检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

arXiv

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arXiv 2023年

作者： Wu, Da Yang, Jingye Liu, Cong Hsieh, Tzung-Chien Marchi, Elaine Blair, Justin Krawitz, Peter Weng, Chunhua Chung, Wendy Lyon, Gholson J. Krantz, Ian D. Kalish, Jennifer M. Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Institute for Genomic Statistics and Bioinformatics University Hospital Bonn Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Human Genetics New York State Institute for Basic Research in Developmental Disabilities Staten IslandNY United States Division of Human Genetics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Pediatrics Boston Children’s Hospital Harvard Medical School BostonMA United States Biology PhD Program The Graduate Center The City University of New York New York United States Department of Genetics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pediatrics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data. © 2023, CC BY.

关键词： Diseases

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Correction: How Can Artificial Intelligence Make Medicine More Preemptive?

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Journal of medical Internet research 2020年第8期22卷 e23645页

作者： Usman Iqbal Leo Anthony Celi Yu-Chuan Jack Li Master Program in Global Health & Development PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan. International Center for Health Information Technology Taipei Medical University Taipei Taiwan. Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States. Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center Boston MA United States. Department of Biostatistics Harvard TH Chan School of Public Health Harvard University Boston MA United States. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Dermatology Taipei Municipal Wan-Fang Hospital Taipei Taiwan. International Medical Informatics Association Geneva Switzerland.

[This corrects the article DOI: .].

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Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions

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BMC bioinformatics 2020年第1期21卷 495页

作者： Samir Rachid Zaim Colleen Kenost Joanne Berghout Wesley Chiu Liam Wilson Hao Helen Zhang Yves A Lussier Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Department of Mathematics College of Sciences The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. Lussier.Y@***. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. Lussier.Y@***. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The Center for Applied Genetic and Genomic Medicine 1295 N. Martin Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona Cancer Center 3838 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona BIO5 Institute 1657 E. Helen Street Tucson AZ 85721 USA. Lussier.Y@***.

An amendment to this paper has been published and can be accessed via the original article.

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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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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Transient stabilization, rather than inhibition, of MYC amplifies extrinsic apoptosis and therapeutic responses in refractory B-cell lymphoma (vol 33, pg 2429, 2019)

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LEUKEMIA 2020年第5期34卷 1485-1485页

作者： Harrington, Colleen T. Sotillo, Elena Robert, Aude Hayer, Katharina E. Bogusz, Agata M. Psathas, James Yu, Duonan Taylor, Deanne Dang, Chi V. Klein, Peter S. Hogarty, Michael D. Geoerger, Birgit El-Deiry, Wafik S. Wiels, Joelle Thomas-Tikhonenko, Andrei Division of Cancer Pathobiology Children’s Hospital of Philadelphia Philadelphia USA Cell & Molecular Biology Graduate Group Perelman School of Medicine at the University of Pennsylvania Philadelphia USA Stanford Cancer Institute Stanford USA CNRS UMR 8126 Univ Paris-Sud - Université Paris-Saclay Institut Gustave Roussy Villejuif France Department of Biomedical and Health Informatics Children’s Hospital of Philadelphia Philadelphia USA Department of Pathology and Laboratory Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia USA The Janssen Pharmaceutical Companies of Johnson & Johnson Malvern USA Noncoding RNA Center Yangzhou University Yangzhou China Department of Pediatrics Perelman School of Medicine at the University of Pennsylvania Philadelphia USA Molecular and Cellular Oncogenesis Program The Wistar Institute Philadelphia USA Department of Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia USA Division of Oncology Children’s Hospital of Philadelphia Philadelphia USA CNRS UMR 8203 Univ Paris-Sud - Université Paris-Saclay Institut Gustave Roussy Villejuif France Department of Pediatric and Adolescent Oncology Univ Paris-Sud - Université Paris-Saclay Institut Gustave Roussy Villejuif France Department of Pathology and Laboratory Medicine Brown University Medical School Providence USA

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

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The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

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四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea National Public Health Laboratory National Centre for Infectious Diseases Singapore Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA Zalgen Labs Frederick MD 21703 USA Global Virus Network (GVN) Baltimore MD 21201 USA Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK Department of Biomathematics David Geffen School of Medicine Un

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

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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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Establishment of a protocol for rapidly expanding Epstein-Barr-virus-specific cytotoxic T cells with enhanced cytotoxicity

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BMC cancer 2024年第1期24卷 980页

作者： Chih-Hao Fang Ya Fang Cheng Shian-Ren Lin Wan-Yu Lai Li-Ren Liao Yen-Ling Chiu Jan-Mou Lee FullHope Biomedical Co. Ltd. 10F. No. 10 Ln. 609 Sec. 5 Chongxin Rd. Sanchong Dist. New Taipei City 241405 Taiwan. Department of Otolaryngology Far Eastern Memorial Hospital New Taipei City 220216 Taiwan. Division of Nephrology Department of Medicine Far Eastern Memorial Hospital New Taipei City 220216 Taiwan. yenling.chiu@***. Department of Medical Research Far Eastern Memorial Hospital No. 121 Sec. 2 Nanya S. Rd. Banqiao Dist. New Taipei City 220216 Taiwan. yenling.chiu@***. Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan 320315 Taiwan. yenling.chiu@***. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei 100233 Taiwan. yenling.chiu@***. FullHope Biomedical Co. Ltd. 10F. No. 10 Ln. 609 Sec. 5 Chongxin Rd. Sanchong Dist. New Taipei City 241405 Taiwan. billlee@fhb.com.tw.

BACKGROUND:Lytic Epstein-Barr virus (EBV) infection plays a major role in the pathogenesis of nasopharyngeal carcinoma (NPC). For patients with recurrent or metastatic NPC and resistant to conventional therapies, adoptive cell therapy using EBV-specific cytotoxic T cells (EBV-CTLs) is a promising option. However, the long production period (around 3 to 4 weeks) and low EBV-CTL purity (approximately 40% of total CD8 T cells) in the cell product limits the application of EBV-CTLs in clinics. Thus, this study aimed to establish a protocol for the rapid production of EBV-CTLs. METHODS:By culturing peripheral blood mononuclear cells (PBMCs) from EBV-seropositive donors with EBV-specific peptides and interleukin (IL)-2, IL-15, and interferon α (IFN-α) for 9 days, we identified that IL-15 can enhance IL-2-mediated CTL activation and significantly increase the yield of CTLs. RESULTS:When IFN-α was used in IL-2/IL-15-mediated CTL production from days 0 to 6, the productivity of EBV-CTLs and EBV-specific cytotoxicity significantly were reinforced relative to EBV-CTLs from IL-2/IL-15 treatment. Additionally, IFN-α-induced production improvement of virus-specific CTLs was not only the case for EBV-CTLs but also for cytomegalovirus-specific CTLs. CONCLUSION:We established a novel protocol to rapidly expand highly pure EBV-CTLs from PBMCs, which can produce EBV-CTLs in 9 days and does not require feeder cells during cultivation.

关键词： Adoptive cell therapy Cytotoxic T lymphocytes Epstein Barr virus Immunotherapy Nasopharyngeal carcinoma

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