Monitoring of disease-induced skin VOC patterns from handheld and wearable chemical sensors

通过手持式和可穿戴化学传感器监测疾病引起的皮肤 VOC 模式

• 批准号: 10426964
• 负责人: CRISTINA ELIZABETH DAVIS
• 金额: $ 98.83万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-22 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10426964
• 关键词: Adult; Aerospace Engineering; Artificial Intelligence; Asthma; Attention deficit hyperactivity disorder; Automatic Data Processing; Benchmarking; Biomedical Technology; Blood Pressure; California; Catalogs; Chemicals; Childhood; Chronic Disease; Chronic Obstructive Pulmonary Disease; Clinical; Connective Tissue Diseases; Consumption; Coupled; Data; Data Analyses; Degenerative polyarthritis; Detection; Development; Devices; Diagnosis; Diagnostic; Diagnostic tests; Differential Diagnosis; Disease; Eczema; Engineering; Enterovirus Infections; Environment; Exhalation; Fever; Fingerprint; Flare; Fragile X Premutation; Funding; Galvanic Skin Response; Gases; Goals; Gold; Hand; Hand functions; Health; Healthcare; Heart Rate; Home; Humidity; Hypersensitivity skin testing; Individual; Joints; Knowledge; Lead; Link; Lung; Lung diseases; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Mechanics; Medical; Mental Health; Methods; Monitor; National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences; Output; Oxygen; Patients; Pattern; Pediatric Hospitals; Phase; Philadelphia; Psoriasis; Psoriatic Arthritis; Publishing; Pulmonary Embolism; Pulmonary Fibrosis; Pulmonary Hypertension; Pulse Rates; RADx Radical; Rapid diagnostics; Reagent; Regulatory Pathway; Respiration; Respiratory Signs and Symptoms; Respiratory Syncytial Virus Infections; Respiratory syncytial virus; Rheumatoid Arthritis; Sampling; Sarcoidosis; Schizophrenia; Sickle Cell Anemia; Site; Skin; Skin Temperature; Spectrometry; Standardization; Symptoms; System; Systemic disease; Testing; Time; Training; United States National Institutes of Health; Urinary tract infection; Urine; Vulnerable Populations; Weight; Work; artificial intelligence algorithm; asthmatic patient; autism spectrum disorder; base; biomarker discovery; clinical research site; cohort; commercialization; cost; data streams; detector; diagnostic tool; disease diagnosis; graphical user interface; health care settings; improved; influenza infection; instrument; mHealth; machine learning algorithm; metabolomics; minimally invasive; novel; novel diagnostics; pediatric patients; point-of-care diagnostics; portability; programs; ranpirnase; real time monitoring; research clinical testing; sensor; skin disorder; telehealth; tool; volatile organic compound; wearable sensor technology

Project Summary/Abstract: This project will bring two skin VOC sensors (hand-held, wearable) into clinical use to improve rapid diagnostics for a range of health conditions. Skin VOC monitoring is a new concept with potential to transform healthcare. Our hypothesis is that miniature skin VOC analysis devices can be coupled with vital sign sensors to measure disease signatures in real-time faster than a traditional differential diagnosis. The proposal has four goals: (1) adapt our current volatile organic compound (VOC) detector into a hand-held format for gas phase skin-emitted metabolites, coupled to non-invasive vital sign sensors and artificial intelligence machine learning (AI/ML) algorithms; (2) deploy our hand-held skin VOC system on 20 diseases over 5 years; (3) adapt our current wearable vital monitoring system to include our skin VOC detector, and use this to monitor persistent asthma patients for disease flares; (4) prepare for our project and devices to move through commercial manufacturing, standardization and FDA regulatory approval. To meet these goals, we plan the following: in Aim #1, we adapt our miniature VOC detection device for skin measurements, and couple it with 7 commercial-off-the-shelf vital sign sensors (skin temperature, pulse rate, respiration rate, heart rate, oxygen saturation, galvanic skin response, skin humidity). Our miniature differential mobility spectrometry detector is coupled with a chip-based preconcentrator and miniature gas chromatograph column for chemical separation and detection. Individual components have already been developed. Under direction of MPI Prof. Davis, UC Davis Chair of Mechanical and Aerospace Engineering, a team of engineers will adapt these pieces together into a hand-held unit for skin VOC sampling/analysis. Co-I Prof. Chuah will guide development of AI/ML capability for automated data processing and interpretation from the integrated VOC and vital sign data streams. In Aim #2, we will use this hand-held system at two different clinical sites to develop AI/ML signatures for 20 different diseases compared to appropriately selected controls. The UC Davis site led by MPI Nicholas Kenyon will focus on: 2 skin diseases (eczema, psoriasis), 7 lung diseases (asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, pulmonary hypertension, pulmonary embolism, sarcoidosis, sickle cell disease with respiratory symptoms), 3 joint and connective tissue diseases (rheumatoid arthritis, psoriatic arthritis, osteoarthritis), 4 mental health diseases (attention deficit hyperactivity disorder, autism, schizophrenia, Fragile X premutation with mental health symptoms). The Children’s Hospital of Philadelphia site lead by Co-I Audrey John will focus on: 4 pediatric fevers (urinary tract infection, enterovirus infection, respiratory syncytial virus infection, influenza infection). In Aim #3, our team will combine our current wearable vital sign sensors with our miniature VOC sensor, and identifying a novel profile for persistent asthma disease flares from both data streams. Aim #4 will develop a manufacturing, commercialization, standardization and FDA regulatory pathway for our devices/tests. These efforts are in conjunction UC Davis start-up company SensIT Ventures.

项目概要/摘要：本项目将把两种皮肤VOC传感器（手持式、可穿戴式）带入临床 用于改善对一系列健康状况的快速诊断。皮肤VOC监测是一个新概念， 改变医疗保健的潜力。我们的假设是，微型皮肤VOC分析设备可以耦合 与传统的鉴别诊断相比，生命体征传感器可以更快地实时测量疾病特征。 该提案有四个目标：（1）将我们目前的挥发性有机化合物（VOC）检测器改造成手持式 用于气相皮肤发射的代谢物的格式，其耦合到非侵入性生命体征传感器和人工 智能机器学习（AI/ML）算法;（2）将我们的手持式皮肤VOC系统部署在20种疾病上 （3）调整我们目前的可穿戴生命监测系统，包括我们的皮肤VOC检测器，并使用 这是为了监测持续性哮喘患者的疾病爆发;（4）为我们的项目和设备移动做准备 通过商业化生产、标准化和FDA监管批准。为了实现这些目标，我们 计划如下：在目标#1中，我们将我们的微型VOC检测设备用于皮肤测量， 它具有7个商业现成的生命体征传感器（皮肤温度，脉搏率，呼吸率，心率， 氧饱和度、皮肤电反应、皮肤湿度）。我们的微型微分迁移率光谱仪 检测器与基于芯片的预浓缩器和用于化学分析的微型气相色谱柱耦合。 分离和检测。各个组成部分已经开发。在MPI教授的指导下 戴维斯，加州大学戴维斯分校机械和航空航天工程系主任，一个工程师团队将改编这些作品 将两种不同的挥发性有机化合物组合成一个手持式装置，用于皮肤挥发性有机化合物采样/分析。蔡教授将指导 AI/ML功能，用于从集成VOC和生命体征数据中自动处理和解释数据 溪流在目标2中，我们将在两个不同的临床研究中心使用该手持式系统开发AI/ML签名 与适当选择的对照组相比，对20种不同疾病的治疗效果。由MPI Nicholas领导的UC Davis网站 凯尼恩将重点关注：2种皮肤病（湿疹、牛皮癣）、7种肺病（哮喘、慢性阻塞性肺病） 肺部疾病、肺纤维化、肺动脉高压、肺栓塞、结节病、镰状细胞 有呼吸道症状的疾病）、3种关节和结缔组织疾病（类风湿性关节炎、银屑病 关节炎，骨关节炎），4种精神健康疾病（注意力缺陷多动障碍，自闭症，精神分裂症， 脆性X前突变伴精神健康症状）。由Co-I领导的费城儿童医院 Audrey John将重点关注：4种儿科发热（尿路感染，肠道病毒感染，呼吸道合胞病毒感染） 病毒感染、流感感染）。在目标3中，我们的团队将联合收割机结合我们目前的可穿戴生命体征传感器， 与我们的微型VOC传感器，并确定一个新的配置文件，为持续性哮喘疾病发作， 数据流目标#4将开发一个制造，商业化，标准化和FDA监管 我们的设备/测试的路径。这些努力与加州大学戴维斯分校的初创公司SensIT Ventures合作。