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The critical role of electrochemically activated adsorbates in neutral OER

science China Materials 2020年第12期63卷 2509-2516页

作者： Longsheng Zhang Haiyang Yuan Liping Wang Hui Zhang Yijing Zang Yao Tian Yunzhou Wen Fenglou Ni Hao Song Haifeng Wang Bo Zhang Huisheng Peng State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Science and Laboratory of Advanced MaterialsFudan UniversityShanghai 200438China Key Laboratory for Advanced Materials Key Laboratory for Advanced MaterialsCentre for Computational Chemistry and Research Institute of Industrial CatalysisEast China University of Science and TechnologyShanghai 200237China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesCAS Center for Excellence in Superconducting ElectronicsShanghai 200050China Key Laboratory of Systems Bioengineering School of Chemical Engineering and TechnologySynBio Research PlatformCollaborative Innovation Centre of Chemical Science and EngineeringTianjin UniversityTianjin 300072China

Developing efficient electrocatalysts for the oxygen evolution reaction(OER)under neutral conditions is important for microbial electrolysis cells(MECs).However,the OER kinetics in neutral electrolytes at present are extremely sluggish,resulting in high overpotentials that greatly limit the energy conversion efficiencies of *** studies failed to probe the adsorbates on surface metal sites of catalysts at the atomic scale and elucidate their influence on the catalytic activities,which has impeded the rational design of efficient neutral OER catalysts with optimal surface ***,using in situ transmission electron microscopy(TEM),in situ X-ray photoelectron spectroscopy(XPS)and in situ low-energy ion scattering studies,we have identified,for the first time,that the electrochemically activated adsorbates on surface metal sites play a critical role in boosting the neutral OER activities of Ru-Ir binary oxide(RuxIryO2)*** adsorbate-activated RuxIryO2on a glassy carbon electrode achieved a low overpotential of 324 m V at10 m A cm-2in neutral electrolyte,with a 36-fold improvement in turnover frequency compared with that of Ir *** application in an MEC system,the resulting full cell showed a decreased voltage of 1.8 V,200 m V lower than the best value reported to date,facilitating efficient synthesis of poly(3-hydroxybutyrate)from bioelectrochemical *** functional theory(DFT)studies revealed that the enhanced OER activity of RuxIryO2catalyst arose from local structural distortion of adjacent adsorbate-covered Ru octahedra at the catalyst surface and the consequently decreased adsorption energies of OER intermediates on Ir active center.

关键词： electrocatalysis oxygen evolution reaction adsorbates neutral electrolytes

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Correction to: Mitigation strategies against cascading failures within a project activity network

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Journal of computational Social science 2021年第1期5卷 1097-1098页

作者： Ellinas, Christos Nicolaides, Christos Masuda, Naoki Nodes and Links Ltd Cambridge UK Department of Business and Public Administration University of Cyprus Nicosia Cyprus Initiative on the Digital Economy Massachusetts Institute of Technology Cambridge USA Department of Mathematics State University of New York at Buffalo Buffalo USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo USA Faculty of Management and Economics Dalian University of Technology Dalian China

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare

arXiv

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arXiv 2024年

作者： Li, Ming Xu, Pengcheng Hu, Junjie Tang, Zeyu Yang, Guang Bioengineering Department Imperial-X Imperial College London LondonW12 7SL United Kingdom National Heart and Lung Institute Imperial College London LondonSW7 2AZ United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Tri-Institutional Computational Biology & Medicine Program Weill Cornell Medicine of Cornell University New York United States Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London LondonWC2R 2LS United Kingdom

Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare. © 2024, CC BY-NC-ND.

关键词： Federated learning

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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

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Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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Environment Scan of Generative AI Infrastructure for Clinical and Translational science

arXiv

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arXiv 2024年

作者： Idnay, Betina Xu, Zihan Adams, William G. Adibuzzaman, Mohammad Anderson, Nicholas R. Bahroos, Neil Bell, Douglas S. Bumgardner, Cody Campion, Thomas Castro, Mario Cimino, James J. Cohen, I. Glenn Dorr, David Elkin, Peter L. Fan, Jungwei W. Ferris, Todd Foran, David J. Hanauer, David Hogarth, Mike Huang, Kun Kalpathy-Cramer, Jayashree Kandpal, Manoj Karnik, Niranjan S. Katoch, Avnish Lai, Albert M. Lambert, Christophe G. Li, Lang Lindsell, Christopher Liu, Jinze Lu, Zhiyong Luo, Yuan McGarvey, Peter Mendonca, Eneida A. Mirhaji, Parsa Murphy, Shawn Osborne, John D. Paschalidis, Ioannis C. Harris, Paul A. Prior, Fred Shaheen, Nicholas J. Shara, Nawar Sim, Ida Tachinardi, Umberto Waitman, Lemuel R. Wright, Rosalind J. Zai, Adrian H. Zheng, Kai Lee, Sandra Soo-Jin Malin, Bradley A. Natarajan, Karthik Price, W. Nicholson Zhang, Rui Zhang, Yiye Xu, Hua Bian, Jiang Weng, Chunhua Peng, Yifan Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Department of Pediatrics Boston Medical Center BostonMA United States Chobanian & Avedisian School of Medicine Boston University BostonMA United States Oregon Clinical and Translational Research Institute Oregon Health and Science University PortlandOR United States Department of Public Health Sciences University of California Davis DavisCA United States Keck School of Medicine University of Southern California Los AngelesCA United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine LexingtonKY United States Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas CityKS United States Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama BirminghamAL United States Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University CambridgeMA United States Department of Biomedical Informatics University at Buffalo BuffaloNY United States Center for Clinical and Translational Science Mayo Clinic RochesterMN United States Technology and Digital Solutions Stanford Medicine Stanford University StanfordCA United States Center for Biomedical Informatics Rutgers Cancer Institute New BrunswickNJ United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California San Diego La Jolla CA United States Department of Biostatistics and Health Data Science School of Medicine Indiana University IndianapolisIN United States Division of Artificial Medical Intelligence in Ophthalmology Univ

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational science Award (CTSA) program led by the National Center for Advancing Translational sciences (NCATS) of the National institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency. © 2024, CC BY.

关键词： Electronic health record

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Observation of Active Galactic Nuclei with the Large-Sized Telescope prototype of the Cherenkov Telescope Array 38

Observation of Active Galactic Nuclei with the Large-Sized T...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Takeishi, R. Álvarez Crespo, N. Arbet-Engels, A. Baquero Larriva, A. Baxter, J. Biederbeck, N. Caroff, S. Di Marco, G. Fallah Ramazani, V. Green, D. Heckmann, L. Láinez, M. Nickel, L. Nievas Rosillo, M. Pons, E. Priyadarshi, C. Sanchez, D.A. Vázquez Acosta, M. Abe, K. Abe, S. Aguasca-Cabot, A. Agudo, I. Antonelli, L.A. Aramo, C. Arcaro, C. Artero, M. Asano, K. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardini, E. Bernardos, M.I. Bernete Medrano, J. Berti, A. Bhattacharjee, P. Bigongiari, C. Bissaldi, E. Blanch, O. Bonnoli, G. Bordas, P. Bulgarelli, A. Burelli, I. Burmistrov, L. Buscemi, M. Cardillo, M. Carosi, A. Carrasco, M.S. Cassol, F. Cauz, D. Cerasole, D. Ceribella, G. Chai, Y. Cheng, K. Chiavassa, A. Chikawa, M. Chytka, L. Cifuentes, A. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. Dazzi, F. De Angelis, A. de Bony de Lavergne, M. De Lotto, B. De Lucia, M. de Menezes, R. Del Peral, L. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Dellaiera, M. Di Piano, A. Di Pierro, F. Di Pilato, A. Di Tria, R. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsässer, D. Emery, G. Escudero, J. Ferrara, G. Ferrarotto, F. Fiasson, A. Foffano, L. Freixas Coromina, L. Fröse, S. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Gasbarra, C. Gasparrini, D. Geyer, D. Giesbrecht Paiva, J. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grau, R. Gunji, S. Günther, P. Hackfeld, J. Hadasch, D. Hahn, A. Hashiyama, K. Hassan, T. Hayashi, K. Heller, M. HerreraLlorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Iarlori, M. Imazawa, R. Inada, T. Inome, Y. Ioka, K. Iori, M. Ishio, K. Jimenez Martinez, I. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kohri, K. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Grupo de Electronica Universidad Complutense de Madrid Av. Complutense s/n Madrid28040 Spain Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany IPARCOS-UCM Instituto de Física de Partículas y del Cosmos EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain Faculty of Science and Technology Universidad del Azuay Cuenca Ecuador Department of Physics TU Dortmund University Otto-Hahn-Str. 4 Dortmund44227 Germany Univ. Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 Annecy74000 France Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Tenerife La Laguna Spain Institut für Theoretische Physik Lehrstuhl IV: Plasma-Astroteilchenphysik Ruhr-Universität Bochum Universitätsstraße 150 Bochum44801 Germany The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Department of Physics Tokai University Japan Departament de Física Quàntica i Astrofísica Institut de Ciències delCosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Spain EMFTEL department IPARCOS Universidad Complutense de Madrid Spain INAF - Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy INFN Sezione di Padova Università degli Studi di Padova Italy Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Universidad del Azuay Ecuador INAF - Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil CIEMAT Spain INFN Sezione di Bari Politecnico di Bari Italy INAF - Osservatorio Astronomico di Brera Italy INFN Sezione di Trieste Università degli Studi di Udine Italy University of Geneva Département de physique nucléaire et corpusculaire Switzerland INFN Sezione di Catania Italy Italy Aix Marseille Uni

Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory. It will consist of three different sizes of telescopes, with a collective improvement of about an order of magnitude in sensitivity, compared with the current generation gamma-ray telescopes. LargeSized Telescopes (LSTs) will be the most sensitive at energies from tens of GeV up to a few TeVs, and will be the best suited to observe gamma-ray sources such as Active Galactic Nuclei (AGN). The first Large-Sized Telescope prototype (LST-1), inaugurated in 2018 and currently in commissioning phase, has accumulated more than one thousand hours of data to date. From 2020 to 2022, we have detected various gamma-ray sources, and prominent among them are several well-known AGN, like Mrk 421, Mrk 501, 1ES 1959+650, 1ES 0647+250, PG 1553+113, etc. We report on light curves and energy spectra reconstructed out of these observations down to energies of tens of GeV, which is very close to the energy threshold of LST-1. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Active Galactic Nuclei

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Phage therapy

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2025年第1期5卷 1页

作者： [Anonymous] Human Microbiome Research Program Department of Bacteriology and Immunology Faculty of Medicine University of Helsinki Helsinki Finland European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Non-traditional Antibacterial Therapy (ESGNTA) Basel Switzerland Institute of Biomedical and Oral Research (IBOR) Faculty of Dental Medicine Hebrew University of Jerusaleum Jerusalem Israel The Israeli Phage Center (IPTC) of the Hebrew University and Hadassah Medical Center Jerusalem Israel Department of Clinical Microbiology and Infectious Diseases Hadassah–Hebrew University Medical Center Jerusalem Jerusalem Israel Laboratory of Gene Technology Department of Biosystems KU Leuven Leuven Belgium Leicester Centre for Phage Research Department of Genetics and Genome Biology University of Leicester Leicester UK Center for Evolutionary Hologenomics Globe Institute University of Copenhagen Copenhagen Denmark Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) AIMST University Bedong Kedah Malaysia Laboratory of Phage Molecular Biology Institute of Immunology and Experimental Therapy Wrocław Poland Wrocław University of Science and Technology Faculty of Medicine Wrocław Poland Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Laboratory for Molecular and Cellular Technology Queen Astrid Military Hospital Brussels Belgium Department of Biological and Environmental Science Nanoscience Center University of Jyväskylä Jyväskylä Finland Chemical Engineering Department Loughborough University Loughborough UK

Bacteriophage (phages) are viruses that exclusively use bacterial cells for propagation, killing the bacterial host in the process. In phage therapy, phages are used to reduce bacterial numbers, thereby curing bacterial infections. Although this principle is conceptually straightforward, its practical application faces several hurdles. In this Primer, the practical aspects of phage therapy are outlined. We introduce the microbiological methods used to prepare and characterize phages and elucidate their interactions with bacteria. The discussion covers how the information in complete phage genome sequences is used, along with how RNA sequencing can enhance our understanding of phage biology. Selection parameters for therapeutic phages for clinical applications and key elements in industrial-scale phage production are provided. A summary of clinical trials both past and present, phage administration and dosing issues is analysed, as well as limitations associated with phage therapy and mitigation strategies. Finally, we speculate on the future of phage therapy.

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Synthetic peptides targeting a mast cell-specific G protein-coupled receptor MRGPRB2 display anti-infective potential

Cell Biomaterials

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Cell Biomaterials 2025年

作者： Aetas Amponnawarat Marcelo D.T. Torres Rakesh Krishnan Cesar de la Fuente-Nunez Hydar Ali Department of Basic and Translational Sciences School of Dental Medicine University of Pennsylvania Philadelphia PA USA Department of Family and Community Dentistry Faculty of Dentistry Chiang Mai University Chiang Mai Thailand Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia PA USA Department of Chemistry School of Arts and Sciences University of Pennsylvania Philadelphia PA USA Penn Institute for Computational Science University of Pennsylvania Philadelphia PA USA

Antibiotics are urgently needed against drug-resistant bacterial infections. We report synthetic peptides with dual antimicrobial and immunomodulatory action. These peptides activate the human Mas-related G protein-coupled receptor-X2 (MRGPRX2) and its mouse ortholog, Mas-related G protein-coupled receptor-B2 (MRGPRB2), while directly killing bacteria. Unlike traditional MRGPRX2 agonists, these peptides contain protease-resistant D -amino acids, enhancing their stability. In a mouse infection model, the peptides demonstrated antimicrobial efficacy, and activated human MRGPRX2 but not its G165E missense variant. They also stimulated tumor necrosis factor alpha (TNF-α), C-C motif chemokine ligand 3 (CCL3), and interleukin-8 (IL-8) release in human mast cells via MRGPRX2 and triggered degranulation in mouse mast cells via MRGPRB2. The peptides increased vascular permeability, promoted neutrophil and monocyte recruitment, and cleared S. aureus infections in wild-type but not Mrgprb2 −/− mice. These findings suggest that synthetic peptides can target G protein-coupled receptors (GPCRs), leveraging mast cell immunomodulation to treat antibiotic-resistant infections. This study opens avenues for designing peptides and other molecules against infections by targeting GPCRs expressed in mast cells.

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HRnV-Calc: A software package for heart rate n-variability and heart rate variability analysis

arXiv

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arXiv 2021年

作者： Niu, Chenglin Guo, Dagang Hock Ong, Marcus Eng Koh, Zhi Xiong Wah Ho, Andrew Fu Lin, Zhiping Liu, Chengyu Clifford, Gari D. Liu, Nan Duke-NUS Medical School National University of Singapore Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Instrument Science and Engineering Southeast University Nanjing China Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Objective: Heart rate variability (HRV) has been proven to be an important indicator of physiological status for numerous applications. Despite the progress and active developments made in HRV metric research over the last few decades, the representation of the heartbeat sequence upon which HRV is based has received relatively little attention. The recently introduced heart rate n-variability (HRnV) offers an alternative to R-to-R peak interval representations which complements conventional HRV analysis by considering HRV behavior on varying scales. Although HRnV has been shown to improve triage in pilot studies, there is currently no open and standard software to support future research of HRnV and its broader clinical applications. We aimed to develop an open, reliable, and easy to use software package implementing HRnV for further research and improvements of HRnV. This package has been designed to facilitate collaborative investigations between clinicians and researchers to study HRnV in various contexts and applications. Approach: We developed an open-source software, HRnV-Calc, based on the PhysioNet Cardiovascular Signal Toolbox (PCST), which features comprehensive graphical user interfaces (GUIs) for HRnV and HRV analysis. Main results: While preserving the core functionalities and performance of PCST, HRnV-Calc enables step-by-step manual inspection and configuration of HRV and HRnV analysis, so that results can be debugged, easily interpreted, and integrated to downstream applications. Significance: The open-source HRnV-Calc software, an accessible and standardized HRV and HRnV analysis platform, enhances the scope of HRV assessment and is designed to assist in future improvements and applications of HRnV and related research. © 2021, CC BY.

关键词： Heart

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