Predictive Modeling of Influenza-Pneumococcal Coinfection

流感-肺炎球菌混合感染的预测模型

基本信息

批准号：
10411579
负责人：
Amber M Smith
金额：
$ 0.86万
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：
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/10411579
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

项目摘要

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.

流感病毒（IAV）和次生细菌感染（SBI）负责大量每年的疾病和死亡。这些疾病的管理很困难，部分原因是了解宿主 - 病原体相互作用的复杂相互作用和无法研究肺炎的复杂相互作用临床环境。为了促进开发有效的治疗剂并预测IAV和SBI风险的目标，新的微生物工具可以评估宿主免疫反应如何限制病毒负担和定量细节增强细菌侵袭至关重要。该项目解决了免疫学的差距在开发预测模型和解释感染数据时，了解IAV和SBI的知识以及差距使用串联数学实验方法来量化肺泡巨噬细胞损失（AIM 1）和与SBI相关的I型干扰素加剧（AIM 2）。这些研究将利用预测模型在这些响应中建立复杂的反馈，确定控制参数和动态管理不同的临床结果，改善对免疫学和临床数据的解释，并揭示新的治疗和预防流感和相关细菌感染的靶标。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Validated Models of Immune Response to Virus Infection.

DOI：
10.1016/j.coisb.2018.10.005
发表时间：
2018-12-01
期刊：
Current opinion in systems biology
影响因子：
3.7
作者：
Smith, Amber M
通讯作者：
Smith, Amber M

COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes.

DOI：
10.1371/journal.ppat.1009753
发表时间：
2021-07
期刊：
PLoS pathogens
影响因子：
6.7
作者：
Jenner AL;Aogo RA;Alfonso S;Crowe V;Deng X;Smith AP;Morel PA;Davis CL;Smith AM;Craig M
通讯作者：
Craig M

Iterative community-driven development of a SARS-CoV-2 tissue simulator.