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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xingjian Wang Rufei Li Jing Fang Shaoping Wang Changhong Lin Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

There are many limitations in passive ankle prosthesis which are widely used so far. Meanwhile succeed commercial product has not been push out yet although the study of powered ankle prosthesis is ongoing. One of the main reason is that it is difficult to integrate the mechanism with limited weight, which have enough instantaneous power to promote the body forward. In this paper, a powered ankle prosthesis driven by EHA (Electro-Hydraulic Actuator) technique is proposed to solve this problem. It is mainly composed of peripheral mechanism and embedded hydraulic system which contains accumulator, plunger pump, motor, solenoid valve, miniature hydraulic circuit. The prototype with this structure is similar in quality and volume to a real ankle and is verified to match the true ankle performance.

关键词： Prosthetics Torque Legged locomotion Valves DC motors Foot

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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Controllability analysis of transcriptional regulatory networks for Saccharomyces cerevisiae 44

Controllability analysis of transcriptional regulatory netwo...

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44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

作者： Liu, Suling Wang, Pei Xu, Qiong Chen, Aimin Lü, Jinhu School of Mathematics and Statistics Henan University Kaifeng China School of Mathematics and Statistics Institute of Applied Mathematics Laboratory of Data Analysis Technology Henan University Kaifeng China School of Mathematics and Statistics Institute of Applied Mathematics Henan University Kaifeng China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data Brain Machine Intelligence Beihang University Beijing China

ISBN: (纸本)9781509066841

Structural controllability of complex networks has been a research focus in recent years. However, few works considered the structural controllability of biological networks, especially for dynamic biological networks. In this paper, structural controllability of one static and five dynamic transcriptional regulatory networks (TRNs) for Saccharomyces cerevisiae (S. cerevisiae) have been investigated. The five dynamic networks included two endogenous and three exogenous ones. We clarified the controllability properties of these networks, as well as explored the differences among different types of networks. Our results revealed that the structural properties of different types of networks are significantly different. We found that the TRNs are rather difficult to be fully controlled. However, the dynamic TRNs were relatively easier to be controlled than the static one, and one needs relatively more external inputs to control the exogenous TRNs than the endogenous ones. Based on the structural controllability analysis of subnetworks with the same sizes, as well as randomly perturbed networks by preserving degree distributions, we further illustrated that the structural controllability of the six networks may be mainly dominated by their degree distributions. Our investigations clarify the control properties of TRNs in S. cerevisiae, it provides some insights on real-world control of biological networks. © 2018 IEEE.

关键词： Complex networks

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Feasibility of multiomics tumor profiling for guiding treatment of melanoma

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Nature medicine 2025年 2025 May 27页

作者： Nicola Miglino Nora C Toussaint Alexander Ring Ximena Bonilla Marina Tusup Benedict Gosztonyi Tarun Mehra Gabriele Gut Francis Jacob Stephane Chevrier Kjong-Van Lehmann Ruben Casanova Andrea Jacobs Sujana Sivapatham Laura Boos Parisa Rahimzadeh Manuel Schuerch Bettina Sobottka Natalia Chicherova Shuqing Yu Rebekka Wegmann Julien Mena Emanuela S Milani Sandra Goetze Cinzia Esposito Jacobo Sarabia Del Castillo Anja L Frei Marta Nowak Anja Irmisch Jack Kuipers Monica-Andreea Baciu-Drăgan Pedro F Ferreira Franziska Singer Anne Bertolini Michael Prummer Ulrike Lischetti Rudolf Aebersold Marina Bacac Gerd Maass Holger Moch Michael Weller Alexandre P A Theocharides Markus G Manz Niko Beerenwinkel Christian Beisel Lucas Pelkmans Berend Snijder Bernd Wollscheid Viola Heinzelmann Bernd Bodenmiller Mitchell P Levesque Viktor H Koelzer Gunnar Rätsch Reinhard Dummer Andreas Wicki Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. NEXUS Personalized Health Technologies ETH Zurich Zurich Switzerland. SIB Swiss Institute of Bioinformatics Lausanne Switzerland. Swiss Data Science Center SDSC Zurich Switzerland. Department of Computer Science Institute of Machine Learning ETH Zurich Zurich Switzerland. Department of Dermatology University Hospital Zurich University of Zurich Zurich Switzerland. Department of Biomedicine University Hospital Basel and University of Basel Basel Switzerland. Department of Quantitative Biomedicine University of Zurich Zurich Switzerland. Department of Biology RWTH Aachen Aachen Germany. Department of Pathology and Molecular Pathology University of Zurich and University Hospital Zurich Switzerland. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. Department of Health Sciences and Technology ETH Zurich Zurich Switzerland. ETH PHRT Swiss Multi-Omics Center (SMOC) ETH Zurich Zurich Switzerland. Department of Molecular Life Sciences University of Zurich Zurich Switzerland. Roche Pharmaceutical Research and Early Development Roche Innovation Center Zurich Switzerland. Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland. Roche Diagnostics GmbH MWG Penzberg Germany. Department of Neurology University Hospital and University of Zurich Zurich Switzerland. Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland. Biomedical Informatics University Hospital Zurich Zurich Switzerland. AI Center at ETH Zurich ETH Zurich Zurich Switzerland. Department of Biology ETH Zurich Zurich Switzerland. Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. andreas.wicki@usz.ch.

There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project. Nine independent technologies, mostly at single-cell level, were used to analyze 126 patient samples, generating up to 500 Gb of data per sample (40,000 potential markers) within 4 weeks. Among established and experimental markers, the molecular tumor board selected 54 to inform its treatment recommendations. In 75% of cases, TuPro-based data were judged to be useful in informing recommendations. Patients received either standard of care (SOC) treatments or highly individualized, polybiomarker-driven treatments (beyond SOC). The objective response rate in difficult-to-treat palliative, beyond SOC patients (n = 37) was 38%, with a disease control rate of 54%. Progression-free survival of patients with TuPro-informed therapy decisions was 6.04 months, (95% confidence interval, 3.75-12.06) and 5.35 months (95% confidence interval, 2.89-12.06) in ≥third therapy lines. The proof-of-concept TuPro project demonstrated the feasibility and relevance of omics-based tumor profiling to support data-guided clinical decision-making. *** identifier: NCT06463509 .

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Developments and Applications of data Deidentification Technology under Big data

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Journal of Electronic science and Technology 2017年第3期15卷 231-239页

作者： Hung-Li Chen Yao-Tung Tsou Bo-Chen Tai Szu-Chuang Li Yen-Nun Huang Chia-Mu Yu Yu-Shian Chiu Department of Communications Engineering Feng Chia University Taichung 400 Research Center for Information Technology Innovation Academia Sinica Taipei 100 Department of Computer Science and Engineering National Chung Hsing University Data Analytics Technology &Applications Institute for Information Industry Taipei 100

In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive *** instance,healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate *** such,privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big *** a strict and verifiable definition of privacy,differential privacy has attracted noteworthy attention and widespread research in recent *** this study,we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection ***,we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.

关键词： Index Terms--Deidentification differential privacy.

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Emergence of a Complete Heavy-Quark Spin Symmetry Multiplet: Seven Molecular Pentaquarks in Light of the Latest LHCb Analysis

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Physical Review Letters 2019年第24期122卷 242001-242001页

作者： Ming-Zhu Liu Ya-Wen Pan Fang-Zheng Peng Mario Sánchez Sánchez Li-Sheng Geng Atsushi Hosaka Manuel Pavon Valderrama School of Physics and Nuclear Energy Engineering Beihang University Beijing 100191 China Centre d’Études Nucléaires CNRS/IN2P3 Université de Bordeaux 33175 Gradignan France Beijing Key Laboratory of Advanced Nuclear Materials and Physics and Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing 100191China Research Center for Nuclear Physics (RCNP) Osaka University Ibaraki 567-0047 Japan Advanced Science Research Center Japan Atomic Energy Agency (JAEA) Tokai 319-1195 Japan International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China

A recent analysis by the LHCb Collaboration suggests the existence of three narrow pentaquarklike states—the Pc(4312), Pc(4440), and Pc(4457)—instead of just one in the previous analysis [the Pc(4450)]. The closeness of the Pc(4312) to the D¯Σc threshold and the Pc(4440) and Pc(4457) to the D¯*Σc threshold suggests a molecular interpretation of these resonances. We show that these three pentaquarklike resonances can be naturally accommodated in a contact-range effective field theory description that incorporates heavy-quark spin symmetry. This description leads to the prediction of all the seven possible S-wave heavy antimeson-baryon molecules [that is, there should be four additional molecular pentaquarks in addition to the Pc(4312), Pc(4440), and Pc(4457)], providing the first example of a heavy-quark spin symmetry molecular multiplet that is complete. If this is confirmed, it will not only give us an impressive example of the application of heavy-quark symmetries and effective field theories in hadron physics, it will also uncover a clear and powerful ordering principle for the molecular spectrum, reminiscent of the SU(3)-flavor multiplets to which the light hadron spectrum conforms.

关键词： Effective field theory Strong interaction Exotic baryons Heavy baryons Heavy mesons

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Operations & Supply Chain Management: Principles and Practice

arXiv

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arXiv 2025年

作者： Petropoulos, Fotios Akkermans, Henk Aksin, O. Zeynep Ali, Imran Babai, Mohamed Zied Barbosa-Povoa, Ana Battaïa, Olga Besiou, Maria Boysen, Nils Brammer, Stephen Brandon-Jones, Alistair Briskorn, Dirk Browning, Tyson R. Buijs, Paul Centobelli, Piera Chiarini, Andrea Cousins, Paul Cudney, Elizabeth A. Davies, Andrew Day, Steven J. de Koster, René Dekker, Rommert Denicol, Juliano Despeisse, Mélanie Disney, Stephen M. Dolgui, Alexandre Duong, Linh El-Qallali, Malek Fahimnia, Behnam Fakhredin, Fatemeh Gershwin, Stanley B. Ghamat, Salar Giannikas, Vaggelis Glock, Christoph H. Godsell, Janet Govindan, Kannan Hannibal, Claire Haug, Anders Hernaus, Tomislav Hsuan, Juliana Ivanov, Dmitry Jahre, Marianne Johansson, Björn Kalidoss, Madan Shankar Kanellopoulos, Argyris Kannan, Devika Karul, Elif Katsikopoulos, Konstantinos V. Kilic-Ararat, Ayse Begüm Kolisch, Rainer Koppenberg, Maximilian Kumar, Maneesh Kuo, Yong-Hong Kusiak, Andrew Lewis, Michael A. Lim, Stanley Frederick W.T. Limère, Veronique Liu, Jiyin Maghazei, Omid Marić, Matija Meissner, Joern Meuwissen, Miranda Micheli, Pietro Nanayakkara, Samudaya Özdemir, Bengü Nur Papadopoulos, Thanos Pavelin, Stephen Perera, Srinath Phillips, Wendy Prak, Dennis Pun, Hubert Qazi, Sharfah Ahmad Ramanathan, Usha Reiner, Gerald Reitsma, Ewout Roehrich, Jens K. Sanders, Nada R. Sarkis, Joseph Schmid, Nico André Schmidt, Christoph G. Schroeder, Andreas Selviaridis, Kostas Seuring, Stefan Shi, Chuan Son, Byung-Gak Spring, Martin Squire, Brian van der Valk, Wendy van Donk, Dirk Pieter van Houtum, Geert-Jan Wilhelm, Miriam Wynstra, Finn Zheng, Ting School of Management University of Bath Bath United Kingdom Department of Information Systems & Operations Management Tilburg School of Economics and Management Tilburg University Netherlands College of Administrative Sciences and Economics Koc University Turkey School of Business and Law Central Queensland University Australia Kedge Business School France Center for Management Studies Instituto Superior Técnico University of Lisbon Portugal Kühne Logistics University Hamburg Germany Friedrich-Schiller-Universität Jena Lehrstuhl für Operations Management Germany Bergische Universität Wuppertal Germany Neeley School of Business Texas Christian University Fort WorthTX United States Department of Operations University of Groningen Netherlands Department of Industrial Engineering University of Naples "Federico II" Italy Department of Management University of Verona Italy Management School University of Liverpool United Kingdom John E. Simon School of Business Maryville University of Saint Louis Missouri United States Science Policy Research Unit University of Sussex Business School Brighton United Kingdom School of Management Zhejiang University China Rotterdam School of Management Erasmus University Netherlands Erasmus School of Economics Erasmus University Netherlands Bartlett School of Sustainable Construction The Bartlett Faculty of the Built Environment University College London United Kingdom Department of Industrial and Material Sciences Chalmers University of Technology Sweden Center for Simulation Analytics and Modelling University of Exeter Business School United Kingdom IMT Atlantique LS2N-CNRS Nantes France College of Business and Law University of the West of England United Kingdom Rabdan Academy United Arab Emirates Institute of Transport and Logistics Studies The University of Sydney Business School Australia Kühne Logistics University Germany Department of Mechanical Engineering Massachusetts Institute of Technology Massachusetts United S

Operations and Supply Chain Management (OSCM) has continually evolved, incorporating a broad array of strategies, frameworks, and technologies to address complex challenges across industries. This encyclopedic article provides a comprehensive overview of contemporary strategies, tools, methods, principles, and best practices that define the field’s cutting-edge advancements. It also explores the diverse environments where OSCM principles have been effectively implemented. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. © 2025, CC BY.

关键词： Supply chain management

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Proctitis and Other Gastrointestinal Manifestations in Mpox Disease: A Systematic Review and Meta-Analysis

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JGH open : an open access journal of gastroenterology and hepatology 2025年第6期9卷 e70190页

作者： Prakasini Satapathy Abhay M Gaidhane Nasir Vadia Soumya V Menon Kattela Chennakesavulu Rajashree Panigrahi Sanjit Sah Suraj Tiwari S Govinda Rao Khang Wen Goh Rachana Mehta Muhammed Shabil Mahendra Singh Ganesh Bushi Center for Global Health Research Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India. Faculty of Data Science and Information Technology INTI International University Nilai Malaysia. Jawaharlal Nehru Medical College and Global Health Academy School of Epidemiology and Public Health Datta Meghe Institute of Higher Education Wardha India. Department of Pharmaceutical Sciences Marwadi University Research Center Faculty of Health Sciences Marwadi University Rajkot Gujarat India. Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to Be University) Bangalore Karnataka India. Department of Chemistry Sathyabama Institute of Science and Technology Chennai Tamil Nadu India. Department of Microbiology IMS and SUM Hospital Siksha 'O' Anusandhan (Deemed to Be University) Bhubaneswar Odisha India. Department of Paediatrics Dr. D. Y. Patil Medical College Hospital and Research Centre Dr. D. Y. Patil Vidyapeeth (Deemed-to-Be-University) Pune Maharashtra India. Department of Public Health Dentistry Dr. D.Y. Patil Dental College and Hospital Dr. D.Y. Patil Vidyapeeth (Deemed-to-Be-University) Pune Maharashtra India. SR Sanjeevani Hospital Siraha Nepal. Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India. Division of Research and Innovation Uttaranchal University Dehradun India. Department of Data Science Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India. Faculty of Mathematics and Natural Sciences Universitas Negeri Padang Padang Indonesia. Clinical Microbiology RDC Manav Rachna International Institute of Research and Studies Faridabad Haryana India. University Center for Research and Development Chandigarh University Mohali Punjab India. Medical Laboratories Techniques Department AL-Mustaqbal University Hillah Babil Iraq. Department of Biotechnology Graphic Era (Deemed to Be University) Dehradun India.

Background:Mpox, caused by the monkeypox virus (MPXV), is primarily recognized for its dermatologic and systemic symptoms. However, emerging evidence suggests a significant prevalence of gastrointestinal (GI) manifestations, particularly proctitis, diarrhea, nausea, vomiting, and abdominal pain. Despite the growing clinical recognition of these symptoms, their epidemiology and impact remain poorly understood. This systematic review and meta-analysis aim to quantify the prevalence of GI manifestations in Mpox patients and assess their clinical significance. Methods:A systematic review following PRISMA guidelines was conducted across PubMed, Embase, and Web of science, including quantitative studies published up until October 2024 that reported GI manifestations in Mpox patients. Screening and data extraction were performed using Nested Knowledge software, and study quality was assessed using the Newcastle-Ottawa Scale. Meta-analysis was conducted using R version 4.4, with heterogeneity evaluated via the statistic. Sensitivity analyses and publication bias were assessed using Doi plots and the Luis Furuya-Kanamori (LFK) index. Results:Out of 1229 records, 33 studies met the eligibility criteria, yielding a pooled prevalence of proctitis in Mpox patients at 24.75% (95% CI: 18.93%-31.04%) across 5878 participants, with high heterogeneity ( = 94.8%). The prediction interval for proctitis ranged from 1.46% to 61.76%. The pooled prevalence of other GI manifestations was 30.45% (95% CI: 18.27%-44.14%) across 2237 participants, with significant heterogeneity ( = 95.2%) and a prediction interval ranging from 0.00% to 85.28%. Sensitivity analyses confirmed the stability of these estimates, while publication bias was indicated by LFK index values exceeding 2.77. Conclusions:This meta-analysis highlights the substantial burden of GI manifestations in Mpox, particularly proctitis, with considerable variability across studies. The findings underscore the need for standardized

关键词： Mpox gastrointestinal manifestations meta‐analysis monkeypox virus proctitis systematic review

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Breadth of influenza A antibody cross-reactivity varies by virus isolation interval and subtype

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Nature microbiology 2025年 2025 Jun 10页

作者： Bingyi Yang Katelyn M Gostic Dillon C Adam Ru Zhang Tal Einav Liping Peng Sook San Wong Tim K Tsang Derek A T Cummings Sheena G Sullivan Sarah Cobey Benjamin J Cowling WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public Health Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong China. yangby@hku.hk. Department of Ecology and Evolution University of Chicago Chicago IL USA. WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public Health Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong China. Laboratory of Data Discovery for Health Limited Hong Kong Science and Technology Park New Territories Hong Kong China. Center for Vaccine Innovation La Jolla Institute for Immunology La Jolla CA USA. Department of Medicine University of California San Diego La Jolla CA USA. HKU-Pasteur Research Pole School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong China. Centre for Immunology and Infection Hong Kong Science Park New Territories Hong Kong China. Department of Epidemiology Johns Hopkins Bloomberg School of Public Health The Johns Hopkins University Baltimore MD USA. Department of Biomedical Engineering Johns Hopkins Whiting School of Engineering The Johns Hopkins University Baltimore MD USA. School of Clinical Sciences Monash University Clayton Australia. Department of Epidemiology University of California Los Angeles Los Angeles CA USA.

Influenza virus infections generate antibodies that cross-react with antigenically similar viruses. However, the breadth of cross-reactivity is unclear. Here we analysed 200,000 haemagglutination inhibition (HAI) titrations from ferrets singly infected with A(H3N2) or A(H1N1)pdm09. We identified consistent influenza A virus subtype-specific patterns where breadth of HAI antibody cross-reactivity was associated with isolation time between tested and immunizing viruses. This was independent of virus strains, passage history and genetic mutations. A 6-year interval between virus isolation resulted in minimal HAI titres for A(H3N2), while A(H1N1)pdm09 demonstrated broader cross-reactivity with moderate titre reductions over the same interval. Analysis of HA substitutions revealed more amino acid substitutions between viruses for A(H3N2) and a greater reduction in HAI titres per substitution compared with A(H1N1). Longitudinal analysis of human antisera across age groups suggests that repeat A(H3N2) exposure increased HAI responses within the cross-reactivity range, with greater increases for more recent viruses. These results provide a framework for antigenically evolving pathogens such as influenza viruses.

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