Predictive Modeling of Influenza-Pneumococcal Coinfection

流感-肺炎球菌混合感染的预测模型

• 批准号: 10409791
• 负责人: Amber M Smith
• 金额: $ 38.86万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10409791
• 关键词: Address; Alveolar Cell; Alveolar Macrophages; Automobile Driving; Bacterial Infections; Biological; Cells; Cessation of life; Clinical; Clinical Data; Complex; Data; Development; Disease; Disease Management; Dose; Drug resistance; Epithelial; Epithelial Cells; Feedback; Goals; Human; Immune; Immune response; Immunologics; Infection; Inflammation; Inflammatory; Influenza; Influenza A virus; Influenza prevention; Innate Immune Response; Integration Host Factors; Interferon Type I; Interferon-alpha; Interferon-beta; Interferons; Investigation; Kinetics; Knowledge; Laboratories; Mathematics; Mediating; Microbiology; Modeling; Morbidity - disease rate; Outcome; Patients; Pneumonia; Production; Public Health; Regulation; Research; Risk; Role; Secondary to; Severity of illness; Source; Streptococcus pneumoniae; Testing; Therapeutic; Therapeutic Agents; Time; TimeLine; Vaccines; Viral; Viral Load result; Virus; Virus Diseases; Work; antimicrobial; co-infection; combat; comorbidity; immune function; improved; in silico; in vivo; infection risk; influenza outbreak; insight; mathematical model; mortality; novel; novel therapeutics; pandemic disease; pathogen; predictive modeling; reconstitution; response; synergism; therapeutically effective; tool; viral rebound

Influenza A virus (IAV) and secondary bacterial infections (SBI) are responsible for a significant number of illnesses and deaths each year. Management of these diseases is difficult, in part due to a lack of understanding of complex interplay of host-pathogen interactions and inability to study pneumonia in clinical settings. To advance the goal of developing effective therapeutics and predicting IAV and SBI risk, new microbiologic tools that can assess how host immune responses work to limit viral burden and enhance bacterial invasion in quantitative detail is essential. This project addresses gaps in immunological knowledge of IAV and SBIs and gaps in developing predictive models and interpreting infection data by using a tandem mathematical-experimental approach to quantify alveolar macrophage loss (Aim 1) and SBI related type I interferon exacerbation (Aim 2). These studies will exploit the predictive models to establish the intricate feedbacks in these responses, identify controlling parameters and dynamics that govern different clinical outcomes, improve interpretation of immunological and clinical data, and reveal new targets for treatment and prevention of influenza and related bacterial infections.

甲型流感病毒（IAV）和继发性细菌感染（SBI）是造成大量死亡的原因