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Challenges for Medical Students in Applying Ethical Principles to Allocate Life-Saving Medical Devices During the COVID-19 Pandemic: Content Analysis

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JMIR Medical Education 2024年第1期10卷 e52711页

作者： Hsieh, Hsing-Yen Lin, Chyi-Her Huang, Ruyi Lin, Guan-Chun Lin, Jhen-Yu Aldana, Clydie School of Medicine for International Students College of Medicine I-Shou University Kaohsiung Taiwan Department of Pediatrics E-Da Hospital Kaohsiung Taiwan Holistic Medicine Department of Family and Community Medicine E-Da Hospital Kaohsiung Taiwan Data Science Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Division of Family Medicine Taipei Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation School of Medicine Tzu Chi University Hualien Taiwan

Background: The emergence of the COVID-19 pandemic has posed a significant ethical dilemma in the allocation of scarce, life-saving medical equipment to critically ill patients. It remains uncertain whether medical students are equipped to navigate this complex ethical process. Objective: This study aimed to assess the ability and confidence of medical students to apply principles of medical ethics in allocating critical medical devices through the scenario of virtual patients. Methods: The study recruited third- and fourth-year medical students during clinical rotation. We facilitated interactions between medical students and virtual patients experiencing respiratory failure due to COVID-19 infection. We assessed the students' ability to ethically allocate life-saving resources. Subsequently, we analyzed their written reports using thematic analysis to identify the ethical principles guiding their decision-making. Results: We enrolled a cohort of 67 out of 71 medical students with a mean age of 34 (SD 4.7) years, 60% (n=40) of whom were female students. The principle of justice was cited by 73% (n=49) of students while analyzing this scenario. A majority of them expressed hesitancy in determining which patient should receive life-saving resources, with 46% (n=31) citing the principle of nonmaleficence, 31% (n=21) advocating for a first-come-first-served approach, and 25% (n=17) emphasizing respect for patient autonomy as key influencers in their decisions. Notably, medical students exhibited a lack of confidence in making ethical decisions concerning the distribution of medical resources. A minority, comprising 12% (n=8), proposed the exploration of legal alternatives, while 4% (n=3) suggested medical guidelines and collective decision-making as potential substitutes for individual ethical choices to alleviate the stress associated with personal decision-making. Conclusions: This study highlights the importance of improving ethical reasoning under time constraints

关键词： COVID-19 pandemic decision support decision-making ethical dilemna ethics life-saving medical device medical devices medical ethical education medical ethics medical guideline medical guidelines medical resources distribution medical student medical students reasoning simulation thematic analysis virtual patient virtual patients virtual platform

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Disentangling high order effects in the transfer entropy

arXiv

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arXiv 2024年

作者： Stramaglia, Sebastiano Faes, Luca Cortes, Jesus M. Marinazzo, Daniele Dipartimento Interateneo di Fisica Università degli Studi di Bari Aldo Moro INFN Sezione di Bari Bari70126 Italy Dipartimento di Ingegneria Università di Palermo Palermo90128 Italy Biocruces-Bizkaia Health Research Institute Barakaldo Spain Department of Data Analysis Ghent University Ghent Belgium Biomedical Research Doctorate Program University of the Basque Country Leioa Spain Department of Cell Biology and Histology University of the Basque Country Leioa Spain IKERBASQUE Basque Foundation for Science Bilbao Spain

Transfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias-either in deficiency or excess-during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, our method searches for both the multiplets of variables that maximize information flow and those that minimize it. This provides a decomposition of TE into unique, redundant, and synergistic atoms. Our approach enables the quantification of the relative importance of high-order effects compared to pure two-body effects in information transfer between two processes, while also highlighting the processes that contribute to building these high-order effects alongside the driver. We demonstrate the application of our approach in climatology by analyzing data from El Niño and the Southern Oscillation. © 2024, CC BY.

关键词： Entropy

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Efficient Bitcoin Address Classification Using Quantum-Inspired Feature Selection

arXiv

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arXiv 2024年

作者： Sie, Ming-Fong Chang, Yen-Jui Lin, Chien-Lung Chang, Ching-Ray Liao, Shih-Wei Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei City10617 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 499 Xingzhong N. Rd. Taoyuan City320317 Taiwan Department of Physics National Taiwan University Taiwan Quantum Information Center Chung Yuan Christian University No. 499 Xingzhong N. Rd. Taoyuan City320317 Taiwan

Over 900 million Bitcoin transactions have been recorded, posing considerable challenges for machine learning regarding computation time and maintaining prediction accuracy. We propose an innovative approach using quantum-inspired algorithms implemented with Simulated Annealing and Quantum Annealing to address the challenge of local minima in solution spaces. This method efficiently identifies key features linked to mixer addresses, significantly reducing model training time. By categorizing Bitcoin addresses into six classes—exchanges, faucets, gambling, marketplaces, mixers, and mining pools—and applying supervised learning methods, our results demonstrate that feature selection with SA reduced training time by 30.3% compared to using all features in a random forest model while maintaining a 91% F1-score for mixer addresses. This highlights the potential of quantum-inspired algorithms to swiftly and accurately identify high-risk Bitcoin addresses based on transaction features. © 2024, CC BY.

关键词： Simulated annealing

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A T cell resilience model associated with response to immunotherapy in multiple tumor types (May, 10.1038/s41591-022-01799-y, 2022)

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NATURE MEDICINE 2022年第10期28卷 2219-2219页

作者： Zhang, Yu Vu, Trang Palmer, Douglas C. Kishton, Rigel J. Gong, Lanqi Huang, Jiao Nguyen, Thanh Chen, Zuojia Smith, Cari Livak, Ferenc Paul, Rohit Day, Chi-Ping Wu, Chuan Merlino, Glenn Aldape, Kenneth Guan, Xin-yuan Jiang, Peng Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Department of Clinical Oncology The University of Hong Kong Hong Kong China Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China Surgery Branch National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory of Pathology Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Experimental Immunology Branch Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory Animal Science Program Leidos Biomedical Research Inc Frederick MD USA Flow Cytometry Core Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Office of the Director Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA

Despite breakthroughs in cancer immunotherapy, most tumor-reactive T cells cannot persist in solid tumors due to an immunosuppressive environment. We developed Tres (tumor-resilient T cell), a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as transforming growth factor-β1, tumor necrosis factor-related apoptosis-inducing ligand and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in melanoma, lung cancer, triple-negative breast cancer and B cell malignancies using bulk T cell transcriptomic data from pre-treatment tumors from patients who received immune-checkpoint inhibitors (n= 38), infusion products for chimeric antigen receptor T cell therapies (n= 34) and pre-manufacture samples for chimeric antigen receptor T cell or tumor-infiltrating lymphocyte therapies (n= 84). Further, Tres identifiedFIBP, whose functions are largely unknown, as the top negative marker of tumor-resilient T cells across many solid tumor *** in murine and human donor CD8+T cells significantly enhanced T cell-mediated cancer killing in in vitro co-cultures. Further,Fibpknockout in murine T cells potentiated the in vivo efficacy of adoptive cell transfer in the B16 tumor *** T cells exhibit reduced cholesterol metabolism, which inhibits effector T cell function. These results demonstrate the utility of Tres in identifying biomarkers of T cell effectiveness and potential therapeutic targets for immunotherapies in solid tumors.

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P662: Estimating UK Biobank population-specific PGx allele and phenotype frequencies using PharmCAT

Genetics in Medicine Open

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Genetics in Medicine Open 2023年第1期1卷 100727-100727页

作者： Katrin Sangkuhl Ryan Whaley Mark Woon Karl Keat Li Gong Michelle Whirl-Carrillo Marylyn Ritchie Teri Klein Department of Biomedical Data Science Stanford University Stanford CA Genomics and Computational Biology PhD Program University of Pennsylvania Philadelphia PA Department of Genetics University of Pennsylvania Philadelphia PA

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Finite-size effects on the convergence time in continuous-opinion dynamics

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Physical Review E 2021年第1期104卷 014309-014309页

作者： Hang-Hyun Jo Naoki Masuda Department of Physics The Catholic University of Korea Bucheon 14662 Republic of Korea Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

We study finite-size effects on the convergence time in a continuous-opinion dynamics model. In the model, each individual's opinion is represented by a real number on a finite interval, e.g., [0,1], and a uniformly randomly chosen individual updates its opinion by partially mimicking the opinion of a uniformly randomly chosen neighbor. We numerically find that the characteristic time to the convergence increases as the system size increases according to a particular functional form in the case of lattice networks. In contrast, unless the individuals perfectly copy the opinion of their neighbors in each opinion updating, the convergence time is approximately independent of the system size in the case of regular random graphs, uncorrelated scale-free networks, and complete graphs. We also provide a mean-field analysis of the model to understand the case of the complete graph.

关键词： Complex systems Nonequilibrium statistical mechanics Social systems Homogeneous networks Scale free & inhomogeneous networks Social dynamics Master equation Monte Carlo methods Network Models

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Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

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Nucleic Acids Research 2024年第3期52卷 E17页

作者： Chang, Lan-Yun Lee, Meng-Zhan Wu, Yujia Lee, Wen-Kai Ma, Chia-Liang Chang, Jun-Mao Chen, Ciao-Wen Huang, Tzu-Chun Lee, Chia-Hwa Lee, Jih-Chin Tseng, Yu-Yao Lin, Chun-Yu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 300 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan Department of Food Science Nutrition and Nutraceutical Biotechnology Shih Chien University Taipei 104 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Cancer and Immunology Research Center National Yang Ming Chiao Tung University Taipei 112 Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge. © 2024 Oxford University Press. All rights reserved.

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ADAM-1: AI and Bioinformatics for Alzheimer’s Detection and Microbiome-Clinical data Integrations

arXiv

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arXiv 2025年

作者： Huang, Ziyuan Sekhon, Vishaldeep Kaur Guo, Ouyang Newman, Mark Sadeghian, Roozbeh Vaida, Maria L. Jo, Cynthia Ward, Doyle Bucci, Vanni Haran, John P. Department of Microbiology UMass Chan Medical School WorcesterMA01655 United States Department of Geriatric Medicine and Gerontology Johns Hopkins University BaltimoreMD21218 United States Data Sciences Harrisburg University of Science and Technology HarrisburgPA17101 United States DevIS Innovation Labs DevIS LLC ArlingtonVA22201 United States Department of Emergency Medicine UMass Chan Medical School WorcesterMA01655 United States Program in Microbiome Dynamics UMass Chan Medical School WorcesterMA01655 United States

The Alzheimer’s Disease Analysis Model Generation 1 (ADAM-1) is a multi-agent large language model (LLM) framework designed to integrate and analyze multi-modal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and detection of Alzheimer’s disease (AD). By leveraging retrieval-augmented generation (RAG) techniques along with its multi-agent architecture, ADAM-1 synthesizes insights from diverse data sources and contextualizes findings using literature-driven evidence. Comparative evaluation against XGBoost revealed similar mean F1 scores but significantly reduced variance for ADAM-1, highlighting its robustness and consistency, particularly in small laboratory datasets. While currently tailored for binary classification tasks, future iterations aim to incorporate additional data modalities, such as neuroimaging and biomarkers, to broaden its scalability and applicability for Alzheimer’s research and *** Codes 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Classification (of information)

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Temporal networks provide a unifying understanding of the evolution of cooperation

arXiv

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arXiv 2023年

作者： Li, Aming Meng, Yao Zhou, Lei Masuda, Naoki Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Mathematics State University of New York at Buffalo BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260 United States

Understanding the evolution of cooperation in structured populations represented by networks is a problem of long research interest, and a most fundamental and widespread property of social networks related to cooperation phenomena is that the node’s degree (i.e., number of edges connected to the node) is heterogeneously distributed. Previous results indicate that static heterogeneous (i.e., degree-heterogeneous) networks promote cooperation in stationarity compared to static regular (i.e., degree-homogeneous) networks if equilibrium dynamics starting from many cooperators and defectors is employed. However, the above conclusion reverses if we employ non-equilibrium stochastic processes to measure the fixation probability for cooperation, i.e., the probability that a single cooperator successfully invades a population. Here we resolve this conundrum by analyzing the fixation of cooperation on temporal (i.e., time-varying) networks. We theoretically prove and numerically confirm that on both synthetic and empirical networks, contrary to the case of static networks, temporal heterogeneous networks can promote cooperation more than temporal regular networks in terms of the fixation probability of cooperation. Given that the same conclusion is known for the equilibrium fraction of cooperators on temporal networks, the present results provide a unified understanding of the effect of temporal degree heterogeneity on promoting cooperation across two main analytical frameworks, i.e., equilibrium and non-equilibrium ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Through the Lens of Patient Partners: Challenges in Accrual of Older Adults to NCI Clinical Trials

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Journal of the National Cancer Institute - Monographs 2022年第60期2022卷 125-134页

作者： BrintzenhofeSzoc, Karlynn Canin, Beverly Casas-Silva, Esmeralda Denicoff, Andrea Braun-Inglis, Christa Okado, Izumi Bakos, Alexis Oncology Social Work Kent School of Social Work University of Louisville Louisville KY United States SCOREboard Patient Advocate Board The Cancer and Aging Research Group United States Center for Biomedical Informatics and Information Technology Informatics and Data Science Program National Institutes of Health National Cancer Institute Rockville MD United States Division of Cancer Treatment and Diagnosis Cancer Therapy and Evaluation Program National Institutes of Health National Cancer Institute Rockville MD United States Clinical Faculty UH Nancy Atmospera-Walch School of Nursing University of Hawaii Cancer Center Hawaii M U NCORP Honolulu HI United States University of HawaiiCancer Center Honolulu HI United States Division of Cancer Prevention Community Oncology and Prevention Trials Research Group National Institutes of Health National Cancer Institute Rockville MD United States

The workshop “Engaging Older Adults in Cancer Clinical Trials Conducted in the NCI Clinical Trials Network: Challenges and Opportunities” included a Patient Stakeholder Workgroup that explored the needs and concerns of older adults with cancer regarding clinical trials. To accomplish this, the workgroup conducted patient focus groups in which participants were interviewed, recorded conversations were analyzed and coded, and salient themes were identified. The focus groups identified general barriers to accrual such as complex consent forms, general communication, restrictive eligibility, nonreferrals, patient costs, cultural insensitivity, limited accessibility in community settings, and transportation issues. They also identified the influence of knowledgeable information presenters, improved care, family or caregiver support, and the desire to help others as drivers or reasons to participate in clinical trials. The workshop concluded that multi-level interventions could be used to increase the accrual of older adults to National Cancer Institute clinical trials as well as others. © The Author(s) 2022. Published by Oxford University Press. All rights reserved.

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