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arXiv 2025年

作者： Lopez, Ivan Haredasht, Fateme Nateghi Caoili, Kaitlin Chen, Jonathan H. Chaudhari, Akshay Stanford University School of Medicine StanfordCA United States Department of Biomedical Data Science StanfordCA United States Center for Biomedical Informatics Research StanfordCA United States The Ohio State University College of Medicine ColumbusOH United States Division of Hospital Medicine Stanford University School of Medicine StanfordCA United States Clinical Excellence Research Center Stanford School of Medicine StanfordCA United States Department of Medicine StanfordCA United States Department of Radiology StanfordCA United States Cardiovascular Institute StanfordCA United States

Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to the need for high-quality data and expert annotators. Synthetic data generation offers an alternative, though pre-trained models may not capture the syntactic diversity of clinical notes. We propose an embedding-driven approach that uses diversity sampling from a small set of real clinical notes to guide large language models in few-shot prompting, generating synthetic text that better reflects clinical syntax. We evaluated this method using the CheXpert dataset on a classification task, comparing it to random few-shot and zero-shot approaches. Using cosine similarity and a Turing test, our approach produced synthetic notes that more closely align with real clinical text. Our pipeline reduced the data needed to reach the 0.85 AUC cutoff by 40% for AUROC and 30% for AUPRC, while augmenting models with synthetic data improved AUROC by 57% and AUPRC by 68%. Additionally, our synthetic data was 0.9 times as effective as real data, a 60% improvement in value. © 2025, CC BY.

关键词： data assimilation

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The development of a blockchain-based business model simulator with an application to civic medical data management

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Digital Business 2025年第1期5卷

作者： Cunningham, James Davies, Nigel Devaney, Sarah Harding, Mike Holm, Søren Neumann, Victoria Ainsworth, John Division of Informatics Imaging & Data Sciences The University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester University Hospitals NHS Foundation Trust Manchester Academic Health Science Centre Manchester United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom Centre for Social Ethics and Policy The University of Manchester Manchester United Kingdom Centre for Medical Ethics University of Oslo Oslo Norway

Blockchain technology is reshaping business models by altering how transactions and data integrity are managed. This shift necessitates evaluating long-term profitability and operational efficiency across various blockchain frameworks. Our research describes the development and application of a business model simulator, beginning with use case analysis, followed by the simulator's design and application to the Civic data Identity Partnership project. We developed a transaction model, ran simulations, and analyzed outcomes to identify key cost drivers and assess the viability of different blockchain architectures. The results highlight the influence of execution costs, legal frameworks, and blockchain selection on financial outcomes, providing insights into the scalability and sustainability of blockchain applications in real-world scenarios. © 2024

关键词： Blockchain Business models Civic data Simulation

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TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools

arXiv

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arXiv 2025年

作者： Gao, Shanghua Zhu, Richard Kong, Zhenglun Noori, Ayush Su, Xiaorui Ginder, Curtis Tsiligkaridis, Theodoros Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Cardiovascular Division Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States MIT Lincoln Laboratory LexingtonMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Precision therapeutics require multimodal adaptive models that generate personalized treatment recommendations. We introduce TXAGENT, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge retrieval across a toolbox of 211 tools to analyze drug interactions, contraindications, and patient-specific treatment strategies. TXAGENT evaluates how drugs interact at molecular, pharmacokinetic, and clinical levels, identifies contraindications based on patient comorbidities and concurrent medications, and tailors treatment strategies to individual patient characteristics, including age, genetic factors, and disease progression. TXAGENT retrieves and synthesizes evidence from multiple biomedical sources, assesses interactions between drugs and patient conditions, and refines treatment recommendations through iterative reasoning. It selects tools based on task objectives and executes structured function calls to solve therapeutic tasks that require clinical reasoning and cross-source validation. The TOOLUNIVERSE consolidates 211 tools from trusted sources, including all US FDA-approved drugs since 1939 and validated clinical insights from Open Targets. TXAGENT outperforms leading LLMs, tool-use models, and reasoning agents across five new benchmarks: DrugPC, BrandPC, GenericPC, TreatmentPC, and DescriptionPC, covering 3,168 drug reasoning tasks and 456 personalized treatment scenarios. It achieves 92.1% accuracy in open-ended drug reasoning tasks, surpassing GPT-4o by up to 25.8% and outperforming DeepSeek-R1 (671B) in structured multi-step reasoning. TXAGENT generalizes across drug name variants and descriptions, maintaining a variance of © 2025, CC BY.

关键词： Drug discovery

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Respiratory syncytial virus vaccine effectiveness among US veterans, September, 2023 to March, 2024: a target trial emulation study

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The Lancet Infectious Diseases 2025年第6期25卷 625-633页

作者： Bajema, Kristina L Yan, Lei Li, Yuli Argraves, Stephanie Rajeevan, Nallakkandi Fox, Alexandra Vergun, Robert Berry, Kristin Bui, David Huang, Yuan Lin, Hung-Mo Hynes, Denise M Lucero-Obusan, Cynthia Schirmer, Patricia Cunningham, Francesca Huang, Grant D Aslan, Mihaela Ioannou, George N Veterans Affairs Portland Health Care System Portland OR United States Division of Infectious Diseases Department of Medicine Oregon Health & Science University Portland OR United States Veterans Affairs Cooperative Studies Program Clinical Epidemiology Research Center Veterans Affairs Connecticut Health Care System West Haven CT United States Department of Biostatistics Yale School of Public Health New Haven CT United States Biomedical Informatics & Data Science Yale School of Medicine New Haven CT United States Department of Internal Medicine Yale School of Medicine New Haven CT United States Seattle Epidemiologic Research and Information Center Veterans Affairs Puget Sound Health Care System Seattle WA United States Research and Development Veterans Affairs Puget Sound Health Care System Seattle WA United States Center of Innovation to Improve Veteran Involvement in Care Veterans Affairs Portland Health Care System Portland OR United States Health Management and Policy College of Health Oregon State University Corvallis OR United States Health Data and Informatics Program Center for Quantitative Life Sciences Oregon State University Corvallis OR United States Public Health National Program Office Veterans Health Administration Palo Alto CA United States Public Health National Program Office Veterans Health Administration Washington DC United States Veterans Affairs Center for Medication Safety—Pharmacy Benefit Management Services Hines IL United States Office of Research and Development Veterans Health Administration Washington DC United States Division of Gastroenterology Veterans Affairs Puget Sound Health Care System and University of Washington Seattle WA United States

Background: New respiratory syncytial virus (RSV) vaccines have been approved in the USA for the prevention of RSV-associated lower respiratory tract disease in adults aged 60 years and older. Information on the real-world effectiveness of these vaccines is needed. Methods: We used electronic health records in the Veterans Health Administration to emulate a target trial comparing a single dose of a recombinant stabilised prefusion F protein RSV vaccine versus no vaccination among veterans aged 60 years and older. We matched eligible vaccine recipients with up to four unvaccinated individuals in four monthly nested sequential trials from Sept 1 to Dec 31, 2023. Outcomes were ascertained up to March 31, 2024. The primary outcome was any positive RSV test from day 14 following the matched index date. Secondary outcomes included hospitalisation and emergency department or urgent care encounter occurring within 1 day before or after a positive RSV test. We estimated vaccine effectiveness as 100 × (1 – risk ratio). Findings: We included 146 852 vaccinated individuals matched to 582 936 unique control individuals, weighted equally to represent 146 852 individuals. Across the two groups, 276 039 (94·0%) of 293 704 veterans were male, 17 665 (6·0%) were female, and median age was 75·9 years (IQR 71·7–79·7). Over a median follow-up of 124 days (IQR 102–150), the incidence rate of documented RSV infection was 1·7 (95% CI 1·4–2·1) events per 1000 person-years (88 total events) in the vaccinated group and 7·3 (6·6–8·1) per 1000 person-years in the unvaccinated group (372 total events), and vaccine effectiveness was estimated as 78·1% (72·6–83·5). Among the secondary outcomes, vaccine effectiveness was estimated at 78·7% (72·2–84·8) against RSV-associated emergency department or urgent care encounters, and 80·3% (65·8–90·1) against RSV-associated hospitalisation. Interpretation: RSV vaccination was effective in preventing RSV-related illness, including associated health-care use,

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Complex wound analysis using AI

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Computers in Biology and Medicine 2025年 190卷 109945-109945页

作者： Robinson, Connor J. Dickie, Bruce Lindner, Claudia Herrera, Jeremy Dingle, Lewis Reid, Adam J. Wong, Jason K.F. Hiebert, Paul Cootes, Timothy F. Kurinna, Svitlana Division of Cell Matrix Biology & Regenerative Medicine School of Biological Sciences FBMH The University of Manchester M13 9PT United Kingdom Division of Informatics Imaging & Data Sciences School of Health Sciences FBMH The University of Manchester M13 9PT United Kingdom Department of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Plastic Surgery & Burns Wythenshawe Hospital Manchester University NHS Foundation Trust Manchester Academic Health Science Centre ManchesterM23 9LT United Kingdom Biomedical Institute for Multimorbidity Centre for Biomedicine Hull York Medical School The University of Hull HU6 7RX United Kingdom

Impaired wound healing is a significant clinical challenge. Standard wound analysis approaches are macroscopic, with limited histological assessments that rely on visual inspection of haematoxylin and eosin (H&E)-stained sections of biopsies. The analysis is time-consuming, requires a specialist trained to recognise various wound features, and therefore is often omitted in practice. We present an automated deep-learning (DL) approach capable of objectively and comprehensively analysing images of H&E-stained wound sections. Our model has a deep neural network (DNN) architecture, optimised for segmentation of characteristic wound features. We employed our model for the first-time analysis of human complex wounds. Histologically, human wounds are extremely variable, which presented a challenge when segmenting the different tissue classes. To validate our approach, we used mouse wound biopsy images across four timepoints of healing and employed the same DNN architecture for training and analysis in this context (89 % mean test set accuracy). We revised our approach for human complex wounds, analysing the biopsies at a cellular level, where our model performance improved (97 % mean test set accuracy). Together, our approach allows: (i) comprehensive analysis of human wound biopsy images;(ii) in-depth analysis of key features of mouse wound healing with accurate morphometric analysis and;(iii) analysis and quantification of immune cell infiltration, to aid clinical diagnosis of human complex wounds. © 2025 The Authors

关键词： Biopsy

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P061 Clinical prediction models for adverse events in rheumatic and musculoskeletal conditions: a systematic review

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Rheumatology 2025年第SUPPLEMENT_3期64卷

作者： Diomatari, Christina Jenkins, David Martin, Glen Jani, Meghna Centre for Epidemiology Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Manchester Academic Health Science Centre NIHR Manchester Biomedical Research Centre Manchester NHS Foundation Trust Manchester United Kingdom Centre for Health Informatics Division of Informatics Imaging and Data Sciences University of Manchester Manchester United Kingdom

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Characterization of a new fluorescence lifetime imaging ophthalmoscope

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ACTA OPHTHALMOLOGICA 2024年第S279期102卷

作者： Nycz, Julia Link, Dietmar Klemm, Matthias Klee, Sascha Haueisen, Jens Institute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems Austria

Aims/Purpose: Fluorescence lifetime imaging ophthalmoscopy (FLIO) allows in vivo measurement of autofluorescence intensity decays of endogenous fluorophores in the ocular fundus. So far, only devices from Heidelberg Engineering based on the Spectralis system have been used in FLIO research. Here, we present and characterize a new FLIO device based on the RETImap system from Roland Consult. Methods: The device is based on a confocal scanning laser ophthalmoscope (35° field, 512 × 512 px). A ps diode laser (BDL-SMN 473 nm, Becker & Hickl GmbH, Berlin, Germany) excites autofluorescence. The fluorescence photons are split into a short (498–560 nm, SSC) and a long (560–720 nm, LSC) spectral channel (one HPM-100-40 detector [Becker & Hickl GmbH] each) and are detected by time-correlated single photon counting (SPC-160, Becker & Hickl GmbH). We determined the maximum laser power (ILT2400, International Light Technologies, Inc. Peabody, MA, USA) and analysed the instrument's behaviour at three different laser power levels (150 μW, 200 μW and max.) in terms of laser spectrum (CAS140CT, Instrument Systems GmbH, Munich, Germany) and instrument response function (IRF). The IRF was determined using a 25 μM Eosin Y solution, mixed with a 5 M solution of potassium iodide, placed in a flat cuvette (110-OS, Hellma GmbH & Co. KG, Müllheim, Germany) in front of the objective lens of the FLIO device. Fluorescence measurements of approximately 1-min duration were performed three times for all three laser powers. The IRF and the full width at half maximum (FWHM) were calculated using FLIMX software ( *** ). Results: The max. laser power was 280 μW. The peak wavelengths of the laser spectra were 467.6 (150 μW), 467.9 (200 μW) and 468.0 nm (280 μW). IRF FWHM in SSC were 298.6 ± 1.1 ps (150 μW), 341.0 ± 2.5 ps (200 μW) and 347.5 ± 6.0 ps (280 μW). In LSC, the IRF FWHM were 290.4 ± 3.8 ps (150 μW), 344.0 ± 3.4 ps (200 μW) and 358.8 ± 1.3 ps (280 μW). Results are mean ± standard devi

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The spatial zonation of the murine placental vasculature is specified by epigenetic mechanisms

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Developmental Cell 2025年第10期60卷 1467-1482.e8页

作者： Gehrs, Stephanie Jakab, Moritz Gutjahr, Ewgenija Gu, Zuguang Weichenhan, Dieter Mallm, Jan-Philipp Mogler, Carolin Schlesner, Matthias Plass, Christoph Schlereth, Katharina Augustin, Hellmut G. Division of Vascular Oncology and Metastasis German Cancer Research Center (DKFZ) Heidelberg 69120 Germany European Center for Angioscience (ECAS) Medical Faculty Mannheim Heidelberg University Mannheim 68167 Germany Faculty of Biosciences Heidelberg University Heidelberg 69120 Germany Institute of Pathology University Clinic Heidelberg Heidelberg 69120 Germany Computational Oncology Group Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Cancer Epigenomics German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and Bioquant Heidelberg 69120 Germany Institute of Pathology TUM School of Medicine Technical University of Munich Munich 80333 Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg 86159 Germany

The labyrinthian fetoplacental capillary network is vital for proper nourishment of the developing embryo. Dysfunction of the maternal-fetal circulation is a primary cause of placental insufficiency. Here, we show that the spatial zonation of the murine placental labyrinth vasculature is controlled by flow-regulated epigenetic mechanisms. Spatiotemporal transcriptomic profiling identified a gradual change in the expression of epigenetic enzymes, including the de novo DNA methyltransferase 3a (DNMT3A). Loss of Dnmt3a resulted in DNA hypomethylation and perturbation of zonated placental gene expression. The resulting global DNA hypomethylation impaired the angiogenic capacity of endothelial cells. Global or endothelium-predominant deletion of Dnmt3a resulted in impaired placental vascularization and fetal growth retardation (FGR). Human placental endothelial gene expression profiling associated preeclampsia with reduced DNMT3A expression. Collectively, our study identified DMNT3A as critical methylome-regulator of placental endothelial gene expression and function with clinical implications for placental dysfunction, as it occurs during preeclampsia or FGR. © 2024 The Authors

关键词： angiogenesis DNA methylation DNMT3A epigenetic regulation growth retardation placental endothelium spatial zonation

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Ophthalmology. Retina 2025年第5期9卷 e40-e40页

作者： Cindy X Cai Akihiko Nishimura George Hripcsak Wilmer Eye Institute Johns Hopkins School of Medicine Baltimore Maryland Biomedical Informatics and Data Science Division of General Internal Medicine Department of Medicine Johns Hopkins University School of Medicine Baltimore Maryland. Electronic address: ccai6@jhmi.edu. Department of Biostatistics Johns Hopkins University Bloomberg School of Public Health Baltimore Maryland. Department of Biomedical Informatics Columbia University New York New York.

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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