检索结果-内蒙古大学图书馆

The overlapping burden of the three leading causes of disability and death in sub-Saharan African children

Nature Communications 2022年第1期13卷 1-14页

作者： Reiner, Robert C. Welgan, Catherine A. Troeger, Christopher E. Baumann, Mathew M. Weiss, Daniel J. Deshpande, Aniruddha Blacker, Brigette F. Miller-Petrie, Molly K. Earl, Lucas Bhatt, Samir Abolhassani, Hassan Abosetugn, Akine Eshete Abu-Gharbieh, Eman Adekanmbi, Victor Adetokunboh, Olatunji O. Aghaali, Mohammad Aji, Budi Alahdab, Fares Al-Aly, Ziyad Alhassan, Robert Kaba Ali, Saqib Alizade, Hesam Aljunid, Syed Mohamed Almasi-Hashiani, Amir Al-Mekhlafi, Hesham M. Altirkawi, Khalid A. Alvis-Guzman, Nelson Amare, Azmeraw T. Amini, Saeed Amugsi, Dickson A. Ancuceanu, Robert Andrei, Catalina Liliana Ansari, Fereshteh Anvari, Davood Appiah, Seth Christopher Yaw Arabloo, Jalal Aremu, Olatunde Atout, Maha Moh’d Wahbi Ausloos, Marcel Ausloos, Floriane Ayanore, Martin Amogre Aynalem, Yared Asmare Azene, Zelalem Nigussie Badawi, Alaa Baig, Atif Amin Banach, Maciej Bedi, Neeraj Bhagavathula, Akshaya Srikanth Bhandari, Dinesh Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bhutta, Zulfiqar A. Bijani, Ali Birhanu, Tesega Tesega Mengistu Bitew, Zebenay Workneh Boloor, Archith Brady, Oliver J. Butt, Zahid A. Car, Josip Carvalho, Felix Casey, Daniel C. Chattu, Vijay Kumar Chowdhury, Mohiuddin Ahsanul Kabir Chu, Dinh-Toi Coelho, Camila H. Cook, Aubrey J. Damiani, Giovanni Daoud, Farah Gela, Jiregna Darega Darwish, Amira Hamed Daryani, Ahmad Das, Jai K. Davis Weaver, Nicole Deribe, Kebede Desalew, Assefa Dharmaratne, Samath Dhamminda Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Dorostkar, Fariba Dubljanin, Eleonora Duko, Bereket Dwyer-Lindgren, Laura Effiong, Andem El Sayed Zaki, Maysaa El Tantawi, Maha Enany, Shymaa Fattahi, Nazir Feigin, Valery L. Fernandes, Eduarda Ferrara, Pietro Fischer, Florian Foigt, Nataliya A. Folayan, Morenike Oluwatoyin Foroutan, Masoud Frostad, Joseph Jon Fukumoto, Takeshi Gaidhane, Abhay Motiramji Gebrekrstos, Hailemikael Gebrekidan G. K. Gebremeskel, Leake Gebreslassie, Assefa Ayalew Gething, Peter W. Gezae, Kebede Embaye Ghadiri, Keyghobad Ghashghaee, Ahmad Golechha, Mahaveer Gubar Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Malaria Atlas Project University of Oxford Oxford United Kingdom Imperial College London London United Kingdom Department of Laboratory Medicine Karolinska University Hospital Huddinge Sweden Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Public Health Debre Berhan University Debre Berhan Ethiopia Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Population Health Sciences King’s College London London United Kingdom Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Stellenbosch South Africa Department of Global Health Stellenbosch University Cape Town South Africa Department of Epidemiology and Biostatistics Qom University of Medical Sciences Qom Iran Faculty of Medicine and Public Health Jenderal Soedirman University Purwokerto Indonesia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center Department of Veterans Affairs St Louis MO United States Institute of Health Research University of Health and Allied Sciences Ho Ghana Department of Information Systems College of Economics and Political Science Sultan Qaboos University Muscat Oman Infectious and Tropical Disease Research Center Hormozgan University of Medical Sciences Bandar Abbas Iran Department of Health Policy and Management Kuwait University Safat Kuwait International Centre for Casemix and Clinical Coding National University of Malaysia Bandar Tun Razak Malaysia Department of Epidemiology Arak University of Medical Sciences Arak Iran Medical Research Center

Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival. © 2022, The Author(s).

关键词： Biogeography Epidemiology Infectious diseases Malaria

来源：评论

学校读者我要写书评

暂无评论

A large-scale binding and functional map of human RNA-binding proteins (vol 583, pg 711, 2020)

引用

NATURE 2021年第7842期589卷 E5-E5页

作者： Van Nostrand, Eric L. Freese, Peter Pratt, Gabriel A. Wang, Xiaofeng Wei, Xintao Xiao, Rui Blue, Steven M. Chen, Jia-Yu Cody, Neal A. L. Dominguez, Daniel Olson, Sara Sundararaman, Balaji Zhan, Lijun Bazile, Cassandra Bouvrette, Louis Philip Benoit Bergalet, Julie Duff, Michael O. Garcia, Keri E. Gelboin-Burkhart, Chelsea Hochman, Myles Lambert, Nicole J. Li, Hairi McGurk, Michael P. Nguyen, Thai B. Palden, Tsultrim Rabano, Ines Sathe, Shashank Stanton, Rebecca Su, Amanda Wang, Ruth Yee, Brian A. Zhou, Bing Louie, Ashley L. Aigner, Stefan Fu, Xiang-Dong Lecuyer, Eric Burge, Christopher B. Graveley, Brenton R. Yeo, Gene W. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Medical Research Institute Wuhan University Wuhan China Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Institut de Recherches Cliniques de Montréal (IRCM) Montreal Quebec Canada Department of Biochemistry and Molecular Medicine Université de Montréal Montreal Quebec Canada Division of Experimental Medicine McGill University Montreal Quebec Canada Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA

来源：评论

学校读者我要写书评

暂无评论

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Cover Image, Volume 5, Number 3, March 2023

引用

Carbon Energy 2023年第3期5卷

作者： Jun Yang Xing Li Ke Qu Yixian Wang Kangqi Shen Changhuan Jiang Bo Yu Pan Luo Zhuangzhi Li Mingyang Chen Bingshu Guo Mingshan Wang Junchen Chen Zhiyuan Ma Yun Huang Zhenzhong Yang Pengcheng Liu Rong Huang Xiaodi Ren David Mitlin School of New Energy and Materials Southwest Petroleum University Chengdu China Energy Storage Research Institute Southwest Petroleum University Chengdu China Ke Qu Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China. China Key Laboratory of Polar Materials and Devices (MOE) and Department of Electronics East China Normal University Shanghai 200062 China. Mingyang Chen Beijing Computational Science Research Center Beijing 100193 China. Center for Green Innovation School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 China. Rong Huang China Key Laboratory of Polar Materials and Devices (MOE) and Department of Electronics East China Normal University Shanghai 200062 China. Xiaodi Ren Hefei National Research Center for Physical Sciences at the Microscale Department of Materials Science and Engineering University of Science and Technology of China Hefei Anhui 230026 China. David Mitlin Materials Science Program and Texas Materials Institute The University of Texas at Austin Austin Texas 78712-1591 USA. Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China China Key Laboratory of Polar Materials and Devices (MOE) Department of Electronics East China Normal University Shanghai China Xing Li School of New Energy and Materials Southwest Petroleum University Chengdu China. Materials Science Program and Texas Materials Institute The University of Texas at Austin Austin Texas USA Beijing Computational Science Research Center Beijing China School of Materials Science and Engineering Center for Green Innovation University of Science and Technology Beijing Beijing China Department of Materials Science and Engineering Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei Anhui China

来源：评论

学校读者我要写书评

暂无评论

Correction to: Near Real Time Satellite Event Detection, Characterization, and Operational Assessment Via the Exploitation of Remote Photoacoustic Signatures

引用

The Journal of the Astronautical sciences 2021年第2期68卷 550-550页

作者： Spurbeck, Justin Jah, Moriba K. Kucharski, Daniel Bennett, James C. S. Webb, James G. Aerospace Engineering and Engineering Mechanics Department Cockrell School of Engineering The University of Texas at Austin Austin USA Research Fellow Space Environment Research Centre SERC Canberra Australia. Visiting Researcher Institute for Computational Engineering and Sciences The University of Texas at Austin Austin USA Astrodynamics Group Leader EOS Space Systems Pty Ltd Queanbeyan Australia. Research Program Leader Space Environment Research Center Canberra Australia Instrument Scientist EOS Space Systems Pty Ltd Queanbeyan Australia

A Correction to this paper has been published: https://***/10.1007/s40295-021-00265-0

关键词：

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

引用

Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

来源：评论

学校读者我要写书评

暂无评论

Innovation and challenges in the implementation of artificial intelligence in critical care

引用

Acta Colombiana de Cuidado Intensivo 2025年

作者： Rojas-Díaz, Ever Leonardo Rojas, Mónica Osorio-Valencia, Juan Sebastian Acevedo Guerrero, Natalia Celi, Leo Anthony Pinilla-Forero, Margoth Cristina Biomedical Technology and Innovation in Critical Care Committee Colombian Association of Critical Medicine and Intensive Care (AMCI) Bogotá Colombia Department of Intensive and Critical Care Medicine Academic Hospital Fundación Santa Fe de Bogotá Bogotá Colombia Biomedical Engineering Program School of Medicine and Health Sciences Universidad del Rosario Bogotá Colombia International Training and Education Center for Health: I-TECH Department of Global Health University of Washington WA United States 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 T.H. Chan School of Public Health Boston MA United States Quality Management Department Biomédica Colombia SAS Bogotá Colombia Consultant Continuing Education School of Medicine and Health Sciences Universidad del Rosario Bogotá Colombia

来源：评论

学校读者我要写书评

暂无评论

Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Correction: Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

引用

BMC Digital Health 2023年第1期1卷 1-1页

作者： Thomas Wetere Tulu Ching Long Chan Chun Hei Wu Kei Hang Katie Chan Tsz Kin Wan Peter Yat Ming Woo Cee Zhung Steven Tseng Asmir Vodencarevic Cristina Menni Department of Biomedical Sciences City University of Hong Kong Hong Kong SAR China Computational Data Science Program Addis Ababa University Addis Ababa Ethiopia Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China Department of Epidemiology and Center for Global Cardiometabolic Health School of Public Health Brown University Providence RI USA Department of Neurosurgery Kwong Wah Hospital Hong Kong SAR China Department of Medicine and Geriatrics Kwong Wah Hospital Hong Kong SAR China Innovative Medicines Novartis Pharma GmbH 90429 Nuremberg Germany Department of Twin Research King’s College London London UK

来源：评论

学校读者我要写书评

暂无评论

Isocitrate dehydrogenase 1 primes group-3 medulloblastomas for cuproptosis

引用

Cancer Cell 2025年第6期43卷 1159-1174.e8页

作者： Dang, Derek Deogharkar, Akash McKolay, John Smith, Kyle S. Panwalkar, Pooja Hoffman, Simon Tian, Wentao Ji, Sunjong Azambuja, Ana P. Natarajan, Siva Kumar Lum, Joanna Bayliss, Jill Manzeck, Katie Sweha, Stefan R. Hamanishi, Erin Pun, Matthew Patel, Diya Rau, Sagar Animasahun, Olamide Achreja, Abhinav Ogrodzinski, Martin P. Diessl, Jutta Cotter, Jennifer Hawes, Debra Yang, Fusheng Doherty, Robert Franson, Andrea T. Hanaford, Allison R. Eberhart, Charles G. Raabe, Eric H. Orr, Brent A. Wechsler-Reya, Robert J. Chen, Brandon Lyssiotis, Costas A. Shah, Yatrik M. Lunt, Sophia Y. Banerjee, Ruma Judkins, Alexander R. Prensner, John R. Koschmann, Carl Waszak, Sebastian M. Nagrath, Deepak Simoes-Costa, Marcos Northcott, Paul A. Venneti, Sriram Laboratory of Brain Tumor Metabolism and Epigenetics Department of Pathology University of Michigan Ann Arbor MI United States Department of Developmental Neurobiology Neurobiology and Brain Tumor Program St. Jude Children's Research Hospital Memphis TN United States Center of Excellence in Neuro-Oncology Sciences St. Jude Children's Research Hospital Memphis TN United States Department of Pediatrics Michigan Medicine Ann Arbor MI United States Department of Pathology Boston Children's Hospital Boston MA United States Department of Systems Biology Harvard Medical School Boston MA United States Cancer Biology and Genetics Program Memorial Sloan Kettering Cancer Center New York NY United States Department of Chemical Engineering University of Michigan Ann Arbor MI United States Laboratory for Systems Biology of Human Diseases University of Michigan Ann Arbor MI United States Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Biomedical Engineering University of Michigan Ann Arbor MI United States Rogel Cancer Center University of Michigan Ann Arbor MI United States Department of Biochemistry and Molecular Biology Michigan State University East Lansing MI United States Department of Biological Chemistry University of Michigan Medical School Ann Arbor 48109 MI United States Department of Pathology and Laboratory Medicine Children's Hospital Los Angeles Los Angeles CA United States Keck School of Medicine University of Southern California Los Angeles CA United States Department of Pathology Johns Hopkins School of Medicine Baltimore MD United States Division of Neuropathology Department of Pathology School of Medicine Johns Hopkins University Baltimore MD United States Sidney Kimmel Comprehensive Cancer Center School of Medicine Johns Hopkins University Baltimore MD United States Division of Pediatric Oncology Department of Oncology School of Medicine Johns Hopkins University Baltimore MD United

MYC-driven group-3 medulloblastomas (MBs) are malignant pediatric brain cancers without cures. To define actionable metabolic dependencies, we identify upregulation of dihydrolipoyl transacetylase (DLAT), the E2-subunit of pyruvate dehydrogenase complex (PDC) in a subset of group-3 MB with poor prognosis. DLAT is induced by c-MYC and targeting DLAT lowers TCA cycle metabolism and glutathione synthesis. We also note upregulation of isocitrate dehydrogenase 1 (IDH1) gene expression in group-3 MB patient tumors and suppression of IDH1 epigenetically reduces c-MYC and downstream DLAT levels in multiple c-MYC amplified cancers. DLAT is a central regulator of cuproptosis (copper-dependent cell death) induced by the copper ionophore elesclomol. DLAT expression in group-3 MB cells correlates with increased sensitivity to cuproptosis. Elesclomol is brain-penetrant and suppresses tumor growth in vivo in multiple group-3 MB animal models. Our data uncover an IDH1/c-MYC dependent vulnerability that regulates DLAT levels and can be targeted to kill group-3 MB by cuproptosis. © 2025 Elsevier Inc.

关键词： cancer metabolism cell death copper cuproptosis elesclomol epigenetics pediatric brain tumor protein lipoylation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：