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.

抽象的 今年年初，冠状病毒 COVID-19 大流行迫使整个国家陷入困境 截至 2020 年 9 月初，全球死亡人数已达 89 万多人。 估计有大量无症状但具有传染性的 COVID-19 病例 感染繁殖率约为2，且突变率较高，预计该病毒将 像流感病毒一样留在人群中。对于服务地区的医院 经济依赖国际旅行、旅游业和游轮旅游业，例如迈阿密戴德 县，与旅行相关的新 COVID-19 病例在感染爆发期间需要治疗 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。当前 COVID-19 爆发期间以及其他时期的患者风险因素 病毒爆发，例如季节性流感，特征不明，因此负面影响 影响病人的护理。唾液微生物组包括病毒和细菌，不是 目前用作诊断工具。然而，它可能揭示与严重相关的风险因素 疾病和/或致命结果，并且它允许检测和研究病毒 RNA 潜在接触者追踪和分子流行病学的序列，所有这些都会影响 疫苗和抗病毒功效。 在这项拟议的研究中，Lifetime Omics 将开发 CLAIRE，一种概念验证的体内集群人工智能 用于预测病毒性传染性呼吸道疾病（例如， 通过唾液宏基因组分析 COVID-19。迈阿密大学医学院 团体感染控制（UMMGIC）部门将通过收集唾液来合作开展这项工作 来自 COVID-19 患者的样本，具有未识别的临床信息。样品将 进行宏基因组测序，Lifetime Omics 将重新利用用于 预测体内 HIV 进化，以对 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识别两者 体内准种簇和患者所属的地理簇。这 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 患者的唾液样本中生成，目的是协助医生 预测 COVID-19 的疾病进展。