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Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos(t)ide analogue therapy

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JHEP Reports 2025年第1期7卷 101229-101229页

作者： Wang, Tingyan Campbell, Cori Stockdale, Alexander J. Todd, Stacy McIntyre, Karl Frankland, Andrew Jaworski, Jakub Glampson, Ben Papadimitriou, Dimitri Mercuri, Luca Mayer, Erik Jones, Christopher R. Salih, Hizni Roadknight, Gail Little, Stephanie Noble, Theresa Várnai, Kinga A. Davis, Cai Heinson, Ashley I. George, Michael Borca, Florina English, Louise Romão, Luis Ramlakhan, David Barnes, Eleanor Matthews, Philippa C. Gelson, William Cooke, Graham S. Khakoo, Salim I. Nastouli, Eleni Davies, Jim Woods, Kerrie Ryder, Stephen Elsharkawy, Ahmed Easom, Nicholas Bernal, William Ahmad, Shazaad Macdonald, Douglas Stanworth, Simon Maynard, Suzanne MacNaughton, Heidi Kim, Yun Meneses, Josune Olza Roberts, Timothy Rogers, Heather Roberts, Lara Higgins, Finola Vilar, Javier Norris, Ruth Tilston, George Serafimova, Ilina Montague, Sarah Verheyden, Juliette Juurlink, Irene Jack, Kathryn Waldren-Glenn, Alex Poole, Lizzie Day, Victoria Reglar, Berit NIHR Oxford Biomedical Research Centre Oxford United Kingdom Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Clinical Infection Microbiology and Immunology Institute of Infection Veterinary and Ecological Sciences University of Liverpool Liverpool United Kingdom Tropical Infectious Diseases Unit Royal Liverpool Hospital Liverpool University Hospitals NHS Trust Liverpool United Kingdom Liverpool Clinical Laboratories Liverpool University Hospitals NHS Trust Liverpool United Kingdom Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom iCARE Secure Data Environment Digital Collaboration Space Imperial College Healthcare NHS Trust London United Kingdom Department of Surgery and Cancer Imperial College London London United Kingdom Department of Infectious Disease Imperial College London London United Kingdom NIHR Health Informatics Collaborative Oxford University Hospitals NHS Foundation Trust Oxford United Kingdom Southampton Emerging Therapies and Technologies Centre University Hospital Southampton NHS Foundation Trust Southampton United Kingdom Clinical Informatics Research Unit Faculty of Medicine University of Southampton Southampton United Kingdom NIHR University College London Hospitals Biomedical Research Centre London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Infection Immunity and Inflammation UCL Great Ormond Street Institute of Child Health London United Kingdom Department of Virology UCLH London United Kingdom School of Clinical and Experimental Sciences Faculty of Medicine University of Southampton Southampton United Kingdom Cambridge Liver Unit Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom Faculty of Medicine Department of Infectious Disease Imperial College London United Kingdom The Francis Crick Institute London United Kingdom Division of Infection and Immunity University College Lon

Background & Aims: The dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos(t)ide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes. Methods: Utilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care research Health Informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase, and with liver fibrosis/cirrhosis. Results: We retrieved data from 1,885 adults on NA treatment (median follow-up 6.2 years, IQR 3.7–9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5–17). Five VL classes were identified from the derivation cohort (n = 1,367, discrimination: 0.93, entropy: 0.90): class 1 ‘long term suppression’ (n = 827, 60.5%), class 2 ‘timely virological suppression’ (n = 254, 18.6%), class 3 ‘persistent moderate viraemia’ (n = 140, 10.2%), class 4 ‘persistent high-level viraemia’ (n = 44, 3.2%), and class 5 ‘slow virological suppression’ (n = 102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n = 518). Alanine transaminase decreased variably over time in VL-suppressed groups (classes 1, 2, 5;all p <0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had twofold increased hazards of fibrosis/cirrhosis compared with class 1 (adjusted hazard ratio, 2.00;95% CI, 1.33–3.02). Conclusions: Heterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression. Impact and implications: Treatment recommendations f

关键词： Antiviral treatment Cirrhosis HBV Health Informatics Collaborative (HIC) Latent class mixed models Liver fibrosis Longitudinal Nucleotide analogue Viral load

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NFDI4Energy Task Area 4: FAIR data for Energy System research

NFDI4Energy Task Area 4: FAIR Data for Energy System Researc...

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Wein, Amanda Reinkensmeier, Jan Weidlich, Anke Lilliestam, Johan Hagenmeyer, Veit Richter, Mascha Auer, Sören Nieße, Astrid Lehnhoff, Sebastian OFFIS E.V. RandD Division Energy Escherweg 2 Oldenburg26121 Germany Friedrichstr. 39 Freiburg79110 Germany Friedrich-Alexander-Universität Erlangen-Nörnberg Lehrstuhl for Sustainability Transition Policy Lange Gasse 20 Nörnberg90403 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Kaiserstr. 89 Karlsruhe76131 Germany Reiner Lemoine Institut GGmbH Research Unit Transformation of Energy Systems Rudower Chaussee 12 Berlin12489 Germany Technische Informationsbibliothek Data Science and Digital Libraries Research Group Welfengarten 1 B Hannover30167 Germany Carl von Ossietzky Universität Oldenburg Department for Informatik Ammerländer Heerstr. 114-118 Oldenburg26129 Germany

ISBN: (纸本)9783885797319

The NFDI4Energy consortium will create a research data infrastructure for energy system research, emphasizing the openness and FAIRness of data and models in this research domain. Within the consortium, Task Area 4 focuses on the development of resources and services that will provide a semantic layer for the overall platform built by NFDI4Energy. The team of this Task Area will produce artifacts including a domain ontology, metadata standards, a knowledge graph, a Persistent Identifier service, and integration infrastructure to join these artifacts to the NFDI4Energy platform. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Metadata

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Power aware scheme for water pipeline monitoring based on Wireless Sensor Networks

Power aware scheme for water pipeline monitoring based on Wi...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Manel Elleuchi Manel Boujelben Mohamed Abid Abdulfattah M. Obeid Mohammed S. BenSaleh Multimedia and Digital Data Processing (CETIC) National School of Engineers of Sfax ResearchCenter in Informatics Sfax Tunisia CES research unit National School of Engineers of Sfax Research Center in Informatics Multimedia and Digital Data Processing (CETIC) Technopark Sfax Tunisia National Center for Electronics Communications and Photonics King Abdulaziz City for Science and Technology Riyadh Kingdom of Saudi Arabi King Abdulaziz City for Science And Technology Riyadh Al Riyadh Province SA

ISBN: (纸本)9781467387101

Recently, using Wireless Sensor Networks (WSNs) is increasing in many areas, whether scientific, logistic, military, health and water monitoring. Water is among the vital material transported by pipeline systems. Any leakage in the pipe can cause major financial losses and possible environmental damages. Therefore, it is necessary to have a real time leak detection system. WSNs can play an important role to effectively detect the leak and minimize the water loss. Sensor size, however, is a significant limitation in terms of energy autonomy. In fact, the battery is very small and in general, it is not replaceable or rechargeable. Therefore, many studies are currently focusing on the management of the energy consumed by sensor nodes. In this paper, we propose a new energy efficient scheme for leak detection in water pipeline based on WSNs. The main goal of our proposal is to solve the problem of losing water and ameliorate network lifetime by reducing energy needs. The proposed scheme is composed of three tasks: deployment of sensors, routing and replacement of failed nodes. Simulations results validate the choice of the best cluster based routing protocol which will be executed in the second task of the proposed scheme.

关键词： Monitoring Routing Wireless sensor networks Logic gates Color Clustering algorithms Algorithm design and analysis

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Cocreating the Visualization of digital Mobility Outcomes: Delphi-Type Process With Patients

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JMIR Formative research 2025年 9卷 e68782页

作者： Lumsdon, Jack Wilson, Cameron Alcock, Lisa Becker, Clemens Benvenuti, Francesco Bonci, Tecla van den Brande, Koen Brittain, Gavin Brown, Philip Buckley, Ellen Caruso, Marco Caulfield, Brian Cereatti, Andrea Delgado-Ortiz, Laura Del Din, Silvia Evers, Jordi Garcia-Aymerich, Judith Gaßner, Heiko Gur Arieh, Tova Hansen, Clint Hausdorff, Jeffrey M. Hiden, Hugo Hume, Emily Kirk, Cameron Maetzler, Walter Megaritis, Dimitrios Rochester, Lynn Scott, Kirsty Sharrack, Basil Sutton, Norman Vereijken, Beatrix Vogiatzis, Ioannis Yarnall, Alison Keogh, Alison Cantu, Alma Population Health Sciences Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom School of Clinical Medicine Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne United Kingdom NIHR Newcastle Biomedical Research Centre Newcastle University The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne United Kingdom Robert Bosch Society for Medical Research Stuttgart Germany Digital Geriatrics Unit Medical Centre University of Heidelberg Heidelberg Germany Mobilise-D Patient and Public Advisory Group Newcastle Upon Tyne United Kingdom School of Mechanical Aerospace and Civil Engineering University of Sheffield Sheffield United Kingdom Insigneo Institute for Silico Medicine University of Sheffield Sheffield United Kingdom Department of Neuroscience Sheffield NIHR Translational Neuroscience BRC Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom Sheffield Institute for Translational Neuroscience University of Sheffield Sheffield United Kingdom The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom Division of Clinical Medicine University of Sheffield Sheffield United Kingdom Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Insights Centre Data Analytics University College Dublin Dublin Ireland School of Public Health Physiotherapy and Sports Science University of College Dublin Dublin Ireland Barcelona Institute for Global Health Barcelona Spain Department of Medicine and Life Sciences Pompeu Fabra University Barcelona Spain Center for Biomedical Research Madrid Spain McRoberts BV The Hague Netherlands Department of Molecular Neurology University Hospital Erlangen Erlangen Germany Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany Departmen

Background: Recent technological advances in wearable devices offer new potential for measuring mobility in real-world contexts. Mobilise-D has validated digital mobility outcomes to provide novel outcomes and end points in clinical research of 4 different long-term health conditions (Parkinson disease, multiple sclerosis, chronic obstructive pulmonary disease, and proximal femoral fracture). These outcomes also provide unique information that is important to patients;however, there is limited literature that explores the optimal methods to achieve this, such as the best way to visualize patients’ data. Objective: This study aimed to identify meaningful outcomes for each condition and how to best visualize them from the perspective of end users. Methods: Using a Delphi-type protocol with patients as subject matter experts, we gathered iterative feedback on the cocreation of visualizations through 3 rounds of questionnaires. An open-ended questionnaire was used in round 1 to understand what aspects of mobility were most influenced by their health condition. These responses were mapped onto relevant digital mobility outcomes and walking experiences and then prioritized for visualization. Using patient responses, we worked alongside researchers, clinicians, and a patient advisory group to develop visualizations that depicted a week of mobility data. During rounds 2 and 3, participants rated usefulness and ease of understanding on a 5-point Likert scale and provided unstructured feedback in comment boxes for each visualization. Visualizations were refined using the feedback from round 2 before receiving further feedback in round 3. Results: Participation varied across rounds 1 to 3 (n=48, n=79, and n=78, respectively). Round 1 identified important outcomes and contexts for each health condition, such as walking speed and stride length for people with Parkinson disease or multiple sclerosis and number of steps for people with chronic obstructive pulmonary disease or prox

关键词： cocreation data visualization digital mobility outcomes mobility wearable devices

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data provenance and integrity of health-care systems data for clinical trials

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The Lancet digital Health 2022年第8期4卷 e567-e568页

作者： Murray, Macey L Love, Sharon B Carpenter, James R Hartley, Suzanne Landray, Martin J Mafham, Marion Parmar, Mahesh K B Pinches, Heather Sydes, Matthew R Institute of Clinical Trials and Methodology University College London LondonWC1V 6LJ United Kingdom Health Data Research UK London United Kingdom NHS DigiTrials Programme NHS Digital Leeds United Kingdom Data Services Directorate NHS Digital Leeds United Kingdom Medical Statistics London School of Hygiene and Tropical Medicine University of London London United Kingdom Clinical Trial Service Unit and Epidemiological Studies Unit University of Oxford Oxford United Kingdom Nuffield Department of Population Health University of Oxford Oxford United Kingdom British Heart Foundation Data Science Centre Health Data Research UK London United Kingdom

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MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

MultiplEYE: Creating a multilingual eye-tracking-while-readi...

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Proceedings of the 2025 Symposium on Eye Tracking research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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Representing Multimorbid Disease Progressions Using Directed Hypergraphs

SSRN

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SSRN 2023年

作者： Burke, Jamie Bailey, Rowena Akbari, Ashley Fasusi, Kevin Lyons, Ronan A. Pearson, Jonathan Rafferty, James Schofield, Daniel School of Mathematics College of Science and Engineering University of Edinburgh EdinburghEH9 3FD United Kingdom Population Data Science Medical School Faculty of Medicine Health & Life Science Swansea University SwanseaSA1 8PP United Kingdom Swansea Trials Unit Medical School Faculty of Medicine Health & Life Science Swansea University SwanseaSA1 8PP United Kingdom Digital Analytics and Research Team Transformation Directorate NHS England and NHS Improvement LeedsLS2 7UE United Kingdom

Objective: To introduce directed hypergraphs as a novel tool for assessing the temporal relationships between coincident diseases, addressing the need for a more accurate representation of multimorbidity and leveraging the growing availability of electronic healthcare databases and improved computational ***: Directed hypergraphs offer a high-order analytical framework that goes beyond the limitations of directed graphs in representing complex relationships such as multimorbidity. We apply this approach to multimorbid disease progressions observed from two multimorbidity sub-cohorts of the SAIL databank, after having been filtered according to the Charlson and Elixhauser comorbidity indices, respectively. After constructing a novel weighting scheme based on disease prevalence, we demonstrate the power of these higher-order models through the use of PageRank centrality to detect and classify the temporal nature of conditions within the two comorbidity ***: In the Charlson population, we found that chronic pulmonary disease (CPD), cancer and diabetes were conditions observed early in a patient's disease progression (predecessors), with stroke and dementia appearing later on (successors) and myocardial infarction acting as a transitive condition to renal failure and congestive heart failure. In Elixhauser, we found renal failure, neurological disorders and arrhythmia were classed as successors and hypertension, depression, CPD and cancer as predecessors, with diabetes becoming a transitive condition in the presence of obesity and alcohol abuse. The dynamics of these and other conditions changed across age and sex but not across deprivation. Unlike the directed graph, the directed hypergraph could model higher-order disease relationships, which translated into stronger classifications between successor and predecessor conditions, alongside the removal of spurious ***: This study underscores the utility of directed hypergraphs as

关键词： Directed graphs

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Making Epidemiological and Clinical Studies FAIR Using the Example of COVID-19

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Datenbank-Spektrum 2024年第2期24卷 117-128页

作者： Pigeot, Iris Ahrens, Wolfgang Darms, Johannes Fluck, Juliane Golebiewski, Martin Hahn, Horst K. Hu, Xiaoming Intemann, Timm Kasbohm, Elisa Kirsten, Toralf Klammt, Sebastian Klopfenstein, Sophie Anne Ines Lassen-Schmidt, Bianca Peters, Manuela Sax, Ulrich Waltemath, Dagmar Schmidt, Carsten Oliver Leibniz Institute for Prevention Research and Epidemiology—BIPS Bremen Germany University Bremen Department of Mathematics and Informatics Bremen Germany —Information Centre for Life Sciences Bonn Germany University of Bonn Institute of Geodesy and Geoinformation Bonn Germany Heidelberg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Greifswald University Medicine Institute for Community Medicine Greifswald Germany University of Leipzig Medical Center Institute for Medical Data Science Leipzig Germany —KKS-Network e. V Berlin Germany Berlin Institute of Health at Charité—Universitätsmedizin Berlin Core Unit eHealth and Interoperability Berlin Germany University Medical Center Göttingen Department of Medical Informatics Göttingen Germany Greifswald University Medicine Department of Medical Informatics Greifswald Germany

FAIRification of personal health data is of utmost importance to improve health research and political as well as medical decision-making, which ultimately contributes to a better health of the general population. Despite the many advances in information technology, several obstacles such as interoperability problems remain and relevant research on the health topic of interest is likely to be missed out due to time-consuming search and access processes. A recent example is the COVID-19 pandemic, where a better understanding of the virus’ transmission dynamics as well as preventive and therapeutic options would have improved public health and medical decision-making. Consequently, the NFDI4Health Task Force COVID-19 was established to foster the FAIRification of German COVID-19 studies. This paper describes the various steps that have been taken to create low barrier workflows for scientists in finding and accessing German COVID-19 research. It provides an overview on the building blocks for FAIR health research within the Task Force COVID-19 and how this initial work was subsequently expanded by the German consortium National research data Infrastructure for Personal Health data (NFDI4Health) to cover a wider range of studies and research areas in epidemiological, public health and clinical research. Lessons learned from the Task Force helped to improve the respective tasks of NFDI4Health. © The Author(s) 2024.

关键词： COVID-19

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Targeted Next Generation Sequencing Reveals Actionable Variants As Potential Therapeutic Targets In Vulvar Melanoma: A Case Study Analysis

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Simona Maria Fragomeni Alex Federico Alice Dal Miglio Angelo Minucci Alessia Perrucci Luciano Giaco' Mina Karimi Giulia Bravetti Tina Pasciuto Ernesto Rossi Giovanni Schinzari Ketty Peris Alessando Di Stefani Angela Santoro Valentina Iacobelli Camilla Nero Giovanni Scambia Giorgia Garganese Unità di Ginecologia Oncologica Dipartimento Scienze della Salute della Donna del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Sezione Ginecologia e Ostetricia Dipartimento Universitario Scienze della Vita e Sanità Pubblica Università Cattolica del Sacro Cuore Rome Italy Genomics Research Core facility Gemelli Science and Technology Park (G-STeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Bioinformatics Research Core Facility Gemelli Science and Technology Park (G-STeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Data Collection Core Facility Gemelli Science and Technology Park (G-STeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Medical Oncology Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Medical Oncology Università Cattolica del Sacro Cuore Rome Italy UOC di Dermatologia Dipartimento di Scienze Mediche e Chirurgiche Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Dipartimento di Medicina e Chirurgia Traslazionale Università Cattolica del Sacro Cuore Rome Italy Unità Operativa Complessa Anatomia Patologica Generale Dipartimento di Scienze Della Salute Della Donna del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Unità operativa di Chirurgia degli Organi Genitali Esterni Femminili Divisione di Ginecologia Oncologica Dipartimento Scienze della Salute della Donna del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS； Gemelli Women Health Center for Digital and Personalized Medicine Sezione Ginecologia e Ostetricia Dipartimento Universitario Scienze della Vita e Sanità Pubblica Università Cattolica del Sacro Cuore Rome Italy Gemelli Women Health Center for Digital and Personalized Medicine Dipartimento Scienze della Vita e Sanità Pubblica all’Università Cattolica del Sacro Cuore Rome Italy

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China Kidney Disease Network (CK-NET) 2017–2018 Annual data Report

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Kidney International Supplements 2025年第1期14卷 e1-e133页

作者： Zhang, Luxia Zhao, Ming-Hui Zuo, Li Zhang, Hong Wang, Yue Wang, Haibo Yu, Feng Yang, Chao Gao, Bixia Chu, Hong Gan, Lanxia Guo, Qi Hao, Jianguo He, Daijun Hong, Shenda Li, Chenglong Li, Pengfei Long, Jianyan Mao, Huijuan Qin, Yingying Shi, Ying Sun, Xiaoyu Tang, Wen Wang, Fulin Wang, Jinwei Wang, Wanzhou Wei, Shaoqing Wen, Fengyu Yao, Xingchen Yang, Guang Yang, Ling Ye, Jianhua Yuan, Qiongjing Zhang, Dongliang Zhang, Feifei Zhang, Ping Zhang, Zhilong Zhao, Xinju Zhou, Zhiye Renal Division Department of Medicine Peking University First Hospital Peking University Institute of Nephrology Beijing China National Institute of Health Data Science at Peking University Beijing China Advanced Institute of Information Technology Peking University Zhejiang Hangzhou China Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases Chinese Academy of Medical Sciences Beijing China Center for Digital Health and Artificial Intelligence Peking University First Hospital Beijing China State Key Laboratory of Vascular Homeostasis and Remodeling Peking University Beijing China Peking-Tsinghua Center for Life Sciences Beijing China Department of Nephrology Peking University People's Hospital Beijing China Department of Nephrology Peking University Third Hospital Beijing China Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangdong Guangzhou China Department of Nephrology Peking University International Hospital Beijing China Renal Division Department of Medicine Peking University First Hospital Peking University Institute of Nephrology Beijing China China Standard Medical Information Research Center Guangdong Shenzhen China Renal Division Department of Medicine Peking University First Hospital Kidney Disease Center The First Affiliated Hospital College of Medicine Zhejiang University Zhejiang Hangzhou China Clinical Trial Unit The First Affiliated Hospital of Sun Yat-sen University Guangdong Guangzhou China Department of Nephrology The First Affiliated Hospital of Nanjing Medical University Nanjing Medical University Jiangsu Nanjing China Science at Peking University Beijing China Department of Nephrology General Hospital of Ningxia Medical University Ningxia Yinchuang China Department of Nephrology Xiangya Hospital Central South University Hunan Changsha China Department of Nephrology Beijing Jishuitan Hospital Beijing China

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