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