COVID-19 detection through scent analysis with a compact GC device

使用紧凑型 GC 设备通过气味分析检测 COVID-19

• 批准号: 10266206
• 负责人: Xudong Fan
• 金额: $ 99.98万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-21 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266206
• 关键词: Agreement; Algorithms; Biological Assay; Biological Markers; Biotechnology; Blood; Breath Tests; COVID-19; COVID-19 detection; COVID-19 diagnosis; COVID-19 diagnostic; COVID-19 monitoring; COVID-19 pandemic; COVID-19 patient; COVID-19 prognosis; Cessation of life; Clinical; Code; Critical Care; Data; Data Analyses; Data Coordinating Center; Data Science; Data Scientist; Devices; Diagnosis; Dimensions; Disease; Engineering; Ensure; Gas Chromatography; Genetic; Health; Hospitals; Human; Human body; Immunologics; In Situ; Institutes; Institutional Review Boards; Intervention; Licensing; Machine Learning; Mechanical ventilation; Medical; Medicine; Methods; Michigan; Monitor; Participant; Patients; Pattern; Performance; Pharmacotherapy; Process; Production; RADx Radical; Research; Resources; SARS-CoV-2 infection; SARS-CoV-2 negative; SARS-CoV-2 positive; Saliva; Sampling; Savings; Services; Severities; Speed; Technology; Testing; Time; Training; Universities; Validation; Virus Diseases; Work; acute hypoxemic respiratory failure; automated algorithm; base; biomarker identification; cohort; commercialization; community setting; computerized data processing; cost; design; fight against; fighting; global health; improved; metabolomics; multidisciplinary; nasopharyngeal swab; pandemic disease; point of care; portability; rapid detection; recruit; screening; severe COVID-19; two-dimensional

Recent studies, including ours, have suggested that breath may allow us to diagnose COVID-19 infection and even monitor its progress. As compared to immunological and genetic based methods using sample media like blood, nasopharyngeal swab, and saliva, breath analysis is non-invasive, simple, safe, and inexpensive; it allows a nearly infinite amount of sample volume and can be used at the point-of-care for rapid detection. Fundamentally, breath also provides critical metabolomics information regarding how human body responds to virus infection and medical intervention (such as drug treatment and mechanical ventilation). The objectives of the proposed SCENT project are: (1) to refine automated, portable, high-performance micro-gas chromatography (GC) device and related data analysis / biomarker identification algorithms for rapid (5-6 minutes), in-situ, and sensitive (down to ppt) breath analysis and (2) to conduct breath analysis on up to 760 patients, and identify and validate the COVID-19 biomarkers in breath. Thus, in coordination with the RADx-rad Data Coordination Center (DCC), we will complete the following specific aims. (1) Refine 5 automated micro-GC devices to achieve higher speed and better separation capability. We will construct 5 new automated and portable one-dimensional micro-GC devices that require only ~6 minutes of assay time (improved from current 20 minutes) at the ppt level sensitivity (Sub-Aim 1a). Then the devices will be upgraded to 2-dimensional micro-GC to significantly increase the separation capability (Sub-Aim 1b). In the meantime, we will optimize and automate our existing data processing and biomarker identification algorithms and codes to streamline the workflow so that the GC device can automatically process and analyze the data without human intervention (Sub-Aim 1c). (2) Identify breath biomarkers that distinguish COVID-19 positive (symptomatic and asymptomatic) and negative patients. We will recruit a training cohort of 380 participants, including 190 COVID-19 positive patients (95 symptomatic and 95 asymptomatic) and 190 COVID-19 negative patients from two hospitals (Michigan Medicine – Ann Arbor and the Henry Ford Hospital – Detroit). We will conduct breath analysis using machine learning to identify VOC patterns that match each COVID-19 diagnostic status. (3) Validate the COVID-19 biomarkers using our refined micro-GC devices. Using the refined 2-D micro-GC devices from Sub-Aim 1b, we will recruit a new validation cohort of 380 participants (190 COVID-19 positive patients and 190 COVID-19 negative patients) to validate the biomarkers identified in Aim 2. We will leverage existing engineering, data science, clinical, regulatory, and commercialization resources throughout the project to hit our milestones, ensuring a high likelihood of rapid patient impact. Upon completion of this work, we will have a portable micro-GC device and accompanying automated algorithms that can detect and monitor COVID-19 status for people in a variety of clinical and community settings.

最近的研究，包括我们的研究，表明呼吸可能使我们能够诊断出Covid-19感染 甚至监控其进度。与使用样品介质的免疫学和基于遗传学的方法相比 像血液，鼻咽拭子和唾液一样，呼吸分析是无创，简单，安全且廉价的。 允许几乎无限的样品体积，可在护理点上使用以进行快速检测。 从根本上讲，呼吸还提供了有关人体如何反应的关键代谢组学信息 病毒感染和医疗干预（例如药物治疗和机械通气）。目标的目标 拟议的气味项目是：（1）改进自动化，便携式，高性能的微气体 媒介（GC）设备和相关数据分析 /生物标志物识别算法的快速算法（5-6 分钟），原位和敏感（降至PPT）呼吸分析，以及（2）进行高达760的呼吸分析 患者，并在呼吸中识别和验证Covid-19-19。那是与radx-rad的协调 数据协调中心（DCC），我们将完成以下特定目标。 （1）完善5个自动化的Micro-GC设备，以实现更高的速度和更好的分离能力。我们 将构建5个新的自动化和便携式一维微型GC设备，仅需要约6分钟 在PPT级别灵敏度（SUB-AIM 1A）处的测定时间（从当前20分钟提高）。然后设备将是 升级到二维微型GC，可显着提高分离能力（Sub-aim 1b）。在 同时，我们将优化并自动化现有的数据处理和生物标志物识别算法 和代码以简化工作流程，以便GC设备可以自动处理和分析数据 没有人类干预（Sub-aim 1C）。 （2）确定呼吸生物标志物，以区分covid-19阳性（有症状和无症状）和 阴性患者。我们将招募一个由380名参与者组成的培训队列，包括190名Covid-19-190阳性患者 （95个症状和95个无症状）和190 COVID-190来自两家医院的阴性患者（密歇根州） 医学 - 安阿伯和亨利·福特医院 - 底特律）。我们将使用机器进行呼吸分析 学会识别与每种covid-19诊断状态相匹配的VOC模式。 （3）使用我们精制的Micro-GC设备验证Covid-19-19生物标志物。使用精制的二维微型GC Sub-aim 1B的设备，我们将招募380名参与者的新验证队列（190 Covid-19-190阳性 患者和190名Covid-190阴性患者）以验证AIM 2中鉴定的生物标志物。 我们将利用现有的工程，数据科学，临床，监管和商业化资源 通过该项目达到我们的里程碑，确保患者迅速影响的可能性很大。完成后 在这项工作中，我们将拥有一种便携式Micro-GC设备和可以检测到的自动化算法 并监视各种临床和社区环境中人们的COVID-19状态。