Mathematical Modeling of Influenza Severity in Outbred Mice

远交小鼠流感严重程度的数学模型

• 批准号: 10308106
• 负责人: John F Alcorn
• 金额: $ 18.98万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-11-25 至 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10308106
• 关键词: Address; Adolescent; Adult; Age; Algorithms; Animals; Bioinformatics; Biological; Biological Markers; Blood; Cause of Death; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Child; Childhood; Clinical; Complex; Computer Models; Cytokine Receptors; Data; Disease; Exposure to; Flow Cytometry; Future; Gender; Goals; Healthcare Systems; Histology; Hospitalization; Human; Immune; Immune response; Immune system; Immunity; Immunologics; Infection; Inflammation Mediators; Inflammatory; Influenza; Influenza A Virus, H1N1 Subtype; Integration Host Factors; Interferons; Intervention; Knowledge; Laboratories; Life; Lung; Lung immune response; Machine Learning; Mediating; Methods; Modeling; Molecular; Molecular Profiling; Molecular Target; Morbidity - disease rate; Mus; Outcome; Pathogenesis; Pathologic; Pathway interactions; Patient Care; Patients; Pattern recognition receptor; Persons; Phenotype; Physiology; Pilot Projects; Pneumonia; Population; Positioning Attribute; Process; Proteins; Publishing; Reporting; Research Design; Seasons; Serum; Severities; Signal Transduction; Supportive care; Testing; United States; Vaccines; Viral Load result; Virus; age group; base; biomarker selection; candidate marker; clinical decision-making; cohort; cost; cytokine; disease phenotype; high risk; immunopathology; infection rate; influenza infection; influenza pneumonia; juvenile animal; lung injury; mathematical methods; mathematical model; mortality; mouse model; novel; novel marker; pandemic disease; pandemic influenza; pathogen; potential biomarker; predictive marker; predictive modeling; pulmonary function; response; transcriptome; transcriptomics; translational study; vaccine access

PROJECT SUMMARY In the United States, pulmonary influenza infection occurs annually in 5-20% of the population with mortality in the range of 30,000 deaths. The recent 2009 influenza H1N1 pandemic illustrated the potential for higher infection rates, which were reported to be as high as 45% in certain age groups. The 2017-18 influenza season had the highest pediatric mortalities since the 2009 pandemic. Influenza infection is known to result in a broad spectrum of disease phenotypes in humans, although severe pneumonia is relatively rare. Despite this, severe disease often requires advanced supportive care in the young, including previously healthy children. Host factors involved in determining the outcome of influenza infection are unclear and children are known to be at higher risk of severe disease. First life exposure to influenza is also thought to dictate life-long immunity. Little is known about the effects of young age and gender on influenza responses and severity. This underscores the importance of understanding influenza pathogenesis in a pediatric population. Influenza pathogenesis is likely mediated in large part by exuberant inflammatory host responses in the lung. It is likely that predictive soluble inflammatory mediators are present in severe infection. Further, predictive biomarkers or mathematical models of influenza pneumonia severity would enhance clinical decision making and patient care. We propose that machine learning and mathematical modeling of host immune endpoints will define a molecular fingerprint of severe influenza pneumonia in juveniles. This hypothesis will be tested in two Aims. Aim 1 will focus on characteristic molecular pathways related to influenza severity in juvenile animals, using outbred mice. We will utilize machine learning and new mathematical approaches for pathway and biomarker selection. Aim 2 will test mathematical models of influenza pathogenesis to elucidate new mechanisms that drive lung injury. The overall goal of the proposed study is to identify novel biomarkers and mechanistic models of influenza pneumonia severity that can be applied to children. To accomplish this we will use a broad, exploratory, and unbiased approach. Candidate biomarkers and pathways would then be evaluated in future mechanistic and translational studies in mice and humans.

项目总结 在美国，每年有5%-20%的人口感染肺部流感，#年死亡。 死亡人数为3万人。最近的2009年甲型H1N1流感大流行说明了更高的 感染率，据报道在某些年龄段高达45%。2017-18年度流感季节 有自2009年大流行以来最高的儿科死亡率。已知流感感染会导致广泛的 人类的疾病表型谱，尽管严重肺炎相对罕见。尽管如此，严重的 疾病通常需要对年轻人进行高级支持性护理，包括以前健康的儿童。寄主 决定流感感染结果的因素尚不清楚，已知儿童处于 患严重疾病的风险更高。第一次接触流感也被认为决定了终身免疫力。一点儿 已知年轻和性别对流感反应和严重程度的影响。这突显了 了解儿童人群中流感发病机制的重要性。流感的发病机制可能是 在很大程度上是由肺部旺盛的炎性宿主反应介导的。很可能是可预测的可解决的 炎症介质在严重感染中存在。此外，预测性生物标志物或数学模型 了解流感肺炎的严重程度将加强临床决策和患者护理。我们建议 机器学习和宿主免疫终点的数学建模将定义一个分子 青少年重症流感肺炎的指纹图谱。这一假设将在两个目标上得到检验。目标1 将重点关注与幼年动物流感严重程度相关的特有分子途径，使用近交系 老鼠。我们将利用机器学习和新的数学方法来选择途径和生物标记物。 目标2将测试流感发病机制的数学模型，以阐明驱动肺的新机制 受伤。拟议研究的总体目标是确定新的生物标志物和发病机制模型。 流感肺炎的严重程度，可适用于儿童。为了实现这一点，我们将使用广泛的， 探索性的、不偏不倚的方法。候选生物标志物和途径将在未来进行评估 在老鼠和人类身上进行的机械学和翻译研究。