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What Constitutes Adequate Control of High Blood Pressure? Current Considerations

Mayo Clinic Proceedings: Innovations, Quality and Outcomes 2024年第4期8卷 384-395页

作者： Casey, Donald E. Blood, Alexander J. Persell, Stephen D. Pohlman, Daniel Williamson, Jeff D. Jefferson College of Population Health Philadelphia PA United States Department of Internal Medicine Rush Medical College Chicago IL United States Division of General Internal Medicine Rush Medical College Chicago IL United States Institute for Healthcare Informatics University of Minnesota Minneapolis MN United States Improving Patient Outcomes for Health (IPO 4 Health) Chicago IL United States Department of Medicine Harvard Medical School Boston MA United States Brigham and Women's Hospital Boston MA United States Cardiac Intensive Care Unit Newton Wellesley Hospital Newton MA United States Mass General Brigham Data Science Office Boston MA United States Brigham and Women's Hospital Accelerator of Clinical Transformation Boston MA United States Shapiro Cardiovascular Center Boston MA United States Division of General Internal Medicine Department of Medicine Feinberg School of Medicine Northwestern University Chicago IL United States Center for Primary Care Innovation Institute for Public Health and Medicine Feinberg School of Medicine Northwestern University Chicago IL United States Northwestern Medical Group Northwestern Medicine Chicago IL United States Center for Healthcare Innovation Chicago IL United States Sticht Center for Healthy Aging and Alzheimer's Prevention Winston-Salem NC United States Section of Gerontology and Geriatric Medicine Department of Gerontology and Geriatric Medicine Wake Forest School of Medicine Winston-Salem NC United States

An estimated 45% of adult Americans currently have high blood pressure (HBP). Effective blood pressure (BP) control is essential for preventing major adverse events from cardiovascular and other vascular-related diseases, such as chronic kidney disease, stroke and dementia. A large and growing number of medical professional societies, health care organizations, and governmental agencies have now endorsed a clinical practice guideline-based target for adequate control of HBP to a systolic BP of less than 130 mm Hg. However, adequate BP control to this goal has been recently estimated to be as low as 30%. The first and most important steps to guide effective BP control include accurate, standardized BP measurement and formal assessment of overall atherosclerotic cardiovascular disease risk. In addition to appropriate pharmacologic treatment, optimal BP management must also include multifaceted guideline-directed lifestyle modifications. High-quality evidence now supports effective uniform HBP control that is consistently achievable for most of people from diverse backgrounds. This can be accomplished through identification and prioritization of social determinants of health enabled by shared decision making that is delivered via team-based care. Such integrated approaches can have a substantial impact for simultaneously reducing several major modifiable atherosclerotic cardiovascular disease risk factors. Hence, moving the “Big Needle” of improved overall cardiovascular, kidney, and brain health of the US population must no longer be solely relegated to primary care and will require a major and coordinated reprioritization of capital and evidence-based human resource allocations by all health care stakeholder organizations. © 2024 The Authors

关键词： AAPA American Association of Physician Assistants ABC Association of Black Cardiologists ABPM ambulatory blood pressure monitoring ACC American College of Cardiology ACPM American College of Preventive Medicine AGS American Geriatrics Society AHA American Heart Association APhA American Pharmacists Association ASCVD atherosclerotic cardiovascular disease ASH American Society of Hypertension ASPC American Association for Preventive Cardiologists BP blood pressure CMS Centers for Medicare and Medicaid CVD cardiovascular disease DBP diastolic blood pressure GDLM guideline-directed lifestyle modification HBP high blood pressure NCQA National Committee on Quality Assurance NMA National Medical Association PCNA Preventive Cardiology Nurses Association SBP systolic blood pressure SDoH social determinants of health SPRINT Systolic Blood Pressure Intervention Trial

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54712 Bipolar disorder among patients with atopic dermatitis: A case-control study in the All of Us research program

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Journal of the American Academy of Dermatology 2024年第3期91卷 AB147-AB147页

作者： Wride, Mitchel, BA Chen, Gloria, BA Fan, Ryan, MD Leasure, Audrey, MD, MHS Levey, Daniel, PhD Damsky, William, MD, PhD Cohen, Jeffrey, MD Yale School of Medicine New Haven CT Department of Dermatology Yale School of Medicine New Haven CT Department of Psychiatry Yale School of Medicine West Haven CT Veterans Affairs Connecticut Healthcare Center West Haven CT Department of Pathology Yale School of Medicine New Haven CT Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT

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Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

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database 2023年第2023期2023卷 baad005-baad005页

作者： Leaman, Robert Islamaj, Rezarta Adams, Virginia Alliheedi, Mohammed A. Almeida, João Rafael Antunes, Rui Bevan, Robert Chang, Yung-Chun Erdengasileng, Arslan Hodgskiss, Matthew Ida, Ryuki Kim, Hyunjae Li, Keqiao Mercer, Robert E. Mertová, Lukrécia Mobasher, Ghadeer Shin, Hoo-Chang Sung, Mujeen Tsujimura, Tomoki Yeh, Wen-Chao Lu, Zhiyong National Center for Biotechnology Information (NCBI) National Library of Medicine National Institutes of Health 8600 Rockville Pike Bethesda 20894 MD United States Nvidia 2788 San Tomas Expressway Santa Clara 95051 CA United States Department of Computer Science Al Baha University 4781 King Fahd Rd Al Aqiq 65779 Saudi Arabia Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Information and Communications Technologies University of A Coruña Camiño Do Lagar de Castro A Coruña 15008 Spain Informatics Department Medicines Discovery Catapult Alderley Park Block 35 Mereside Macclesfield SK10 4ZF United Kingdom Graduate Institute of Data Science Taipei Medical University No. 172-1 Section 2 Keelung Rd Da’an District Taipei City 106 Taiwan Department of Statistics Florida State University 117 N. Woodward Ave Tallahassee 32306 FL United States Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata Tempaku-ku Nagoya Aichi 468-8511 Japan Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea Department of Computer Science The University of Western Ontario Middlesex College Room 355 London N6A 5B7 ON Canada Scientific Databases and Visualization Group Heidelberg Institute for Theoretical Studies (HITS GGmbH) Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Institute of Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg 69120 Germany Institute of Information Systems and Applications National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and - as highlighted during the coronavirus disease 2019 pandemic - their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substanti

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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State of the art in selection of variables and functional forms in multivariable analysis-outstanding issues

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Diagnostic and prognostic research 2020年第1期4卷 3页

作者： Willi Sauerbrei Aris Perperoglou Matthias Schmid Michal Abrahamowicz Heiko Becher Harald Binder Daniela Dunkler Frank E Harrell Patrick Royston Georg Heinze 1Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany. Data Science and Artificial Intelligence AstraZeneca Cambridge UK. 3Department of Medical Biometry Informatics and Epidemiology Faculty of Medicine University of Bonn Bonn Germany. 4McGill University Health Centre McGill University Montreal Canada. 5Institute for Medical Biometry and Epidemiology University Medical Center Hamburg-Eppendorf Hamburg Germany. 6Section for Clinical Biometrics Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria. 7Department of Biostatistics School of Medicine Vanderbilt University Nashville TN USA. 8MRC Clinical Trials Unit at UCL Institute of Clinical Trials and Methodology University College London London UK.

BACKGROUND:How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc 'traditional' approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics. METHODS:We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling. RESULTS:Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research. CONCLUSIONS:Selection of variables and of functional forms are important topics in multivariable analysis. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, further comparative research is required.

关键词： Bias Categorisation Descriptive modelling Empirical evidence Fractional polynomials Methods for variable selection STRATOS initiative Shrinkage Spline procedures

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Thalamo-cortical structural co-variation networks are related to familial risk for schizophrenia in the context of lower nuclei volume estimates in patients: an ENIGMA study

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Biological psychiatry 2025年

作者： Annalisa Lella Linda A Antonucci Roberta Passiatore Loredana Bellantuono Pierluigi Selvaggi Teresa Popolizio Guido Di Sciascio Alessandro Saponaro Patrizia Ricci Mario Altamura Giuseppe Blasi Antonio Rampino Chris Vriend Vince D Calhoun Kelly Rootes-Murdy Aaron L Goldman Inmaculada Baeza Josefina Castro-Fornieles Gisela Sugranyes Elena De la Serna Edith Pomarol-Clotet Mar Fatjó-Vilas Raymond Salvador Andriana Karuk Paola Fuentes-Claramonte David C Glahn Amanda L Rodrigue John Blangero Lei Wang Taeyoung Lee Karolin E Einenkel Saskia Hamers Oliver Gruber Adrian Preda Young-Chul Chung Soyolsaikhan Odkhuu Corentin Vallée Paola Dazzan Machteld Marcelis Stijn Michielse Katharina Brosch Frederike Stein Igor Nenadić Benjamin Straube Florian Thomas-Odenthal Tilo Kircher Sean Carruthers Susan L Rossell Phillip J Sumner Tamsyn E Van Rheenen Caroline Demro Ian S Ramsay Scott R Sponheim Rebekka Lencer Susanne Meinert Tim Hahn Udo Dannlowski Dominik Grotegerd Mariateresa Ciccarelli Felice Iasevoli Giuseppe Pontillo Godfrey D Pearlson Derin Cobia Fabrizio Piras Nerisa Banaj Daniela Vecchio Marjolein E A Barendse Neeltje E M van Haren Hang Joon Jo Kang Sim Yann Quidé Melissa J Green Rachael Slate Giacomo Cecere Wolfgang Omlor Stephanie Homan Philipp Homan Sophia I Thomopoulos Jessica A Turner Theo G M van Erp Paul M Thompson Alessandro Bertolino Giulio Pergola Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy. Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy Lieber Institute for Brain Development Johns Hopkins Medical Campus Baltimore MD USA. Istituto Nazionale di Fisica Nucleare Bari Italy. Azienda Ospedaliero-Universitaria Consorziale Policlinico Bari Italy. Neuroradiology Unit Scientific Institute for Research Hospitalization and Health Care Casa Sollievo della Sofferenza 71013 San Giovanni Rotondo Foggia Italy. Department of Mental Health ASL Bari Bari Italy. Department of Mental Health ASL Brindisi Brindisi Italy. Department of Mental Health ASL Barletta Andria Trani BAT Italy. Department of Clinical and Experimental Medicine University of Foggia Italy. Amsterdam UMC Vrije Universiteit Amsterdam Department of Psychiatry and Department of Anatomy and Neuroscience de Boelelaan 1117 Amsterdam the Netherlands Compulsivity Impulsivity and Attention Amsterdam Neuroscience de Boelelaan 1117 Amsterdam the Netherlands. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University Atlanta GA USA. Department of Child and Adolescent Psychiatry and Psychology 2021 SGR 01319 Institute of Neuroscience Hospital Clínic Barcelona Barcelona University of Barcelona Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) CIBERSAM Spain. FIDMAG Germanes Hospitalàries Research Foundation CIBERSAM ISCIII Barcelona Spain. Department of Psychiatry and Behavioral Sciences Boston Children's Hospital Boston MA USA Department of Psychiatry Harvard Medical School Boston MA USA Olin Neuropsychiatry Research Center Institute of Living Hartford CT USA. Department of Psychiatry Harvard Medical School Boston MA USA. Department of Human Genetics and South Texas Diabetes and Obesity Institute School of

BACKGROUND:Structural brain differences in the thalamus and the cortex have been widely reported in schizophrenia (SCZ) relative to neurotypical controls (NC). Most prior studies examined the thalamus as a whole, single region-of-interest. Additionally, findings in individuals at familial high-risk for schizophrenia (FHR) remain inconclusive. Here, we investigated whether local and network-wide thalamic-related structural alterations vary as a function of familial risk for schizophrenia. METHODS:Structural MRI scans were obtained from 5,197 participants (3,409 NC, 257 FHR, 1,531 SCZ) across 32 cross-sectional samples within the ENIGMA Consortium. Random-effects meta-analyses, and network analyses were conducted on (i) local thalamic alterations (volume estimates of seven thalamic subdivisions), and (ii) network-wide thalamic alterations (thickness and surface-related thalamo-cortical/cortico-cortical co-variation patterns) across groups (NC, FHR, SCZ). RESULTS:Individuals with SCZ showed significantly lower gray matter volume estimates in the anterior, pulvinar, medial, posterior, and ventral thalamic subdivisions compared to NC (q<0.05). FHR did not differ from NC. At the network-wide level, thalamo-cortical co-variations discriminated FHR from NC (q<0.05), with FHR showing intermediate co-variation between SCZ and NC. Cortico-cortical co-variation patterns revealed that SCZ and FHR shared similarly disconnected clustering configurations, distinct from NC (q<0.05). CONCLUSIONS:Results revealed lower thalamic volume estimates in SCZ but not in FHR, hence yielding no evidence of a familial risk trait, whereas thalamo-cortical and cortico-cortical co-variation estimates were associated with familial risk for SCZ These findings suggest that, once the thalamus is parsed into subdivisions, network-wide thalamo-cortical features may identify trait-dependent, neurobiological correlates of genetic risk for SCZ.

关键词： Familial high risk for schizophrenia meta-analyses morphometric measures structural neuroimaging thalamic subdivisions thalamo-cortical networks

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The science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

JACC: Advances

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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

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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