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

最近的研究，包括我们的研究表明，呼吸可能会让我们诊断出新冠肺炎感染 甚至监控它的进展。与使用样本介质的免疫学和遗传学方法进行比较 与血液、鼻咽拭子和唾液一样，呼气分析是非侵入性的、简单、安全和廉价的；它 允许几乎无限数量的样品体积，并可在护理点使用，以进行快速检测。 从根本上说，呼吸还提供了有关人体如何应对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 建议的气味项目是：(1)提炼自动化、便携、高性能的微气 用于快速(5-6)的层析(GC)设备和相关数据分析/生物标志物识别算法 分钟)、现场和灵敏(低至ppt)呼气分析，以及(2)对高达760的呼气进行分析 并识别和验证呼气中的新冠肺炎生物标记物。因此，与RADX-RAD协调 数据协调中心(DCC)，我们将完成以下具体目标。 (1)精制5个自动化微型GC装置，以实现更快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，只需要大约6分钟的 在ppt级别灵敏度下的分析时间(从目前的20分钟改进)(分目标1a)。那么这些设备将会是 升级到二维微型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 并编写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 没有人为干预(次级目标1c)。 (2)确定区分新冠肺炎阳性(有症状和无症状)的呼吸生物标志物 阴性患者。我们将招募380名参与者的培训队列，其中包括190名新冠肺炎阳性患者 来自两家医院(密歇根州)的95名有症状和95名无症状的新冠肺炎阴性患者 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气分析 学习识别与每个新冠肺炎诊断状态匹配的挥发性有机碳模式。 (3)使用我们改进的微型GC设备对新冠肺炎生物标志物进行验证。利用改进的二维微气相色谱 来自子目标1b的设备，我们将招募380名参与者(190名新冠肺炎阳性)的新验证队列 以验证AIM 2中确定的生物标记物。 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，确保快速对患者产生影响的可能性很高。完工后 在这项工作中，我们将有一个便携式微型GC设备和伴随的自动化算法，可以检测到 并在各种临床和社区环境中监控人们的新冠肺炎状态。

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.