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

将唾液转录组学和蛋白质组学与多神经网络智能相结合用于儿童 SARS-CoV2 感染的严重程度预测 (SPITS MISC)

• 批准号: 10733697
• 负责人: Steven Daniel Hicks
• 金额: $ 71.39万
• 依托单位: CENTRAL MICHIGAN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10733697
• 关键词: 2019-nCoV; Adult; Anti-Inflammatory Agents; Area; Artificial Intelligence; Bioinformatics; Biological Markers; Biological Testing; Blood; Body Fluids; COVID-19; COVID-19 impact; COVID-19 severity; Cell physiology; Characteristics; Child; Childhood; Clinical; Clinical Data; Clinical Informatics; Coronavirus; Critically ill children; Data; Development; Devices; Diagnostic; Disease; Disease Progression; Disease stratification; Early identification; Early treatment; Electronic Health Record; Ensure; Epigenetic Process; Future; Goals; Healthcare; Immune response; Individual; Infection; Inflammatory; Intelligence; Interleukin-10; Intervention; Knowledge; Measurement; Measures; MicroRNAs; Modality; Modeling; Molecular; Molecular Profiling; Multisystem Inflammatory Syndrome in Children; Outcome; Patients; Phase; Phenotype; Post-Transcriptional Regulation; Prognosis; Proteomics; Public Health; Regulation; Research; Resource Allocation; Resources; Respiratory Failure; Risk; Risk Factors; Role; SARS-CoV-2 exposure; SARS-CoV-2 genome; SARS-CoV-2 infection; Saliva; Salivary; Sampling; Severities; Severity of illness; Signal Transduction; Site; Syndrome; System; TNF gene; Techniques; Technology; Testing; Training; Virus Diseases; Virus Replication; cloud based; comorbidity; cytokine; disease phenotype; disorder risk; epigenetic regulation; experience; health data; health determinants; immunoregulation; improved outcome; innovation; mobile application; mortality; neural network; novel; pandemic disease; personalized medicine; point of care; portability; potential biomarker; predictive modeling; profiles in patients; prognostic model; risk stratification; severe COVID-19; social; social determinants; social health determinants; targeted treatment; tool; transcriptomics; web portal

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.

摘要 儿童受到冠状病毒病2019(新冠肺炎)的影响小得不成比例 由严重急性呼吸综合征冠状病毒2(SAR-CoV-2)与成人进行比较。然而， 出现了包括多系统炎症综合征(MIS-C)和呼吸衰竭在内的严重疾病 在一小部分感染SARS-CoV-2的儿童中。近80%患有MIS-C的儿童病情危重 死亡率为2-4%。目前还没有模式来描述疾病严重程度的谱 并预测接触SARS-CoV-2病毒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不同的疾病表型和 风险分层疾病。由于感染而发生的microRNA(MiRNA)图谱的表观遗传变化可能 通过改变体内可能检测到的免疫反应和细胞因子调节来影响疾病的严重性 包括唾液在内的液体。我们的长期目标是及早改善感染SARS-CoV-2的儿童的预后 对有严重疾病风险的人进行识别和治疗。我们的中心假设是一个模型 将唾液生物标记物与社会和临床健康决定因素相结合将预测 感染SARS-CoV-2的儿童。核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阶段实现，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)建立和验证预测儿童严重SARS-CoV-2疾病的敏感和特异的模型。 在R33阶段，我们将追求以下两个目标：3)开发一种便携式、快速的量化设备 唾液miRNAs的准确性与预测技术(qRT-PCR)相当，以及4)开发出一种人造的 智能(AI)辅助云和移动系统早期识别严重SARS-CoV-2感染 孩子们。我们将利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。拟议的研究具有重要意义，因为 一种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 通过早期识别和及时干预感染。这项提议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 期望在未来带来个性化的治疗。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，从而能够及早识别 在大流行期间控制严重疾病和适当的资源分配。