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: 10321006
• 负责人: Xudong Fan
• 金额: $ 94.29万
• 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 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321006
• 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; Persons; 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，呼吸可以帮助我们诊断 COVID-19 感染 甚至与使用样本介质的基于免疫学和遗传的方法相比来监测其进展。 与血液、鼻咽拭子和唾液一样，呼吸分析是非侵入性的、简单、安全且便宜； 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，呼吸还提供了有关人体如何反应的重要代谢组学信息。 病毒感染和医疗干预（如药物治疗和机械通气）。 拟议的SCENT项目是：（1）精炼自动化、便携式、高性能微气体 色谱（GC）装置和相关数据分析/生物标志物识别算法，用于快速（5-6 分钟）、原位、灵敏（低至 ppt）呼吸分析，以及 (2) 对多达 760 个样本进行呼吸分析 患者，并与 RADx-rad 配合识别和验证呼吸中的 COVID-19 生物标志物。 数据协调中心（DCC），我们将完成以下具体目标。 （1）完善5台自动化微型气相色谱装置，实现更高的速度和更好的分离能力。 将建造 5 台新型自动化便携式一维微型气相色谱设备，仅需约 6 分钟的时间 ppt 水平灵敏度（子目标 1a）的检测时间（从当前的 20 分钟改进）然后设备将被检测。 升级为二维微型气相色谱仪，显着提高分离能力（子目标 1b）。 同时，我们将优化和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，使 GC 设备能够自动处理和分析数据 无需人工干预（子目标 1c）。 (2) 识别区分 COVID-19 阳性（有症状和无症状）的生物标志物 我们将招募 380 名参与者组成的培训队列，其中包括 190 名 COVID-19 阳性患者。 （95 名有症状的患者和 95 名无症状的患者）和来自两家医院（密歇根州）的 190 名 COVID-19 阴性患者 医学 - 安娜堡和亨利福特医院 - 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) 使用我们的精制微型 GC 设备验证 COVID-19 生物标志物 使用精制 2-D 微型气相色谱仪。 来自 Sub-Aim 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（其中 190 名 COVID-19 呈阳性） 患者和 190 名 COVID-19 阴性患者）来验证目标 2 中确定的生物标志物。 我们将利用现有的工程、数据科学、临床、监管和商业化资源 在整个项目中达到我们的里程碑，确保完成后极有可能对患者产生快速影响。 在这项工作中，我们将拥有一个便携式微型气相色谱设备和随附的自动化算法，可以检测 监测各种临床和社区环境中人们的 COVID-19 状态。

项目成果

Portable comprehensive two-dimensional micro-gas chromatography using an integrated flow-restricted pneumatic modulator.

• DOI: 10.1038/s41378-022-00452-5
• 发表时间: 2022
• 期刊: MICROSYSTEMS & NANOENGINEERING
• 影响因子: 7.9
• 作者: Huang, Xiaheng;Li, Maxwell Wei-hao;Zang, Wenzhe;Huang, Xiaolu;Sivakumar, Anjali Devi;Sharma, Ruchi;Fan, Xudong
• 通讯作者: Fan, Xudong