In Vivo Cluster AI Prediction (CLAIRE) of COVID-19 Disease Progression

COVID-19 疾病进展的体内集群 AI 预测 (CLAIRE)

• 批准号: 10256828
• 负责人: Patricia Buendia
• 金额: $ 24.87万
• 依托单位: LIFETIME OMICS, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256828
• 关键词: 2019-nCoV; Affect; Algorithms; Antibiotics; Area; Artificial Intelligence; Bacteria; Biological Markers; COVID-19; COVID-19 outbreak; COVID-19 pandemic; COVID-19 patient; Cessation of life; China; Cities; Clinical; Communities; Contact Tracing; Coronavirus; Country; County; Data; Data Set; Detection; Deterioration; Diagnosis; Diagnostic; Diagnostic Sensitivity; Disease; Disease Outbreaks; Disease Progression; Evolution; Fatal Outcome; Gene Frequency; Genetic; Geography; Goals; HIV; Health system; Hospitals; Immune; Infection; Infection Control; Influenza; International; Lifting; Lung diseases; Measures; Medical; Metagenomics; Microbe; Minor; Modeling; Molecular; Molecular Epidemiology; Mutation; Outcome; Pathogenicity; Patient Care; Patient risk; Patients; Performance; Phylogenetic Analysis; Physicians; Population; Predictive Value; Primary Infection; Process; Prognosis; Quarantine; RNA Sequences; ROC Curve; Records; Reporting; Resources; Risk Factors; SARS-CoV-2 positive; Saliva; Sampling; Seasons; Sensitivity and Specificity; Services; Severities; Ships; Testing; Time; Training; Travel; Universities; Validation; Viral; Virus; Virus Diseases; anti-viral efficacy; base; co-infection; computerized tools; data modeling; health care service utilization; improved outcome; in vivo; infection rate; influenzavirus; markov model; metagenome; metagenomic sequencing; microbiome; microbiota; negative affect; pathogen; pathogenic bacteria; patient subsets; phase 1 study; predictive modeling; predictive tools; pressure; reproductive; saliva analysis; saliva sample; seasonal influenza; supervised learning; tool; vaccine efficacy; viral RNA

ABSTRACT The coronavirus COVID-19 pandemic, which early this year forced entire countries into lockdown, has reached a global death toll of 890,000+ by early September 2020. Based on the high number of COVID-19 cases that are asymptomatic but infectious, an estimated reproductive rate of infection of about 2 and a high mutation rate, it is expected that the virus will remain in the population as the influenza virus does. For hospitals serving areas whose economy relies on international travel, tourism, and cruise ship tourism, such as Miami-Dade county, new COVID-19 cases related to travel will require treatment during infection outbreaks which will strain health systems, especially during the infectious respiratory disease season in the winter. Patient risk factors during the current COVID-19 outbreak as well as during other viral outbreaks, such as seasonal influenza, are poorly characterized, consequently negatively affecting patient care. The saliva microbiome, which includes viruses and bacteria, is not currently used as in diagnostic tools. However, it may reveal risk factors associated with severe disease and/or a fatal outcome, and it allows for the detection and study of the viral RNA sequence for potential contact tracing and molecular epidemiology, all of which affect both vaccine and antiviral efficacy. In this proposed study, Lifetime Omics will develop CLAIRE, a proof-of-concept in vivo cluster AI platform for predicting disease progression of viral infectious respiratory diseases such as COVID-19 through the analysis of the saliva metagenome. The University of Miami Medical Group Infection Control (UMMGIC) division will collaborate in this effort by collecting saliva samples from COVID-19 patients with de-identified clinical information. The samples will undergo metagenomic sequencing and Lifetime Omics will repurpose algorithms used for prediction of in vivo HIV evolution to perform genetic/phylogenetic analysis on SARS-CoV-2 RNA sequences, estimating mutation rate and immune selection pressures and identifying both the in vivo quasispecies clusters and the geographic cluster to which the patient belongs. The CLAIRE models will be trained with public datasets and tested on the metagenomic sequences generated from saliva samples of UMMGIC patients with the goal of assisting physicians in predicting disease progression in COVID-19.

摘要 今年早些时候，新冠肺炎疫情迫使整个国家进入 截至2020年9月初，全球死亡人数已达89万多人。基于 据估计，大量无症状但具有传染性的COVID-19病例 生殖感染率约为2和高突变率，预计该病毒将 就像流感病毒一样留在人群中。对于服务于以下地区的医院， 经济依赖于国际旅行，旅游和游轮旅游，如迈阿密-戴德 县，与旅行有关的新的COVID-19病例将需要在感染爆发期间接受治疗 这将给卫生系统带来压力，特别是在传染性呼吸道疾病季节， 冬天在当前COVID-19爆发期间以及在其他 病毒爆发，如季节性流感，特征不明确， 影响病人护理唾液微生物组，包括病毒和细菌， 目前用于诊断工具。然而，它可能揭示与严重 疾病和/或致命的结果，它允许检测和研究病毒RNA 用于潜在接触者追踪和分子流行病学的序列，所有这些都影响到 疫苗和抗病毒功效。 在这项拟议的研究中，Lifetime Omics将开发CLAIRE，这是一种概念验证的体内集群AI 用于预测病毒性传染性呼吸道疾病的疾病进展的平台， 通过唾液宏基因组分析COVID-19。迈阿密大学医学院 群体感染控制（UMMGIC）部门将通过收集唾液来合作 从具有去识别临床信息的COVID-19患者中提取样本。样品将 进行宏基因组测序，Lifetime Omics将重新使用用于 预测体内HIV进化，对SARS-CoV-2进行遗传/系统发育分析 RNA序列，估计突变率和免疫选择压力，并确定两者 体内准种聚类和患者所属的地理聚类。的 CLAIRE模型将使用公共数据集进行训练，并在宏基因组序列上进行测试。 从UMMGIC患者的唾液样本中产生，目的是帮助医生 预测COVID-19的疾病进展。