea. The bladder cancer study population consisted of 232 study population consisted of 232 risk. histologically confirmed male histologically confirmed male adder cancer cases and 165 adder cancer cases and 165 male controls enrolled from male controls enrolled from urology departments with no urology departments with no previous history of cancer or previous history of cancer or systemic diseases in Seoul during systemic diseases in Seoul during 1997-1999. The GSTMl null 1997-1999. The GSTMl null genotype was significantly genotype was significantly associated with bladder cancer associated with bladder cancer (OR: 1.6, 95% CI: 1.0-2.4), (OR: 1.6, 95% CI: 1.0-2.4), whereas the association observed whereas the association observed for GSTTl null genotype did not for GSTTl null genotype did not reach statistical significance (OR: reach statistical significance (OR: 1.3, 95% CI: 0.9-2.0). There was a 1.3, 95% CI: 0.9-2.0). There was a statistically significant multiple statistically significant multiple interaction between GSTMl and nteraction between GSTMl and GSTTl genotype for risk of GSTTl genotype for risk of bladder cancer (P=O.04); the risk bladder cancer (P=0.04); the risk associated with the concurrent associated with the concurrent lack of both of the genes (OR: 2.2, ack of both of the genes (OR: 2.2, 95% CI: 1.2-4.3) was greater than 95% CI: 1.2-4.3) was greater than the product of risk in men with the product of risk in men with GSTMl null/GSTTl present (OR: GSTMl null/GSTTl present (OR: 1.3, 95% CI: 0.7-2.5) or GSTMl 1.3, 95% CI: 0.7-2.5) or GSTMl present/GSTTl null (OR: 1.1, present/GSTTl null (OR: 1.1, 95% CI: 0.6-2.2) genotype 95% CI: 0.6-2.2) genotype combinations. . combinaisons. \nAPPENDICE
"},{"id":"text-36","type":"text","heading":"","plain_text":"Genowl edge 340431_00001 Appendi x E . txt — MySQL dump 10.11\n— Host : l ocal host Database : DPA — Server version 5.0.45\n/*! 40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;\n/*! 40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;\n/*! 40101 SET @OLD_COLLATION_CONNECTION=®@COLLATION_CONNECTION */;\n/*! 40101 SET NAMES Utf8 */;\n/* 140103 SET @OLD_TIME_ZONE=@@TIME_ZONE */ \n/* 140103 SET TIME_ZONE='+00:00' */;\n/* 140014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=O */;\n/* 140014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=O */;\n/* 140101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE= ' NO_AUTO_VALUE_ON_ZERO ' */]\n/* 140111 SET @OLD_SQL_NOTES=@@SQL_NOTES , SQL_NOTES=0 */;\n— Table structure for table Disease'\nDROP TABLE IF EXISTS 'Maladie'; CREER LA TABLE 'Maladie" (\n 'Disease_Id' int(ll) default NULL,\n 'Disease_Generic_τerm' varchar(30) default NULL,\n 'Disease_Name' varchar(40) default NULL,\n 'Disease_θntology' varchar(150) default NULL,\n 'Disease_Type' varchar(25) default NULL ) ENGINE=MyISAM DEFAULT CHARSET=I ati nl;\n— Dumping data for table 'Maladie'\nLOCK TABLES 'Maladie' WRITE; /*! 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"}],"sections":[{"id":"text-1","heading":"Text","content":"ESPÉRANCE DE VIE ET ASSURANCE-VIE GÉNÉTIQUEMENT PRÉVUES\nÉVALUATION\nCONTEXTE DE L'INVENTION\n[0001] Traditionnellement, le marché de l’assurance vie offrait des alternatives limitées à un preneur d’assurance qui souhaitait se départir de ses polices actuelles. En règle générale, le titulaire de la police cède la police et reçoit les liquidités énumérées dans les valeurs de déchéance de la police ou laisse celle-ci expirer et reçoit une couverture d'assurance supplémentaire sous la forme d'une assurance temporaire supplémentaire, aussi longtemps que les valeurs de rachat le permettent. Ces valeurs de non-confiscation sont au mieux minimales. Avant les lois types sur la non-confiscation, qui prévoient désormais le calcul de valeurs minimales, l’absence de péremption empêchait l’assuré de ne rien recevoir du tout. Cette forme classique du marché de l’assurance est un monopsone avec la dynamique de marché d’un acheteur, la compagnie d’assurance, qui fait face à de nombreux vendeurs, les preneurs d’assurance, ce qui entraîne un pouvoir de fixation des prix considérable pour les compagnies d’assurance. Cette situation s'apparente à un monopole dans lequel un seul vendeur est confronté à de nombreux acheteurs. Les assureurs en place appliquent une tarification à la monopsone à celle des assurés. Toutefois, la valeur intrinsèque d’un contrat d’assurance-vie dépasse toujours la valeur de rachat offerte à l’assuré. En raison de cette dynamique de marché, un marché secondaire a évolué, appelé marché de règlement à vie.\n[0002] Dans le marché de règlement à vie, un tiers soumissionnaire achète la police auprès du titulaire de la police et en devient le titulaire remplaçant, avec les mêmes droits de propriété que le titulaire initial. Les tiers propriétaires sont généralement disposés à payer beaucoup plus au titulaire initial du contrat qu'à la compagnie d'assurance monopsone. Le marché de l'assurance secondaire, cependant, est extrêmement inefficace pour évaluer les transactions sur polices. Les propriétaires remplaçants sont des acheteurs financiers qui versent au propriétaire initial davantage que les autres soumissionnaires et qui perçoivent les indemnités de décès sous forme de rendement financier.\n[0003] Il est utile de comprendre le rôle des participants dans le processus de transaction de stratégie. La personne assurée est la personne dont la vie est couverte par la police considérée et qui est généralement le titulaire initial de la police. Habituellement, le\n La personne assurée est le vendeur de la police dans la transaction, bien que, après la transaction de règlement initial, le vendeur puisse alors être tout titulaire de police successif. Un conseiller, tel qu'un conseiller financier ou un agent d'assurance, agit généralement en tant que consultant pour conseiller le vendeur sur les solutions de remplacement disponibles. Les offres générées pour les contrats d'assurance vie peuvent être appelées offres de règlement en vie. Un courtier est la personne responsable des achats, sollicite plusieurs soumissionnaires et travaille de préférence avec quatre à cinq soumissionnaires, appelés fournisseurs de règlement à vie. Un fournisseur de vie-règlement est l'entité qui formule l'offre d'achat et la transmet aux courtiers. Les fournisseurs de colonies de vie peuvent souscrire des polices pour leur propre compte ou pour d'éventuels investisseurs économiques en aval. Un fournisseur d'espérance de vie est une société de services spécialisée qui examine les dossiers médicaux afin de fournir des estimations de souscription de l'espérance de vie de l'assuré au fournisseur de règlement vie pour la formulation de l'offre. Les investisseurs financent généralement les prestataires de règlement vie (par exemple, par l’intermédiaire de fonds de couverture, de banques d’investissement). Dans certains cas, les investisseurs peuvent créer leur propre fournisseur interne. Parfois, les investisseurs peuvent être des fiducies qui émettent des obligations (à leurs détenteurs) sous forme de titres dérivés. Ces obligations financent les acquisitions de polices et sont remboursées par le règlement des polices acquises.\n[0004] Initialement, le titulaire de la police ou le client peut consulter un conseiller afin de décider de la vente de sa police. Le client et le conseiller peuvent travailler ensemble pour décider si un courtier sera impliqué dans la transaction ou s'ils iront directement aux fournisseurs. Le client et le conseiller peuvent soumettre la police pour évaluation et le propriétaire de la police publie des informations médicales. Les fournisseurs de colonies de vie ordonnent ensuite un rapport d’espérance de vie auprès des fournisseurs d’espérance de vie afin d’accéder au risque associé à la transaction proposée. Ce rapport examinera les antécédents médicaux de l'assuré pour voir si la police répond aux critères de soumission. Si la politique répond aux critères d'un règlement viager, le fournisseur peut ensuite envoyer des offres directement au client ou au client par l'intermédiaire d'un courtier. Voici quelques exemples de critères pour un règlement viager: 1) si la personne assurée a une espérance de vie limitée en raison d'un âge avancé ou de problèmes de santé, 2) la police est transférable et est en vigueur pour une période allant au-delà de la contestabilité\n période, 3) la police est émise par une compagnie d’assurance américaine, et 4) un capital-décès d’au moins 50 000 $ est associé à la police. À ce stade, le client et le conseiller peuvent examiner les offres et le client peut accepter une offre préférentielle. Le client et le conseiller peuvent compléter le dossier de clôture du fournisseur et renvoyer les documents essentiels. Le fournisseur peut placer en encaissement le paiement en espèces pour la police et soumettre des formulaires de changement de propriété à la compagnie d’assurance. Les documents peuvent être vérifiés et les fonds transférés au vendeur de police.\n[0005] Tout type de police d'assurance-vie peut être acheté lors d'une transaction, telle que la vie universelle, la vie temporaire, la vie entière ou la vie de survie. Le titulaire du contrat peut être un ou plusieurs particuliers, une fiducie, une société ou une organisation à but non lucratif, une banque ou une autre institution financière, une société à responsabilité limitée, une société de personnes ou une autre entité commerciale. La valeur nominale d'une police d'assurance fournit une valeur maximale à partir de laquelle la valeur de rachat est déterminée. Pour une personne de santé normale, une courbe de survie est générée par l'analyse de l'âge par rapport à la valeur de la politique, le point de départ étant l'âge de l'achat de la police et le résultat final étant prédite par l'espérance de vie estimée d'un individu de «santé normale». et se situe à l'âge de décès prédit, où la valeur économique de la politique est égale à la valeur nominale réelle de la politique. Cette courbe de survie fournit une représentation graphique de la valeur économique de la police d'assurance sur le marché secondaire de l'assurance. La connaissance supplémentaire des conditions médicales d'un individu permet une plus grande précision dans la prévision de l'espérance de vie, mais à ce jour, les applications générales reposent uniquement sur les dossiers médicaux et les antécédents familiaux. Lors de l'examen des dossiers médicaux, la valeur d'une politique individuelle sur le marché secondaire peut se situer en dehors de la courbe de survie «santé normale» si cette personne est en bonne santé ou en mauvaise santé.\n[0006] La valeur de rachat d'une police d'assurance-vie est déterminée au moment de l'émission et est basée sur des données de mortalité standard entièrement souscrites. Ces valeurs sont définies et ne changent pas lorsque l'état de santé du titulaire de la police change. La valeur des règlements viagers est déterminée au moment du règlement et est basée sur les\n la mortalité altérée au règlement, l'espérance de vie, selon l'estimation du fournisseur d'espérance de vie, et le taux de rendement, l'horizon temporel et la tolérance au risque requis des acquéreurs financiers successifs. Ces valeurs sont définies par les sociétés de règlement à vie et varient en fonction du niveau de dépréciation du titulaire de la police. L’espérance de vie de l’assuré est cruciale pour la formation d’une offre d’entreprise de règlement à vie. À ce jour, ces offres de règlement vie sont basées sur une souscription vie conventionnelle et utilisent des dossiers médicaux.\n[0007] L'évaluation traditionnelle des polices d'assurance-vie n'a pas de valeur prédictive et, comme indiqué ci-dessus, repose sur des informations historiques (par exemple, dossiers médicaux, antécédents médicaux familiaux et habitudes de vie). Les méthodes décrites dans le présent document tiennent compte des raisons sous-jacentes ayant une incidence sur l'espérance de vie et non prises en compte actuellement par les acheteurs, les vendeurs et les investisseurs Il existe un marché et un besoin d'amélioration de la précision d'évaluation des polices d'assurance-vie.\n[0008] Le séquençage du génome humain a permis de mieux comprendre les bases génétiques de la maladie et de la mortalité humaines, deux facteurs importants de l'espérance de vie. Cela a également permis de mieux comprendre les causes génomiques sous-jacentes des différences qui surviennent entre les personnes en réponse à leur environnement. Plusieurs modifications génomiques (telles que les variations du nombre de copies) et des modifications structurelles à petite échelle (telles que les inversions et les délétions) ont été impliquées dans la pathologie de la maladie. Par exemple, les modifications d'un seul nucléotide dans des positions spécifiques du génome humain, appelées polymorphismes d'un nucléotide simple (SNP), ont un effet sur les différences phénotypiques observées entre les individus. Les différences entre les SNP peuvent influer sur la vulnérabilité des individus aux facteurs environnementaux, tels que le tabagisme, et sur leur probabilité de réagir aux interventions médicales. Les SNP sont l'un des facteurs qui affectent la prédisposition génétique d'un individu à développer une certaine maladie et peuvent également être prédictifs de la mortalité d'un individu due à une maladie. \n [0009] Les progrès récents en matière de technologie de génotypage à grande vitesse ont permis à la communauté scientifique de progresser dans l'identification et la validation de nombreux polymorphismes génétiques courants associés au risque de maladie.\n[00010] Depuis 1977, la méthode de Sanger est la méthode choisie pour les études de séquençage de l’ADN, y compris le projet du génome humain. Cependant, au cours des dernières années, un certain nombre de technologies de séquençage ne s'appuyant plus sur la méthode de Sanger et présentant des améliorations dans les domaines fondamentaux de longueur, de débit et de coût de lecture (Chan. 2005. Mutation Research. 573: 12-40 Lander et al., 2001. Nature 409: 860-921, Shaffer, 2007. Nature Biotechnology 25 (2): 149; Nature Methods, janvier 2008. 5 (1)). Des exemples de ces techniques incluent: la technologie de pyroséquençage de 454 Sciences de la vie; technologie de polymérisation-colonies développée par Solexa, Inc. et actuellement détenue et commercialisée par Illumina, Inc .; et séquençage par ligature, développé par Agencourt Bioscience Corp., qui constitue désormais la base des séquenceurs du système SoLID d’Applied Biosystems; et le séquençage d'une molécule, tel que celui développé et commercialisé par Helicos Biosciences.\n[00011] Par rapport au coût du projet du génome humain, les technologies ci-dessus peuvent séquencer le génome humain pour beaucoup moins cher. Des technologies (telles que celles proposées par Helicos Biosciences, Pacific Biosciences et Oxford Nanopore Technologies) ont démontré la capacité de réduire davantage ce coût.\n[00012] Les matrices SNP peuvent être utilisées pour profiler plusieurs centaines de milliers à un million de marqueurs SNP pour un individu donné à un coût raisonnable. Ces tableaux sont utilisés pour étudier la variation génétique dans l'ensemble du génome. Une société de génétique personnelle, 23andMe, a dévoilé un tableau qui génotypera près de 600 000 SNPs pour 399 $. Les coûts de séquençage diminuent considérablement chaque année, ce qui diminue le coût du séquençage du génome.\n[00013] Plusieurs approches ont été proposées pour caractériser la contribution de la génétique à la susceptibilité aux maladies et à la longévité ou à la durée de vie.\n Kenedy et al., (2008/0228818), décrit dans son intégralité ici une méthode, un logiciel, une base de données et un système de bioinformatique dans lesquels les profils d'attribut d'individus positifs d'attribut requête et d'attributs négatifs sont comparés. Voir également les demandes de brevet US n ° 2008/0076120, 2007/0259351, 2007/0042369, 2008/0228772, 2008/0187483, 2003/0040002, 2006/0068432, 2008/0131887, 2008/0195327, les brevets américains n ° 7 406 453 et 6 653 073. , Publication internationale n ° WO 2004/048591, WO 2004/050898, WO 2006/138696, WO 2006121558, WO 2007127490. Ces sources n'expliquent pas la capacité de préparer une méta-analyse des données disponibles sur une multitude de gènes et variantes génétiques et corréler ces données collectives pour déterminer une espérance de vie en relation avec l’évaluation des polices d’assurance vie.\n[00014] La contribution génétique à l'espérance de vie est multiplicative sur l'échelle de risque, comme l'attend le nombre important de traits héréditaires transmis de génération en génération (Risch. 2001. Cancer Epidemiology Biomarkers & Prevention. 10: 733-741). Cependant, la capacité de détecter les interactions entre les allèles à risque est limitée en raison de la taille des échantillons des études épidémiologiques en cours. Par conséquent, la présente invention propose une nouvelle approche pour intégrer les données d'études épidémiologiques de manière utile, par rapport à la prédiction personnalisée du risque génétique et à la prédiction personnalisée de l'espérance de vie. Cette approche est démontrée dans des modes de réalisation de la présente invention.\nRésumé de l'invention\n[00015] La présente invention concerne un procédé d'utilisation d'un appareil de base de données central pour évaluer une police d'assurance-vie pour un membre d'une population. L'appareil de base de données central contient une base de données génétique et une base de données sur l'espérance de vie. Le procédé d'évaluation de politique comprend: a) l'identification d'au moins un gène candidat; b) utiliser un appareil de récupération adapté pour récupérer de la littérature afin de collecter une littérature contenant des données de risque relatives aux données du gène candidat et de l'espérance de vie; d) télécharger les données de risque de la littérature recueillie dans la base de données génétiques; e) télécharger les données d'espérance de vie tirées de la littérature recueillie dans la base de données sur l'espérance de vie; g) en utilisant un ordinateur pour calculer un\n indice de risque collectif basé sur les données de risque téléchargées et les données d'espérance de vie téléchargées; h) collecte des données d'entrée du membre de la population; i) en utilisant les données d'entrée collectées et l'indice de risque collectif calculé pour déterminer une espérance de vie génétiquement prédite (GPLE) pour le membre; et j) évaluer la police d'assurance-vie sur la base du GPLE.\n[00016] Dans un autre mode de réalisation, la présente invention fournit un procédé pour évaluer les niveaux de prime de police d'assurance-vie pour une population dans un appareil de base de données central, comprenant les étapes consistant à: a) identifier au moins un gène candidat; b) utiliser un appareil de récupération adapté pour récupérer de la littérature afin de collecter une littérature contenant des données de risque relatives aux données du gène candidat et de l'espérance de vie; d) télécharger les données de risque de la littérature recueillie dans la base de données génétiques; e) télécharger les données d'espérance de vie tirées de la littérature recueillie dans la base de données sur l'espérance de vie; g) utiliser un ordinateur pour calculer un indice de risque collectif basé sur les données de risque téléchargées et les données d'espérance de vie téléchargées; h) collecte des données d'entrée du membre de la population; i) en utilisant les données d'entrée collectées et l'indice de risque collectif calculé pour déterminer un GPLE pour le membre; et j) évaluer la valeur de la prime de la police d’assurance vie sur la base du GPLE.\n[00017] La présente invention concerne également un système d'évaluation d'une police d'assurance-vie pour un membre d'une population. Dans ce mode de réalisation, le système comprend un serveur informatique et un appareil de base de données central, cet appareil comprenant une base de données génétique et une base de données d'espérance de vie, et le serveur étant configuré pour: a) inviter un utilisateur à identifier au moins un gène candidat; ; b) invite l'utilisateur à rassembler des ouvrages contenant des données de risque relatives à au moins un gène candidat et des données d'espérance de vie; c) télécharger les données de risque de la littérature recueillie dans la base de données génétiques; d) télécharger les données d'espérance de vie tirées de la littérature recueillie dans la base de données sur l'espérance de vie; e) calculer un indice de risque collectif basé sur les données de risque téléchargées et les données d'espérance de vie téléchargées; f) invite l'utilisateur à fournir des données d'entrée relatives au membre de la population; g) utiliser les données d'entrée fournies et le collectif calculé\n indice de risque pour déterminer un GPLE pour le membre; et h) évaluer la police d'assurance-vie en fonction du GPLE déterminé.\n[00018] Dans un autre mode de réalisation, les données d'entrée comprennent un échantillon biologique collecté à partir de l'élément. Dans ce mode de réalisation, l'échantillon biologique contient de l'ADN génomique.\n[00019] Dans un autre mode de réalisation, une séquence d'ADN génomique est isolée de l'échantillon biologique du membre. Dans encore un autre mode de réalisation, un gène candidat est contenu dans la séquence d'ADN génomique isolée.\n[00020] La présente invention concerne en outre un procédé permettant d’utiliser le profil génomique d’un individu pour évaluer sa police d’assurance vie en 1) obtenant un échantillon biologique de l’individu, 2) déterminant la séquence génomique à partir de l’échantillon biologique, 3) mettant en corrélation la séquence génomique avec la base de données centrale contenant les données de risque génétique et d'espérance de vie, 4) le calcul d'un GPLE pour l'individu et 5) l'évaluation de la police d'assurance-vie pour l'individu sur la base de la GPLE ou la détermination des niveaux de prime d'un contrat d'assurance-vie pour l'individu sur la base de la GPLE.\n[00021] Dans un autre mode de réalisation, la police d'assurance-vie est catégorisée sur la base du GPLE.\n[00022] Dans d’autres modes de réalisation de la présente invention, des facteurs supplémentaires peuvent être utilisés pour évaluer la valeur d’une police d’assurance vie, tels que des marqueurs génétiques, des antécédents médicaux, des habitudes personnelles, des habitudes d’exercice, des habitudes alimentaires, des habitudes de santé, des habitudes sociales, des expositions professionnelles, des expositions environnementales. le même. Dans un mode de réalisation, les marqueurs génétiques peuvent être choisis parmi des mutations ponctuelles d'ADN, des mutations de décalage de cadre d'ADN, des délétions d'ADN, des insertions d'ADN, des inversions d'ADN, des mutations d'expression de l'ADN, des modifications chimiques de l'ADN, etc. Dans un autre mode de réalisation, les marqueurs génétiques peuvent être des polymorphismes mononucléotidiques (SNP). \n [00023] Dans un autre mode de réalisation, les antécédents médicaux comprennent des informations relatives à une maladie manifestée, un trouble, une condition pathologique et / ou une séquence d'ADN génomique.\n[00024] Dans un autre mode de réalisation de la présente invention, l'indice de risque collectif peut être un risque relatif, un rapport de risque ou un rapport de cotes. Dans un mode de réalisation préféré, l'indice de risque collectif est un rapport de cotes de méta-analyse.\n[00025] Dans encore un autre mode de réalisation, l'appareil de base de données central est mis à jour de manière itérative avec des données de risque et des données d'espérance de vie supplémentaires.\nDESCRIPTION BRÈVE DES DESSINS\n[00026] FIGUE. 1 est un exemple d'interface de fenêtre d'affichage permettant de rechercher de la littérature dans une base de données.\n[00027] FIGUE. 2 est un exemple d'interface de fenêtre d'affichage permettant de rechercher des résumés dans une base de données.\n[00028] FIGUE. 3 est un organigramme illustrant des aspects des procédés décrits ici.\n[00029] FIGUE. 4 est un exemple de champs de données liés aux gènes candidats et à la maladie.\n[00030] FIGUE. 5 est un organigramme illustrant des aspects des procédés décrits ici.\n[00031] FIGUE. 6 est un organigramme illustrant des aspects des procédés décrits ici.\n[00032] FIGUE. 7 est un exemple de courbe de survie calculée en relation avec l'exemple 4.\nDESCRIPTION DÉTAILLÉE\n[00033] La présente invention concerne des procédés, des systèmes informatiques et des bases de données permettant d’évaluer et d’évaluer les polices d’assurance vie d’une population en fonction de facteurs tels que l’information génétique, les antécédents médicaux, les habitudes personnelles, les habitudes d’exercice, les habitudes alimentaires, les habitudes sociales et les habitudes. Divulgué ici sont\n bases de données, ainsi que des systèmes permettant de créer des bases de données et d’y accéder, décrivant ces facteurs pour les populations et permettant d’effectuer des analyses en fonction de ces facteurs. Les méthodes, systèmes informatiques et logiciels peuvent être utiles pour identifier des combinaisons complexes de facteurs pouvant être mis en corrélation avec des calculs d'espérance de vie et des prévisions de survie. Les méthodes, systèmes informatiques et bases de données peuvent également être utilisés pour analyser la valeur des polices d’assurance vie en fonction de la présence de ces facteurs et de leur influence sur les taux d’espérance de vie et de survie calculés. Les méthodes, systèmes informatiques et bases de données peuvent également être utilisés pour déterminer la valeur marchande des polices d’assurance vie pour le marché de l’assurance secondaire.\n[00034] La présente invention concerne des procédés améliorés d'évaluation de polices d'assurance-vie. Plus spécifiquement, la présente invention fournit de nouveaux procédés pour incorporer des informations génétiques dans la détermination de l'espérance de vie et de la valeur de la police d'assurance économique ou marchande. Cette information génétique procure des avantages directs en permettant aux acheteurs de polices d'accéder à de nouveaux segments de marché. À l’heure actuelle, les méthodes disponibles permettent d’évaluer la politique de la personne présentant une déficience médicale sur la base des antécédents médicaux et familiaux et à l’aide de tables d’espérance de vie. En utilisant les procédés de la présente invention, des polices d'assurance-vie pour des personnes possédant une information génétique altérée dans des gènes candidats ou des gènes associés à une espérance de vie améliorée ou diminuée deviennent des atouts précieux. En outre, les nouveaux procédés de la présente invention fournissent des avantages et des améliorations directs par rapport aux procédés de l’état de la technique en ce qu’ils identifient une population de personnes qui seraient sinon négligées sur le marché de l’assurance secondaire (par exemple, des individus en bonne santé présentant des mutations génétiques à haut risque).\n[00035] L'arrivée prochaine de réseaux de SNP plus complets et moins chers permettra le génotypage rapide d'individus à travers le spectre économique. En tant que tels, les modèles qui intègrent les résultats des dernières études d'association génétique pour prédire le risque de maladie et de mortalité deviendront très importants. Par conséquent, avec une compréhension croissante des causes génétiques de la\n maladies polygéniques, un mode de réalisation de la présente invention démontre la capacité de prédire le risque de maladie, la GPLE et l'évaluation de la politique d'assurance-vie en tenant compte de la présence de marqueurs génétiques spécifiques.\n[00036] Ces marqueurs génétiques peuvent être n’importe quel génome, génotype, haplotype, chromatine, chromosome, locus chromosomique, matériel chromosomique, acide désoxyribonucléique (ADN), allèle, gène, grappe de gène, locus de gène, polymorphisme de gène, mutation génique, marqueur de gène, nucléotide, simple nucléotide polymorphisme (SNP), polymorphisme de longueur de fragment de restriction (RPLP), répétition tandem à nombre variable (VNTR), variation du nombre de copies (CNV), marqueur de séquence, site de marqueurs de séquence (STS), plasmide, unité de transcription, produit de transcription, acide ribonucléique ( ARN), micro-ARN, ADN de copie (ADNc) et séquence d’ADN contenant des mutations ponctuelles, des mutations de décalage de cadre, des délétions, des insertions, des inversions, des mutations d’expression et des modifications chimiques (par exemple, méthylation de l’ADN). Les marqueurs génétiques comprennent la séquence nucléotidique et, le cas échéant, la séquence d'acides aminés codée de l'un quelconque des marqueurs ci-dessus ou de tout autre marqueur génétique connu de l'homme du métier.\n[00037] Des modes de réalisation de la présente invention concernent des procédés permettant de déterminer le GPLE associé à la valeur d'un contrat d'assurance-vie en utilisant des associations génétiques pour la susceptibilité aux maladies et la longévité. La présente invention concerne également des procédés permettant d’identifier la contribution d’une information génétique à la prédiction de son état de santé médical et de son espérance de vie et de l’effet de cette information sur les courbes de survie utilisées pour évaluer les polices d’assurance vie.\n[00038] La présente invention concerne un procédé permettant de déterminer le GPLE selon trois perspectives: 1) l’identification d’informations génétiques ou d’associations gène / maladie et l’utilisation des odds ratios (OR) associés pour construire des courbes de survie modifiées pour la population de génotypes donnée; 2) identification des gènes candidats impliqués dans la détermination de la durée de vie (longévité) ou des probabilités d'espérance de vie et utilisation des variations au niveau des locus génétiques associés pour calculer\n évolution positive ou négative des probabilités d'espérance de vie; 3) l'identification des changements dans les probabilités d'espérance de vie pour évaluer les polices d'assurance-vie.\n[00039] Bien qu'ils soient applicables à n'importe quel gène, les gènes candidats préférés de la présente invention peuvent être ceux impliqués dans une maladie, des maladies liées au vieillissement, et des gènes impliqués dans le maintien et la réparation du génome. Le vieillissement est un phénomène biologique complexe, susceptible d'être contrôlé par de multiples mécanismes et processus, génétiques et épigénétiques. Grâce à l'interaction et à l'interdépendance des systèmes biologiques, il est possible de déterminer la survie ou la durée de vie d'un organisme. Le rôle des gènes sur la survie ou la durée de vie a été étudié chez des jumeaux, des mutants génétiques humains du vieillissement prématuré, des études de liaison génétique pour la transmission de la durée de vie et des études sur des marqueurs génétiques de longévité exceptionnelle. Les gènes impliqués dans le processus de vieillissement, tels que les gènes d'assurance de la longévité, les gènes associés à la longévité, les vitagènes et les gérontogènes, sont des exemples de gènes candidats. Les gènes d'assurance de la longévité peuvent être des variants (ou des allèles) de certains gènes qui permettent à un organisme de vivre plus longtemps. Des mutations dans ces gènes peuvent modifier la pente des courbes de mortalité en fonction de l'âge. Sans se limiter à aucune théorie, certains gérontogènes peuvent réduire la durée de vie en bloquant l’expression des gènes d’assurance de la longévité.\n[00040] Les études d'association pangénomique (GWAS) montrent que la majorité des variants génétiques de la population ne présentent qu'un risque légèrement accru de maladie (Wray et al. 2007. Genome Research. 17 (10): 1520-1528; Wray et al. 2008 Opinion actuelle en génétique et développement, 18: 1-7; Consortium de contrôle des cas Wellcome Trust, 2007. Nature 447 (7145): 661-78). Wray et al. 2007, Wray et al. 2008 et Wellcome Trust Case Control Consortium 2007 sont incorporés dans leur intégralité par référence. Ce risque est reflété dans les OR numériques, on observe généralement un OR inférieur à 1,5, avec de nombreux OR autour de 1,1 à 1,2, avec un effet neutre pour un variant génétique ayant un odds ratio égal à 1. Les variants génétiques présentant des effets plus significatifs sur les risques de maladie possèdent généralement des rapports de cotes supérieurs à 2. \n [00041] Une simulation de GWAS par Wray et al. montre que, pour une étude cas-témoins portant sur 10 000 cas et contrôles, il sera possible d'identifier les plus gros loci (~ 75) expliquant plus de 50% de la variance génétique dans la population (Wray et al. 2007. Genome Research. 17 (10): 1520-1528). En outre, un regroupement des données permet de prédire un pourcentage élevé du risque génétique, même lorsque des mutations avec des RUP relativement faibles constituent la base de cette prédiction. Par exemple, Wray et al. ont identifié une corrélation> 0,7 entre le risque génétique prédit et le risque génétique réel (expliquant> 50% de la variance génétique) même pour les maladies contrôlées par 1 000 locus avec un risque relatif moyen de seulement 1,04.\n[00042] Les procédés de la présente invention offrent de nombreux avantages. Premièrement, la puissance statistique des données d'association génétique peut être augmentée en regroupant les résultats en utilisant des modes de réalisation de la présente invention provenant de plusieurs GWAS, ce qui peut aider à identifier de nombreux autres variants à risque avec des effets de petite taille. En outre, ces variantes de risque peuvent être utilisées pour expliquer un pourcentage plus élevé de variance génétique.\n[00043] Deuxièmement, des méthodes statistiques optimales peuvent être utilisées pour sélectionner et combiner plusieurs risques génétiques (tels que les SNP) dans une équation de prédiction du risque. C'est un défi commun à la plupart des études de génomique car le nombre de variables mesurées est beaucoup plus grand que le nombre d'échantillons. Dans la présente invention, plusieurs techniques d'apprentissage automatique, telles que les machines à vecteurs de support et les forêts à décision aléatoire, peuvent être appliquées aux données d'expression génétique de micropuces pour améliorer le diagnostic et la stratification du risque dans les études cliniques. Ces méthodes et un certain nombre d’autres méthodes qui ont été appliquées à la sélection des PNS peuvent être utiles pour la construction d’une équation de prédiction du risque.\n[00044] Des modes de réalisation de la présente invention prévoient l’intégration de données provenant d’un large éventail d’études d’associations génétiques afin d’améliorer efficacement la probabilité de prédiction de contracter une maladie donnée (par exemple, risque relatif, rapport de cotes, rapport de risque, etc.) et la mortalité due à cette maladie pendant trois mois. une personne compte tenu de son profil génomique. Dans certains modes de réalisation, la génomique d’un individu\n Le profil peut être combiné à des informations médicales et démographiques supplémentaires pour améliorer encore la probabilité de prédiction. En outre, les prédictions d'espérance de vie générées par les modes de réalisation de l'invention peuvent être utilisées pour évaluer les polices d'assurance-vie détenues par ces personnes.\n[00045] La présente invention fournit un procédé par lequel des données de risque de susceptibilité génétique peuvent être extraites de la littérature et compilées dans un appareil de base de données central. Les données de risque peuvent être des données contenant des contributions statistiques d'attributs génétiques liés à une maladie (par exemple, risque relatif, rapports de cotes, rapports de risque, valeurs prédictives, etc.). Dans la première phase de la collecte de données (curation primaire), des études ayant été effectuées sur un grand nombre de sujets tels que la méta-analyse, l'analyse groupée, des articles de synthèse et des études d'association pangénomique (GWAS) peuvent être incluses. La présente invention prévoit des cycles ultérieurs de collecte et de curation de données. Les phases ultérieures de la collecte de données (par exemple, la curation secondaire et la curation finale) peuvent utiliser des études d'association génétique à plus petite échelle pour affiner ces résultats. Un procédé selon cette invention est décrit ci-dessous:\n[00046] identifier les maladies à haute mortalité et leurs associations génétiques pertinentes (gènes candidats);\n[00047] rechercher, récupérer et filtrer la littérature pertinente;\n[00048] conservation des données de la littérature;\n[00049] déposer les données pertinentes dans la base de données centrale;\n[00050] construire un cadre statistique pour intégrer les données;\n[00051] recevoir des données d'entrée (par exemple, profil génomique de gènes candidats);\n[00052] calculer un score de susceptibilité à la maladie ou de mortalité, et un GPLE basé sur le profil génétique de l'individu (séquence génomique); et\n[00053] corréler le score GPLE à une valeur ou à un niveau de prime d'assurance vie prédit.\n Identifier les maladies à haute mortalité et leurs associations génétiques pertinentes\n[00054] Des maladies spécifiques à mortalité élevée ont été identifiées sur la base d'une enquête sur les données de mortalité provenant de diverses ressources publiques. Lors de l'identification d'une maladie particulière, toutes les associations d'intérêt génétiques et environnementales peuvent être explorées par des équipes scientifiques composées d'individus désignés pour examiner la littérature identifiée (l'équipe scientifique comprend par exemple un responsable de projet, un conservateur principal, un conservateur secondaire et un gestionnaire de base de données). La liste des associations peut être revue et modifiée sur une base continue, ce qui donne une liste de plus en plus longue, en termes de nombre de maladies incluses et de nombre de gènes candidats (déterminants génétiques) ayant un effet établi sur les taux de mortalité de ces maladies. déjà répertorié et sous enquête.\n[00055] Des exemples de maladies abordées par les procédés de la présente invention comprennent: polypose coli adénomateuse, maladie d'Alzheimer, sclérose latérale amyotrophique, tumeur cérébrale, bronchite chronique, carcinome, cancer de l'endomètre, carcinome hépatocellulaire, carcinome du poumon non à petites cellules, carcinome canalaire pancréatique, cancer le carcinome cellulaire, le carcinome à petites cellules, la thrombose de l'artère carotide, l'infarctus cérébral, les troubles cérébrovasculaires, le néoplasie intraépithéliale cervicale, les néoplasmes coliques, le syndrome de Mellitus , néoplasmes œsophagiens, syndrome de Gardner, néoplasmes gastriques, néoplasmes de la tête et du cou, thrombose de la veine hépatique, néoplasmes colorectaux héréditaires, anévrisme intracrânien, embolie intracrânienne, embolie intracrânienne et thrombose, thrombose, voie respiratoire. LEOPARD syndrome, leukemia, T-cell leukemia-lymphoma, acute B-cell leukemia, chronic B-cell leukemia, lymphocytic leukemia, acute lymphocytic leukemia, acute Ll lymphocytic leukemia, acute L2 lymphocytic leukemia, chronic lymphocytic leukemia, lymphocytic, acute megakaryocytic leukemia, acute myelocytic leukemia, myeloid leukemia, chronic myeloid leukemia, chronic myelomonocytic leukemia, acute nonlymphocytic leukemia, pre B-cell leukemia,\n acute promyelocyte leukemia, acute T-cell leukemia, liver disease, liver neoplasms, long QT syndrome, longevity, lung neoplasms, mammary neoplasms, Marfan syndrome, microvascular angina, mitral valve insufficiency, mitral valve prolapse, mitral valve stenosis, myocardial infarction, myocardial ischemia, myocardial reperfusion injury, myocardial stunning, myocarditis, nephritis, hereditary nephritis, ovarian neoplasms, pancreatic neoplasms, prostate neoplasm, chronic obstructive pulmonary disease, pulmonary embolism, pulmonary emphysema, pulmonary heart disease, pulmonary valve stenosis, rectal neoplasms, retinal vein occlusion, rheumatic heart disease, Romano-Ward syndrome, cardiogenic shock, sick sinus syndrome, sigmoid neoplasms, intracranial sinus thrombosis, tachycardia, supraventricular tachycardia, ventricular tachycardia, thromboembolism, thrombophlebitis, thrombosis, torsades de pointes, tricuspid atresia, tricuspid valve insufficiency, and other diseases known to one of ordinary skill in the art. In preferred embodiments, the disease(s) is bladder cancer, lung cancer, breast cancer, and/or pancreatic cancer.\n[00056] Exemplary candidate genes are those involved in disease, aging- associated diseases, and genes that are involved in genome maintenance and repair. Some examples of candidate genes are apoliprotein E, apolipoprotein C3, microsomal triglyceride transfer protein, cholesteryl ester transfer protein, angiotensin I-converting enzyme, insulin-like growth factor 1 receptor, growth hormone 1, glutathione- S -transferase Ml (GSTMl), catalase, superoxide dismutases 1 and 2, heat shock proteins, paraoxonase 1 , interleukin 6, hereditary haemochromatosis, methyenetetrahydrofolate reductase, sirtuin 3, tumor protein p53, transforming growth factor βl, klotho, werner syndrome, mutL homologue 1, mitochondrial mutations (Mt5178A, Mt8414T, Mt3010A and J haplotype), cardiac myosin binding protein C (MYBPC3) as well as other candidate genes involved in longevity known to one of ordinary skill in the art. In preferred embodiments, the candidate gene is glutathione-S-transferase Ml (GSTMl) or cardiac myosin binding protein C (MYBPC3). \n Searching, retrieving and filtering of relevant literature\n[00057] Embodiments of the present invention provide tools for automated searching, retrieval and filtering of results from databases, such as PubMed and HuGE. PubMed is an online database of indexed articles, citations and abstracts from medical and life sciences journals maintained by the National Library of Medicine. HuGE (Human Genome Epidemiology) is a searchable knowledge base of genetic associations. HuGE Literature Finder is a continuously updated literature information system that systematically curates and annotates publications on human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene-environment interactions, and evaluation of genetic tests. In addition to PubMed and HuGE, databases and sources known to one of ordinary skill in the art that contain the appropriate information could also be used.\n[00058] The present invention provides a computer system wherein databases are searched and desired information is collected based on the search parameters entered by the user through an interface. The present invention provides a code for searching the database and selecting relevant articles based on search criteria (e.g., Appendix A illustrates computer system coding for the HuGE metasearch – Advanced software). A user interface as an exemplary search related to GSTMl is shown in FIG. 1. The additional filters for searching provided in the code and on the interface can allow the user to limit searching to articles that contain or do not contain specific words. For example, Appendix B illustrates the first five results of the search hits identified from running the criteria presented in FIG. 1 through the code in Appendix A.\n[00059] The present invention also provides a computer system wherein abstracts are searched and desired information is collected based on the search parameters entered by the user through an interface. The present invention provides a search code for identifying and parsing the relevant information from abstracts in the literature (e.g., Appendix C illustrates computer system coding for the abstract fetcher – parser software). A user interface as an exemplary search \n related to bladder cancer with five identified studies (PubMed IDs entered) is shown in FIG. 2. For example, Appendix D shows the results of the search run through the interface of FIG. 2, utilizing the coding of Appendix C.\n[00060] Embodiments of the present invention also provide search and retrieval tools that permit searching a combination of generic or specific disease terms (e.g., heart disease) and gene symbol (e.g., APOE) on a public resource of choice in an automated fashion. These tools take into account the various ontologically associated disease terms from UMLS (Unified Medical Language System) and MeSH (Medical Subject Headings) vocabulary. For example, the associated terms with "heart disease" can include "coronary aneurysm" and "myocardial stunning". The search tool can also take into account gene name synonyms or sub-types (e.g., "apolipoprotein E2" and "apolipoprotein E3" as subtypes for the gene symbol "APOE"). This preferred comprehensive approach ensures retrieval of an extensive literature set for the particular disease-gene combination of interest.\n[00061] Embodiments of the present invention also provide search and retrieval tools that can be used to limit the culled results based on a variety of factors. These factors can include: country or region in which the study was performed or type of study (e.g., genetic association, gene-environment interactions, clinical trial, genome-wide association study and the like). Several publication parameters for each document (such as the title, abstract, PubMed ID, journal, author list and year of publication) can be automatically parsed by these tools. All of this information can be uploaded into the central database apparatus.\n[00062] Embodiments of the present invention provide a filtering tool that enables searching the titles and abstracts of the retrieved records based on any combination of terms. Several types of terms can be supported by the tool. Exemplary terms are: statistical terms (e.g., odds ratio (OR), hazard ratio (HR), relative risk (RR), p-values, primary statistic, number of cases and controls, adjusting variable, confidence intervals and the like); environmental effect terms \n (e.g., smoking, exercise, geographic location, language, temperature, altitude, and the like); personal terms (e.g., ethnicity, gender, age distribution of the study population); interaction terms (e.g., gene/gene interaction terms, gene/environment interaction terms); and other general terms (e.g., statistical significance, phenotype description, time of onset, study model used, study approach (classical or Bayesian), endpoints and outcomes such as, accelerated disease progression or sudden death). The filtering tool can also provide for the use of markers such as binary data fields to enter review status information (e.g., indication as to whether the article and the electronic record have been marked for additional review, whether the electronic record of data collected is ready to proceed to upload into the genetic database, and the like)\n[00063] Boolean logic can be implemented, which allows the user to enter any combination of the above described terms or additional terms known to one of ordinary skill in the art. Case-sensitive searches can be preformed to aid in narrowing the results. The methods of the present invention can be created by systems using a variety of programming languages including but not limited to C, Java, PHP, C++, Perl, Visual Basic, sql and other languages which can be used to cause the computing system of the present invention to perform the steps of the methods described herein.\nCurating data from literature\n[00064] A preferred embodiment of the present invention is shown in FIG. 3, the scientific articles and literature containing risk data (e.g., statistical contributions of genetic attributes related to disease) identified by the exemplary search methods of the present invention (11) can be passed through a primary curation phase (12) where the articles can be retrieved using a retrieval apparatus and filtered by article content prior to collecting the first set of data in an electronic record (13). Upon initiation of primary curation (12), the curation fields can be mapped to the data fields (18) in the genetic database (20). This process can be done iteratively as additional curation fields could be entered into the electronic record of data collected (13, 15, 17). The scientific articles and \n literature containing risk data can be subject to additional review. A review mechanism can be utilized that marks the article of concern for additional review [shown as secondary curation (14) or final curation (16)]. Without being limited to a specific number of review/curation rounds, the present invention provides for single or multiple rounds of article searching and curation of data. The publications identified and curated can be archived in the genetic database and/or central database apparatus to facilitate quick referencing.\n[00065] A secondary curation phase (14) can follow the primary curation phase (12) where additional literature and experimental results can be retrieved and the appropriate risk data can be obtained and collected in an electronic record (15). A final curation phase (16) can also follow the secondary curation phase (14) where additional literature and experimental results can be retrieved or the collected data can be reviewed to produce an electronic record of data collected (17) that can be uploaded into the genetic database (19). The genetic database (20) can serve as a central repository for the risk data associated with gene/gene interactions and/or gene/environment interactions.\nDeposition of relevant data into the central database apparatus\n[00066] The central database apparatus can be the central location of all the automatically searched, retrieved and filtered literature as well as curated literature. Curated literature and electronic records pending final curation can also be stored in the central database apparatus. A secondary set of tables can store pending results and final results in order to preserve the quality of the final statistical model.\n[00067] The electronic record of data collected can be stored in tables comprising fields of information related to the genetic markers identified. As shown by example in FIG. 4, the data fields can include various information related to the candidate gene [e.g. synonym names for the candidate genes or disease (33), information related to the disease (34), information related to candidate gene (35), information related to the article/literature searched (36), \n statistical information (37) and information related to the genetic marker (38)]. The electronic record of data can be stored in a master file after population of the data in the designated fields. For exemplary purposes, a representative GSTMl field database can be created using the code of Appendix E.\n[00068] The central database apparatus can also be used to log information associated with the curation process, such as identification of the user, date and time of data upload, and curation status of the publication and electronic record. For security purposes, users of the central database apparatus can be granted different access privileges to the tables and database.\n[00069] A number of interfaces to the database can be developed by one of ordinary skill in the art to enable easy and intuitive access to the data set of interest. Interfaces can also be developed for direct entry of curation results into the database or uploading of the full text of the article from which the data was collected.\n[00070] Due to the evolving process of scientific research, newly determined studies in genetic association are being conducted on a regular basis. To address this, the database can have a field that specifies the date when the database was last updated. At periodic intervals, the database can be queried for literature resources for all curated diseases in the database, and new references can be identified that have not been curated and deposited into the electronic record or the central database apparatus. The central database apparatus can then be augmented by these references through the curation process. The new date when this comparative search is performed can be recorded, and all records in the database can be updated to reflect the new curation date.\nBuilding a statistical framework to integrate the data (risk data)\n[00071] Hazard ratio (HR), relative risk (RR) and odds ratio (OR) calculations can be used as risk data to determine the statistical contribution of genetic attributes to occurrence of an event (such as disease). In a prospective study, RR is the ratio of the proportion of cases having a pre-defined disease in \n the exposed group (e.g., those with the genetic variant of interest) over that in the control group (e.g., those without the genetic variant of interest). In a case- control retrospective study, such as GWAS, calculation of the OR is preferred and can be estimated as the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or the ratio of being exposed to an event for the case group (e.g., those with allele of interest) over that in the control group (e.g., those without the allele of interest).\n[00072] In one embodiment of the present invention, the relative risk is used. For example, if the number of observations in each exposure/outcome combination is labeled as those shown in Table 1, the calculation of RR is A/(A+B)/C/(C+D). In a rare disease/outcome with incidence < 10%, A (C) is much smaller than B (D). Therefore, RR can be approximated by A/B/C/D, which is equal to A/C/B/D, the OR. However, for more common outcomes, the OR always overstates the RR, sometimes dramatically. Alternative statistical methods can be used for estimating an adjusted RR when the outcome is common (Localio et al. 2007. J Clin Epidemiol. 60(9):874-882; McNutt et al. Am J 2003. Epidemiol. 157(10):940-943; Zhang et al. 1998. Jama. 280(19):1690-1691)."},{"id":"text-2","heading":"Text","content":"[00073] In another embodiment, the hazard ratio is used. The hazard ratio (HR) is the ratio of the hazards of the treatment and control groups at a particular point in time. There is no direct mathematical relationship between the OR and the HR. However, the HR can be approximated by the odds ratio (OR) using a Taylor series expansion assuming disease prevalence is small (Walker. 1985. Appl Statist. 34(l):42-48). \n [00074] Since the sample size of most genetic-association studies is small to moderate leading to inconsistent results, meta-analysis, that combine multiple studies with similar measures are warranted to evaluate the significance of the genetic associations. Meta-analysis permits the calculation of summary ORs, which are weighted averages of ORs from individual studies. Both Mantel Haenszel and Peto's methods are commonly used by one of skill in the art to estimate such summary ORs in meta-analysis. These methods require 2 x 2 tables that cannot control for confounding factors.\n[00075] In addition, it is preferred to select an effect model. Usually the choice is between a fixed effects model, which indicates that the conclusions derived in the meta-analysis are valid for the studies included in the analysis, and a random effects model, which assumes that the studies included in the metaanalysis belong to a random sample of a universe of such studies. When the studies are found to be homogeneous, random and fixed effects models are indistinguishable.\n[00076] Engels et al. systematically evaluated 125 meta-analysis studies, and concluded that random effects estimates, which incorporate heterogeneity, tended to be less precisely estimated than fixed effects estimates (Stat Med. 2000 JuI 15;19(13):1707-28). Furthermore, summary odds ratios and risk differences agreed in statistical significance, leading to similar conclusions about whether treatments affected the outcome. Heterogeneity was common regardless of whether treatment effects were measured by odds ratios or risk differences. However, risk differences usually displayed more heterogeneity than odds ratios.\n[00077] Meta analysis techniques have been implemented in several statistical software packages, including R (The R Project for Statistical Computing; http://www.r-project.org/). Most of these packages also allow investigators to test studies for heterogeneity and publication bias, which refers to the greater likelihood of research with statistically significant results to be reported in comparison to those with null or non significant results. \n [00078] In still another embodiment of the present invention, an odds ratio (OR) is used. The OR is the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or to a sample-based estimate of that ratio. These groups might be men and women, an experimental group and a control group, or any other dichotomous classification (e.g., with and without a specific risk allele). If the probabilities of the event in each of two groups are p (first group) and q (second group), then the OR is expressed by the following formula:\nWO – p ) _ p Q – g ) q /( l – q ) q (l ~ p )\n[00079] An OR = 1 indicates that the condition or event under study is equally likely in both groups. An OR > 1 indicates that the condition or event is more likely in the first group.\n[00080] In another embodiment, the central database apparatus contains a panel of risk SNPs (SNPs located in risk alleles of candidate genes) with their corresponding ORs for each disease. In an additional embodiment, the central database apparatus also contains a list of ORs for implicated environmental factors and optionally ORs for interactions between SNPs and environmental factors. These ORs can be indicative of how likely a person is to develop a disease given his genetic makeup and environmental factors. The ORs for SNPs and environmental factors can be assumed to be additive within a particular disease.\nReceiving input data (e.g. genomic sequence including sequence of candidate genes) from an individual\n[00081] Genetic information can be collected from an individual by a variety of methods known in the art. In one embodiment collection involves the contribution by the individual of a buccal swab (i.e., inside the cheek), a blood sample, or a contribution of other biological materials containing genetic information for that individual. The genetic sequence can be determined by \n known methods such as that disclosed in Stephan et al, US 2008/0131887, incorporated in its entirety by reference, as well as methods employed by companies such as Seq Wright, GenScript, GenoMex, Illumina, ABI, 454 Life Sciences, Helicos and additional methods known to persons of ordinary skill in the art.\nCalculation of disease susceptibility, fatality scores and GPLE\n[00082] From the central database apparatus, data can be extracted to calculate statistical parameters such as an individual's ORs of disease susceptibility based on the specific SNPs that individual possesses. These ORs can be used to calculate fatality scores. Curated ORs from a wide range of high mortality diseases along with fatality scores for the diseases can be generated in the central database apparatus. The fatality score can qualitatively take into account several relevant factors such as mortality, average age of disease manifestation and prevalence within the population. The list of fatality scores can be customizable based on user or external third party databases results and preferences, and can reflect results from external databases results about the relative importance of the diseases in predicting mortality.\n[00083] The ORs calculated by the meta-analysis approach of the method provided by the present invention can be used as weights for the fatality scores to calculate an overall life expectancy for an individual given his/her genotype (i.e. GPLE). The GPLE is an individual age-specific probability for living an additional number of years given that individuals genetic profile (i.e. genomic DNA sequence) for the candidate genes of interest. This GPLE will be strongly indicative of mortality, with higher values corresponding to individuals at greater risk of contracting or succumbing to a high mortality disease. As more GWAS are completed, more gene/gene and gene/environment interaction ORs can be reported and calculated and as next-generation sequencing technologies are widely adapted these calculations will increase in precision. \n [00084] In one embodiment, the methods of the present invention can be utilized to provide survivorship data for people with specific risk genotype patterns. For these individuals, a panel of risk alleles in candidate genes can be identified in the electronic record of data collected. Individuals with a specific combination of these risk alleles can be monitored until their death in order to provide actual mortality data for the particular risk alleles of these candidate genes and more accurately determine life expectancy. Many GWAS are based on case-control design to identify risk alleles associated with certain diseases or traits. With actual mortality data for individuals with known genetic profiles, the methods of the present invention provide a database that can be populated with actual mortality data, resulting in an additional sample population to utilize in calculating probabilities and predicted genetic life expectancy for individuals with these risk alleles. This can provide more precise estimates and life tables (also called mortality tables or actuarial tables) based on genetic profiles.\n[00085] In another embodiment, the genetic information from the deceased individuals can be used to calculate mortality rates and/or life expectancies for those carrying specific risk alleles of candidate genes. Life tables show the probability of surviving until the next year for someone of a given age. Classification of the data in life tables is subdivided by gender, personal habits, economic condition, ethnicity, medical conditions and other factors attributable to life expectancy. There are multiple sources for mortality tables, such as The Society of Actuaries, National Center for Health Statistics (NCHS), CDC, and others known to a person of ordinary skill in the art. Life tables can provide basic statistical data for deaths and diagnosed cause of death correlated with personal factors (e.g., sex, race, lifestyle habits, social habits, education, and the like) and mortality. See National Vital Statistics Report. CDC. 56(10): 1-124.\n[00086] Life expectancy is the average number of years of life remaining at a given age. The starting point for calculating life expectancies is the age- specific death rates of the population members. For example, if 10% of a group of \n people alive at their 90th birthday die before their 91st birthday, then the age- specific death rate at age 90 would be 10%.\n[00087] These values can be used to calculate a life table, which can be used to calculate the probability of surviving to each age. In actuarial notation, the probability of surviving from age x to age x+n is denoted nPχ and the probability of dying during age x (i.e. between ages x and x+1) is denoted Qx.\n[00088] The life expectancy at age x, denoted e* , is then calculated by adding up the probabilities to survive to every age. This is the expected number of complete years lived:\nOO OO"},{"id":"text-3","heading":"Text","content":"[00089] Because age is rounded down to the last birthday, on average people live half a year beyond their final birthday, so half a year is added to the life expectancy to calculate the full life expectancy.\n[00090] Life expectancy is by definition an arithmetic mean. It can be calculated also by integrating the survival curve from ages 0 to positive infinity. For an extinct population of individuals, life expectancy can be calculated by averaging the ages at death. For a population of individuals with some survivors it is estimated by using mortality experience in recent years.\n[00091] Using this life expectancy calculation, no allowance has been made for expected changes in life expectancy in the future. Usually when life expectancy figures are quoted, they have been calculated in this manner with no allowance for expected future changes. This means that quoted life expectancy figures are not generally appropriate for calculating how long any given individual of a particular age is expected to live, as they effectively assume that current death rates will be "frozen" and not change in the future. Instead, life expectancy figures can be thought of as a useful statistic to summarize the current health status of a population. Some models do exist to account for the evolution of \n mortality (e.g., the Lee-Carter model) (R.D. Lee and L.Carter 1992. J. Amer. Stat. Assoc. 87:659-671) and can be used in the embodiments of the invention.\n[00092] Given the age, gender, race (AGR) of a person, the median life expectancy of the person can be calculated from mortality tables. Life expectancy calculations, in general, are heavily dependent on the criteria used to select the members of the population from which it is calculated. The baseline life expectancy (BLE) can be defined as the median life expectancy of individuals with matched AGR parameters.\n[00093] The inclusion of information on additional parameters such as medical factors (e.g., disease, stage of disease, treatment regimen, medical history and the like), environmental factors (e.g., exercise, smoking, occupational exposure and the like) and extended demographic information (e.g., geographical region, socioeconomic status and the like) can substantially enhance the life expectancy estimate for an individual. The specific life expectancy (SLE) of an individual for a given disease can be defined as the median life expectancy of individuals affected with that disease, with matched demographic, medical and environmental parameters. The specificity of the SLE for an individual for a given disease can depend on the availability of detail in the literature.\n[00094] The present invention provides a method for improved calculation of life expectancy based on genetic profiles, resulting in a GPLE. The inclusion of genetic information for an individual, such as SNPs, can increase the accuracy of life expectancy estimates. The GPLE is the median life expectancy of individuals with matched genetic profiles for individual candidate genes. In addition, calculation of GPLE by the methods herein, utilizes a central database apparatus under constant evolvement, continually factoring in the newest developments in genetic association scientific research reported in the literature.\n[00095] In preferred embodiments, the GPLE for an individual can be calculated from a blended approach, a minimum approach or any other approach known to one of ordinary skill in the art (in cases where the SLEs are not \n available, BLEs can be used). An example of a blended approach for three diseases is shown below. This approach calculates GPLE based on a combination of SLEs for three diseases (ij, i2, je3), where all the corresponding OR(i) values contribute to the GPLE:\n_ ORQ1) * SLEQ1) + OR(J2) • SLE(J2) + OR(J3) • SLE(J3) OR(I1) + OR(i2) + ORQ3)\n[00096] An example of a minimum approach for three diseases is shown below. This approach calculates GPLE based on the minimum of scaled SLEs for the diseases, where the scale factor for a corresponding ORQ) value is dependent on age and gender:\nmm • SLE(h) SLEJi2) SLE(J3)[SlOR(JySlOR(I[SlOR(JySlOR(I[SlOR(JySlOR(I[SlOR(JySlOR(I2) ' φR(h)\n[00097] The advantages of the GPLE calculation methods of the present invention above are twofold: 1) they combine a measure of the likelihood of an individual developing a disease (ORQ)) with the life expectancy of the individual with the genetic markers for that disease (reflected in the GPLE) and 2) a numerical value is provided that is indicative of the life expectancy of a person taking into account multiple input data or parameters, such as genetic, medical, environmental, demographic parameters.\n[00098] A preferred embodiment of the present invention is shown in FIG. 5. The determination of GPLE (28) can be based on information contained in a genetic database (20) and a life expectancy database (25). The genetic database can be comprised of information as discussed in FIG. 3. The life expectancy database (25) can contain information related to life expectancy data (21) and life table data (23). The retrieval of a specific life expectancy (22) from reported life expectancy data and the retrieval or construct of a baseline life expectancy (23) from reported life table data can be collectively housed in the life expectancy database (25). To determine GPLE, a user can calculate a collective risk index (26) based on multiple genetic factors and, along with the input data \n (27) from an individual, calculate a GPLE (28). The calculated GPLE can take into account individual or multiple genetic markers affiliated with disease susceptibility and longevity.\nDetermination of life insurance policy value based on GPLE\n[00099] The resultant GPLE can be utilized in the evaluation of life insurance policies. The GPLE can be inserted into standard time value of money equations, such as Present Value, Future Value, IRR and Net Present Value methods to calculate the theoretical value of a policy given the resultant life expectancy based on the genetic disposition of the insured. The GPLE can be used as a time interval in any standard financial valuation equation that calls for discounting or accruing in the analysis of life insurance products.\n[000100] Time value of money approaches can discount an amount of funds in the future to determine their worth at a prior period, generally the present. This technique is applied to both lump sums and streams of cash flow. Adjustments in the calculations can be made for whether the cash flow takes place at the beginning or the end of the period. Additional mathematical adjustments may also be made to adjust for certain policy features, such as minimum guaranteed returns, compounding periods and the like.\n[000101] The present value v'-" of a single payment made at n periods in the future is\n[000102] où n is the number of periods until payment, P is the payment amount, and r is the periodic discount rate. The present value v« of equal payments made each successive period in perpetuity (a.k.a. the present value of a perpetuity) is given by\nΣ J (l + ιT r ' (2) \n [000103] The present value v' of equal payments made each successive period for « periods (i.e. the present value of an annuity) is given by"},{"id":"text-4","heading":"Text","content":"[000104] where P is the periodic payment amount.\n[000105] In applying the GPLE to value a policy, the GPLE can be used to project the date of death by adding the GPLE, which is essentially a time interval to the current date. The GPLE would represent the time interval in the future that the insured would be projected to expire, thereby generating a payment inflow of the face value of the policy at that date in the future. In order to calculate the theoretical value of the policy, the life insurance face value or policy proceeds would be discounted back from that projected future date to the present using either a market or required interest rate. In addition, the present value of the future stream of cash outlays representing the periodic premium payments required to keep the policy in force would be deducted from the present value of the policy proceeds received.\n[000106] A preferred embodiment of the present invention is shown in FIG. 6. The evaluation of a life insurance policy can be conducted using input from the GPLE (28) and from external input variables (e.g., interest rates, expenses, investments, returns, and the like) (29). The input conditions (27 and 28) can be used in actuarial calculations to determine a value for the life insurance policy as an asset (32) or to determine the value for the policy premium of a life insurance policy for an individual (31).\nExample 1: Calculation of OR(disease) for an individual with GSTMl null genotype\n[000107] For example, an OR for bladder cancer can be determined. To calculate the odds ratio, thirty-one population-based case-control studies were curated from PubMed to investigate the risk of bladder cancer associated with glutathione-S-transferase Ml (GSTMl) null genotype. To avoid confounding by \n ethnicity, five Caucasian-based studies were used, which included 896 cases and 1,241 controls. Odds ratios from these five individual studies range from 1.15 to 2.2 (Arch. Toxicol. 2000 74(9):521-6, Cytogen. Cell. Gen. 2000 91(l-4):234-8, Int. J. Cancer 2004 110(4):598-604, Cancer Lett. 2005 219(l):63-9, Carcinogenesis 2005 26(7): 1263-71.). The summary OR calculated using the Mantel-Haenszel method was 1.37 (95% CI [1.15, 1.64]) for the fixed effect model and 1.56 (95% CI [1.12, 1.91]) for the random effect model. This result also showed no significant heterogeneity in study outcomes among these five studies (p=0.08). The OR estimate from this analysis is similar to the summary OR from a meta-analysis conducted by Engel et al. that included seventeen individual studies (OR=I.44; 95% CI [1.23, 1.68]; 2,149 cases and 3,646 controls).\nExample 2: Calculation of OR(disease) for lung cancer, breast cancer and pancreatic cancer\n[000108] Assuming a list of three diseases (wherein for disease i, let OR(i) represent the cumulative additive effect of all relevant ORs for a given person): lung cancer (lung), breast cancer (breast) and pancreatic cancer (pancreatic), and each with ten known SNPs. For the example below, the following assumptions can be made; each SNP has an OR of 1.2. Environmental effect of smoking has an OR of 1.5 for lung cancer in general, and 1.6 when found in combination with SNP 1 for lung cancer. The OR of smoking for breast and pancreatic cancer is not known.\n[000109] For a given person, their SLE can be estimated for lung, breast and pancreatic cancer from the best matched life expectancy or life table data from literature, for example:\n[000110] SLE(lung) = 1.5 years, SLE(breast) = 10 years, SLE(pancreatic) = 1 year\n[000111] The OR(lung) for a given person can be calculated as follows based on the different scenarios: \n [000112] If an individual has SNPs 2-10, but not SNP 1, and is a non- smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)*9 + 1 = 2.8\n[000113] If an individual has SNPs 1-10, and is a non-smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2- 1)* 10 + 1 = 3\n[000114] If an individual has SNPs 1-10, and is a smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)* 10 + (0.6) + 1 = 3.6\n[000115] If an individual has SNPs 2-10, and is a smoker, the OR(lung) can be calculated as follows: OR(lung) = (1.2-1)*9 + (0.5) + 1 = 3.3\n[000116] Similar to the OR(lung) calculations above, the OR(breast) and OR(pancreatic) can be similarly calculated to be OR(breast) = 0.5 and OR(pancreatic) = 1.2\nExample 3: Calculation of GPLE for an individual with SNPs 1-10 who is a smoker using a blended approach.\n[000117] The GPLE for the individual in Example 2 can be calculated using a blended approach that does not prioritize one disease over another. This type of approach evaluates the diseases in combination and provides for an overall perspective. The blended approach can be calculated as follows:\n_ OR(lung) • SLE(lung) + OR(breast) • SLE(breast) + OR(pancreatic) • SLE(pancreatic)\nOR(lung) + OR(breast) + OR(pancreatic) _ 3.4«1.5 + 0.5 «10 + 1.2«l 3.4 + 0.5 + 1.2\n= 2.22\nExample 4: Calculation of GPLE for an individual with SNPs 1-10 who is a smoker using a minimum approach.\n[000118] The GPLE for the individual in Example 2 can also be calculated using a minimum approach that factors in age and sex, resulting in a \n GPLE generated by the disease with the greatest contribution. The minimum approach can be calculated as follows:\n.[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—[SLEQung)SLEφreast)SLE(pancreatic)}mmx=—j– >\n[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J[ξjOR(lung)ξjORφreast)ξjOR(pancreatic)J\n[000119] where p is a function of age and sex. Specifically, p = 1 + a ■ exp(-/? • age I λsexe), a,β > 0. Note that p is a monotonic decrease function of age, and α and β are two tuning parameters that can be determined by the mortality table. λsexe is a constant factor for sex, which is also determined by mortality table. λsexe=l for female if OR(disease)>l; otherwise, λsexe=l for male. If α=4, β=l/25, and λseχ=0.94, using the equation above, a GPLE minimum of (3.97, 17.50, 6.13), which is 3.97 for a male and min (4.12, 17.62, 6.16) = 4.12 for a female is generated. FIGUE. 7 illustrates a survival curve representing the relation between ξJθR(lung) and age/sex.\nExample 5: Calculation of GPLE for an individual with a high risk genetic mutation\n[000120] A high prevalence of mutation (4%, deletion of 25 bp) in the gene encoding cardiac myosin binding protein C (MYBPC3) is associated with high risk of heart failure (OR=7) [Dhandapany PS et al. (2009). A common MYBPC3 (cardiac myosin binding protein C) variant associated with cardiomyopathies in South Asia. Nat Genet. 41(2):187-91.]. Assuming SLE is 15 for individuals at age 55. If α=8, β=l/30, and λsexe=0.9, applying the minimum approach for life expectancy calculation, the GPLE is 5.8 for men and 6.4 for women with this gene mutation, e.g, 38% or 42% of SLE. Similarly, if SLE is 25 for individuals at age 45, the GPLE is 11.5 for men and 12.4 for women (46% or 50% of SLE). \n Example 6: Determination of life insurance policy value based on fatality score\n[000121] In continuation of the individual presented in Example 4 (the male, age 55 who has a mutation for the gene encoding cardiac myosin binding protein C (MYBPC3) and has a fatality score of 5.8), the calculations below assume the insured has a policy that has a face value of $1,000,000 and has monthly premiums due of $1000 a month to keep the policy in force. In addition, annual interest rate of 6% is assumed.\n[000122] The life expectancy fatality score of 5.8 can be converted into 69.6 months.\n[000123] Applying the formula for Present Value results in the present value of the policy proceeds would be $706,711.41.\n[000124] From this we must subtract the Present Value of the 69.6 payments which equals -$58,657.72 as the total cost in present value terms of the 69.6 payments.\n[000125] Therefore the theoretical value of this policy assuming an interest rate of 6% is $706,711.41- $58,657.72= $648,053.69. \nAPPENDICE"},{"id":"text-5","heading":"Text","content":"# ! /usr/bin/perl use strict; use warnings ; use LWP 5.64; # Loads all important LWP classes, and makes sure your version is reasonably recent. use Data : : Dumper ; use CGI ' : standard ' ; use CGI :: Carp qw(fatalsToBrowser) ; use File:: Temp qw/ tempfile tempdir /; my $num_of_terms = 5; # Change this setting to change the number of search terms allowed for each filtering level.\n#my $adminemail = ; print header; if ( !param)\n print << 'EOF' ;"},{"id":"text-6","heading":"Text","content":"EOF print "", start_html ( 'HuGE meta- search' ) , "" , hi ( ' HuGE metasearch – Advanced ' ) , "" ; print "This is a powerful yet convenient and simple front end to the HuGE Literature Finder tool.",br,\n' Important : You will need to read the Très bref ' ,\n 1 Documentation ' , ' in order to use it correctly . ' ,p, start_multipart_form; print "Enter search terms for HuGE navigator database: ",br, textfield(-name=> ' condition' , -size=>40) ; print " (Do Not enter boolean queries into this box.)"; print "Enter search tags to further filter context by and highlight or eliminate: ",p; print 'Must contain tout of these words ' ,br,- \n foreach my $i (1.. $num_of_terms)\n my $paramname = "and_searchterm" . $i; print textfield(-name=>$paramname, – size=>15) , '  ,- ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "and_casesensitive" . $i; print checkbox ( -name=>$paramname ,\n-selected => 0,\n-value=> 1Y ',\n-label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p; print 'Must contain tout of these words ' , br; foreach my $i (1.. $num_of_terms)\n my $paramname = "or_searchterm" . $i; print textfield ( -name=>$paramname, – size=>15) , '   ;   ;   ; ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "or_casesensitive" . $i; print checkbox (-name=>$paramname, -selected => 0, -value=>'Y' ; -label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p ; print 'Must ne pas contain any of these words ',br; foreach my $i (1.. $num_of_terms)\n my $paramname = "not_searchterm" . $i; print textfield (-name=>$paramname, – size=>15) , '   ;   ;   ; ' ;\n print br; foreach my $i (1.. $num_of_terms)\n my $paramname = "not_casesensitive" . $i; print checkbox (-name=>$paramname, -selected => 0, -value=> ' Y' , -label=> ' case sensitive ' ) , "   ;   ;   ;   ; " ;\n print p; print "All filter terms are assumed to be exact phrases. Non\n wild cards . " ; print br , checkbox ( -name=> ' showabstract ' , – selected => 1 ,\n-value=> ' Y ' ,\n-label=>' ■ ) , " Check here if you want to see full abstract ." ,hr; print "Use the engine that is " ; print ' ' , "n" ; print 'faster but cuts corners and can fail' , "n" ; print 'slower but rigorous and failsafe' , "n" ; print ' ' , "n" ; print '  ,-  ,- ' , submit (' SUBMIT '), " Scnbsp    " , reset, ' ' , end_form, hr;\n else\n{ my $dir = tempdir (DIR => " /var/www/vhosts/default/htdocs/tmpdir/ " ) ; if (! (-d $dir) ) system ("mkdir $dir"); \n# print ' ' ; print ' ' ; my $searchcondition = param ( "condition" ); my %searchterm = ( ) ; my %casesens = ( ) ; foreach my $lo ("and", "or", "not")\n{ foreach my $i (1.. $num_of_terms)\n my $paramtag = $lo. "_searchterm" . $i; if (param ($paramtag) && param ($paramtag) =~ /S/)"},{"id":"text-7","heading":"Text","content":"$searchterm$lo $i = param ($paramtag) ;\n$searchterm$lo $i =~ s/s+$//g;\n$searchterm$lo $i =~ s/UNEs+//g; \n$paramtag = $lo. "_casesensitive" . $i; if (param ($paramtag) && param ($paramtag) =~ /S/) $casesens$lo $i = param ($paramtag) ; my $showabstract = param ( "showabstract" ); my $outfile = "HuGE_fetched. csv" ; open (OUTCSV, ">$dir/$outfile") or die "Cannot open $dir/$outfile \nfor writingn" ; print OUTCSV "HuGE Query, $searchconditionn" ; print OUTCSV "Highlighting/Filtering Tag(s)n"; print OUTCSV "All these terms are required:";\n# Tagging all the required terms with the actual HuGE query is a good idea because it\n# will reduce the actual number of hits that need to be fetched. But the user better not enter\n# an OR into the HuGE query (because HuGE does not tolerate mixing logical operators) . my $full_hugestring = $searchcondition; if (param ( 'version' ) eq 'hardhack')\n{ foreach my $key (keys % $searchterm "and" )\n my $srchterm = $searchterm "and" $key ; if (defined $srchterm && $srchterm =~ /S/)\n print OUTCSV " , $srchterm" ;\n$full_hugestring .= " AND $srchterm" ; \n print OUTCSV "nAny of these terms are required:"; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (defined $srchterm && $srchterm =- /S/) print OUTCSV "$srchterm, " ; \n print OUTCSV "n"; print OUTCSV "All these terms are avoided:"; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (defined $srchterm && $srchterm =~ /S/) print OUTCSV "$srchterm, " ; \n print OUTCSV "nn"; my $browser = LWP: :UserAgent->new; my $url = "http: //hugenavigator .net/HuGENavigator/searchSummary .do" ; my $response = $browser->post ( $url,[[[[\n'User-Agent' => 'Mozilla/4.76 [en] (Win98; U) ', 'Accept' => 'image/gif, image/x-xbitmap, image/jpeg, image/pjpeg, image/png, */* ' ,\n 1 Accept -Charset' => ' iso-8859-1, * ,utf-8 ' , ' Accept -Language ' => 'en-US',\n'firstQuery' => $full_hugestring, 'publitSearchType ' => "now", \n 1 whichContinue ' => "firststart" , 'check' => "n", 'dbType' => "publit", 1Mysubmit' => "go" ], ); die "$url error: ", $response->status_line unless $response->is_success; die "Weird content type at $url — ", $response->content_type unless $response->content_type eq ' text/html ' ; my @pmids = ( ) ; if ( $response->content =~ /No articles found/)\n # print $response->content ; print "Couldn't find the match-string in the responsen" ; exit; open (TEMP, " >$dir/huge_metasearcher .html" ) or die "Cannot open huge_metasearcher.html for writingn" ; print TEMP $response->content ; close TEMP; my $startindex = index ($response->content, "fileDownloadForm" ) ; my $subtextl = substr ($response->content, $startindex) ; my $endindex = index ($subtextl, "Search Criteria: " ); my $subtext2 = substr ($subtextl, 0, $endindex) ;\n$subtext2 =~ s/ . *value="//g; $subtext2 =~ s/">.*//g; $subtext2 =~ s/file.*//g; $subtext2 =~ s/\n.*//g; $subtext2 =~ s/ . *Text. *//g; $subtext2 =~ s/s+//g; $subtext2 =~ s/pubmedid//g;\n@pmids = split (/,/, $subtext2) ; print 'Final HuGE query: ' . $full_hugestring. "n" ; print "Number of records hit from the HuGE database = " , scalar (Opmids) , "" ; open (LOG, " >$dir/huge_metasearcher . log" ) or die "Cannot open $dir/huge_metasearcher . log for writingn" ; print LOG "PMIDs are n" . join( "n" ,@pmids) . "nn" ;\n## It ' s faster to lump 12 PMIDs together and fetch at a time rather than sending an\n## HTTP request to pubmed for each one separately (try higher at own risk with lynx) . So.. my $i=0; my $lumpsize = 18; my @medline_articles = ( ) ; while ($i<scalar (Opmids) -1)"},{"id":"text-8","heading":"Text","content":"$i=$i+$lumpsize; \n if ( $i>=scalar (Θpmids) ) $i=scalar (@pmids) -1 ; my @current_pmids = @pmids [ ($i- $lumpsize) . . $i] ; my $url =\n 1 http : //www . ncbi . nlm . nih . gov/pubmed/ ' .joint", " , @current_j?mids ) . ' ?report =medline&format=text ' ; print LOG "Current URL: $urln" ; my $current_medline_articles_lumped = "lynx -dump – dont_wrap_jpre ' $url ' – ; my @current_medline_articles = split (/PMID-/, $current_medline_articles_lumped) ; shift (@current_medline_articles) ; push (@medline_articles, @current_medline_articles) ;"},{"id":"text-9","heading":"Text","content":"# End of lumped fetching procedure print LOG "nn"; my %Articles = () ; foreach my $medline_article (@medline_articles)\n{\n$medline_article = "PMID- ". $medline_article; my $pmid = 0 ; my @medline_lines = split (/n/, $medline_article) ; my %medline_hash = ( ) ; my $current_key = " " ; foreach my $line (@medline_lines)\n if ($line =~ /S/)\n if ($line =~ /ΛS/ && substr ($line, 4, 1) eq "-"},{"id":"text-10","heading":"Text","content":"$current_key = substr ($line, 0, 4) ; $current_key =~ s/s+//g;\n my $current_value_line = substr ($line, 5) ;\n$current_value_line =~ s/UNE //g; chomp $current_value_line; if (defined $medline_hash$current_key )"},{"id":"text-11","heading":"Text","content":"$medline_hash$current_key .=\n$current value line;\n else $medline_hash$current_key =\n$current_value_line,- if ($current_key eq "TI" $current_key .eq\n"AB")"},{"id":"text-12","heading":"Text","content":"$medline_hash$current_key .= "n";\n elsif ($current_key eq "PMID")"},{"id":"text-13","heading":"Text","content":"$pmid = $current_value_line,- $pmid =~ s/s+//g,- \n# print "Addingn $current_value_linen TOn€current_key";"},{"id":"text-14","heading":"Text","content":"if ($pmid == 0) die "PMID is still unresolved for this article \n$medline_article\n" ; \n$medline_hash"PMID" =~ s/s+//g; $Articles$pmid = %medline_hash;\n# print "" , $Articles$pmid-> "AB" , "" ,- print LOG Dumper (%Articles) , "n======================================nnn" ; close LOG;\n# print join("", @pmids) , "\n" ; print "Highlighted tag (s) : " ; print "All-are-required terms: "; foreach my $key (keys %$searchterm "and" )\n my $srchterm = $searchterm "and" $key ,- if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "nAny-one- is-required terms: " ; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "nMust-be-absent terms: " ; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (defined $srchterm && $srchterm =~ /S/) print " $srchterm " ; \n print "n" ;\n##### FILTERING STEP BEGINS ##### my Ofiltered_pmidsl = (); if (scalar(keys %$searchterm "and" ) > 0 && parara ( 'version' ) eq ' rigorous ' )\n{ foreach my $pmid (@pmids)\n my ($ab, $ti) = ($Articles$pmid-> "AB" , $Articles$pmid->"TI") ; my $yes = 1; foreach my $key (keys %$searchterm "and" )\n my $srchterm = $searchterm "and" $key ; if (uc ($casesens"and" $key) =~ /Y/) \n if ( found ($srchterm, $ti) == 0 && found ($srchterm, $ab) == 0) $yes = 0; \n else\n if (found_i ($srchterm, $ti) == 0 && found_i ($srchterm, $ab) == 0) $yes = 0; \n if ($yes == 1) @filtered_jpmidsl = addtolist (@filtered_jpmidsl, $pmid) ; else @filtered_pmidsl = Opmids,- if (scalar (@filtered_pmidsl) == 0)\n print "No articles pass the ALL-ARE-REQUIRED search terms. Try altering the highlighting requirements . n" ; exit;\n else print scalar (Ofiltered_praidsl) ." articles passed the ALL- ARE-REQUIRED filtersn"; my @filtered_pmids2 = (); if (scalar(keys %$searchterm "or" ) > 0)\n{ foreach my $pmid (@filtered_jpmidsl)\n{ my ($ab, $ti) = ($Articles$praid -> "AB" , $Articles$pmid->"TI") ; my $yes = 0 ; foreach my $key (keys %$searchterm "or" )\n my $srchterm = $searchterm "or" $key ; if (uc($casesens"or"$key) =~ /Y/)\n if ( found ($srchterm, $ti) == 1 else\n if (found_i ($srchterm, $ti) == 1 if ($yes == 1) @filtered_pmids2 = addtolist (@filtered_jpmids2 , $pmid) ; else @filtered_jpmids2 = @filtered_pmidsl; if (scalar (@filtered_pmids2) == 0)\n print "No articles pass after the ANY-ARE-REQUIRED search terms. Try altering the highlighting requirements . n" ; exit; \n else print scalar (@filtered_j?mids2) ." articles passed the ANYONE- IS -REQUIRED filtersn" ; my @filtered_pmids3 = (); if (scalar (keys %$searchterm "not" ) > 0)\n{ foreach my $pmid (@filtered_jpmids2)\n my ($ab, $ti) = ($Articles$pmid -> "AB" , $Articles$pmid->"TI") ; my $yes = 1; foreach my $key (keys %$searchterm "not" )\n my $srchterm = $searchterm "not" $key ; if (uc($casesens"not"$key) =~ /Y/)\n found ($srchterm, $ab) == 1)\n print "\nSearch term $srchterm exists in title $ti or abstract $ab\nn" ; ; \n else\n found_i ($srchterm, $ab) == 1) $yes = 0; if (found_i ($srchterm, $ti) == 1 if ($yes == 1) push (@filtered_jpmids3 , $pmid) ; \n else @filtered_pmids3 = @filtered_j)mids2; if (scalar (@filtered_pmids3) == 0)\n print "No articles pass after the MUST-BE-ABSENT search terms. Try altering the highlighting requirements. n" ; exit;\n else print scalar (Ofiltered_jpmids3) ." articles passed the MUST- BE-ABSENT filtersn" ; my $webdir = $dir;\n$webdir =~ s//var/www/vhosts/default/htdocs//g; print 'Click ici to download output in CSV format\n'; print "\nn" ; , print "\nn"; \n if (uc ($showabstract) =~ /Y/)\n print "#"; print "PMID"; print "Titre" ; print "Context" ; print "Abstraitn" ; print OUTCSV " # , PMID, Title, Context ,Abstractn" ;\n else\n print "#"; print "PMID" ; print "Titre" ; print "Contextn" ,- print OUTCSV "#, PMID, Title, Contextn";\n print "\nn"; foreach my $i (1..scalar (Ofiltered_pmids3) )\n{ my $pmid = $filtered_jomids3 [$i-l] ; if (defined $Articles$pmid ) else die "No article for PMID $pmid or some other unknown error . n" ; # print "Currently processing PMID $pmidn" ; my %medline_hash = %$Articles$pmid ; print "\nn"; print '\n ' . "$i\n" ; my $pmid_link = "http: //www.ncbi .nlm.nih.gov/pubmed/" . $medline_hash "PMID" ; print '\n ' ; print ' ' . $medline_hash "PMID" . ""; my $modti = $medline_hash "TI" ; my $modab = $medline_hash "AB" ; foreach my $lo ("and", "or")\n{ foreach my $key (keys %$searchterm$lo )\n my $srchterm = $searchterm$lo $key ; if (uc ($casesens$lo $key ) =~ /Y/)"},{"id":"text-15","heading":"Text","content":"$modti = bolden($modti, $srchterm) ; $modab = bolden($modab, $srchterm) ;\n else"},{"id":"text-16","heading":"Text","content":"$modti = bolden_i ($modti, $srchterm) ;\n$modab = bolden_i ($modab, $srchterm) ; \n print ' \n ' . $modti . "\n";\n my ©sentences = split (/. /, $medline_hash "AB" ) ; print ' \n ' ; my $local_output = " " ; foreach my $sentence (©sentences)\n my $modsent = $sentence; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo )\n my $srchterm = $searchterm$lo $key ; if (uc($casesens$lo$key) =~ /Y/)"},{"id":"text-17","heading":"Text","content":"$modsent = bolden ($modsent, $srchterm) ;\n else"},{"id":"text-18","heading":"Text","content":"$modsent = bolden_i ( $modsent , $srchterm) ;"},{"id":"text-19","heading":"Text","content":"if ($modsent ne $sentence) $local_output .= $modsent . " . " ; print ""; if ($local_output =~ /S/) print $local_output; else print " – " ; print ""; print "\n"; if (uc ($showabstract) =~ /Y/)\n print ' \n'; print ""; if ($modab =~ /S/) print $modab; else print "-\n"; print " " ; print "\n"; print OUTCSV\n"$i, $pmid, " . $medline_hash "TI" . " , $local_output, " . $medline_hash "AB" . " n" ;\n else\n print OUTCSV "$i, $pmid, " . $medline_hash "TI" . " , $local__outputn" ;\n print "\nn";\n print "\nn" ; close OUTCSV; } \nsub found\n{ my ($searchterm, $text) = ($_[0] , $_[1]) if (defined $searchterm && $searchterm =~ /S/) else return (1) ; if (defined $text && $text =~ /S/) else { return (0) ; ) if ($text =~ / Q€searchtermEW/ | | $text =~ /UNEQ€searchtermEW/ | | $text =~ /WQ€searchtermEW/)"},{"id":"text-20","heading":"Text","content":"# print "$text\nA\n$searchterm\nn" ; return (1) ;\n else\n return ( 0 ) ;"},{"id":"text-21","heading":"Text","content":"sub found__i\n{ my ($searchterm, $text) = ($_[0] , $_[1]) if (defined $searchterm && $searchterm =~ /S/) else return (1) ; if (defined $text && $text =~ /S/) else { return(O); ' if ($text =~ / Q€searchtermEW/i | | $text =~ /UNEQ€searchtermEW/i | | $text =~ /WQ€searchtermEW/i)"},{"id":"text-22","heading":"Text","content":"# print "$text\nA\n$searchterm\nn" ; return ( 1 ) ;\n else\n return ( 0 ) ;"},{"id":"text-23","heading":"Text","content":"sub addtolist\n my ($array_ref, $element) = ($_[0] , $_[1]) my ©array = @$array_ref ,■ my $found = 0 ; foreach my $exel (©array)\n if ($exel == $element) $found = 1; \n if ($found == 0)\n push (©array, $element) ;\n return (©array) ; \n sub bolden\n my ($text, $string) = ($_[0] , $_[1]) if ($text =~ /ΛQ€stringEW/ sub bolden_i\n my ($text, $string) = ($_[0] , $_[1]) if ($text =~ /ΛQ€stringEW/i \nAPPENDICE"},{"id":"text-24","heading":"Text","content":"Below we show the results from querying the HuGE database using our *.cgi script (see Appendix A and Figure 1) and the search term, "GSTMl". To reduce the number of hits from 1132 to 480, we required that each abstract include "GSTMl" and any of the following terms: "OR", "Ratio", "Odds" (all case-sensitive). Below are the tabulated results showing 1) a running index of abstracts; 2) the PMID of the abstract; 3) the title of the abstract; 4) the context within which the additional query terms were found in the abstract (for example, "OR" in the first record retrieved); 5) the entire PubMed abstract corresponding to the PMID in the second column. The first five hits are shown.\nFinal HuGE query: GSTMl\nNumber of records hit from the HuGE database = 1132\nHighlighted tag(s):\nAll-are-required terms:\nAny-one-is-required terms: OR Ratio Odds\nMust-be-absent terms:\n1132 articles passed the ALL- ARE-REQUIRED filters 480 articles passed the ANY-ONE-IS-REQUIRED filters 480 articles passed the MUST-BE-ABSENT filters Click here to download output in CSV format \n#[PMIDTitleContextAbstract[PMIDTitleContextAbstract[PMIDTitleContextAbstract[PMIDTitleContextAbstract\n1 19338664 GSTMl and The results showed BACKGROUND: Previous GSTTl that the overall OR evidence implicates polymorphisms andlwas 1.42 (95%CI = polymorphisms of GSTMl and nasopharyngeal 1.21-1.66) for GSTMl GSTTl, candidates of phase II cancer risk: an polymorphism. While enzymes, as risk factors for vidence-based forGSTTl various cancers. A number of meta-analysis. polymorphism, the studies have conducted on the overall C > R was 1.12 association of GSTMl and (95% CI = 0.93-1.34). GSTTl polymorphismwith susceptibility to nasopharyngeal carcinoma (NPC). However, inconsistent and inconclusive results have been obtained. In the present study, we aimed to assess the possible associations of NPC risk with GSTMl and GSTMl null genotype, respectively. METHODS: The associated literature was acquired through deliberate searching and selected based on the established inclusion criteria for publications, then the extracted data were further analyzed using systematic metaanalyses. RESULTS: A total of 85 articles were identified, of which eight case-control studies concerning NPC were selected. The results showed that the overall OK was 1.42 (95%CI = 1.21-1.66) for GSTMl polymorphism. While forGSTTl polymorphism, the overall OR was 1.12 (95% CI = 0.93-1.34). CONCLUSION: The data were proven stable via sensitivity analyses. The results suggest GSTMl deletion as a risk factor for NPC and failed to suggest a marked correlation of GSTTl polymorphisms with NPC risk. \n #|PMID JTitϊe Context Abstract\n19347979 Evaluation of Patients carrying the GPXl- INTRODUCTION: We evaluated the role glutathione metabolic CC genotype had a of glutathionelrelated genotypes on genes on outcomes in clinically significant overall survival, time to progression, advanced non-small decline in the UNISCALE adverse events, and quality of life (QOL) cell lung cancer (odds ratio (OR) : 7.5; p = in stage IIIB/IV non-small cell lung patients after initial 0.04), total Functional cancer patients who were stable or treatment with Assessment of Cancer respondingfrom initial treatment with platinum-based Therapy-Lung score (OR: platinum-based chemotherapy and chemotherapy: an 11.0; p = 0.04), physical subsequently randomized to receive daily NCCTG-97-24-51 (OR: 7.1; p = 0.03), oral carboxyaminoimidazole or a placebo. based study. functional (OR: 5.2; p = METHODS: Of the 186 total patients, 113 0.04), and emotional well- had initial treatment with platinum being constructs (OR: 23.8; therapy and DNA samplesof whom 46 p = 0.01). also had QOL data. These samples were analyzed using six polymorphic DNA markers that encode five important enzymes in the glutathione metabolic pathway. Patient QOL was assessed using the Functional Assessment of Cancer Therapy-Lung and the UNISCALE QOL questionnaires. A clinically significant decline in QOL was defined as a 10% decrease from baseline to week-8. Multivariate analyses were used to evaluate the association of the genotypes on the four endpoints. RESULTS: Patients carrying a GCLC 77 genotype had a worse overall survival (hazardratio (HR) = 1.5, p = 0.05). Patients carrying the GPXl-CC genotype had a clinically significant decline in the UNISCALE (odds ratio (HH) : 7.5; p = 0.04), total Functional Assessment of Cancer Therapy-Lung score (OR: 11.0; p = 0.04), physical (OR: 7.1; p = 0.03), functional (OR: 5.2; p = 0.04), and emotional well- being constructs (OR: 23.8; p = 0.01). CONCLUSIONS: Genotypes of glutathione-related enzymes, especially GCLC, may be used as host factors in iredicting patients' survival after latinum-based chemotherapy. GPXl may e an inherited factor in predicting atients' QOL. Further investigation to define and measure theeffects of these genes in chemotherapeutic regimens, drug toxicities, disease progression, and QOL are critical."},{"id":"text-25","heading":"Text","content":"#PMID Title Context Abstract\n19303722 Association of NAT2, Results: It was found that Objective: To explore the\nGSTMl, GSTTl, significant associations of the association of polymorphisms in CYP2A6, and CYP2A13 NAT2 slow-acetylator genotype N-acetyltransferase 2 (NAT2), gene polymorphismswith (odds ratio, CM: 2.42; 95% glutathione S-transferase (GST), susceptibility and clinicopathologic •onfidence interval, CI: 1.47-3.99), cytochrome P450 (CYP) 2A6, and characteristics of bladder GSTMl null genotype (OR: 1.64; CYP 2A13 genes with cancer inCentral China. 95% CI: 1.11-2.42) and susceptibility and clinicopathologic GSTMl/GSTTl-double null characteristics of bladder cancer in genotype (OR: 1.72; 95% CI: 1.00- a Chinese population. Methods: In 2.95) with increased risk of a hospital-based case-control study bladder cancer. Conversely, of 208 cases and 212 controls carriers with at least one matched on age and gender, CYP2A6*4 allele showed lower genotypes were determined by risk than the non-carriers (OR: PCR-based methods. Risks were 0.47; 95% CI: 0.28-0.79). evaluated by unconditional logistic regression analysis. Results: It was found that significant associations of the NAT2 slow-acetylator genotype (odds ratio, C)H: 2.42; 95% confidence interval, CI: 1.47- 3.99), GSTMl null genotype (OR: 1.64; 95% CI: 1.11-2.42) and GSTMl/GSTTl-double null genotype (OR: 1.72; 95% CI: 1.00- 2.95) with increased risk of bladder cancer. Conversely, carriers with at least one CYP2A6*4 allele showed lower risk than the non-carriers (OR: 0.47; 95% CI: 0.28-0.79). The adjusted ORs (95% CI) for smokers with NAT2 slow- acetylator, GSTMl null, GSTMl/GSTTl-double null genotype, and variant CYP2A6 genotypes were 2.99 (1.44-6.25), 1.98 (1.13-3.48), 2.66 (1.22-5.81) and 0.41 (0.20-0.86), respectively. Furthermore, NAT2 slow- acetylator, GSTMl null, and GSTMl/GSTTl-double null genotypes were associated with higher tumor grade (P=0.001, 0.022, and 0.036, respectively), and only NAT2 slow-acetylator genotype was associated with higher tumor stage (P=0.007). CYP2A13 was not associated with risk or tumor characteristics. Conclusion: It is suggested that NAT2 slow-acetylator, GSTMl null, GSTMl/GSTTl-double null, and variant CYP2A6 genotypes may play important roles in the development of bladder cancer in Henan area, China. \n #1PMID ffϊtie Context Abstract\n5)19303595 Negative effects of The risk of low motility with high OBJECTIVE: Effects of ambient serum p,p'-DDE on DDE-DDT exposure was increased exposure to DDT and its metabolites sperm parameters in men with the GSTTl null (DDE-DDT) on human sperm and modification by genotype compared to those with parameters and the role of genetic genetic GSTTl intact (odds ratio (C)R) polymorphisms in modifyingthe polymorphisms. =4.19, 95% confidence interval association were investigated. (CI) 1.05-16.78 and OR=3.57, 1.43- METHODS: Demographics, 8.93, respectively). Risk for low medical history data, blood and morphology in men with high semen samples were obtained from DDE-DDT and one or both the first 336 male partners of CYPlAl *2A alleles was lower couples presenting to 2 infertility compared to men with the common clinics. Serum was analyzed for CYPlAl alleles ^GR- 2.18, 0.78- organochlorines (OC) and DNA for 6.07 vs. OR 3.45, 1.32-9.03, polymorphisms in GSTMl, GSTTl, respectively). Effects of high DDE- GSTPl and CYPlAl . Men with DDT on low sperm concentration each sperm parameter considered\n>R- 2.53, 1.0-6.31) was low by WHO criteria (concentration unaffected by the presence of the <20million/mL, motility <50%, polymorphisms. morphology <4%) were compared to men with all normal sperm parameters in logistic regression models, controlling for sum of other OC pesticides. RESULTS: High DDE-DDT level was associated with significantly increased odds for all 3 low sperm parameters. The risk of low motility with high DDE-DDT exposure was increased in men with the GSTTl null genotype compared to those with GSTTl intact (odds ratio"},{"id":"text-26","heading":"Text","content":"=4.19, 95% confidence interval (CI) 1.05-16.78 and OR=3.57, 1.43-8.93, respectively). Risk for low morphology in men with high DDE-DDT and one or both CYPlAl *2A alleles was lower compared to men with the common CYPlAl alleles (OR=2.18, 0.78- 6.07 vs. OR=3.45, 1.32-9.03, respectively). Similar results were obtained for men with low DDE- DDT exposure. Effects of high DDE-DDT on low sperm concentration (OR=2.53, 1.0-6.31) was unaffected by the presence of the polymorphisms. CONCLUSION: High DDE-DDT exposure adversely affected all 3 sperm parameters and its effects were exacerbated by the GSTTl null polymorphism and by the CYPlAl common alleles. \nAPPENDICE"},{"id":"text-27","heading":"Text","content":"#!/usr/bin/perl\nuse strict; use warnings; use LWP 5.64; # Loads all important LWP classes, and makes sure your version is reasonably recent. use Data::Dumper; use CGI ':standard'; use CGI:: Carp qw(fatalsToBrowser); use File::Temp qw/ tempfile tempdir /; my $num_of_terms = 5; # Change this setting to change the number of search terms allowed for each filtering level.\n#my $adminemail = print header; if (! par am)\n print « 'EOF';"},{"id":"text-28","heading":"Text","content":"EOF\nprint "ici to download output in CSV format\n'; print "<table cellpadding = V1OV cellspacing = VOV border = V3V align =\n"left">n"; \n print "\nn"; if (uc($showabstract) =~ IYI)\n print "\n#\n"; print "\nPMID\n"; print "\nTitre\n"; print "\nContext\n"; print "\nAbstrait\nn"; print OUTCSV "#,PMID,Title,Context,Abstractn";\n else\n print "\n#\n"; print "\nPMID\n"; print "\nTitre"; print "\nContext\nn" ; print OUTCSV M#,PMID,Title,Contextn";\n print "\nn"; foreach my $i(l..scalar(@filtered_pmids3))\n{ my $pmid = $filtered_pmids3[$i-l];\n# print "Currently processing PMID $pmidn"; my %medline_hash = %$ Articles $pmid; print "\nn"; print '\n'."$i\n"; my $pmid_link =\n"http://www.ncbi.nlm.nih.gov/pubmed/".$medline_hashMPMIDM; print '\n'; print '<a href="l.$pmid_link.'">'.$medline_hash "PMID" . "\n" ; my $modti = $medline_hash"TI"; my $modab = $medline_hash"AB"; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo)\n my $srchterm = $searchterm$lo$key; if (uc($casesens$lo $key) =~ IYI)"},{"id":"text-29","heading":"Text","content":"$modti = bolden($modti, $srchterm);\n$modab = bolden($modab, $srchterm); \n autre"},{"id":"text-30","heading":"Text","content":"$modti = bolden_i($modti, $srchterm); $modab = bolden_i($modab, $srchterm); print '\n'.$modti."\n"; my @sentences = split (Λ. /, $medline_hash"AB"); print '\n'; my $local_output = ""; foreach my $sentence (@sentences)\n my $modsent = $sentence; foreach my $lo ("and", "or")\n foreach my $key (keys %$searchterm$lo)\n my $srchterm = $searchterm$lo $key; if (uc($casesens$lo $key) =~ IYI)"},{"id":"text-31","heading":"Text","content":"$modsent = bolden($modsent, $srchterm);\n else"},{"id":"text-32","heading":"Text","content":"$modsent = bolden_i($modsent, $srchterm);\n if ($modsent ne $sentence) $local_output .= $modsent". "; print ""; if ($local_output =~ ASI) print $local_output; else print "-"; print ""; print "\n"; if (uc($showabstract) =~ IYI)\n print '\n'; print ""; if ($modab =~ ΛS/) print $modab; else print "-"; print ""; print "\n";\n print OUTCSV M€i,$pmid,".$medline_hash"Trje.",$local_output,M.$medline_hash"AB"."n";\n else\n print OUTCSV\n"$i,$pmid," .$medline_hash "TI" . ",$local_outputn" ;\n print "\nn";\n} print "\nn"; close OUTCSV; } sub found\n $text =~ /ΛQ€searchtermEW/ 1 sub found_i\n sub addtolist\n{ my ($array_ref, $element) = ($_[0], $_[1]) my @array = @$array_ref); my $found = 0; foreach my $exel(@array)\n if ($exel = $element) $found = 1; \n if ($found == 0)\n push (@array, $element);\n return (@array);\n sub bolden\n my ($text, $string) = ($_[0], $_[1]) if ($text =~ /ΛQ€stringEW/ sub bolden_i\n{ my ($text, $string) = ($_[0], $_[1]) if ($text =~ /ΛQ€stringEW/i || $text =~ ΛWQ€stringEW/i || $text =~ / Q€stringEW/i)"},{"id":"text-33","heading":"Text","content":"$text =~ sΛQ$&E/ $& /ig;"},{"id":"text-34","heading":"Text","content":"return ($text); \nAPPENDICE"},{"id":"text-35","heading":"Text","content":"Five PMEDs were fetched and filtered using the word "Bladder" (see Appendix C which shows our *.cgi script and Figure 2 which shows the graphical interface for the Abstract Fetcher and Parser). The filtering process reduced the number of abstracts from five to four. Below are the tabulated results showing 1) a running index of abstracts; 2) the PMID of the abstract; 3) the title of the abstract; 4) the context within the abstract in which the word, "bladder", was found; 5) the entire PubMed abstract corresponding to the PMID in the second column.\nAbstract Fetcher and Parser\nHighlighted tag(s): All-are-required terms: 'bladder' Any-one-is-required terms: Must-be-absent terms:\n4 articles passed the ALL-ARE-REQUIRED filters 4 articles passed the ANY-ONE-IS-REQUIRED filters 4 articles passed the MUST-BE-ABSENT filters Click here to download output in CSV format \n#PMID JTitle Context (Abstract\n1 11131031-Glutathione Genotype distributions for Genotype distributions for transferase GSTPl, GSTMl, and GSTTl GSTPl, GSTMl, and GSTTl isozyme were determined in 91 patients were determined in 91 patients genotypes in with prostatic carcinoma and 135 with prostatic carcinoma and 135 patients with patients with Madder carcinoma patients with bladder carcinoma prostate and and compared with those in 127 and compared with those in 127 h I adder abdominal surgery patients abdominal surgery patients carcinoma. without malignancies. 3%, chi2 without malignancies. None of the P=0.02, Fisher P =0.03). genotypes differed significantly Homozygosity for the GSTMl with respect to age or sex among null allele was more frequent controls or cancer patients. In the among bladder carcinoma group of prostatic carcinoma patients (59% in bladder patients, GSTTl nullallele carcinoma patients vs 45% in homozygotes were more prevalent controls, Fisher P=0.03, chi2 (25% in carcinoma patients vs. P=0.02, OR=I .76, CI=I.08-2.88). 13% in controls, Fisher P =0.02, These findings suggest that chi2 P=0.02, OR=2.31, CI = 1.17- pecific single polymorphic GST ■4.59) and the combined M1-/T1 – genes, that is GSTMl in the case null genotype was also more of bladder cancer and GSTTl in frequent (9% vs. 3%, chi2 P=0.02, the case of prostatic carcinoma, Fisher P =0.03). Homozygosity are most relevant for the for the GSTMl null allele was development of these urological more frequent among lifecldei malignancies among the general carcinoma patients (59% in population in Central Europe. . bladder carcinoma patients vs 45% in controls, Fisher P=0.03, chi2 P=0.02, OR=I.76, CI=I.08- 2.88). In contrast to a previous report, no significant increase in the frequency of the GSTPIb allele was found in the tumor patients. Except for the combined GSTMl-/ Tl -null genotype in prostatic carcinoma, none of the combined genotypes showed a significant association with either of the cancers. These findings suggest that specific single polymorphic GST genes, that is GSTMl in the case of bladder cancer and GSTTl in the case of prostatic carcinoma, are most relevant for the development of these urological malignancies among the general population in Central Europe. \n #PMID Title Context Abstract\n211173863 Susceptibility As a result of this mutation, the Glutathione S-transferase (GST, E.C. genes: GSTMl :χpression of GSTM3 can be 2.5.1.18) comprises a family of and GSTM3 as influenced. The mutated GSTM3 isoenzymes that play a key role in the genetic risk gene has been reported to be involved detoxification of such exogenous factors in in increased susceptibility for the substrates as xenobiotics, bladder cancer. development of cancer, but no environmental substances, and information is available concerning carcinogenic compounds. At least five its role in bladder cancer. We have mammalian GST gene families have identified patients with a been identified to be polymorphic, and heterozygous GSTM3 geno- type mutations or deletions of these genes who carry a significantly increased contribute to the predisposition for risk for the development of bladder several diseases, including cancer. cancer. Here we report that the The gene cluster of GSTMl -GSTM5 mutation of intronβ of GSTM3 has been reported to be localized on increases the risk for bladder cancer chromosome Ip and spans a length of (odds ratio: 2.31; 95% confidence nearly 100 kb. One mutation of the interval [CI], 1.79-2.82). GSTM3 gene generates a recognition Heterozygous carriers of the GSTMl site for the transcription factor yin null genotype have a significantly yang 1. As a result of this mutation, levated risk of developing biaddcr the expression of GSTM3 can be ancer.We calculated an odds ratio of influenced. The mutated GSTM3 gene 3.54 (95% CI, 2.99-4.11) for this has been reported to be involved in ;enotype. These observations lead to increased susceptibility for the the assumption that the lack of development of cancer, but no detoxification by glutathione information is available concerning its conjugation predispose to bladder role in Maddfc"* cancer. We have cancer when at least one oftwo alleles identified patients with a heterozygous is affected. Furthermore, individuals GSTM3 geno- type who carry a presenting the homozygous wild type significantly increased risk for the of GSTMl and GSTM3 are development of hlmhhr cancer. Here significantly protected against we report that the mutation of intronδ hiaritlei cancer. . of GSTM3 increases the risk for Madder cancer (odds ratio: 2.31; 95% confidence interval [CI], 1.79-2.82). We developed a procedure to identify heterozygous or homozygous carriers of the GSTMl alleles. Heterozygous carriers of the GSTMl null genotype have a significantly elevated risk of developing iji<J!Jei cancer. We calculated an odds ratio of 3.54 (95% CI, 2.99-4.11) for this genotype. These observations lead to the assumption that the lack of detoxification by glutathione conjugation predispose to bladder cancer when at least one oftwo alleles is affected. Furthermore, individuals presenting the homozygous wild type of GSTMl and GSTM3 are significantly protected against bladder cancer. \n #|PMID Title Context JAbstract 3 11757669 Polymorphisms of We investigated the effect of We investigated the effect of glutathione S- the GSTMl and GSTTl null the GSTMl and GSTTl null transferase genes genotypes, and GSTPl 313 genotypes, and GSTPl 313 (GSTMl5 GSTPl andiA/G polymorphism on btotider A/G polymorphism on bladder GSTTl)and bhtddei cancer susceptibility in a case cancer susceptibility in a case cancer susceptibility control studyof 121 btatkk-i control studyof 121 O!add<τ in the Turkish cancer patients, and 121 age- cancer patients, and 121 age- population. and sex-matched controls of and sex-matched controls of the Turkish population. GSTTl the Turkish population. The was shown notto be associated adjusted odds ratio for age, sex, with bladder cancer. In and smoking status is 1.94 individuals with the combined [95% confidence intervals (CI) risk factors of cigarette 1.15-3.26] for the GSTMl null smoking and the GSTMl null genotype, and 1.75 (95% CT genotype, the risk of hladkUv 1.03-2.99) for the GSTPl 313 ancer is 2.81 times (95% CI A/G or G/G genotypes. GSTTl 1.23-6.35) that of persons who was shown notto be associated both carry the GSTMl -present with 1HHiUiCf cancer. genotype and do not smoke. Combination of the two high- Similarly, the risk is 2.38-fold risk genotypes. GSTMl null (95% CI 1.12-4.95) for the and GSTPl 313 A/G or G/G, combined GSTPl 313 A/G and revealed that the risk increases G/ G genotypes and smoking. to 3.91-fold (95% CI 1.88- These findings support the role 8.13) compared with the for the GSTMl null and the combination of the low-risk GSTPl 313 AG or GG genotypes of these loci. In genotypes in the development individuals with the combined of bladder cancer. risk factors of cigarette Furthermore, gene-gene smoking and the GSTMl null (GSTMl -GSTPl) and gene- genotype, the risk of bhuickr :nvironment (GSTMl- cancer is 2.81 times (95% CI moking, GSTPl -smoking) 1.23-6.35) that of persons who interactions increase this risk both carry the GSTMl -present substantially. . genotype and do not smoke. Similarly, the risk is 2.38-fold (95% CI 1.12-4.95) for the combined GSTPl 313 A/G and G/ G genotypes and smoking. These findings support the role for the GSTMl null and the GSTPl 313 AG or GG genotypes in the development of Madder cancer. Furthermore, gene-gene (GSTMl -GSTPl) and gene- environment (GSTMl- smoking, GSTPl -smoking) interactions increase this risk substantially.\n #[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract[PMID(Title[Context(Abstract\n411825664 Combined To evaluate the association To evaluate the association effect of between genetic polymorphism of between genetic polymorphism of glutathione S- GSTMl, GSTTl and development STMl, GSTTl and development transferase Ml of Madder cancer, a hospital- of bladder cancer, a hospital- and Tl based case-control study was based case-control study was genotypes on conducted in South Korea. The conducted in South Korea. The bladder cancer study population consisted of 232 study population consisted of 232 risk. histologically confirmed male histologically confirmed male adder cancer cases and 165 adder cancer cases and 165 male controls enrolled from male controls enrolled from urology departments with no urology departments with no previous history of cancer or previous history of cancer or systemic diseases in Seoul during systemic diseases in Seoul during 1997-1999. The GSTMl null 1997-1999. The GSTMl null genotype was significantly genotype was significantly associated with bladder cancer associated with bladder cancer (OR: 1.6, 95% CI: 1.0-2.4), (OR: 1.6, 95% CI: 1.0-2.4), whereas the association observed whereas the association observed for GSTTl null genotype did not for GSTTl null genotype did not reach statistical significance (OR: reach statistical significance (OR: 1.3, 95% CI: 0.9-2.0). There was a 1.3, 95% CI: 0.9-2.0). There was a statistically significant multiple statistically significant multiple interaction between GSTMl and nteraction between GSTMl and GSTTl genotype for risk of GSTTl genotype for risk of bladder cancer (P=O.04); the risk bladder cancer (P=0.04); the risk associated with the concurrent associated with the concurrent lack of both of the genes (OR: 2.2, ack of both of the genes (OR: 2.2, 95% CI: 1.2-4.3) was greater than 95% CI: 1.2-4.3) was greater than the product of risk in men with the product of risk in men with GSTMl null/GSTTl present (OR: GSTMl null/GSTTl present (OR: 1.3, 95% CI: 0.7-2.5) or GSTMl 1.3, 95% CI: 0.7-2.5) or GSTMl present/GSTTl null (OR: 1.1, present/GSTTl null (OR: 1.1, 95% CI: 0.6-2.2) genotype 95% CI: 0.6-2.2) genotype combinations. . combinaisons. \nAPPENDICE"},{"id":"text-36","heading":"Text","content":"Genowl edge 340431_00001 Appendi x E . txt — MySQL dump 10.11\n— Host : l ocal host Database : DPA — Server version 5.0.45\n/*! 40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;\n/*! 40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;\n/*! 40101 SET @OLD_COLLATION_CONNECTION=®@COLLATION_CONNECTION */;\n/*! 40101 SET NAMES Utf8 */;\n/* 140103 SET @OLD_TIME_ZONE=@@TIME_ZONE */ \n/* 140103 SET TIME_ZONE='+00:00' */;\n/* 140014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=O */;\n/* 140014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=O */;\n/* 140101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE= ' NO_AUTO_VALUE_ON_ZERO ' */]\n/* 140111 SET @OLD_SQL_NOTES=@@SQL_NOTES , SQL_NOTES=0 */;\n— Table structure for table Disease'\nDROP TABLE IF EXISTS 'Maladie'; CREER LA TABLE 'Maladie" (\n 'Disease_Id' int(ll) default NULL,\n 'Disease_Generic_τerm' varchar(30) default NULL,\n 'Disease_Name' varchar(40) default NULL,\n 'Disease_θntology' varchar(150) default NULL,\n 'Disease_Type' varchar(25) default NULL ) ENGINE=MyISAM DEFAULT CHARSET=I ati nl;\n— Dumping data for table 'Maladie'\nLOCK TABLES 'Maladie' WRITE; /*! 40000 ALTER TABLE 'Maladie' DISABLE KEYS */; /* 140000 ALTER TABLE 'Maladie' ENABLE KEYS */; UNLOCK TABLES;\n— Table structure for table 'Gene'\nDROP TABLE IF EXISTS 'Gene'; CREER LA TABLE 'Gene' (\n 'Gene_id~ int(ll) default NULL, 'Gene_Name' varchar(lθ) default NULL, 'Gene_Synonyms" varchar(50) default NULL, 'GO_Cellular_Components' varchar(lOO) default NULL, GO_Biological_Processes' varchar(lOO) default NULL, "GO_Molecular_Functions' varchar(lOO) default NULL, 'OMlM_Id' int(ll) default NULL ) ENGINE=MyISAM DEFAULT CHARSET=I ati nl;\n— Dumping data for table 'Gene'\nLOCK TABLES 'Gene' WRITE;\n/* 140000 ALTER TABLE 'Gene' DISABLE KEYS */; /* 140000 ALTER TABLE 'Gene' ENABLE KEYS */; UNLOCK TABLES; \n Genowledge 340431_00001 Appendix E.txt — Table structure for table "Littérature"\nDROP TABLE IF EXISTS Literature"; CREER LA TABLE "Littérature" (\n "Pub_id" int(ll) default NULL,\n "PMID" int(ll) default NULL,\n "Titre" varchar(lOO) default NULL,\n "Abstrait" varchar(lOOO) default NULL,\n "Mots clés' varchar(50) default NULL ) ENGINE=MyISAM DEFAULT CHARSET=I ati nl;\n— Dumping data for table "Littérature"\nLOCK TABLES "Littérature" WRITE;\n/*! 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