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，表明呼吸可以让我们诊断COVID-19感染 甚至监控它的进程。与使用样品培养基的基于免疫学和遗传学的方法相比 与血液、鼻咽拭子和唾液一样，呼吸分析是非侵入性的、简单、安全和便宜;它 允许几乎无限量的样品体积，并且可以在护理点用于快速检测。 从根本上说，呼吸还提供了关于人体如何响应的关键代谢组学信息。 病毒感染和医疗干预（如药物治疗和机械通气）。的目标 建议SCENT项目是：（1）提炼自动化、便携式、高性能的微气体 色谱（GC）装置和相关数据分析/生物标记物鉴定算法用于快速（5-6 分钟）、现场和灵敏（低至ppt）的呼吸分析，以及（2）对高达760 患者，并识别和验证呼吸中的COVID-19生物标志物。因此，在与RADx-rad的协调下， 数据协调中心（DCC），我们将完成以下具体目标。 (1)优化5台自动化微型GC设备，以实现更高的速度和更好的分离能力。我们 将建造5个新的自动化和便携式一维微型GC装置，仅需约6分钟， ppt水平灵敏度下的测定时间（从当前的20分钟改进）（子目标1a）。然后这些设备将 升级为二维微型GC，以显著提高分离能力（子目标1b）。在 同时，我们将优化和自动化我们现有的数据处理和生物标志物识别算法 以及简化工作流程的代码，以便GC设备可以自动处理和分析数据 没有人为干预（子目标1c）。 (2)识别区分COVID-19阳性（有症状和无症状）和 阴性患者我们将招募380名参与者的培训队列，其中包括190名COVID-19阳性患者 (95有症状和95无症状）和190名来自两家医院（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 学习识别与每个COVID-19诊断状态相匹配的VOC模式。 (3)使用我们改进的微型GC设备检测COVID-19生物标志物。使用改进的二维微气相色谱 对于SubAim 1b的设备，我们将招募一个由380名参与者组成的新验证队列（190名COVID-19呈阳性 患者和190名COVID-19阴性患者），以验证目标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 在整个项目中，我们都在努力达到我们的里程碑，确保快速影响患者的可能性很高。完成后 这项工作，我们将有一个便携式微型GC设备和配套的自动化算法，可以检测 并在各种临床和社区环境中监测人们的COVID-19状况。