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: 10266337
• 负责人: CRISTINA ELIZABETH DAVIS
• 金额: $ 97.55万
• 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 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266337
• 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; Communities; 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; 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; base; biomarker signature; clinical care; detector; deviant; differential expression; disease transmission; graphical user interface; instrument; intelligent algorithm; ion mobility; large scale production; machine learning algorithm; metabolomics; 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.

项目摘要/摘要：此提案有两个主要目标：1）定义签名呼气呼吸易变 有机化合物（VOC）诊断SARS-COV-2感染，2）开发便携式化学感应 可以捕获和检测呼出的VOC的设备，包括自动化的机器学习算法 数据处理和结果解释。该项目将使便携式传感器向前发展 用无试剂替代方法补充Covid-19诊断的目的。呼气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。 我们的总体假设是微型呼吸分析设备可以测量呼出的签名 实时呼吸VOC，并将其轮廓与病毒上呼吸道感染（例如SARS-COV-2）相关联 甚至无疑。在AIM＃1中，我们提出了一项前瞻性的观察性研究来分析呼吸样本 从共同的-19阳性和负面受试者，仅出于通过黄金标准GC-进行分析的目的 MS定义呼吸VOC生物标志物感染。我们将在两个本地站点招募主题，分校戴维斯分校 医疗中心（加利福尼亚州萨克拉曼多）和VA北加州医疗保健系统（Mather，CA），MPI Kenyon博士和Co-IS Drs。 Harper和Schivo有联合临床任命。我们的小组有一个验证的轨道 记录进行这些类型的临床呼吸研究。在AIM＃2中，我们将进行便携式呼吸分析 使用我们的新型微型差异迁移率（DMS）检测器的设备，并与基于芯片的探测器 气相色谱。 DMS是离子迁移率光谱的子集，并在环境温度下检测VOC 和压力，使其非常适合便携式设备。该设备将包括我们的自定义芯片 - 基于基于的预浓缩剂，该浓缩装有化学吸附剂，用于从呼吸中提取VOC，并将 将紧凑型市售GC列的功能与Micro-GC色谱柱的芯片进行比较 Deviant，这项工作中的分包商。该设备的各个组件已经开发，并且 在机械和航空工程主席MPI戴维斯教授的指导下，研究团队 工程师将将这些碎片集成在一起。合作者Chuah教授将指导 使用机器学习和人工智能为设备开发自定义软件包 自动数据处理和解释的功能。该设备将放在 临床医生，他们将提供反馈，工程师将立即将其纳入设备和 返回临床医生进行更多测试。在AIM＃3下，我们的团队将处理GC-MS和GC-DMS数据 在这项工作中生成，确定了Covid-19诊断的新型VOC概况。 AIM＃4将启动 这项研究的结束是制定大规模的监管途径和合同制造计划 生产和部署该设备以供临床批准。这些努力得到了合作者博士的支持。 Nam Tran，加州大学戴维斯分校医疗中心临床病理与临床化学主任。