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)
基本信息
- 批准号:10256828
- 负责人:
- 金额:$ 24.87万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:2019-nCoVAffectAlgorithmsAntibioticsAreaArtificial IntelligenceBacteriaBiological MarkersCOVID-19COVID-19 outbreakCOVID-19 pandemicCOVID-19 patientCessation of lifeChinaCitiesClinicalCommunitiesContact TracingCoronavirusCountryCountyDataData SetDetectionDeteriorationDiagnosisDiagnosticDiagnostic SensitivityDiseaseDisease OutbreaksDisease ProgressionEvolutionFatal OutcomeGene FrequencyGeneticGeographyGoalsHIVHealth systemHospitalsImmuneInfectionInfection ControlInfluenzaInternationalLiftingLung diseasesMeasuresMedicalMetagenomicsMicrobeMinorModelingMolecularMolecular EpidemiologyMutationOutcomePathogenicityPatient CarePatient riskPatientsPerformancePhylogenetic AnalysisPhysiciansPopulationPredictive ValuePrimary InfectionProcessPrognosisQuarantineRNA SequencesROC CurveRecordsReportingResourcesRisk FactorsSARS-CoV-2 positiveSalivaSamplingSeasonsSensitivity and SpecificityServicesSeveritiesShipsTestingTimeTrainingTravelUniversitiesValidationViralVirusVirus Diseasesanti-viral efficacybaseco-infectioncomputerized toolsdata modelinghealth care service utilizationimproved outcomein vivoinfection rateinfluenzavirusmarkov modelmetagenomemetagenomic sequencingmicrobiomemicrobiotanegative affectpathogenpathogenic bacteriapatient subsetsphase 1 studypredictive modelingpredictive toolspressurereproductivesaliva analysissaliva sampleseasonal influenzasupervised learningtoolvaccine efficacyviral RNA
项目摘要
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。
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
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Patricia Buendia其他文献
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