A Handheld Microchip for GC analysis of breath to screen for COVID-19

项目摘要 COVID-19大流行造成了前所未有的社会痛苦和经济破坏。在 在美国，超过600万人感染了COVID-19，超过190万人感染了COVID-19。 到目前为止，已有数千名患者死于这种疾病。尽管目前的COVID-19诊断检测技术 对于减缓病毒传播和防止未来爆发至关重要，但它们不适用于现场使用。 目前的诊断测试进行起来很麻烦，因为它们使用水溶液，需要多个步骤， 几个小时到几天来获得结果。自5月份美国开始重新开放经济以来， COVID-19病例数量大幅增加。因此，迫切需要开发一种诊断 这种方法是非侵入性的，便携式的，可以快速提供测试结果。 该项目的总体目标是开发一种移动的呼吸分析技术，用于快速筛查 使用手持式呼吸收集工具和带光电离检测器（PID）的便携式GC检测COVID-19。的 手持工具将是一个封闭的系统，用于在微加工的 芯片捕获的VOC将用乙醇洗脱，然后使用市售便携式 GC-PID仪表人工智能（AI）和机器学习算法将被应用于识别 与COVID-19感染相关的VOC模式。核心创新是微加工芯片， 捕获呼出气体中的羰基化合物，从而用作预浓缩器， 羰基挥发性有机化合物的便携式GC-PID。假设呼出气中的羰基代谢组是 直接关系到身体对新型冠状病毒感染的反应，以及羰基VOC的变化 相对于健康对照，呼出气中的组合物可用于检测有症状和 无症状COVID-19患者。 为实现总体目标，提出了三个具体目标。目标1是建立一个一次性手持呼吸器 用于浓缩羰基VOC的分析仪工具。目标2是识别COVID-19呼吸中的VOC模式 通过机器学习算法来治疗患者。目的三是将便携式气相色谱技术与呼吸采样技术相结合 人工智能系统引导的COVID-19筛查工具。路易斯维尔大学是唯一适合迅速 由于PI和Co-PI在呼吸分析方面的经验，将微芯片技术转移到现场使用， 转化研究，以及项目团队在病毒学，传染病，生物统计学， 人工智能以及最先进的设施，包括微纳米技术中心， 生物安全3级区域生物防护实验室和NIH资助的REACH计划。

Project Summary The COVID-19 pandemic has caused unprecedented societal suffering and economic disruption. In the United States, more than six million people have contracted COVID-19 and more than one hundred ninety thousand patients have died of this disease to date. Although current COVID-19 diagnostic testing technologies are critical for slowing the spread of the virus and preventing future outbreaks, they are not practical for field use. Current diagnostic tests are cumbersome to perform because they use aqueous solutions, require multiple steps, and hours-to-days to obtain results. Since the US began to reopen the economy in May, there has been a significant increase in the number of COVID-19 cases. Therefore, there is an urgent need to develop a diagnostic approach that is non-invasive, portable, and can rapidly provide test results. The overall goal of the project is to develop a mobile breath analysis technology for rapid screening for COVID-19 using a handheld breath collection tool and a portable GC with a photoionization detector (PID). The handheld tool will be a closed system for trapping select volatile organic compounds (VOCs) on a microfabricated chip. The captured VOCs will be eluted with ethanol and then analyzed using a commercially available, portable GC-PID instrument. Artificial intelligence (AI) and machine learning algorithms will be applied to recognize the VOC pattern that correlates with COVID-19 infection. The central innovation is the microfabricated chip that captures carbonyl compounds in exhaled breath and thus serves as a preconcentrator, which enables analysis of carbonyl VOCs by the portable GC-PID. The hypothesis is that the carbonyl metabolome in exhaled breath is directly related to the body’s reaction to the novel coronavirus infection, and changes in the carbonyl VOC composition in exhaled breath relative to healthy controls can be used to detect both symptomatic and asymptomatic COVID-19 patients. Three specific aims are proposed to fulfill the overall goal. Aim 1 is to build a disposable handheld breath analyzer tool for concentrating carbonyl VOCs. Aim 2 is to identify VOC patterns in the breath of COVID-19 patients by machine learning algorithms. Aim 3 is to integrate portable GC technology with the breath sampling tool for COVID-19 screening guided by an AI system. The University of Louisville is uniquely suited to rapidly transition the microchip technology to field use because of the PI and Co-PI’s experience in breath analysis and translational research, and the project team’s experience in virology, infectious diseases, biostatistics, and artificial intelligence as well as the state-of-the-art facilities that include a MicroNano Technology Center, Biosafety Level 3 Regional Biocontainment Lab, and an NIH-funded REACH program.