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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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The electronic medical record management systems may improve monitoring and control of disease activity in patients with ankylosing spondylitis

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Scientific Reports 2023年第1期13卷 1-8页

作者： Huang, Pei-Ju Chen, Yi-Hsing Huang, Wen-Nan Chen, Yi-Ming Lai, Kuo-Lung Hsieh, Tsu-Yi Hung, Wei-Ting Lin, Ching-Tsai Tseng, Chih-Wei Tang, Kuo-Tung Chou, Yin-Yi Wu, Yi-Da Huang, Chin-Yin Hsieh, Chia-Wei Chen, Yen-Ju Liao, Yu-Wan Liu, Yen-Tze Chen, Hsin-Hua Department of Family Medicine Changhua Christian Hospital No. 135 Nanxiao Street Changhua 500 Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital No. 1650 Boulevard Sect. 4 Taichung 40705 Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Department of Business Administration Ling-Tung University Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Medical Education Taichung Veterans General Hospital Taichung Taiwan PhD Program of Business College of Business Feng Chia University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Holistic Wellness Mingdao University Changhua Taiwan Division of General Medicine Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Institute of Biomedical Science and Rong-Hsing Research Center for Translational Medicine Chung-Hsing University Taichung Taiwan Institute of Public Health and Community Medicine Research Center National Yang Ming Chiao Tung University Taipei Taiwan

To investigate the impact of an electronic medical record management system (EMRMS) on disease activity and the frequency of outpatient visits among patients with ankylosing spondylitis (AS). We identified 652 patients with AS who were followed up for at least 1 year before and after the first Ankylosing Spondylitis Disease Activity Score (ASDAS) assessment and compared the number of outpatient visits and average visit time within 1 year before and after the initial ASDAS assessment. Finally, we analyzed 201 patients with AS who had complete data and received ≥ 3 continuous ASDAS assessments at an interval of 3 months, and we compared the results of the second and third ASDAS assessments with those of the first. The number of annual outpatient visits increased after ASDAS assessment (4.0 (4.0, 7.0) vs. 4.0 (4.0, 8.0), p < 0.001), particularly among those with a high initial disease activity. The average visit time was reduced within 1 year after ASDAS assessment (6.4 (8.5, 11.2) vs. 6.3 (8.3, 10.8) min, p = 0.073), especially among patients whose with an inactive disease activity was < 1.3 (ASDAS C-reactive protein (CRP) 6.7 (8.8, 11.1) vs. 6.1 (8.0, 10.3) min, p = 0.033;ASDAS erythrocyte sedimentation rate (ESR) 6.4 (8.7, 11.1) vs. 6.1 (8.1, 10.0) min, p = 0.027). Among patients who received at least three ASDAS assessments, the third ASDAS-CRP tended to be lower than the first (1.5 (0.9, 2.1) vs. 1.4 (0.8, 1.9), p = 0.058). The use of an EMRMS increased the frequency of ambulatory visits among AS patients with high and very high disease activity and reduced the visit time among those with an inactive disease. Continual ASDAS assessments may help control the disease activity of patients with AS. © 2023, The Author(s).

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The wonders of BMP9:From mesenchymal stem cell differentiation,angiogenesis,neurogenesis,tumorigenesis,and metabolism to regenerative medicine

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Genes & Diseases 2019年第3期6卷 201-223页

作者： Sami Mostafa Mikhail Pakvasa Elam Coalson Allen Zhu Alex Alverdy Hector Castillo Jiaming Fan Alex Li Yixiao Feng Di Wu Elliott Bishop Scott Du Mia Spezia Alissa Li Ofir Hagag Alison Deng Winny Liu Mingyang Li Sherwin S·Ho Aravind Athiviraham Michael J·Lee Jennifer Moriatis Wolf Guillermo A·Ameer Hue H·Luu Rex C·Haydon Jason Strelzow Kelly Hynes Tong-Chuan He Russell R·Reid The University of Chicago Pritzker School of Medicine ChicagoIL 60637USA Laboratory of Craniofacial Biology and Development Section of Plastic and Reconstructive SurgeryDepartment of SurgeryThe University of Chicago MedicineChicagoIL 60637USA Molecular Oncology Laboratory Department of Orthopaedic Surgery and Rehabilitation MedicineThe University of Chicago Medical CenterChicagoIL 60637USA Rosalind Franklin University Chicago Medical SchoolNorth ChicagoIL 60064USA SIR Program Illinois Math and Science Academy(IMSA)AuroraIL 60506USA Department of Biomedical Engineering Northwestern UniversityEvanstonIL60208USA The Department of Surgery Feinberg School of MedicineNorthwestern UniversityChicagoIL60616USA

Although bone morphogenetic proteins(BMPs)initially showed effective induction of ectopic bone growth in muscle,it has since been determined that these proteins,as members of the TGF-b superfamily,play a diverse and critical array of biological *** roles include regulating skeletal and bone formation,angiogenesis,and development and homeostasis of multiple organ *** of the members of the TGF-b/BMP superfamily result in severe skeletal and extra-skeletal irregularities,suggesting high therapeutic potential from understanding this family of BMP *** it was once one of the least characterized BMPs,BMP9 has revealed itself to have the highest osteogenic potential across numerous experiments both in vitro and in vivo,with recent studies suggesting that the exceptional potency of BMP9 may result from unique signaling pathways that differentiate it from other *** effectiveness of BMP9 in inducing bone formation was recently revealed in promising experiments that demonstrated efficacy in the repair of critical sized cranial defects as well as compatibility with bone-inducing bio-implants,revealing the great translational promise of ***,emerging evidence indicates that,besides its osteogenic activity,BMP9 exerts a broad range of biological functions,including stem cell differentiation,angiogenesis,neurogenesis,tumorigenesis,and *** review aims to summarize our current understanding of BMP9 across biology and the body.

关键词： Adipogenesis Bone morphogenetic proteins(BMPs) BMP9/GDF2 Mesenchymal stem cells(MSCs) Metabolism Osteogenesis Regenerative medicine Tumorigenesis

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study ( vol 14, 17380, 2024 )

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Kember, Allan J. Zia, Hafsa Elangainesan, Praniya Hsieh, Min-En Adijeh, Ramak Li, Ivan Ritchie, Leah Akbarian, Sina Taati, Babak Hobson, Sebastian R. Dolatabadi, Elham Department of Obstetrics and Gynaecology University of Toronto 123 Edward Street Suite 1200 Toronto ON M5G 0A8 Canada Institute of Health Policy Management and Evaluation University of Toronto 155 College Street Suite 425 Toronto ON M5T 3M6 Canada Maternal-Fetal Medicine Division Department of Obstetrics and Gynaecology Mount Sinai Hospital 600 University Avenue Toronto ON M5G 1X5 Canada School of Health Policy and Management York University Stong College Room 314 4700 Keele Street Toronto ON M3J 1P3 Canada Temerty Faculty of Medicine University of Toronto Medical Sciences Building 1 King’s College Circle Toronto ON M5S 1A8 Canada Electrical Engineering and Computer Science National Cheng Kung University No.1 University Road Tainan City 701 Taiwan Regulatory Affairs Program Northeastern University First Canadian Place 100 King Street West Suite 4620 Toronto ON M5X 1E2 Canada Department of Computer Science University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada KITE Research Institute Toronto Rehabilitation Institute University Health Network 550 University Avenue #12-165 Toronto ON M5G 2A2 Canada Department of Biological Sciences University of Toronto Scarborough 1265 Military Trail Toronto ON M1C 1A4 Canada Vector Institute 661 University Avenue Suite 710 Toronto ON M5G 1M1 Canada

Sleeping on the back after 28 weeks of pregnancy has recently been associated with giving birth to a small-for-gestational-age infant and late stillbirth, but whether a causal relationship exists is currently unknown and difficult to study prospectively. This study was conducted to build a computer vision model that can automatically detect sleeping position in pregnancy under real-world conditions. Real-world overnight video recordings were collected from an ongoing, Canada-wide, prospective, four-night, home sleep apnea study and controlled-setting video recordings were used from a previous study. Images were extracted from the videos and body positions were annotated. Five-fold cross validation was used to train, validate, and test a model using state-of-the-art deep convolutional neural networks. The dataset contained 39 pregnant participants, 13 bed partners, 12,930 images, and 47,001 annotations. The model was trained to detect pillows, twelve sleeping positions, and a sitting position in both the pregnant person and their bed partner simultaneously. The model significantly outperformed a previous similar model for the three most commonly occurring natural sleeping positions in pregnant and non-pregnant adults, with an 82-to-89% average probability of correctly detecting them and a 15-to-19% chance of failing to detect them when any one of them is present.

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Caffeic acid phenethyl ester suppresses EGFR/FAK/Akt signaling, migration, and tumor growth of prostate cancer cells

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Phytomedicine 2023年 116卷

作者： Jen-Chih Tseng Bi-Juan Wang Ya-Pei Wang Ying-Yu Kuo Jen-Kun Chen Tzyh-Chyuan Hour Li-Kuo Kuo Po-Jen Hsiao Chien-Chih Yeh Cheng-Li Kao Li-Jane Shih Chih-Pin Chuu Institute of Cellular and System Medicine National Health Research Institutes Miaoli County 35053 Taiwan Immunology Research Center National Health Research Institutes Miaoli County 35053 Taiwan Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes Miaoli County 35053 Taiwan Neuroscience Research Center Kaohsiung Medical University Kaohsiung 80737 Taiwan Department of Biochemistry School of Medicine Kaohsiung Medical University Kaohsiung 80737 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung 80737 Taiwan Division of Pulmonary and Critical Care Medicine Department of Internal Medicine Mackay Memorial Hospital Taipei City 104217 Taiwan Department of Nursing Mackay Medical College Taipei City Taiwan Division of Nephrology Department of Internal Medicine Taoyuan Armed Forces General Hospital Taoyuan 325 Taiwan Division of Nephrology Department of Internal Medicine Tri-Service General Hospital National Defense Medical Center Taipei 114 Taiwan Department of Education and Medical Research Taoyuan Armed Forces General Hospital Taoyuan Taiwan Division of Colon and Rectal Surgery Department of Surgery Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Division of Urology Departments of Surgery Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Division of Urology Department of Surgery Taoyuan Armed Forces General Hospital Taoyuan Taiwan Graduate Institute of Medical Science National Defense Medical Center Taipei 114 Taiwan Department of Life Sciences National Central University Taoyuan 32031 Taiwan PhD Program for Aging and Graduate Institute of Basic Medical Science China Medical University Taichung City 40402 Taiwan Biotechnology Center National Chung Hsing University Taichung City 40227 Taiwan

Background Epidermal growth factor receptor (EGFR) is upregulated in prostate cancer (PCa). However, suppression of EGFR did not improve the patient outcome, possibly due to the activation of PI3K/Akt signaling in PCa. Compounds able to suppress both PI3K/Akt and EGFR signaling may be effective for treating advanced PCa. Purpose We examined if caffeic acid phenethyl ester (CAPE) simultaneously suppresses the EGFR and Akt signaling, migration and tumor growth in PCa cells. Methods Wound healing assay, transwell migration assay and xenograft mice model were used to determine the effects of CAPE on migration and proliferation of PCa cells. Western blot, immunoprecipitation, and immunohistochemistry staining were performed to determine the effects of CAPE on EGFR and Akt signaling. Results CAPE treatment decreased the gene expression of HRAS, RAF1, AKT2, GSK3A , and EGF and the protein expression of phospho-EGFR (Y845, Y1069, Y1148, Y1173), phospho-FAK, Akt, and ERK1/2 in PCa cells. CAPE treatment inhibited the EGF-induced migration of PCa cells. Combined treatment of CAPE with EGFR inhibitor gefitinib showed additive inhibition on migration and proliferation of PCa cells. Injection of CAPE (15 mg/kg/3 days) for 14 days suppressed the tumor growth of prostate xenografts in nude mice as well as suppressed the levels of Ki67, phospho-EGFR Y845, MMP-9, phospho-Akt S473, phospho-Akt T308, Ras, and Raf-1 in prostate xenografts. Conclusions Our study suggested that CAPE can simultaneously suppress the EGFR and Akt signaling in PCa cells and is a potential therapeutic agent for advanced PCa.

关键词： Prostate cancer Caffeic acid phenethyl ester Metastasis Epidermal growth factor receptor Fak Akt CAPE caffeic acid phenethyl ester CRPC castration-resistant prostate cancer EGFR epidermal growth factor receptor EMT epithelial-mesenchymal transition GAPDH glyceraldehyde-3-phosphate dehydrogenase mTOR mammalian target of rapamycin PCa prostate cancer PI3K phosphoinositide 3-kinase PSA prostate specific antigen PSMA prostate specific-membrane antigen PTEN phosphatase and tensin homolog

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Stem cell therapy for chronic skin wounds in the era of personalized medicine:From bench to bedside

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Genes & Diseases 2019年第4期6卷 342-358页

作者： Elam Coalson Elliot Bishop Wei Liu Yixiao Feng Mia Spezia Bo Liu Yi Shen Di Wu Scott Du Alexander J.Li Zhenyu Ye Ling Zhao Daigui Cao Alissa Li Ofir Hagag Alison Deng Winny Liu Mingyang Li Rex C.Haydon Lewis Shi Aravind Athiviraham Michael J.Lee Jennifer Moriatis Wolf Guillermo A.Ameer Tong-Chuan He Russell R.Reid The University of Chicago Pritzker School of Medicine ChicagoIL 60637USA Molecular Oncology Laboratory Department of Orthopaedic Surgery and Rehabilitation MedicineThe University of Chicago Medical CenterChicagoIL 60637USA Department of Surgery Section of Plastic SurgeryThe University of Chicago Medical CenterChicagoIL 60637USA Key Laboratory of Diagnostic Medicine(Ministry of Education) College of Laboratory MedicineChongqing Medical UniversityChongqingChina Department of Orthopaedic Surgery Xiangya Second Hospital of Central South UniversityChangsha 410011China Student Inquiry Research Program Illinois Mathematics and Science Academy(IMSA)AuroraIL60506USA Department of General Surgery The Second Affiliated Hospital of Soochow UniversitySuzhou 215004China Department of Orthopaedic Surgery Chongqing General HospitalChongqing 400013China Department of Biomedical Engineering Northwestern UniversityEvanstonIL 60208USA Department of Surgery Northwestern University Feinberg School of MedicineChicagoIL 60616USA Center for Advanced Regenerative Engineering(CARE) EvanstonIL 60208USA

With the significant financial burden of chronic cutaneous wounds on the healthcare system,not to the personal burden mention on those individuals afflicted,it has become increasingly essential to improve our clinical *** requires the translation of the most recent benchtop approaches to clinical wound repair as our current treatment modalities have proven *** most promising potential treatment options rely on stem cellbased *** cell proliferation and signaling play crucial roles in every phase of the wound healing process and chronic wounds are often associated with impaired stem cell *** approaches involving stem cells could thus be utilized in some cases to improve a body’s inhibited healing *** aim to present the laboratory research behind the mechanisms and effects of this technology as well as current clinical trials which showcase their therapeutic *** the current problems and complications presented by chronic wounds,we hope to show that developing the clinical applications of stem cell therapies is the rational next step in improving wound care.

关键词： Chronic inflammation Chronic wounds Growth factors Personalized medicine Skin Stem cells Wound healing

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TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A (vol 603, pg 131, 2022)

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NATURE 2024年第8020期631卷 E7-E7页

作者： Brown, Anna-Leigh Wilkins, Oscar G. Keuss, Matthew J. Hill, Sarah E. Zanovello, Matteo Lee, Weaverly Colleen Bampton, Alexander Lee, Flora C. Y. Masino, Laura Qi, Yue A. Bryce-Smith, Sam Gatt, Ariana Hallegger, Martina Fagegaltier, Delphine Phatnani, Hemali Phatnani, Hemali Newcombe, Jia Gustavsson, Emil K. Seddighi, Sahba Reyes, Joel F. Coon, Steven L. Ramos, Daniel Schiavo, Giampietro Fisher, Elizabeth M. C. Raj, Towfique Secrier, Maria Lashley, Tammaryn Ule, Jernej Buratti, Emanuele Humphrey, Jack Ward, Michael E. Fratta, Pietro UCL Queen Square Motor Neuron Disease Centre Department of Neuromuscular Diseases UCL Queen Square Institute of Neurology UCL London UK The Francis Crick Institute London UK UK Dementia Research Institute University College London London UK Department of Molecular Biology and Nanobiotechnology National Institute of Chemistry Ljubljana Slovenia National Institute of Neurological Disorders and Stroke NIH Bethesda MD USA Medical Scientist Training Program Johns Hopkins University School of Medicine Baltimore MD USA Center for Alzheimer’s and Related Dementias National Institutes of Health Bethesda MD USA Queen Square Brain Bank UCL Queen Square Institute of Neurology University College London London UK Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology University College London London UK Great Ormond Street Institute of Child Health Genetics and Genomic Medicine University College London London UK Center for Genomics of Neurodegenerative Disease New York Genome Center (NYGC) New York NY USA NeuroResource Department of Neuroinflammation UCL Queen Square Institute of Neurology London UK Molecular Genomics Core Eunice Kennedy Shriver National Institute of Child Health and Human Development NIH Bethesda MD USA Nash Family Department of Neuroscience and Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY USA Ronald M. Loeb Center for Alzheimer’s Disease Icahn School of Medicine at Mount Sinai New York NY USA Department of Pathology Fishberg Department of Neuroscience Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY USA Estelle and Daniel Maggin Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics Evolution and Environment UCL Genetics Institute University College London London UK Mole

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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