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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Dongjae Kim Aria S. Hahn Niels W. Hanson Kishori M. Konwar Steven J. Hallam Department of Computer Science University of British Columbia Vancouver BC Canada Koonkie Inc. Menlo Park CA USA Department of Microbiology & Immunology University of British Columbia Vancouver BC Canada Data & Analytics KPMG LLC Seattle WA USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Graduate Program in Bioinformatics University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781509000128

FAST is a multi-threaded, I/O optimized Seed-and-Extend alignment program. FAST is extensible to nucleotide sequences making it comparable to both BLASTn and BLASTp, and also features several new usage flags reporting only HSPs meeting user defined e-value cut-offs. FASTs threaded database construction allows fast, low memory database construction e.g., RefSeq (9.4GB) can be indexed in under 5 minutes using 20 threads. The threaded database construction gives users the ability to create custom databases on demand which is useful for tasks requiring self-alignment, such as network construction [30]. Finally, FAST supports incremental database construction enabling users to keep their databases up-to-date by adding sequences without suffering the cost of reformatting the entire database. The FAST source is freely available through GitHub https:// ***/hallamlab/FAST and test datasets can be found on Dropbox https://***/sh/xvvavweuzgqybc4/ AAAWCrRnol67ZsXi2qLQWUuOa?

关键词： Instruction sets Synchronization Random access memory Algorithm design and analysis data structures Indexes

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WhendataIs a Dirty Word: A Survey to Understand data Management Needs Across Diverse Research Disciplines

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Bulletin of the Association for Information science and Technology 2016年第1期42卷

作者： Alicia Hofelich Mohr data management specialist Josh Bishoff Carolyn Bishoff Steven Braun Christine Storino Lisa R. Johnston College of Liberal Arts University of Minnesota Staff of the University Libraries University of Minnesota Graduate students in the data science program University of Minnesota

EDITOR'S SUMMARY Prompted by federal mandates regarding data management planning, researchers sought to understand the needs and expectations of faculty at the University of Minnesota Twin Cities for managing their data. An initial survey revealed weak identification with the word data . Researchers then customized surveys for four colleges, using terminology that would resonate best for each. Survey results showed a distinction between those who identify the primary output of their work as data vs. research materials. Among those in the College of Food, Agriculture and Natural Resource sciences, 91% responded they generate data, along with 65% of health center researchers, 68% of College of science and Engineering respondents and 54% of those from the College of Liberal Arts. Those whose focus was data wanted support with long term data preservation and preparing for sharing materials. Respondents who stated they produce research materials rather than data indicated different needs, though help with long term preservation and better access to resources to support data management were common themes.

关键词： data curation materials preservation research data sets needs assessment faculty

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NGS-Based Copy Number Analysis in 1,185 Patients with Myeloid Neoplasms

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BLOOD 2016年第22期128卷 955-955页

作者： Saiki, Ryunosuke Shiozawa, Yusuke Yoshizato, Tetsuichi Yoshida, Kenichi Shiraishi, Yuichi Tanaka, Hiroko Chiba, Kenichi Atsuta, Yoshiko Onizuka, Makoto Itonaga, Hidehiro Kanda, Yoshinobu Sekeres, Mikkael A. Guinta, Kathryn M. Miyawaki, Shuichi Nakamaki, Tsuyoshi Chiba, Shigeru Shih, Lee-Yung Miyazaki, Yasushi Miyano, Satoru Makishima, Hideki Maciejewski, Jaroslaw P. Ogawa, Seishi Department of Pathology and Tumor Biology Graduate School of Medicine Kyoto University Kyoto Japan Department of Pediatrics Graduate School of Medicine The University of Tokyo Tokyo Japan Human Genome Center Institute of Medical Science The University of Tokyo Tokyo Japan Laboratory of DNA information Analysis & Laboratory of Sequence Analysis Human Genome Center The University of Tokyo Tokyo Japan Japanese Data Center for Hematopoietic Cell Transplantation Nagoya Japan Nagoya University Graduate School of Medicine Nagoya Japan Department of Hematology and Oncology Tokai University School of Medicine Isehara Japan Department of Hematology Atomic Bomb Disease and Hibakusha Medicine Unit Atomic Bomb Disease Institute Nagasaki University Nagasaki Japan Division of Hematology Saitama Medical Center Jichi Medical University Saitama Japan Department of Translational Hematology and Oncology Research Taussig Cancer Institute Cleveland Clinic Cleveland OH Leukemia Program Department of Hematology and Medical Oncology Cleveland Clinic Cleveland OH Department of Translational Hematology and Oncology Research Taussig Cancer Institute Cleveland Clinic Cleveland OH Division of Hematology Tokyo Metropolitan Ohtsuka Hospital Tokyo Japan Department of Hematology Showa University School of Medicine Tokyo Japan Department of Hematology Faculty of Medicine University of Tsukuba Tsukuba Japan Division of Hematology-Oncology Department of Internal Medicine Chang Gung Memorial Hospital Chang Gung University Taoyuan Taiwan

Background Copy number alteration (CNA) is a hallmark of cancer genomes and has been implicated in the development of human cancers, including myeloid neoplasms. We developed a novel, next-generation sequencing-based platform for highly sensitive detection of CNAs with a single exon resolution, which was applied to sequencing data from 1, 185 patients to delineate a comprehensive landscape of CNAs in myeloid neoplasms. Materials and Methods We enrolled 1, 185 patients with different myeloid neoplasms including myelodysplastic syndromes (n = 607), myelodysplastic/myeloproliferative neoplasms (n = 80), de novo acute myeloid leukemia (AML) (n = 136), secondary AML (sAML) (n = 226), and unknown myeloid malignancies (n = 136). Whole-exome sequencing (WES) was performed on samples from 260 patients, while samples from 925 patients including pre-transplantation peripheral blood samples provided by Japan Marrow Donor program were subjected to targeted deep sequencing. Eight cases were serially evaluated before and after progression tosAML. RNA baits for targeted deep sequencing were designed to cover 69 driver genes in myeloid neoplasms and 1,158 single-nucleotide polymorphisms (SNPs)for assessment of allelic imbalance. In WES, allelic imbalance was examined using allele frequencies of SNPs within coding regions. Focal CNAs were defined as CNAs whose lengths relative to the chromosomal arms were below 10%. Results To obtain a landscape of CNAs in coding regions, a comprehensive copy number analysis was performed on 260 patients including 136 with de novo AML and 124 with myeloid neoplasms with myelodysplasia, all of whom were studied by WES. A total of 755 CNAs (502 deletions and 253 amplifications) were identified, where 52% of the patients harbored at least one alteration. Using GISTIC 2.0 algorism, we identified 21 significantly altered regions involving known or putative driver genes (Figure 1): losses of 7q22.1 (CUX1 ), 12p13.2 (ETV6 ), 13q14 (RB1 ),17p13.1(TP53 ), and

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A second update on mapping the human genetic architecture of COVID-19

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Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes

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Nature genetics 2019年第7期51卷 1192-1193页

作者： Rainer Malik Ganesh Chauhan Matthew Traylor Muralidharan Sargurupremraj Yukinori Okada Aniket Mishra Loes Rutten-Jacobs Anne-Katrin Giese Sander W van der Laan Solveig Gretarsdottir Christopher D Anderson Michael Chong Hieab H H Adams Tetsuro Ago Peter Almgren Philippe Amouyel Hakan Ay Traci M Bartz Oscar R Benavente Steve Bevan Giorgio B Boncoraglio Robert D Brown Adam S Butterworth Caty Carrera Cara L Carty Daniel I Chasman Wei-Min Chen John W Cole Adolfo Correa Ioana Cotlarciuc Carlos Cruchaga John Danesh Paul I W de Bakker Anita L DeStefano Marcel den Hoed Qing Duan Stefan T Engelter Guido J Falcone Rebecca F Gottesman Raji P Grewal Vilmundur Gudnason Stefan Gustafsson Jeffrey Haessler Tamara B Harris Ahamad Hassan Aki S Havulinna Susan R Heckbert Elizabeth G Holliday George Howard Fang-Chi Hsu Hyacinth I Hyacinth M Arfan Ikram Erik Ingelsson Marguerite R Irvin Xueqiu Jian Jordi Jiménez-Conde Julie A Johnson J Wouter Jukema Masahiro Kanai Keith L Keene Brett M Kissela Dawn O Kleindorfer Charles Kooperberg Michiaki Kubo Leslie A Lange Carl D Langefeld Claudia Langenberg Lenore J Launer Jin-Moo Lee Robin Lemmens Didier Leys Cathryn M Lewis Wei-Yu Lin Arne G Lindgren Erik Lorentzen Patrik K Magnusson Jane Maguire Ani Manichaikul Patrick F McArdle James F Meschia Braxton D Mitchell Thomas H Mosley Michael A Nalls Toshiharu Ninomiya Martin J O'Donnell Bruce M Psaty Sara L Pulit Kristiina Rannikmäe Alexander P Reiner Kathryn M Rexrode Kenneth Rice Stephen S Rich Paul M Ridker Natalia S Rost Peter M Rothwell Jerome I Rotter Tatjana Rundek Ralph L Sacco Saori Sakaue Michele M Sale Veikko Salomaa Bishwa R Sapkota Reinhold Schmidt Carsten O Schmidt Ulf Schminke Pankaj Sharma Agnieszka Slowik Cathie L M Sudlow Christian Tanislav Turgut Tatlisumak Kent D Taylor Vincent N S Thijs Gudmar Thorleifsson Unnur Thorsteinsdottir Steffen Tiedt Stella Trompet Christophe Tzourio Cornelia M van Duijn Matthew Walters Nicholas J Wareham Sylvia Wassertheil-Smoller James G Wilson Kerri L Wiggins Qiong Yang Salim Yusuf Joshua C Bis Tomi Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany. Centre for Brain Research Indian Institute of Science Bangalore India. INSERM U1219 Bordeaux Population Health Research Center University of Bordeaux Bordeaux France. Stroke Research Group Division of Clinical Neurosciences University of Cambridge Cambridge UK. Department of Neurology Institute for Neurodegenerative Disease Bordeaux University Hospital Bordeaux France. Laboratory for Statistical Analysis RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Osaka Japan. Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan. Department of Neurology Massachusetts General Hospital (MGH) Harvard Medical School Boston MA USA. Laboratory of Experimental Cardiology Department of Cardiology Division of Heart and Lungs University Medical Center Utrecht University of Utrecht Utrecht the Netherlands. deCODE genetics/AMGEN Inc. Reykjavik Iceland. Center for Genomic Medicine MGH Boston MA USA. J. Philip Kistler Stroke Research Center Department of Neurology MGH Boston MA USA. Program in Medical and Population Genetics Broad Institute Cambridge MA USA. Population Health Research Institute McMaster University Hamilton Ontario Canada. Department of Epidemiology Erasmus University Medical Center Rotterdam the Netherlands. Department of Radiology and Nuclear Medicine Erasmus University Medical Center Rotterdam the Netherlands. Department of Medicine and Clinical Science Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Clinical Sciences Lund University Malmö Sweden. INSERM Institut Pasteur de Lille LabEx DISTALZ-UMR1167 Risk Factors and Molecular Determinants of Aging-Related Diseases Université Lille Lille France. Centre Hospitalier Université Lille Epidemiology and Public Health D

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

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P2.02-026 Impact of PD-L1 Expression on 18F-FDG-PET in Pulmonary Squamous Cell Carcinoma

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Journal of Thoracic Oncology 2017年第11期12卷 S2108-S2108页

作者： N. Kasahara K. Kaira B. Alatan T. Higuachi Y. Arisaka E. Bilguun N. Sunaga T. Oyama T. Yokobori T. Asao M. Nishiyama K. Shimizu A. Mogi H. Kuwano Department of Respiratory Medicine Gunma University Graduate School of Medicine Maebashi Gunma Japan/JP Department of Oncology Clinical Development Gunma University Graduate School of Medicine Maebashi Gunma Japan/JP Department of Diagnostic Radiology and Nuclear Medicine Gunma University Graduate School of Medicine Maebashi Gunma Japan/JP Oncology Center Gunma University Hospital Maebashi Gunma Japan/JP Department of Diagnostic Pathology Gunma University Graduate School of Medicine Maebashi Gunma Japan/JP Research Program for Omics-Based Medical Science Division of Integrated Oncology Research Gunma University Graduate School of Medicine Maebashi Gunma Japan/JP Big Data Center for Integrative Analysis Gunma University Initiative for Advance Research Maebashi Gunma Japan/JP Department of Molecular Pharmacology and Oncology Gunma University Graduate School of Medicine Maebashi Gunma Japan/JP Division of General Thoracic Surgery Integrative Center of General Surgery Gunma University Hospital Maebashi/JP Integrative Center of General Surgery Gunma University Hospital Maebashi Gunma Japan/JP

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A data Mining Analysis of Asthma Risk Factors

A Data Mining Analysis of Asthma Risk Factors

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International Conference on Applied Computing and Information Technology/International Conference on Computational science and Intelligence (ACIT-CSI)

作者： Alma Pochini Ben M. Williams Hasanboy M. Isomitdinov Gongzhu Hu Departments of Finance Law & Departments of Mathematics Data Mining Graduate Certificate Program Department of Economics Department of Computer Science Central Michigan University Michigan USA

Asthma is a chronic inflammatory disease of the airways. Although causes of asthma may directly related to environment factors such as dust mites, pollens, cold air, etc.,we would like to find out if demographic, social and behavior factors also play a role. In this paper, we present a study of the behavioral rick factors in relation to people getting asthma. This study applies data mining techniques to the 2013 Behavioral Risk Factor Surveillance System (BRFSS) dataset of over 490,000 observations with 336 variables. The results reveal some relationships between some of these variables and asthma that may help asthma prevention.

关键词： Logistics Diseases data mining Neural networks Regression tree analysis

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Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis (vol 50, pg 1368, 2020)

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PSYCHOLOGICAL MEDICINE 2020年第16期50卷 2816-2816页

作者： Wu, Yin Levis, Brooke Riehm, Kira E. Saadat, Nazanin Levis, Alexander W. Azar, Marleine Rice, Danielle B. Boruff, Jill Cuijpers, Pim Gilbody, Simon Ioannidis, John P. A. Kloda, Lorie A. McMillan, Dean Patten, Scott B. Shrier, Ian Ziegelstein, Roy C. Akena, Dickens H. Arroll, Bruce Ayalon, Liat Baradaran, Hamid R. Baron, Murray Bombardier, Charles H. Butterworth, Peter Carter, Gregory Chagas, Marcos H. Chan, Juliana C. N. Cholera, Rushina Conwell, Yeates de Manvan Ginkel, Janneke M. Fann, Jesse R. Fischer, Felix H. Fung, Daniel Gelaye, Bizu Goodyear-Smith, Felicity Greeno, Catherine G. Hall, Brian J. Harrison, Patricia A. Harter, Martin Hegerl, Ulrich Hides, Leanne Hobfoll, Stevan E. Hudson, Marie Hyphantis, Thomas Inagaki, Masatoshi Jette, Nathalie Khamseh, Mohammad E. Kiely, Kim M. Kwan, Yunxin Lamers, Femke Liu, Shen-Ing Lotrakul, Manote Loureiro, Sonia R. Lowe, Bernd McGuire, Anthony Mohd-Sidik, Sherina Munhoz, Tiago N. Muramatsu, Kumiko Osorio, Flavia L. Patel, Vikram Pence, Brian W. Persoons, Philippe Picardi, Angelo Reuter, Katrin Rooney, Alasdair G. Santos, Ina S. Shaaban, Juwita Sidebottom, Abbey Simning, Adam Stafford, Lesley Sung, Sharon Tan, Pei Lin Lynnette Turner, Alyna van Weert, Henk C. White, Jennifer Whooley, Mary A. Winkley, Kirsty Yamada, Mitsuhiko Benedetti, Andrea Thombs, Brett D. [a1]Lady Davis Institute for Medical Research Jewish General Hospital Montréal Québec Canada [a2]Department of Psychiatry McGill University Montréal Québec Canada [a3]Department of Epidemiology Biostatistics and Occupational Health McGill University Montréal Québec Canada [a4]Department of Psychology McGill University Montréal Québec Canada [a5]Schulich Library of Physical Sciences Life Sciences and Engineering McGill University Montreal Quebec Canada [a6]Department of Clinical Neuro and Developmental Psychology Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam the Netherlands [a7]Hull York Medical School and the Department of Health Sciences University of York Heslington York UK [a8]Department of Medicine Department of Health Research and Policy Department of Biomedical Data Science Department of Statistics Stanford University Stanford California USA [a9]Library Concordia University Montréal Québec Canada [a10]Department of Community Health Sciences University of Calgary Calgary Alberta Canada [a11]Hotchkiss Brain Institute and O’Brien Institute for Public Health University of Calgary Calgary Alberta Canada [a12]Department of Medicine Johns Hopkins University School of Medicine Baltimore Maryland USA [a13]Department of Psychiatry Makerere University College of Health Sciences Kampala Uganda [a14]Department of General Practice and Primary Health Care University of Auckland Auckland New Zealand [a15]Louis and Gabi Weisfeld School of Social Work Bar Ilan University Ramat Gan Israel [a16]Endocrine Research Center Institute of Endocrinology and Metabolism Iran University of Medical Sciences Tehran Iran [a17]Ageing Clinical & Experimental Research Team Institute of Applied Health Sciences School of Medicine Medical Sciences and Nutrition University of Aberdeen Aberdeen Scotland UK [a18]Department of Medicine McGill University Montréal Québec Canada [a19]Department of Rehabilitation Medicine University of Washington S

BackgroundItem 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and *** conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic ***16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).ConclusionsPHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

关键词： Depression diagnostic accuracy individual participant data meta-analysis meta-analysis PHQ-8 PHQ-9 screening systematic review

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Author Correction: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk

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Nature genetics 2020年第3期52卷 354页

作者： Iris E Jansen Jeanne E Savage Kyoko Watanabe Julien Bryois Dylan M Williams Stacy Steinberg Julia Sealock Ida K Karlsson Sara Hägg Lavinia Athanasiu Nicola Voyle Petroula Proitsi Aree Witoelar Sven Stringer Dag Aarsland Ina S Almdahl Fred Andersen Sverre Bergh Francesco Bettella Sigurbjorn Bjornsson Anne Brækhus Geir Bråthen Christiaan de Leeuw Rahul S Desikan Srdjan Djurovic Logan Dumitrescu Tormod Fladby Timothy J Hohman Palmi V Jonsson Steven J Kiddle Arvid Rongve Ingvild Saltvedt Sigrid B Sando Geir Selbæk Maryam Shoai Nathan G Skene Jon Snaedal Eystein Stordal Ingun D Ulstein Yunpeng Wang Linda R White John Hardy Jens Hjerling-Leffler Patrick F Sullivan Wiesje M van der Flier Richard Dobson Lea K Davis Hreinn Stefansson Kari Stefansson Nancy L Pedersen Stephan Ripke Ole A Andreassen Danielle Posthuma Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research Amsterdam Neuroscience VU University Amsterdam the Netherlands. Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam the Netherlands. Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden. deCODE Genetics/ Amgen Reykjavik Iceland. Interdisciplinary Graduate Program Vanderbilt University Nashville TN USA. Institute of Gerontology and Aging Research Network-Jönköping (ARN-J) School of Health and Welfare Jönköping University Jönköping Sweden. NORMENT K.G. Jebsen Centre for Psychosis Research Institute of Clinical Medicine University of Oslo Oslo Norway. Division of Mental Health and Addiction Oslo University Hospital Oslo Norway. Institute of Psychiatry Psychology and Neuroscience King's College London London UK. Institute of Clinical Medicine University of Oslo Oslo Norway. Center for Age-Related Diseases Stavanger University Hospital Stavanger Norway. Department of Neurology Akershus University Hospital Lørenskog Norway. AHUS Campus University of Oslo Oslo Norway. Department of Psychiatry of Old Age Oslo University Hospital Oslo Norway. Department of Community Medicine University of Tromsø Tromsø Norway. Norwegian National Advisory Unit on Ageing and Health Vestfold Hospital Trust Tønsberg Norway. Centre for Old Age Psychiatry Research Innlandet Hospital Trust Ottestad Norway. Department of Geriatric Medicine Landspitali University Hospital Reykjavik Iceland. Geriatric Department Oslo University Hospital Oslo Norway. Department of Neuromedicine and Movement Science Norwegian University of Science and Technology Trondheim Norway. Department of Neurology St Olav's Hospital Trondheim University Hospital Trondheim Norway. Neuroradiology Section Department of Radiology and Biomedical Imaging University of California San Francisco CA USA. Department

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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