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：该提案有两个主要目标：1)定义特征呼气挥发性 有机化合物(VOC)用于诊断SARS-CoV-2感染，以及2)开发便携式化学传感器 可以捕获和检测呼出的VOCs并包括机器学习算法的设备 数据处理和结果解释。该项目将把一种便携式传感器提前到临床使用 目的是用一种无试剂替代方法补充新冠肺炎诊断。呼气VOC呼气试验 生物标记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。 我们的主要假设是，微型呼气分析设备可以测量呼气的特征 实时呼吸VOCs，并将其特征与SARS-CoV-2等病毒性上呼吸道感染相关联， 即使是无症状的。在目标1中，我们提出了一项分析呼气样本的前瞻性观察性研究。 来自新冠肺炎阳性和阴性受试者，仅用于通过黄金标准GC-分析的目的- MS定义呼吸VOC感染的生物标志物。我们将在两个地方招募受试者，加州大学戴维斯分校 医疗中心(加利福尼亚州萨克拉门托)和退伍军人事务部北加州医疗保健系统(加利福尼亚州马瑟)，其中MPI 凯尼恩博士和合作伙伴哈珀博士和希沃博士有联合的临床预约。我们的团队有一条经过验证的赛道 记录进行这些类型的临床呼吸研究。在目标2中，我们将开发一种便携式呼吸分析 使用我们的新型微型差动迁移率光谱(DMS)探测器和基于芯片的设备 气相色谱。DMS是离子迁移率光谱的一个子集，可以在环境温度下检测VOCs 和压力，使其非常适合便携设备。这个装置将包括我们的定制芯片- 基于预浓缩器，其中填充了从呼气中提取VOCs的化学吸着剂，并将 将紧凑型商用GC柱的功能与来自 越野者，这项工作的分包商。该设备的各个组件已经开发出来，并且 在MPI机械和航空航天工程主席戴维斯教授的指导下，一个研究团队 工程师们会将这些部件集成到一个单元中。合作者蔡教授将指导 为该设备开发具有机器学习和人工智能的定制软件包 具有自动数据处理和解释的能力。这个装置将会被放在 临床医生，他们会提供反馈，工程师会立即将其整合到设备中，并 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 在这项工作中生成的，确定了用于新冠肺炎诊断的新的VOC配置文件。目标#4将开始朝着 这项研究的结束，以制定一条监管途径和大规模的合同制造计划 生产和部署该设备以供临床批准。这些努力得到了合作者Dr。 加州大学戴维斯分校医学中心临床病理学和临床化学部主任纳曼·陈说。