Portable GC detector for breath-based COVID diagnostics

用于基于呼吸的新冠肺炎诊断的便携式 GC 检测器

• 批准号: 10321008
• 负责人: CRISTINA ELIZABETH DAVIS
• 金额: $ 89.85万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321008
• 关键词: 2019-nCoV; Aerospace Engineering; Appointment; Artificial Intelligence; Automatic Data Processing; Benchmarking; Biological Markers; Breath Tests; COVID diagnostic; COVID-19; COVID-19 diagnosis; COVID-19 diagnostic; COVID-19 test; California; Catalogs; Chemicals; Clinical; Clinical Chemistry; Clinical Pathology; Clinical Trials; Computer software; Consultations; Contact Tracing; Contracts; Coupled; Custom; Data; Data Analyses; Detection; Development; Devices; Diagnosis; Diagnostic; Diagnostic tests; Disease; Engineering; Environment; Exhalation; Feedback; Fingerprint; Flushing; Funding; Gas Chromatography; Gases; Goals; Gold; Healthcare; Healthcare Systems; Heating; Human Resources; Individual; Industry; Infection; Intuition; Joints; Lead; Liquid substance; Machine Learning; Manufacturer Name; Maps; Mass Spectrum Analysis; Materials Testing; Mathematics; Measurement; Measures; Mechanics; Medical center; National Institute of Biomedical Imaging and Bioengineering; Observational Study; Output; Pattern; Persons; Polymerase Chain Reaction; Process; Production; Public Health; Publishing; Rapid diagnostics; Reagent; Regulatory Pathway; Research; SARS-CoV-2 infection; SARS-CoV-2 negative; SARS-CoV-2 positive; Sampling; Sensitivity and Specificity; Site; Skin; Spectrometry; Standardization; System; Technology; Temperature; Testing; Time; Training; Translational Research; United States; United States National Institutes of Health; Upper Respiratory Infections; Viral; Work; artificial intelligence algorithm; base; biomarker signature; clinical care; community transmission; detector; deviant; differential expression; disease transmission; graphical user interface; instrument; ion mobility; large scale production; machine learning algorithm; metabolomics; mobile sensor; novel; novel diagnostics; pandemic disease; portability; pressure; programs; prospective; prototype; recruit; risk mitigation; scale up; sensor; standard of care; volatile organic compound

Project Summary/Abstract: This proposal has two major goals: 1) Define signature exhaled breath volatile organic compounds (VOCs) to diagnose SARS-CoV-2 infections, and 2) Develop a portable chemical sensing device that can capture and detect exhaled VOCs and includes machine learning algorithms for automated data processing and results interpretation. This project will bring a portable sensor forward into clinical use with the aim of supplementing COVID-19 diagnostics with a reagentless alternative. Breath testing of exhaled VOC biomarkers is a relatively new concept that has the potential to transform healthcare in the US and globally. Our overarching hypothesis is that a miniature breath analysis device can measure signatures of exhaled breath VOCs in real-time and correlate their profile to viral upper respiratory infections such as SARS-CoV-2, even asymptomatically. In Aim #1, we propose a prospective, observational study to analyze breath samples from COVID-19 positive and negative subjects, solely for the purpose of analysis through gold standard GC- MS to define breath VOC biomarkers of infection. We will recruit subjects at two local sites, the UC Davis Medical Center (Sacramento, CA) and VA Northern California Health Care System (Mather, CA), where MPI Dr. Kenyon and Co-Is Drs. Harper and Schivo have joint clinical appointments. Our group has a proven track record to conduct these types of clinical breath studies. In Aim #2, we will develop a portable breath analysis device using our novel miniature differential mobility spectrometry (DMS) detector, coupled with chip-based gas chromatography. DMS is a subset of ion mobility spectrometry and detects VOCs at ambient temperatures and pressures, making it highly appropriate for portable devices. This device would include our custom chip- based preconcentrator, which is packed with a chemical sorbent for extraction of VOCs from breath, and will compare functionality of a compact commercially available GC column to a micro-GC column chip from Deviant, a subcontractor in this work. Individual components of this device have already been developed, and under direction of MPI Prof. Davis, Chair of Mechanical and Aerospace Engineering, a team of research engineers would integrate these pieces together into a single unit. Collaborator Prof. Chuah would guide development of a custom software package for the device with machine learning and artificial intelligence capabilities for automated data processing and interpretation. The device would be placed in the hands of clinicians, who would provide feedback that engineers would immediately incorporate into the device and return to the clinicians for more testing. Under Aim #3, our team would process the GC-MS and GC-DMS data generated in this work, identifying a novel VOC profile for COVID-19 diagnostics. Aim #4 would initiate towards the end of this study to develop both a regulatory pathway & contract manufacturing plan for large scale production and deployment of the device for clinical approval. These efforts are supported by collaborator Dr. Nam Tran, Director of Clinical Pathology & Clinical Chemistry at the UC Davis Medical Center.

项目概要/摘要：本提案有两个主要目标：1）定义呼出气挥发物特征 有机化合物（VOCs）诊断SARS-CoV-2感染，以及2）开发便携式化学传感器 该设备可以捕获和检测呼出的VOC，并包括机器学习算法，用于自动 数据处理和结果解释。该项目将使便携式传感器进入临床使用， 旨在用无试剂替代品补充COVID-19诊断。呼出VOC的呼吸测试 生物标志物是一个相对较新的概念，有可能改变美国和全球的医疗保健。 我们的首要假设是微型呼吸分析设备可以测量呼出的特征 实时呼吸VOC，并将其特征与病毒性上呼吸道感染（如SARS-CoV-2）相关联， 甚至是无症状的在目标1中，我们提出了一项前瞻性的观察性研究，以分析呼吸样本 来自COVID-19阳性和阴性受试者，仅用于通过金标准GC进行分析- MS定义呼吸VOC感染生物标志物。我们将在加州大学戴维斯分校和 医学中心（萨克拉门托，CA）和VA北方加州卫生保健系统（Mather，CA），其中MPI 博士凯尼恩和同事哈珀和斯基沃博士有联合临床任命。我们的团队有一个行之有效的轨道 记录进行这些类型的临床呼吸研究。在目标#2中，我们将开发便携式呼吸分析仪， 使用我们的新型微型差分迁移率光谱（DMS）检测器的设备，再加上基于芯片的 气相色谱DMS是离子迁移谱的一个子集，可在环境温度下检测VOC 和压力，使其非常适合便携式设备。这个装置会包含我们的定制芯片 基于预浓缩器，其填充有用于从呼吸中提取VOC的化学吸附剂，并且将 将紧凑的市售GC柱的功能与来自 Deviant，一个分包商在这项工作。该设备的各个组件已经开发出来， 在MPI教授戴维斯的指导下，机械和航空航天工程主席，一个研究小组 工程师们会把这些部件整合成一个单元。合作者蔡教授将指导 利用机器学习和人工智能为设备开发定制软件包 自动化数据处理和解释的能力。该装置将被放置在手中， 临床医生，他们将提供反馈，工程师将立即将其纳入设备， 回到临床医生那里进行更多的测试。在目标#3下，我们的团队将处理GC-MS和GC-DMS数据 在这项工作中产生，确定了新的VOC概况COVID-19诊断。目标#4将启动 本研究的最后，为大规模生产制定了监管途径和合同制造计划， 用于临床批准的器械的生产和部署。这些努力得到了合作者Dr. 南陈，临床病理学和临床化学主任在加州大学戴维斯分校医学中心。