Immunologic and Predictive Features of MIS-C

新型SARS冠状病毒（SARS-CoV-2）引起严重肺炎样冠状病毒 疾病（COVID-19）。SARS-CoV-2感染了超过1.7亿人， 3.5迄今为止，全世界有100万人。如果其他方面健康，儿童被认为是主要的 免于SARS-CoV-2疾病。然而，在SARS-CoV-2感染率高的地区， 一些儿童在SARS-CoV后4-6周开始到儿科重症监护室就诊， 2例川崎病样感染。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的特征性临床特征变得越来越清楚，病理生理学仍然是 未知在这里，我们建议在急性和急性期评估三个独立的MIS-C队列， 在临床、遗传和免疫水平上，使用最新的 技术.我们将不仅进行MIS-C的系统免疫定位， 控制，而且还利用机器学习算法来描绘如何最好地预测，诊断， 和结果分层MIS-C。我们期待发现免疫学和遗传学特征， 可以帮助我们评估MIS-C发展，诊断和预后的风险。总之，我们的 MIS-C的临床和免疫特征的系统分析和计算建模 不仅有助于阐明这种综合征的发病机制，而且还将为我们提供 用于疾病风险、诊断和进展的可行生物标志物。

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.