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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

Science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

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BPS2025 - Cross-scale conundrum: Slow masticatory myosin in jaw muscles that are both fast and strong

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Biophysical Journal 2025年第3期124卷 516a-516a页

作者： Varun V. Dhorajia Nicholas LaFave Brandon Reder Robert Brocklehurst Nicolai Konow Sam Walcott Jeffrey R. Moore Biomedical Engineering and Biotechnology Program University of Massachusetts Lowell Lowell MA USA Department of Biological Sciences University of Massachusetts Lowell Lowell MA USA Department of Mathematical Sciences Worcester Polytechnic Institute Worcester MA USA Department of Bioinformatics and Computational Biology Worcester Polytechnic Institute Worcester MA USA

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Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

SSRN

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SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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Functional neuroplasticity of facilitation and interference effects on inhibitory control following 3-month physical exercise in aging

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Scientific Reports 2024年第1期14卷 1-13页

作者： Wu, Hong-Yi Huang, Chih-Mao Hsu, Ai-Ling Chen, Chiao-Nan Wu, Changwei W. Chen, Jyh-Horng Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan Taiwan Department of Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Physical Therapy and Assistive Technology National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University New Taipei Taiwan Research Center of Sleep Medicine Taipei Medical University Hospital Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Preservation of executive function, like inhibition, closely links to the quality of life in senior adults. Although neuroimaging literature has shown enhanced inhibitory function followed by aerobic exercise, current evidence implies inconsistent neuroplasticity patterns along different time durations of exercise. Hence, we conducted a 12-week exercise intervention on 12 young and 14 senior volunteers and repeatedly measured the inhibitory functionality of distinct aspects (facilitation and interference effects) using the numerical Stroop task and functional Magnetic Resonance Imaging. Results showcased improved accuracy and reduced reaction times (RT) after 12-week exercise, attributed to frontoparietal and default mode network effects. In young adults, the first phase (0 to six weeks) exercise increased the activation of the right superior medial frontal gyrus, associated with reduced RT in interference, but in the second intervention phase (six to twelve weeks), the decreased activation of the left superior medial frontal gyrus positively correlated with reduced RT in facilitation. In senior adults, the first six-week intervention led to reduced activations of the inferior frontal gyrus, inferior parietal gyrus, and default mode network regions, associated with the reduced RT in interference. Still, in the second intervention phase, only the visual area exhibited increased activity, associated with reduced RT in interference. Except for the distinctive brain plasticity between the two phases of exercise intervention, the between-group comparison also presented that the old group gained more cognitive benefits within the first six weeks of exercise intervention;however, the cognitive improvements in the young group occurred after six weeks of intervention. Limited by the sample size, these preliminary findings corroborated the benefits of aerobic exercise on the inhibitory functions, implying an age × exercise interaction on the brain plasticity for both facilita

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Measurement and models accounting for cell death capture hidden variation in compound response (vol 82, pg 631, 2020)

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CELL DEATH & DISEASE 2020年第8期11卷 1-9页

作者： Bae, Song Yi Guan, Ning Yan, Rui Warner, Katrina Taylor, Scott D. Meyer, Aaron S. Department of Pharmacology University of Minnesota-Twin Cities Minneapolis MN USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Biological and Biomedical Sciences Program Harvard University Cambridge MA USA Department of Bioengineering University of California Los Angeles CA USA Department of Bioinformatics University of California Los Angeles CA USA Jonsson Comprehensive Cancer Center University of California Los Angeles CA USA Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research University of California Los Angeles CA USA

Cancer cell sensitivity or resistance is almost universally quantified through a direct or surrogate measure of cell number. However, compound responses can occur through many distinct phenotypic outcomes, including changes in cell growth, apoptosis, and non-apoptotic cell death. These outcomes have divergent effects on the tumor microenvironment, immune response, and resistance mechanisms. Here, we show that quantifying cell viability alone is insufficient to distinguish between these compound responses. Using an alternative assay and drug-response analysis amenable to high-throughput measurement, we find that compounds with identical viability outcomes can have very different effects on cell growth and death. Moreover, additive compound pairs with distinct growth/death effects can appear synergistic when only assessed by viability. Overall, these results demonstrate an approach to incorporating measurements of cell death when characterizing a pharmacologic response.

关键词： Apoptosis Drug development Necroptosis

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Rapid antibiotic sensitivity prediction in Pseudomonas aeruginosa using UV–vis-NIR spectroscopy and gray-box one-vs-all models

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Journal of Microbiological Methods 2025年

作者： Tsung-Han Chou Chi-Wei Chen Su-Hua Huang Ying-Tsong Chen Yen-Wei Chu Doctoral Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Graduate Degree Program of Smart Healthcare & Bioinformatics I-Shou University Kaohsiung Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan Graduate Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Institute of Molecular Biology National Chung Hsing University Taichung Taiwan Smart Sustainable New Agriculture Research Center (SMARTer) Taichung Taiwan

Pseudomonas aeruginosa is a widespread pathogen known to cause infections in various hosts, particularly threatening immunocompromised patients. Although determining antibiotic sensitivity is crucial for appropriate patient care, existing diagnostic methods remain time-consuming, which can delay targeted therapy. In this study, we propose a novel, interpretable, and cost-effective framework that combines ultraviolet-visible-near-infrared (UV–Vis-NIR) spectroscopy with subgroup discovery and a one-vs-all multilayer perceptron (MLP) model to predict antibiotic sensitivity without the need for traditional culture methods. Unlike prior approaches that depend on expensive instruments or black-box algorithms, our method leverages spectral pattern interpretability to identify key wavelength features associated with distinct resistance categories. Testing on clinical isolates of P. aeruginosa , the model achieved optimal prediction accuracy within 10 min of culture time, significantly reducing the typical 48–72 h turnaround time of conventional culture-based susceptibility testing. This work demonstrates a promising direction for rapid, low-cost, and clinically actionable antimicrobial susceptibility testing that balances performance with explainability.

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Comparative analysis of gene regulation in single cells using Compass

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Cell Reports Methods 2025年第5期5卷 101035页

作者： Wan, Changxin Qu, Yilong Ye, Zhiyou Zhang, Tianbei Ma, Huifang Chen, Ming Hou, Wenpin Ji, Zhicheng Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham NC United States Program of Computational Biology and Bioinformatics Duke University School of Medicine Durham NC United States Department of Biomedical Engineering Pratt School of Engineering Duke University Durham NC United States Department of Pathology Duke University School of Medicine Durham NC United States Duke Cancer Institute Duke University Durham NC United States Department of Biostatistics Columbia University Mailman School of Public Health New York City NY United States

Single-cell multi-omics is a transformative technology that measures both gene expression and chromatin accessibility in individual cells. However, most studies concentrate on a single tissue and are unable to determine whether a gene is regulated by a cis-regulatory element (CRE) in just one tissue or across multiple tissues. We developed Compass for comparative analysis of gene regulation across a large number of human and mouse tissues. Compass consists of a database, CompassDB, and an open-source R software package, CompassR. CompassDB contains processed single-cell multi-omics data of more than 2.8 million cells from hundreds of cell types. Building upon CompassDB, CompassR enables visualization and comparison of gene regulation across multiple tissues. We demonstrated that CompassR can identify CRE-gene linkages specific to a tissue type and their associated transcription factors in real examples. © 2025 The Author(s)

关键词： cis-regulatory elements CP: Systems biology gene regulation single-cell ATAC-seq single-cell multi-omics

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WARMING EFFECT of BLANKETS with HIGH FAR-INFRARED EMISSIVITY on SKIN MICROCIRCULATION in TYPE 2 DIABETIC PATIENTS

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biomedical engineering - Applications, Basis and Communications 2020年第6期32卷

作者： Bau, Jian-Guo Yang, Chun-Pai Huang, Bo-Wen Lin, Yung-Sheng Wu, Ming-Feng Hungkuang University Department of Biomedical of Engineering Taichung Taiwan Department of Neurology Kuang Tien General Hospital Taichung Taiwan Department of Nutrition Hungkuang University Taichung Taiwan Feng Chia University Master's Program of Bioinformatics and Biomedical Engineering Taichung Taiwan National United University Department of Chemical Engineering Miaoli Taiwan Department of Medical Intensive Care Unit Kuang Tien General Hospital Taichung Taiwan

Vascular complications are responsible for most of the morbidity and mortality in diabetic patients. Effective strategies to improve circulation appear to be another important issue in addition to the interventions of blood glucose control. Previous studies have shown that the biological effects on humans after using the materials containing ceramic particles emits far infrared radiation (FIR). The present study is to investigate the warming effect of the fabrics containing specified metals in diabetic patients. A total of 28 diabetic patients were blinded and randomly assigned to treatment group (N = 18) and control group (N = 10), respectively. The subjects of treatment group were ministered with the blankets with fibers containing the specified metals, while the subjects of control group were provided with blankets of ordinary material. The skin temperature and microcirculatory perfusion were monitored before and after 20-min use of the blankets at shoulder and bilateral calves. After warming with blankets, the treatment group revealed a higher increased ratio of skin perfusion than control (p © 2020 National Taiwan University.

关键词： Infrared radiation

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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