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Virtual Patient Cohorts to Illuminate Immunologic Drivers of Influenza Severity

虚拟患者队列阐明流感严重程度的免疫驱动因素

基本信息

批准号：
10628017
负责人：
Morgan Craig
金额：
$ 60.14万
依托单位：
UNIVERSITY OF TENNESSEE HEALTH SCI CTR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10628017
关键词：
Address Affect African American Age Animals Automobile Driving Biological Biological Factors Biological Markers CD8B1 gene COVID-19 Calibration Cells Cessation of life Clinical Computer Models Computing Methodologies Correlation Studies Cytomegalovirus Data Disease Disease Management Disease Outcome Disease Progression Dose Economic Burden Environmental Risk Factor Future Genetic Goals Health Heritability Heterogeneity Human Immune Immune response Immunity Immunological Models Immunologics Immunology Individual Infection Inflammation Inflammatory Influenza Influenza vaccination Infrastructure Integration Host Factors Interleukin-6 Knowledge Lung Macrophage Mathematics Methods Modeling Morbidity - disease rate Mus Natural Immunity Outcome Parainfluenza Virus Infections Pathogenicity Patient-Focused Outcomes Pattern Population Process Public Health Pulmonary Pathology Research Respiratory Tract Infections Seasons Serology Severities Severity of illness Source South American T-Lymphocyte Vaccination Validation Variant Viral Virus Vitamin A Deficiency Work clinical examination cohort comorbidity coronavirus disease cytokine digital twin experimental study human data immunopathology improved in silico in vivo influenza infection influenzavirus monocyte mortality mouse model neutrophil pathogen predictive marker predictive modeling respiratory virus response risk prediction seasonal influenza sex success time use tool virtual patient

项目摘要

Project Summary Influenza viruses result in a significant number of illnesses and deaths each year highlighting its health and economic burden. Management of this disease is difficult, and little is known about how different host factor contribute to heterogenous outcomes. To advance the goals of understanding the diverse immune responses to influenza and predict risk, it is essential to develop new tools that can define individualized immune trajectories, simultaneously account for multiple sources of heterogeneity, and accurately predict dynamics that drive disease progression. This project addresses gaps in identifying the impact that host factors have disease outcome and gaps in developing computational methods for respiratory infections that accurately predict inflammation and disease severity. The studies will develop and exploit new predictive systemic immune models and simulate human populations using virtual patient cohorts aims at differentiating clinical outcomes and identify downstream effects of varying levels of basal immunity.

项目摘要流感病毒每年导致大量的疾病和死亡，突出了其健康和经济负担。这种疾病的管理是困难的，而且很少有人知道不同的宿主因素导致异质性结果。为了推进理解不同免疫反应的目标，流感和预测风险，至关重要的是开发新的工具，可以定义个性化的免疫轨迹，同时解释异质性的多种来源，并准确预测驱动疾病的动力学进展该项目旨在解决在确定宿主因素对疾病结果的影响方面存在的差距，在开发准确预测炎症的呼吸道感染计算方法方面存在差距，疾病严重程度。这些研究将开发和利用新的预测性系统免疫模型，使用虚拟患者队列的人群旨在区分临床结果并识别下游不同水平的基础免疫力。