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.

抽象的 儿童受2019年电晕病毒疾病（Covid-19）的影响降低了 与成年人相比，通过严重的急性呼吸综合症病毒2（SAR-COV-2）。然而， 发生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 SARS-COV-2感染的一小部分儿童。近80％的MIS-C儿童患病患病 死亡率为2-4％。目前没有表征疾病严重程度的方式 并预测哪个患有SARS-COV-2暴露的孩子可能会出现包括MIS-C在内的严重疾病。因此 迫切需要开发一种诊断方式，以区分疾病的不同表型和 风险分层疾病。由于感染而发生的microRNA（miRNA）特征的表观遗传变化可以 通过改变免疫反应和细胞因子调节来影响疾病的严重程度，这可以在体内检测到 包括唾液在内的液体。我们的长期目标是早期改善SARS-COV-2儿童的结果 识别和治疗患有严重疾病风险的人。我们的核心假设是一个模型 将唾液生物标志物与健康和临床决定因素整合在一起，将预测疾病的严重程度 SARS-COV-2感染的儿童。中心假设将通过分阶段的四个特定目标进行。 前两个目标将在R61阶段进行，其中包括：1）定义和比较唾液 患有不同表型（严重和非重度）SARS-COV-2感染的儿童的分子宿主反应 2）开发和验证一个敏感和特定的模型，以预测儿童严重的SARS-COV-2疾病。 在R33阶段，我们将追求以下两个目的：3）开发一种量化的便携式，快速的设备 唾液miRNA具有与谓词技术（QRT-PCR）相当精度的唾液miRNA，4）发展人造 情报（AI）协助云和移动系统，以早期识别严重的SARS-COV-2感染 孩子们。我们将使用唾液学和生物信息学的创新组合来追求上述目标， AI和临床信息学的分析技术。拟议的研究很重要，因为发展 预计将疾病分层的敏感模型有望改善严重的SARS-COV-2儿童的预后 通过早期识别和及时干预感染。该提议的直接预期结果是 更好地理解宿主对病毒感染的免疫反应的表观遗传调节 期望将来会导致个性化疗法。结果将立即产生积极的影响 将基于个体风险因素创建患者概况，这些因素可以早期识别 大流行期间的严重疾病和适当的资源分配。