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

点击翻译按钮获取中文摘要

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.