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Sex differences in proteomics of cardiovascular disease – Results from the Yale-CMD registry

IJC Heart and Vasculature 2025年 58卷 101667-101667页

作者： Liu, Yihan Wang, Zuoheng Collins, Sean P. Testani, Jeffery Safdar, Basmah Program in Computational Biology & Biomedical informatics Yale University New Haven CT United States Department of Biostatistics Yale University School of Public Health New Haven CT United States Department of Biomedical Informatics & Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Vanderbilt University Medical Center Veterans Affairs Tennessee Valley Healthcare System Geriatric Research Education and Clinical Center (GRECC) Nashville TN United States Section of Cardiovascular Medicine Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States

Aims This study assessed sex-specific proteomic profiles by cardiovascular disease (CVD) phenotype (coronary artery disease [CAD] vs coronary microvascular dysfunction [CMD]) and describe their role in sex-specific pathways. Methods: In a secondary biobank analysis of the Yale-CMD registry, adults with ischemic symptoms who underwent cardiac positron emission test/computed tomography were categorized as a) controls (normal coronary flow reserve (CFR) > 2 without perfusion defect or coronary calcification), b) having CMD (CFR < 2 without defect or calcification), or c) having CAD (known CAD or new perfusion defect). Using proximity extension assays (Olink® Explore 3072), we examined 2944 proteins. Differential protein expression was assessed using linear regression models, adjusting for age, race, body mass index, diabetes, dyslipidemia, hypertension, or smoking. Results: Of 190 patients, 91 provided blood samples (mean age, 56 years;66 %, females;48 %, controls;24 %, CAD;27 %, CMD). Among controls, 15 proteins showed sex differences (5 proteins upregulated in females, 10 in males;false discovery rate [FDR < 0.05]). Upregulated in CAD patients were FSHB in females and INSL3 and EDDM3B in males (FDR < 0.05). Among CMD patients, SCGB3A1 and HGFAC were higher in females;INSL3, SPINT3, EDDM3B, and KLK3 were higher in males (FDR < 0.05). Per pathway analysis, females showed upregulation of immune pathways in CAD and lipid and glucose metabolism pathways in CMD. Males showed upregulated endothelial regulation of blood flow in CAD and increased angiogenesis in CMD. Conclusions: Sex differences exist in the proteomic profiles of CAD and CMD patients, highlighting a need for precision medicine. © 2025

关键词： Coronary artery disease Coronary microvascular dysfunction Olink Precision medicine Proteomics Sex differences

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Integration of Clinical, Biological, and Computational Perspectives to Support Cerebral Autoregulatory Informed Clinical Decision Making Decomposing Cerebral Autoregulation using Mechanistic Timescales to Support Clinical Decision-Making

arXiv

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arXiv 2022年

作者： Briggs, Jennifer K. Stroh, J.N. Bennett, T.D. Park, S. Albers, D.J. Department of Bioengineering College of Engineering Design and Computing Aurora CO United States Section of Informatics and Data Science Department of Pediatrics University of Colorado School of Medicine AuroraCO United States Section of Critical Care Medicine Department of Pediatrics University of Colorado School of Medicine AuroraCO United States Department of Neurology Columbia University Irving Medical Center NewYork-Presbyterian Hospital New YorkNY10032 United States Department of Biomedical Informatics Columbia University Irving Medical Center NewYork-Presbyterian Hospital New YorkNY10032 United States

Adequate brain perfusion is required for proper brain function and life. Maintaining optimal brain perfusion to avoid secondary brain injury is one of the main concerns of neurocritical care. Cerebral autoregulation is responsible for maintaining optimal brain perfusion despite pressure derangements. Knowledge of cerebral autoregulatory function should be a key factor in clinical decision-making, yet it is often insufficiently and incorrectly applied. Multiple physiologic mechanisms impact cerebral autoregulation, each of which operate on potentially different and incompletely understood timescales confounding conclusions drawn from observations. Because of such complexities, clinical conceptualization of cerebral autoregulation has been distilled into practical indices defined by multimodal neuromonitoring, which removes mechanistic information and limits decision options. The next step towards cerebral autoregulatory-informed clinical decision-making is to quantify cerebral autoregulation mechanistically, which requires decomposing cerebral autoregulation into its fundamental processes and partitioning those processes into the timescales at which each operates. In this review, we scrutinize biologically, clinically, and computationally focused literature to build a timescales-based framework around cerebral autoregulation. We conclude that the myogenic mechanism acts on timescales faster than one second, the endothelial mechanism acts in one to two minutes, the metabolic mechanism will act on different timescales depending on the circumstance but will not be reflected in dynamic CA testing due to timescale constraints, and the neurologic mechanism acts within two seconds. These timescales have never been synthesized in the literature and lay the foundation for a new framework in which to view and test cerebral autoregulatory function. This new framework will allow us to quantify mechanistic interactions and directly infer which mechanism(s) are functioning based o

关键词： Decision making

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Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature

arXiv

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arXiv 2023年

作者： Zhou, Huixue Austin, Robin Lu, Sheng-Chieh Silverman, Greg Zhou, Yuqi Kilicoglu, Halil Xu, Hua Zhang, Rui Institute for Health Informatics University of Minnesota MinneapolisMN United States School of Nursing University of Minnesota MinneapolisMN United States The University of Texas MD Anderson Cancer Center Department of Symptom Research HoustonTX United States Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States Department of Pharmaceutical Care & Health Systems University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois Urbana-ChampaignIL United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven United States

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts. © 2023, CC BY-NC-SA.

关键词： Terminology

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Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

arXiv

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arXiv 2023年

作者： Yang, Rui Zeng, Qingcheng You, Keen Qiao, Yujie Huang, Lucas Hsieh, Chia-Chun Rosand, Benjamin Goldwasser, Jeremy Dave, Amisha D. Keenan, Tiarnan D.L. Chew, Emily Y. Radev, Dragomir Lu, Zhiyong Xu, Hua Chen, Qingyu Li, Irene Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Linguistics Northwestern University EvanstonIL United States Department of Computer Science Yale University New HavenCT United States Yale School of Public Health Yale University New HavenCT United States Yale New Haven Hospital Yale School of Medicine Yale University New HavenCT United States Division of Epidemiology and Clinical Applications National Eye Institute National Institutes of Health BethesdaMD United States National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD United States Section of Biomedical Informatics and Data Science Yale School of Medicine Yale University New HavenCT United States Information Technology Center University of Tokyo Tokyo Japan

This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. © 2023, CC0.

关键词： Machine learning

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Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

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Computer Methods and Programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition

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Generative Artificial Intelligence in Clinical Medicine and Impact on Gastroenterology

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Gastroenterology 2025年

作者： Ali Soroush Mauro Giuffrè Sunny Chung Dennis L Shung Division of Data-Driven and Digital Medicine (D3M) Icahn School of Medicine at Mount Sinai Henry D. Janowitz Division of Gastroenterology Icahn School of Medicine at Mount Sinai Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai. Section of Digestive Diseases Department of Medicine Yale School of Medicine. Section of Digestive Diseases Department of Medicine Yale School of Medicine Department of Biomedical Informatics & Data Science Yale School of Medicine. Electronic address: dennis.shung@yale.edu.

The pace of artificial intelligence (AI) integration into healthcare has accelerated with rapid advances in generative AI (GenAI). Gastroenterology and hepatology in particular will be transformed due to the multimodal workflows that integrate endoscopic video, radiologic imaging, tabular data, and unstructured note text. GenAI will impact the entire spectrum of clinical experience, from administrative tasks, diagnostic guidance and treatment recommendations. Unlike traditional machine learning approaches, GenAI is more flexible, with one platform able to be used across multiple tasks. Initial evidence suggests benefit in lower-level administrative tasks such as clinical documentation, medical billing and scheduling, and information tasks such as patient education and summarization of the medical literature. No evidence exists for GenAI solutions for more complex tasks relevant to clinical care, such as clinical reasoning for diagnostic and treatment decisions that may affect patient outcomes. Challenges of output reliability, data privacy, and useful integration remain, with potential solutions include robust validation, regulatory oversight, and "human-AI teaming" strategies to ensure safe, effective deployment. We remain optimistic in the potential of GenAI to augment clinical expertise due to the adaptability of GenAI to handle multiple data modalities to obtain and focus relevant information flows and the human-friendly interfaces that facilitate ease of use. We believe that the potential of GenAI for dynamic human-algorithmic interactions may allow for a degree of clinician-directed customization to enhance human presence.

关键词： AI Safety Gastroenterology Generative Artificial Intelligence Human-AI Collaboration

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Artificial Intelligence in Gastroenterology and Hepatology: Potential and Perils

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Gastroenterology 2025年

作者： Dennis L Shung Marietta Iacucci Section of Digestive Diseases Department of Medicine Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States. Electronic address: dennis.shung@yale.edu. Microbiome Ireland College of Medicine and Health University College Cork Mercy/Cork University Hospitals Cork Ireland. Electronic address: miacucci@ucc.ie.

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Factors Associated With Influenza Vaccination in a National Veteran Cohort

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AJPM Focus 2025年第1期4卷 100290-100290页

作者： Chen, Alissa S. Farmer, Melissa M. Han, Ling Runels, Tessa Bade, Brett Crothers, Kristina Bastian, Lori A. Bazan, Isabel S. Bean-Mayberry, Bevanne A. Brandt, Cynthia A. Akgün, Kathleen M. Section of General Medicine Department of Medicine Yale University School of Medicine New Haven Connecticut United States Center for Study of Healthcare Innovation Implementation and Policy (CSHIIP) VA Greater Los Angeles Healthcare System Los Angeles California United States Pain Research Informatics Multimorbidities and Education (PRIME) Center VA Connecticut Healthcare System West Haven CT United States Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Hempstead New York United States VA Puget Sound Seattle WA United States Division of Pulmonary Critical Care and Sleep Medicine University of Washington Seattle WA United States Pulmonary Critical Care & Sleep Medicine Yale University School of Medicine New Haven Connecticut United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles California United States Department of Emergency Medicine Yale University School of Medicine New Haven Connecticut United States Section of Biomedical Informatics and Data Science Yale University School of Medicine New Haven Connecticut United States

Introduction: Only 53% of American adults receive influenza vaccination, and disparities in vaccination exist among particular racial and ethnic groups. This study determines how race, ethnicity, sex, and rurality are associated with influenza vaccination adherence in a national Veteran Health Affairs Administration cohort. Methods: The authors examined differences in documented influenza vaccinations for the 2019–2020 influenza season among Veteran Health Affairs Administration patients in a retrospective cohort study using Veteran Health Affairs Administration administrative electronic health record data. The author used logistic regression to model receipt of influenza vaccination in association with race, ethnicity, sex, and rurality while controlling for clinical diagnoses, demographics, and ambulatory care utilization. The authors also stratified the models by sex and rurality. Results: Among 5,943,918 veterans, 48.6% received influenza vaccination. Unadjusted comparisons showed that those who were vaccinated were more likely to be White, to be of male sex, and to be older. Similar proportions of unvaccinated and unvaccinated veterans were from rural settings. In adjusted models, Black race was most strongly associated with decreased vaccination (AOR=0.69;95% CI=0.69, 0.70), and American Indian/Alaskan Native race also had reduced odds of vaccination (AOR=0.94;95% CI=0.92, 0.95) compared with White race. Female veterans had increased odds of vaccination (AOR=1.20;95% CI=1.19, 1.20) compared with men. Rurality (AOR=0.97;95% CI=0.96, 0.97) was associated with a small decreased odds of vaccination compared with urban. In stratified models, Black veterans were less likely to receive influenza vaccination regardless of sex and rurality than White veterans. American Indian/Alaska Native female veterans had equal odds of vaccination as White female veterans, whereas American Indian/Alaska Native male veterans had reduced odds of vaccination compared with White male v

关键词： Influenza vaccination vaccination coverage vaccine hesitancy veterans

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54712 Bipolar disorder among patients with atopic dermatitis: A case-control study in the All of Us research program

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Journal of the American Academy of Dermatology 2024年第3期91卷 AB147-AB147页

作者： Wride, Mitchel, BA Chen, Gloria, BA Fan, Ryan, MD Leasure, Audrey, MD, MHS Levey, Daniel, PhD Damsky, William, MD, PhD Cohen, Jeffrey, MD Yale School of Medicine New Haven CT Department of Dermatology Yale School of Medicine New Haven CT Department of Psychiatry Yale School of Medicine West Haven CT Veterans Affairs Connecticut Healthcare Center West Haven CT Department of Pathology Yale School of Medicine New Haven CT Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT

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Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

arXiv

引用

arXiv 2021年

作者： Metzler, Hannah Baginski, Hubert Niederkrotenthaler, Thomas Garcia, David Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Austria Unit Suicide Research & Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Austria Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Institute of Globally Distributed Open Research and Education Austria Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Background Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th

关键词： Learning algorithms

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