检索结果-内蒙古大学图书馆

Author Correction: Developmental exposure of California mice to endocrine disrupting chemicals and potential effects on the microbiome-gut-brain axis at adulthood

引用

Scientific reports 2020年第1期10卷 12524页

作者： Sarabjit Kaur Saurav J Sarma Brittney L Marshall Yang Liu Jessica A Kinkade Madison M Bellamy Jiude Mao William G Helferich A Katrin Schenk Nathan J Bivens Zhentian Lei Lloyd W Sumner John A Bowden Jeremy P Koelmel Trupti Joshi Cheryl S Rosenfeld Christopher S Bond Life Sciences Center University of Missouri Columbia MO 65211 USA. Biomedical Sciences University of Missouri Columbia MO 65211 USA. MU Metabolomics Center University of Missouri Columbia MO 65211 USA. MU Institute of Data Science and Informatics University of Missouri Columbia MO 65211 USA. Food Science and Human Nutrition University of Illinois Urbana IL 61801 USA. Physics Randolph College Lynchburg VA 24503 USA. DNA Core Facility University of Missouri Columbia MO 65211 USA. Department of Biochemistry University of Missouri Columbia MO 65211 USA. Department of Physiological Sciences College of Veterinary Medicine University of Florida Gainesville FL 32610 USA. Center for Environmental and Human Toxicology College of Veterinary Medicine University of Florida Gainesville FL 32610 USA. Environmental Health Sciences Yale University New Haven CT 06510 USA. Department of Health Management and Informatics School of Medicine University of Missouri Columbia MO 65211 USA. Christopher S Bond Life Sciences Center University of Missouri Columbia MO 65211 USA. rosenfeldc@missouri.edu. Biomedical Sciences University of Missouri Columbia MO 65211 USA. rosenfeldc@missouri.edu. MU Institute of Data Science and Informatics University of Missouri Columbia MO 65211 USA. rosenfeldc@missouri.edu. Thompson Center for Autism and Neurobehavioral Disorders University of Missouri Columbia MO 65211 USA. rosenfeldc@missouri.edu. Genetics Area Program University of Missouri Columbia MO 65211 USA. rosenfeldc@missouri.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification

引用

Nature Communications 2025年第1期16卷 1-18页

作者： Maria Zanti Denise G. O’Mahony Kristia Yiangou Kyriaki Michailidou Michael T. Parsons Amanda B. Spurdle Leila Dorling Joe Dennis Marc Naven Antonis C. Antoniou Manjeet K. Bolla Qin Wang Douglas F. Easton Nicholas J. Boddicker Wenan Chen Chunling Hu Steven N. Hart Fergus J. Couch Thomas U. Ahearn Jonine D. Figueroa Montserrat García-Closas Christine B. Ambrosone Song Yao Irene L. Andrulis Gord Glendon Paul L. Auer Caroline Baynes Don M. Conroy Alison M. Dunning Craig Luccarini Clara Bodelon James M. Hodge Alpa V. Patel Lauren R. Teras Natalia V. Bogdanova Stig E. Bojesen Kristen D. Brantley Nicola J. Camp Archie Campbell Jose E. Castelao Melissa H. Cessna Jenny Chang-Claude Fei Chen Christopher A. Haiman Georgia Chenevix-Trench Kamila Czene Arcangela De Nicolo Susan M. Domchek Katherine L. Nathanson Thilo Dörk A. Heather Eliassen D. Gareth Evans Peter A. Fasching Henrik Flyger Manuela Gago-Dominguez Anna González-Neira Rocio Nuñez-Torres Felix Grassmann Andreas Hadjisavvas Mihalis I. Panayiotidis Ute Hamann Muhammad U. Rashid Diana Torres Mikael B. A. Hartman Weang-Kee Ho Reiner Hoppe Sacha J. Howell Anna Jakubowska Elza K. Khusnutdinova Yon-Dschun Ko Peter Kraft Vessela N. Kristensen James V. Lacey Jingmei Li Geok Hoon Lim Veronique Kiak-Mien Tan Sara Lindström Artitaya Lophatananon Kenneth Muir Arto Mannermaa Maria Elena Martinez Dimitrios Mavroudis Roger L. Milne Melissa C. Southey Nadia Obi Janet E. Olson Celine M. Vachon Julie R. Palmer Gary R. Zirpoli Paul D. P. Pharoah Eric C. Polley Kathryn J. Ruddy Siddhartha Yadav Emmanouil Saloustros Elinor J. Sawyer Marjanka K. Schmidt Soo Hwang Teo Amy Trentham-Dietz Thérèse Truong Jeffrey N. Weitzel Melissa S. Cline Peter Devilee Sean V. Tavtigian David E. Goldgar Biostatistics Unit The Cyprus Institute of Neurology & Genetics Nicosia Cyprus Department of Medical Genetics Oslo University Hospital Oslo Norway Population Health Program QIMR Berghofer Medical Research Institute Brisbane Queensland Australia Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge UK Centre for Cancer Genetic Epidemiology Department of Oncology University of Cambridge Cambridge UK Department of Quantitative Health Sciences Division of Computational Biology Mayo Clinic Rochester MN USA Department of Laboratory Medicine and Pathology Mayo Clinic Rochester MN USA Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Department of Health and Human Services Bethesda MD USA Usher Institute of Population Health Sciences and Informatics The University of Edinburgh Edinburgh UK Cancer Research UK Edinburgh Centre The University of Edinburgh Edinburgh UK Department of Cancer Prevention and Control Roswell Park Comprehensive Cancer Institute Buffalo NY USA Fred A. Litwin Center for Cancer Genetics Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Toronto Ontario Canada Department of Molecular Genetics University of Toronto Toronto Ontario Canada Division of Biostatistics Data Science Institute and Cancer Center Medical College of Wisconsin Milwaukee WI USA Department of Population Science American Cancer Society Atlanta GA USA Radiation Oncology Research Unit Hannover Medical School Hannover Germany Copenhagen General Population Study Herlev and Gentofte Hospital Copenhagen University Hospital Herlev Denmark Department of Clinical Biochemistry Herlev and Gentofte Hospital Copenhagen University Hospital Herlev Denmark Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Internal Medicine and Hu

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data ofBRCA1andBRCA2from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207BRCA1andBRCA2variants, with 6909 being coding, covering 23.4% ofBRCA1andBRCA2VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.3% specificity forBRCA1and 93.3% sensitivity and 86.6% specificity forBRCA2. This approach provides case-control evidence for 787 unclassified variants; these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Assessing Compliance with Best Practices for the Prevention of Surgical Site Infections: Statewide Survey of Infection Preventionists and Perioperative Personnel

引用

American Journal of Infection Control 2017年第6期45卷 S93-S93页

作者： Margaret Drake Laura Tyner Xiao Wang Michelle Schwedhelm Maureen Tierney Mark Rupp Muhammad Ashraf State Infection Preventionist Nebraska Medicine Department of Health and Human Services Public Health Nebraska Infection Prevention Lead Nebraska Infection Control Assessment and Promotion Program (ICAP) Nebraska Medicine Health Survillance Data Analyst Nebraska Department of Health and Human Services Executive Director Infection Prevention & Emergency Preparedness Nebraska Medicine NDHHS Infection Prevention Epidemiologist Nebraska Divison of Public Health- Epidemiology and Informatics Unit Medical Director Department of Infection Control and Epidemiology UNMC Nebraska Medicine Associate Professor University of Nebraska Medical Center

来源：评论

学校读者我要写书评

暂无评论

Precision Rehabilitation for Patients Post-Stroke based on Electronic health Records and Machine Learning

arXiv

引用

arXiv 2024年

作者： Gao, Fengyi Zhang, Xingyu Sivarajkumar, Sonish Denny, Parker E. Aldhahwani, Bayan M. Visweswaran, Shyam Shi, Ryan Hogan, William Bove, Allyn Wang, Yanshan Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Communication Science and Disorders University of Pittsburgh PittsburghPA United States Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Physical Therapy University of Pittsburgh PittsburghPA United States Department of Medical Rehabilitation Sciences Umm Al-Qura University Makkah Saudi Arabia Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Department of Computer Science University of Pittsburgh Medical Center PittsburghPA United States Data Science Institute Medical College of Wisconsin MilwaukeeWI United States Hillman Cancer Center University of Pittsburgh Medical Center PittsburghPA United States

Objective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients’ rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. Through this study, our ultimate goal is to pinpoint the specific rehabilitation exercises that can effectively aid post-stroke patients in improving their functional outcomes in basic mobility (BM) and applied cognitive (AC) domains. data sources We collected data for 265 stroke patients from the University of Pittsburgh Medical Center, accessed through the Rehabilitation datamart With informatics iNfrastructure for Research (ReDWINE). Methods We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. AM-PAC scores were collected at the initial rehabilitation visit and followed up at one and two months—key recovery periods. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models—logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)—to predict outcomes in functional ability. Results Statistical analyses revealed significant associations between functional improvements and specific exercises. In the AC domain, the BALANCE exercise showed substant

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular health Disparities

JACC: Advances

引用

JACC: Advances 2024年第1期3卷 100759-100759页

作者： Pearson, Thomas A. Vitalis, Debbie Pratt, Charlotte Campo, Rebecca Armoundas, Antonis A. Au, David Beech, Bettina Brazhnik, Olga Chute, Christopher G. Davidson, Karina W. Diez-Roux, Ana V. Fine, Lawrence J. Gabriel, Davera Groenveld, Peter Hall, Jaclyn Hamilton, Alison B. Hu, Hui Ji, Heng Kind, Amy Kraus, William E. Krumholz, Harlan Mensah, George A. Merchant, Raina M. Mozaffarian, Dariush Murray, David M. Neumark-Sztainer, Dianne Petersen, Maya Goff, David College of Medicine and College of Public Health and Health Professions University of Florida Health Science Center Gainesville FL United States Division of Cardiovascular Sciences National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Cardiovascular Research Center Massachusetts General Hospital and Broad Institute Massachusetts Institute of Technology Cambridge MA United States Center of Innovation for Veteran-Centered and Value-Driven Care University of Washington Seattle WA United States UH Population Health University of Houston Houston TX United States Johns Hopkins Medicine Institute for Clinical and Translational Research Baltimore MD United States Institute of Health System Science Feinstein Institutes for Medical Research Northwell Health New Hyde Park NY United States Feinstein Institutes for Medical Research Northwell Health Manhasset NY United States Urban Health Collaborative Drexel Dornsife School of Public Health Philadelphia PA United States Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine Baltimore MD United States Center for Health Care Transformation and Innovation University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Department of Health Outcomes and Biomedical Informatics Institute for Child Health Policy College of Medicine University of Florida Gainesville FL United States Center for the Study of Healthcare Innovation Implementation & Policy VA Greater Los Angeles Healthcare System Los Angeles CA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Department of Computer Science University of Illinois Urbana-Champaign Champaign IL United States Center for Health Disparities Research (CHDR) University of Wisconsin School of Medicine and Public Health Madison WI United States Duke Molecular Physiology Insti

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce. © 2023 The Authors

关键词： data science health equity health promotion implementation science personalized medicine precision analytics

来源：评论

学校读者我要写书评

暂无评论

Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

arXiv

引用

arXiv 2024年

作者： Zhang, Yuezhou Folarin, Amos A. Ranjan, Yatharth Cummins, Nicholas Rashid, Zulqarnain Conde, Pauline Stewart, Callum Sun, Shaoxiong Vairavan, Srinivasan Matcham, Faith Oetzmann, Carolin Siddi, Sara Lamers, Femke Simblett, Sara Wykes, Til Mohr, David C. Haro, Josep Maria Penninx, Brenda W.J.H. Narayan, Vaibhav A. Hotopf, Matthew Dobson, Richard J.B. Pratap, Abhishek Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Institute of Health Informatics University College London London United Kingdom NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust London United Kingdom Health Data Research UK London University College London London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom Department of Computer Science University of Sheffield Sheffield United Kingdom Janssen Research and Development LLC TitusvilleNJ United States Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom School of Psychology University of Sussex Falmer United Kingdom Teaching Research and Innovation Unit Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Mental Health Program Amsterdam Public Health Research Institute Amsterdam Netherlands Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom South London and Maudsley NHS Foundation Trust London United Kingdom Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University ChicagoIL United States Boehringer Ingelheim Pharmaceuticals Inc. RidgefieldCT United States University of Washington Seattle United States

Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the interplay between weather, behavior, and depression has not been fully quantified. This study analyzed real-world data from 428 participants (a subset;68.7% of the cohort) in the RADAR-MDD longitudinal mobile health study to investigate seasonal variations in depression (measured through a remote validated assessment - PHQ-8) and examine the potential interplay between dynamic weather changes, physical activity (monitored via wearables), and depression severity. The clustering of PHQ-8 scores identified four distinct seasonal variations in depression severity: one stable trend and three varying patterns where depression peaks in different seasons. Among these patterns, participants within the stable trend had the oldest average age (p=0.002) and the lowest baseline PHQ-8 score (p=0.003). Mediation analysis assessing the indirect effect of weather on physical activity and depression showed significant differences among participants with different affective responses to weather. Specifically, the temperature and day length significantly influenced depression severity, which in turn impacted physical activity levels (p Copyright © 2024, The Authors. All rights reserved.

关键词： Radar measurement

来源：评论

学校读者我要写书评

暂无评论

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity (vol 50, pg 765, 2017)

引用

NATURE GENETICS 2018年第5期50卷 765-766页

作者： Turcot, Valerie Lu, Yingchang Highland, Heather M. Schurmann, Claudia Justice, Anne E. Fine, Rebecca S. Bradfield, Jonathan P. Esko, Tonu Giri, Ayush Graff, Mariaelisa Guo, Xiuqing Hendricks, Audrey E. Karaderi, Tugce Lempradl, Adelheid Locke, Adam E. Mahajan, Anubha Marouli, Eirini Sivapalaratnam, Suthesh Young, Kristin L. Alfred, Tamuno Feitosa, Mary F. Masca, Nicholas G. D. Manning, Alisa K. Medina-Gomez, Carolina Mudgal, Poorva Ng, Maggie C. Y. Reiner, Alex P. Vedantam, Sailaja Willems, Sara M. Winkler, Thomas W. Abecasis, Goncalo Aben, Katja K. Alam, Dewan S. Alharthi, Sameer E. Allison, Matthew Amouyel, Philippe Asselbergs, Folkert W. Auer, Paul L. Balkau, Beverley Bang, Lia E. Barroso, Ines Bastarache, Lisa Benn, Marianne Bergmann, Sven Bielak, Lawrence F. Bluher, Matthias Boehnke, Michael Boeing, Heiner Boerwinkle, Eric Boger, Carsten A. Bork-Jensen, Jette Bots, Michiel L. Bottinger, Erwin P. Bowden, Donald W. Brandslund, Ivan Breen, Gerome Brilliant, Murray H. Broer, Linda Brumat, Marco Burt, Amber A. Butterworth, Adam S. Campbell, Peter T. Cappellani, Stefania Carey, David J. Catamo, Eulalia Caulfield, Mark J. Chambers, John C. Chasman, Daniel I. Chen, Yii-Der I. Chowdhury, Rajiv Christensen, Cramer Chu, Audrey Y. Cocca, Massimiliano Collins, Francis S. Cook, James P. Corley, Janie Galbany, Jordi Corominas Cox, Amanda J. Crosslin, David S. Cuellar-Partida, Gabriel D'Eustacchio, Angela Danesh, John Davies, Gail Bakker, Paul I. W. Groot, Mark C. H. Mutsert, Renee Deary, Ian J. Dedoussis, George Demerath, Ellen W. Heijer, Martin Hollander, Anneke I. Ruijter, Hester M. Dennis, Joe G. Denny, Josh C. Angelantonio, Emanuele Drenos, Fotios Du, Mengmeng Dube, Marie-Pierre Dunning, Alison M. Easton, Douglas F. Edwards, Todd L. Ellinghaus, David Ellinor, Patrick T. Elliott, Paul Evangelou, Evangelos Farmaki, Aliki-Eleni Farooqi, I. Sadaf Faul, Jessica D. Fauser, Sascha Feng, Shuang Ferrannini, Ele Ferrieres, Jean Florez, Jose C. Ford, Ian Fornage, Myriam Franco, Oscar H. Franke, Andre Franks, Paul W. Friedrich, Ne Montreal Heart Institute Université de Montréal Montreal Quebec Canada Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University School of Medicine Nashville TN USA Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA Genetics of Obesity and Related Metabolic Traits Program Icahn School of Medicine at Mount Sinai New York NY USA Department of Epidemiology University of North Carolina Chapel Hill NC USA Human Genetics Center University of Texas School of Public Health University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences University of Texas Health Science Center at Houston Houston TX USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Genetics Harvard Medical School Boston MA USA Division of Endocrinology and Center for Basic and Translational Obesity Research Boston Children’s Hospital Boston MA USA Center for Applied Genomics Division of Human Genetics Children’s Hospital of Philadelphia Philadelphia PA USA Quantinuum Research LLC San Diego CA USA Estonian Genome Center University of Tartu Tartu Estonia Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA Institute for Translational Genomics and Population Sciences LABioMed at Harbor-UCLA Medical Center Torrance CA USA Wellcome Trust Sanger Institute Hinxton UK Department of Mathematical and Statistical Sciences University of Colorado Denver CO USA Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK Department of Biological Sciences Faculty of Arts and Sciences Eastern Mediterranean University Famagusta Cyprus Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (***35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

关键词： Genome-wide association studies Obesity

来源：评论

学校读者我要写书评

暂无评论

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

引用

NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

来源：评论

学校读者我要写书评

暂无评论

A Causal Roadmap for Generating High-Quality Real-World Evidence

arXiv

引用

arXiv 2023年

作者： Dang, Lauren E. Gruber, Susan Lee, Hana Dahabreh, Issa Stuart, Elizabeth A. Williamson, Brian D. Wyss, Richard Díaz, Iván Ghosh, Debashis Kıcıman, Emre Alemayehu, Demissie Hoffman, Katherine L. Vossen, Carla Y. Huml, Raymond A. Ravn, Henrik Kvist, Kajsa Pratley, Richard Shih, Mei-Chiung Pennello, Gene Martin, David Waddy, Salina P. Barr, Charles E. Akacha, Mouna Buse, John B. van der Laan, Mark Petersen, Maya Department of Biostatistics University of California BerkeleyCA United States TL Revolution CambridgeMA United States Office of Biostatistics Office of Translational Sciences Center for Drug Evaluation and Research U.S. Food and Drug Administration Silver SpringMD United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Department of Mental Health Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Biostatistics Division Kaiser Permanente Washington Health Research Institute SeattleWA United States Division of Pharmacoepidemiology and Pharmacoeconomics Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Biostatistics Department of Population Health New York University Grossman School of Medicine New YorkNY United States Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Microsoft Research RedmondWA United States Global Biometrics and Data Management Pfizer Inc. New YorkNY United States Department of Epidemiology Mailman School of Public Health Columbia University New YorkNY United States Syneos Health Clinical Solutions Amsterdam Netherlands Syneos Health Clinical Solutions MorrisvilleNC United States Novo Nordisk Søborg Denmark AdventHealth Translational Research Institute OrlandoFL United States Cooperative Studies Program Coordinating Center VA Palo Alto Health Care System Palo AltoCA United States Department of Biomedical Data Science Stanford University StanfordCA United States Division of Imaging Diagnostics and Software Reliability Office of Science and Engineering Laboratories Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD United States Global Real World Evidence Group Moderna CambridgeMA United States National Center for Advancing Translational Sciences BethesdaMD United States G

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using RWD to fully observational studies. Yet many RWE study proposals lack sufficient detail to evaluate adequacy, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to pre-specify analytic study designs;it addresses a wide range of guidance within a single framework. By requiring transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on pre-specified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers, with companion papers demonstrating application of the Causal Roadmap for specific use cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Ontologizing health Systems data at Scale: Making Translational Discovery a Reality

arXiv

引用

arXiv 2022年

作者： Callahan, Tiffany J. Stefanski, Adrianne L. Wyrwa, Jordan M. Zeng, Chenjie Ostropolets, Anna Banda, Juan M. Baumgartner, William A. Boyce, Richard D. Casiraghi, Elena Coleman, Ben D. Collins, Janine H. Deakyne-Davies, Sara J. Feinstein, James A. Haendel, Melissa A. Lin, Asiyah Y. Martin, Blake Matentzoglu, Nicolas A. Meeker, Daniella Reese, Justin Sinclair, Jessica Taneja, Sanya B. Trinkley, Katy E. Vasilevsky, Nicole A. Williams, Andrew Zhang, Xingman A. Denny, Joshua C. Robinson, Peter N. Ryan, Patrick Hripcsak, George Bennett, Tellen D. Hunter, Lawrence E. Kahn, Michael G. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States Department of Computer Science Georgia State University AtlantaGA30303 United States Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15260 United States Computer Science Università degli Studi di Milano Milan20122 Italy Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States The Jackson Laboratory for Genomic Medicine FarmingtonCT06032 United States Department of Haematology University of Cambridge Cambridge United Kingdom Department of Research Informatics & Data Science Analytics Resource Center Children’s Hospital Colorado AuroraCO80045 United States University of Colorado Anschutz School of Medicine AuroraCO80045 United States Center for Health AI University of Colorado Anschutz Medical Campus AuroraCO80045 United States Departments of Biomedical Informatics and Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Semanticly Athens Greece Yale School of Medicine New HavenCT06510 United States HealthLinc ValparaisoIN46383 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Family Medicine University of Colorado Anschutz School of Medicine AuroraCO80045 United States Translational and Integrative Sciences Lab University of Colorado Anschutz Medical Campus AuroraCO80045 United States Tufts Institute for Clinical Research and Health Policy Studies Tufts University Bos

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping. © 2022, CC BY.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：