Severity Predictors Integrating salivary Transcriptomics and proteomics with Multi neural network Intelligence in SARS-CoV2 infection in Children (SPITS MISC)

摘要 儿童受2019年冠状病毒病（COVID-19）的影响不成比例地小 严重急性呼吸系统综合征冠状病毒2（SAR-CoV-2）与成人相比。但是，在这方面， 严重的疾病包括多系统炎症综合征（MIS-C）和呼吸衰竭， 在一小部分SARS-CoV-2感染的儿童中。近80%的MIS-C患儿病情危重 死亡率为2-4%目前还没有模式来描述疾病严重程度的谱 并预测哪些接触SARS-CoV-2的儿童可能会患上包括MIS-C在内的严重疾病。因此 迫切需要开发一种诊断模式来区分疾病的不同表型， 风险分层疾病。由于感染而发生的microRNA（miRNA）谱的表观遗传变化可以 通过改变免疫应答和细胞因子调节（可在体内检测到）来影响疾病严重程度 液体包括唾液。我们的长期目标是通过早期干预改善SARS-CoV-2儿童的预后。 确定和治疗那些有严重疾病风险的人。我们的中心假设是， 将唾液生物标志物与健康的社会和临床决定因素相结合， SARS-CoV-2感染的儿童。中心假设将通过分阶段的四个具体目标来实现。 前两个目标将在R61阶段进行，包括：1）定义和比较唾液 不同表型（严重和非严重）SARS-CoV-2感染儿童的分子宿主反应 （2）开发并验证一个敏感和特异的模型来预测儿童严重的SARS-CoV-2疾病。 在R33阶段，我们将追求以下两个目标：3）开发一种便携、快速的设备， 唾液miRNA具有与等同技术（qRT-PCR）相当的准确性，以及4）开发人工的 智能（AI）辅助云和移动的系统，用于早期识别严重的SARS-CoV-2感染， 孩子我们将使用唾液组学和生物信息学的创新组合来实现上述目标， 人工智能和临床信息学的分析技术。该研究具有重要意义，因为 一个敏感的疾病风险分层模型有望改善严重SARS-CoV-2儿童的预后 及早发现和及时干预。该提案的最接近预期结果是 更好地理解宿主对病毒感染的免疫反应的表观遗传调节， 希望在未来能带来个性化的治疗。结果将立即产生积极的影响，因为它 将导致根据个人风险因素创建患者档案，从而能够早期识别 在大流行期间，我们必须确保严重疾病的预防和适当的资源分配。

Abstract Children have been disproportionately less impacted by the Corona Virus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Corona Virus 2 (SAR-CoV-2) compared to adults. However, severe illnesses including Multisystem Inflammatory Syndrome (MIS-C) and respiratory failure have occurred in a small proportion of children with SARS-CoV-2 infection. Nearly 80% of children with MIS-C are critically ill with a 2-4% mortality rate. Currently there are no modalities to characterize the spectrum of disease severity and predict which child with SARS-CoV-2 exposure will likely develop severe illness including MIS-C. Thus there is an urgent need to develop a diagnostic modality to distinguish the varying phenotypes of disease and risk stratify disease. The epigenetic changes in microRNA (miRNA) profiles that occur due to an infection can impact disease severity by altering immune response and cytokine regulation which may be detected in body fluids including saliva. Our long-term goal is to improve outcomes of children with SARS-CoV-2 by early identification and treatment of those at risk for severe illness. Our central hypothesis is that a model that integrates salivary biomarkers with social and clinical determinants of health will predict disease severity in children with SARS-CoV-2 infection. The central hypothesis will be pursued through phased four specific aims. The first two aims will be pursued during the R61 phase and include: 1) Define and compare the salivary molecular host response in children with varying phenotypes (severe and non severe) SARS-CoV-2 infections and 2) Develop and validate a sensitive and specific model to predict severe SARS-CoV-2 illness in children. During the R33 phase we will pursue the following two aims: 3) Develop a portable, rapid device that quantifies salivary miRNAs with comparable accuracy to predicate technology (qRT-PCR), and 4) Develop an artificial intelligence (AI) assisted cloud and mobile system for early recognition of severe SARS-CoV-2 infection in children. We will pursue the above aims using an innovative combination of salivaomics and bioinformatics, analytic techniques of AI and clinical informatics. The proposed research is significant because development of a sensitive model to risk stratify disease is expected to improve outcomes of children with severe SARS-CoV-2 infection via early recognition and timely intervention. The proximate expected outcome of this proposal is better understanding of the epigenetic regulation of host immune response to the viral infection which we expect to lead to personalized therapy in the future. The results will have a positive impact immediately as it will lead to the creation of patient profiles based on individual risk factors which can enable early identification of severe disease and appropriate resource allocation during the pandemic.