Immunologic and Predictive Features of MIS-C

MIS-C 的免疫学和预测特征

基本信息

批准号：
10667530
负责人：
Dusan Bogunovic
金额：
$ 57.43万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
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：
2022-07-18 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10667530
关键词：
2019-nCoV Acute Adult Age Algorithms Antibody Repertoire Area Autoantibodies Biological Biological Assay Biological Markers Blood COVID-19 COVID-19 mortality COVID-19 pandemic Cells Cellular Indexing of Transcriptomes and Epitopes by Sequencing Characteristics Child Child Development Childhood Clinical Computer Models Critical Care Data Data Collection Data Set Development Diagnosis Disease Disease Outbreaks Functional disorder Genetic Genomics Immune Immunologics Immunophenotyping Individual Infection Inflammation Inflammatory Influenza A virus Influenza B Virus Institutional Review Boards Life London Machine Learning Maps Measurement Modeling Mononuclear Morbidity - disease rate Mucocutaneous Lymph Node Syndrome Multisystem Inflammatory Syndrome in Children New York City Outcome Pathogenesis Patients Phase Pneumonia Prognosis Publishing Relative Risks Reporting Respiratory Disease Risk Risk Assessment SARS coronavirus SARS-CoV-2 antibody SARS-CoV-2 infection SARS-CoV-2 positive Sampling Serology Shock Site Spain Surface Symptoms Syndrome Technology Time Virus World Health Organization biomarker identification clinical center cohort comparison control coronavirus disease disease diagnosis disease prognosis disorder risk early childhood exome sequencing follow-up improved infection rate long-term sequelae machine learning algorithm machine learning method novel pathogen pediatric patients post SARS-CoV-2 infection recruit risk prediction risk stratification risk variant single-cell RNA sequencing

项目摘要

The novel SARS coronavirus (SARS-CoV-2) causes the severe pneumonia-like coronavirus disease (COVID-19). SARS-CoV-2 infected over 170 million individuals and has claimed over 3.5 million lives worldwide to date. If otherwise healthy, children were thought to be largely spared from SARS-CoV-2 disease. However, in areas of high SARS-CoV-2 infection rates, some children started presenting to pediatric critical care units 4-6 weeks following SARS-CoV- 2 infection with Kawasaki-like disease. Clinically, we now know that this is a distinct disease, which was recently termed - multisystem inflammatory syndrome in children (MIS-C). While the characteristic clinical features of MIS-C are becoming clear, the pathophysiology remains unknown. Here we propose to evaluate three independent cohorts of MIS-C during acute and convalescent phases of disease at clinical, genetic and immunologic levels using the latest technology. We will not only perform systemic immunological mapping of MIS-C as compared to controls, but also utilize machine learning algorithms to delineate how best to predict, diagnose and outcome stratify MIS-C. We anticipate discovering immunologic and genetic features which can aid us in assessing risks of MIS-C development, diagnosis and prognosis. In summary, our systematic analysis and computational modeling of the clinical and immune features of MIS-C will not only help illuminate the pathogenesis of this syndrome, but will also provide us with actionable biomarkers for disease risk, diagnosis and progression.

新型SARS冠状病毒（SARS-COV-2）引起严重的肺炎冠状病毒疾病（Covid-19）。 SARS-COV-2感染了超过1.7亿个人，并声称迄今为止，全球350万人居住。如果其他健康，则认为儿童在很大程度上是免于SARS-COV-2疾病。但是，在高SARS-COV-2感染率的地区，在SARS-COV - 2感染川崎样疾病。临床上，我们现在知道这是一种独特的疾病，最近被称为儿童多系统炎症综合征（MIS -C）。而 MIS-C的特征临床特征越来越清楚，病理生理学仍然存在未知。在这里，我们建议在急性期间评估三个独立的MIS-C队列使用最新技术。与控件，但还利用机器学习算法来描述如何最好地预测，诊断结果分层MIS-C。我们预计会发现免疫和遗传特征可以帮助我们评估MIS-C开发，诊断和预后的风险。总而言之，我们的 MIS-C的临床和免疫特征的系统分析和计算建模不仅会帮助阐明该综合征的发病机理，还将为我们提供可行的生物标志物，用于疾病风险，诊断和进展。