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

用于呼吸气相色谱分析以筛查 COVID-19 的手持式微芯片

基本信息

批准号：
10266377
负责人：
Xiao-An Fu
金额：
$ 102.67万
依托单位：
UNIVERSITY OF LOUISVILLE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-12-21 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10266377
关键词：
2019-nCoV Acute Address Artificial Intelligence Biochemical Process Biometry Breath Tests COVID-19 COVID-19 detection COVID-19 diagnostic COVID-19 pandemic COVID-19 patient COVID-19 screening COVID-19 test Cancer Detection Clinic Collaborations Collection Communicable Diseases Contracts Coronavirus Infections Detection Device or Instrument Development Devices Diagnosis Diagnostic Diagnostic tests Disease Disease Outbreaks Economics Epithelial Cells Ethanol Exhalation Expert Systems Foundations Funding Future Goals Hour Human Influenza Institutes Institution Laboratories Machine Learning Malignant neoplasm of lung Mass Fragmentography Medical Device Modeling Monitor Nasal Epithelium Oxidative Stress Patients Pattern Process Production Protocols documentation Rapid screening Reaction Reagent Research Project Grants Role SARS-CoV-2 infection Sampling Sensitivity and Specificity Signal Transduction Silicon Sterilization System Technology Test Result Testing Training Translational Research Tuberculosis United States United States National Institutes of Health Universities Vial device Viral Viral Respiratory Tract Infection Virulent Virus Virus Diseases adduct aqueous asymptomatic COVID-19 biosafety level 3 facility bronchial epithelium carbonyl compound detection sensitivity detector experience innovation instrument machine learning algorithm metabolome microchip mobile computing novel coronavirus photoionization point of care portability prevent programs prototype reagent testing symptomatic COVID-19 tool virology volatile organic compound

项目摘要

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.

项目概要 COVID-19 大流行造成了前所未有的社会苦难和经济混乱。美国，超过 600 万人感染了 COVID-19，超过 190 人感染了尽管目前的 COVID-19 诊断检测技术已导致数千名患者死于这种疾病。虽然对于减缓病毒传播和防止未来爆发至关重要，但它们不适合现场使用。目前的诊断测试执行起来很麻烦，因为它们使用水溶液，需要多个步骤，自美国五月开始重新开放经济以来，出现了数小时至数天的结果。因此，迫切需要开发一种诊断方法。该方法是非侵入性的、便携式的并且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呼吸分析技术，用于快速筛查使用手持式呼吸采集工具和带有光电离检测器 (PID) 的便携式 GC 来检测 COVID-19。手持式工具将是一个封闭系统，用于在微制造的设备上捕获选定的挥发性有机化合物（VOC）捕获的 VOC 将用乙醇洗脱，然后使用市售便携式芯片进行分析。 GC-PID 仪器将应用人工智能 (AI) 和机器学习算法来识别。与 COVID-19 感染相关的 VOC 模式核心创新是微加工芯片。捕获呼出气中的羰基化合物，从而充当预浓缩器，从而能够进行分析通过便携式 GC-PID 分析羰基 VOC 假设呼出气中的羰基代谢组是与人体对新型冠状病毒感染的反应直接相关，羰基VOC的变化与健康对照相比，呼出气中的成分可用于检测症状和症状无症状的 COVID-19 患者。为实现总体目标，提出了三个具体目标：打造一次性手持式呼吸器。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。目标 3 是将便携式 GC 技术与呼吸采样相结合。由人工智能系统引导的 COVID-19 筛查工具非常适合快速筛查。由于 PI 和 Co-PI 在呼吸分析和转化研究，以及项目团队在病毒学、传染病、生物统计学等方面的经验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，以及 NIH 资助的 REACH 计划。