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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Deep learning based computer-aided diagnosis for neuroimaging data: focused review and future potential

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Neuroimmunology and Neuroinflammation 2018年第1期5卷 1-3页

作者： V.B.Surya Prasath Computational Imaging and VisAnalysis(CIVA)Lab Department of Computer ScienceUniversity of Missouri-ColumbiaColumbiaMO 65211USA Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center(CCHMC)CincinnatiOH 45229USA Department of Biomedical Informatics College of MedicineUniversity of CincinnatiOH 45267USA

Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) tools in neuroscience(1-4)Recently, the advancements in machine learning techniques combined with the wide availability of computational power have proven to be efficient in solving previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the usage of deep (artificial) neural network architectures that led robust learning based techniques to attack challenging problems such as segmentation and classification in neuroimaging data[5-8].

关键词： Deep learning neuroimaging data future potential

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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A large annotated medical image dataset for the development and evaluation of segmentation algorithms

arXiv

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arXiv 2019年

作者： Simpson, Amber L. Antonelli, Michela Bakas, Spyridon Bilello, Michel Farahani, Keyvan Van Ginneken, Bram Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc Golia-Pernicka, Jennifer Heckers, Stephan H. Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Vorontsov, Eugene Maier-Hein, Lena Jorge Cardoso, M. Department of Surgery Memorial Sloan Kettering Cancer Center Centre for Medical Image Computing University College London Center for Biomedical Image Computing and Analytics University of Pennsylvania Division of Cancer Treatment and Diagnosis National Cancer Institute Department of Pathology Radboud University Medical Center Division of Biostatistics German Cancer Research Center Department of Electrical Engineering and Computer Science Vanderbilt University Department of Informatics Technische Universität München Google DeepMind Imaging Biomarkers and Computer-aided Diagnosis Lab Radiology and Imaging Sciences National Institutes of Health Clinical Center Department of Radiology Memorial Sloan Kettering Cancer Center Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center Department of Radiology Stanford University Department of Computer Science and Software Engineering École Polytechnique de Montréal Division of Computer Assisted Medical Interventions German Cancer Research Center Department of Imaging and Biomedical Engineering King's College London

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

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Cell 2025年第12期188卷 3167-3183.e18页

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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Pathogenesis, symptomatology, and transmission of SARS-CoV-2 through analysis of viral genomics and structure

arXiv

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arXiv 2021年

作者： Rando, Halie M. MacLean, Adam L. Lee, Alexandra J. Lordan, Ronan Ray, Sandipan Bansal, Vikas Skelly, Ashwin N. Sell, Elizabeth Dziak, John J. Shinholster, Lamonica D'Agostino McGowan, Lucy Guebila, Marouen Ben Wellhausen, Nils Knyazev, Sergey Boca, Simina M. Capone, Stephen Qi, Yanjun Park, Yoson Sun, Yuchen Mai, David Boerckel, Joel D. Brueffer, Christian Byrd, James Brian Kamil, Jeremy P. Wang, Jinhui Velazquez, Ryan Szeto, Gregory L. Barton, John P. Goel, Rishi Raj Mangul, Serghei Lubiana, Tiago Gitter, Anthony Greene, Casey S. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States University of Colorado School of Medicine AuroraCO United States Department of Quantitative and Computational Biology University of Southern California Los AngelesCA United States Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-5158 United States Department of Biotechnology Indian Institute of Technology Hyderabad Kandi Sangareddy Telangana 502285 India Biomedical Data Science and Machine Learning Group German Center for Neurodegenerative Diseases Tubingen72076 Germany Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Immunology University of Pennsylvania Perelman School of Medicine Philadelphia United States Edna Bennett Pierce Prevention Research Center The Pennsylvania State University University ParkPA United States LSH2126 Mercer University MaconGA United States Department of Mathematics and Statistics Wake Forest University Winston-SalemNC United States Department of Biostatistics Harvard School of Public Health BostonMA United States Georgia State University AtlantaGA United States Innovation Center for Biomedical Informatics Georgetown University Medical Center WashingtonDC United States St. George's University School of Medicine Saint George's Grenada Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Department of Orthopaedic Surgery Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Bioengineering University of Pennsylvania Philadelphia PhiladelphiaPA United States Department of Clinical Sciences L

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. © 2021, CC BY.

关键词： Coronavirus

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Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences

arXiv

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arXiv 2025年

作者： Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernández-Díaz, Raúl Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C.E. Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece 1Department of Biomedical Sciences University of Padova Padova35121 Italy Department of Biomedical Sciences University of Padova Via Ugo Bassi 58/B Padova35131 Italy Athena Research & Innovation Center Aigialias & Chalepa Marousi15125 Greece VIB Data Core VIB.AI Center for AI & Computational Biology Technologiepark-Zwijnaarde 75 Ghent9052 Belgium ELIXIR Europe Hub EMBL-EBI South Building Wellcome Genome HinxtonCB10 1SD United Kingdom Plaça Eusebi Güell 1-3 Barcelona08034 Spain Department of Pharmacy and Biotechnology University of Bologna Bologna40126 Italy Computational Genomics Platform IRCCS University Hospital of Bologna Bologna40138 Italy ZB MED Information Centre for Life Sciences Gleueler Straße 60 Cologne50931 Germany SequenceAnalysis.co.uk United Kingdom University of East Anglia Research Park NorwichNR4 7TJ United Kingdom Department of Computational Biology University of Lausanne Genopode 2024.3 Lausanne1015 Switzerland Swiss Institute of Bioinformatics Amphipôle Quartier UNIL-Sorge Lausanne1015 Switzerland Institute for Fundamental Biomedical Science Biomedical Sciences Research Center Alexander Fleming Vari Greece Department of Informatics & Telematics School of Digital Technology Harokopio University Athens Greece School of Medicine University College Dublin Belfield Dublin 4 Ireland Conway Institute of Biomolecular and Biomedical Research University College Dublin Belfield Dublin 4 Ireland IBM Research Dublin DublinD15 HN66 Ireland Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki57001 Greece Department of Biology National & Kapodistrian University of Athens Athens15772 Greece Department of Computer Science University of Manchester Oxford Road ManchesterM19 3PL United Kingdom Luxembourg National Data Service 6 avenue des Hauts-Fourneaux Esch-sur-AlzetteL-4362 Luxembourg Insti

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI *** Codes 92 © 2025, CC BY-NC-ND.

关键词： Reusability

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Cross vendor test-retest validation of diffusion tensor analysis along the perivascular space (DTI-ALPS) method for evaluating glymphatic system function in vascular cognitive impairment and dementia (VCID)

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Xiaodan Liu Giuseppe Barisano Xingfeng Shao Kay Jann John M Ringman Hanzhang Lu Konstantinos Arfanakis Caprihan Arvind Charles Decarli Brian T. Gold Pauline Maillard Clandia L. Satizabal Elyas Fadaee Mohamad Habes Lara Stables Herpreet Singh Bruce Fischl Andre J. van der Kouwe Kristin Schwab Karl G. Helmer Steven M. Greenberg Danny J.J. Wang University of Southern California Los Angeles CA USA University of California San Francisco San Francisco CA USA Zilkha Neurogenetic Institute University of Southern California Los Angeles CA USA Correspondece Stanford University Stanford CA USA Laboratory of Neuro Imaging USC Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Los Angeles CA USA Laboratory of FMRI Technology (LOFT) Mark & Mary Stevens Neuroimaging and Informatics Institute University of Southern California Los Angeles CA USA Department of Neurology Keck School of Medicine at USC Los Angeles CA USA Department of Radiology Johns Hopkins University School of Medicine Baltimore MD USA Department of Biomedical Engineering Illinois Institute of Technology Chicago IL USA Department of Diagnostic Radiology and Nuclear Medicine Rush UniversityRush University Medical Center Chicago IL USA The Mind Research Network Albuquerque NM USA University of California at Davis Davis CA USA University of Kentucky Lexington KY USA University of California Davis Davis CA USA Population Health Sciences and Glenn Biggs Institute for Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio TX USA Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core Glenn Biggs Institute for Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio TX USA University of Texas Health San Antonio San Antonio TX USA Memory and Aging Center UCSF Weill Institute for Neurosciences University of California San Francisco San Francisco CA USA Massachusetts General Hospital Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA Department of Radiology Harvard Medical School Boston MA USA Department of Radiology Massachusetts General Hospital Boston MA USA Department of Computer Science and AI Lab Massachusetts

Background Glymphatic system (GS) is a recently discovered brain-wide perivascular fluid transport system in the cerebral nervous system (CNS)(Benveniste H et al., 2019) The impairment of glymphatic transport has been demonstrated to be associated with several neurological diseases, in particular the vascular cognitive impairment and dementia (VCID)(Tang J et al., 2022). The diffusion tensor analysis along the perivascular space (DTI-ALPS) was proposed to non-invasively evaluate the GS clearance dysfunction which has been used to investigate the GS in various pathologies(Taoka T et al., 2017, Hsu JL et al., 2022). However, studies on the cross-vendor and test-retest reliability of DTI-ALPS method are lacking. The current study aimed to perform cross-vendor, inter-rater and test-retest validations of DTI-ALPS method by using a cohort from the MarkVCID consortium. Method Fifty participants’ DTI data from the MarkVCID consortium (consists of 7 sites) were included in this study: 15 participants were scanned on four MRI scanners; 35 participants underwent two MR scans within 14 days. The DTI data were acquired used a single shell, b = 1000s/mm2, 40-direction with a voxel size of 2.02.02.0mm 3 and six b = 0 s/mm 2 on 3T MR scanners from seven sites, including two Siemens systems, one Philips system and one GE system. Two pipelines by using DSI studio and FSL software were developed for data processing and ALPS index calculation (Figure 1). The mean ALPS (mALPS) index was obtained by the average of bilateral ALPS index and was used for testing the inter-rater, cross-vendor and test-retest reliability. The association between mALPS index and Montreal Cognitive assessment (MoCA) scores were evaluated using a general linear model with age and gender as covariate. Result The Bland-Altman plot and scatterplot illustrated the validation results of the mALPS index analyzed using DSI studio and FSL pipelines (Figure 2). The mALPS index demonstrated favorable inter-scanner reprodu

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Prediction of cardiac arrest in intensive care patients through machine learning 3rd

Prediction of cardiac arrest in intensive care patients thro...

引用

3rd International Conference on biomedical and Health informatics, ICBHI 2017

作者： Akrivos, E. Papaioannou, V. Maglaveras, N. Chouvarda, I. Lab of Computing Medical Informatics and Biomedical Imaging Technologies Medical School Aristotle University of Thessaloniki Thessaloniki Greece Department of Internal Medicine 424 Military Hospital of Thessaloniki Thessaloniki Greece Alexandroupolis University Hospital/Intensive Care Unit Alexandroupoli Greece McCormick School of Engineering & Applied Sciences Department of Electrical Engineering & Computer Science Northwestern University EvastonIL60201 United States

ISBN: (纸本)9789811074189

Cardiac arrest is a critical health condition characterized by absence of traceable heart rate, patient’s loss of consciousness as well as apnea, with inhospital mortality of ~80%. Accurate estimation of patients at high risk is crucial to improve not only the survival rate, but also the quality of life as patients who survived from cardiac arrest have severe neurological effects. Existing research has focused on demonstrating static risk scores without taking account patient’s physiological condition. In this study, we are implementing an integrated model of sequential contrast patterns using Multichannel Hidden Markov Model. These models can capture relations between exposure and control group and offer high specificity results, with an average sensitivity of 78%, and have the ability to identify patients in high risk. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pattern recognition

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Correction to: COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data

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European radiology 2022年第7期32卷 5034页

作者： Jianhong Cheng John Sollee Celina Hsieh Hailin Yue Nicholas Vandal Justin Shanahan Ji Whae Choi Thi My Linh Tran Kasey Halsey Franklin Iheanacho James Warren Abdullah Ahmed Carsten Eickhoff Michael Feldman Eduardo Mortani Barbosa Ihab Kamel Cheng Ting Lin Thomas Yi Terrance Healey Paul Zhang Jing Wu Michael Atalay Harrison X Bai Zhicheng Jiao Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University 932 Lushan S Rd Yuelu District Changsha Hunan China. Department of Diagnostic Radiology Rhode Island Hospital 593 Eddy St. Providence RI 02903 USA. Warren Alpert Medical School of Brown University Providence RI 02903 USA. Department of Diagnostic Radiology Perelman School of Medicine at the University of Pennsylvania Philadelphia PA 19104 USA. Department of Data Science University of London London UK. Center for Biomedical Informatics Brown University Providence RI 02912 USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia PA 19104 USA. Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine 601 N Caroline St Baltimore MD 21205 USA. Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine 601 N Caroline St Baltimore MD 21205 USA. hbai7@jh.edu.

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