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ACS Sens

Title:		Noninvasive Detection of Stress by Biochemical Profiles from the Skin
Author(s):		Mansour E; Palzur E; Broza YY; Saliba W; Kaisari S; Goldstein P; Shamir A; Haick H;
Address:		"Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel. Eliachar Research Laboratory, Galilee Medical Center, P.O. Box 21, Nahariya 2210001, Israel. Integrative Pain Laboratory (iPainLab), School of Public Health, University of Haifa, Haifa 2611001, Israel. Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3200003, Israel. Mazor Mental Health Center, Akko 2423314, Israel. Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel"
Journal Title:		ACS Sens
Year:		2023
Volume:		20230227
Issue:		3
Page Number:		1339 - 1347
DOI:		10.1021/acssensors.3c00011
ISSN/ISBN:		2379-3694 (Electronic) 2379-3694 (Linking)
Abstract:		"Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist (e.g., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats (n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group (n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health"
Keywords:		"Male Animals Rats Rats, Sprague-Dawley *Skin/chemistry Mass Spectrometry *Volatile Organic Compounds/analysis Breath Tests behavioral sensor skin stress volatile organic compound;"
Notes:		"MedlineMansour, Elias Palzur, Eilam Broza, Yoav Y Saliba, Walaa Kaisari, Sharon Goldstein, Pavel Shamir, Alon Haick, Hossam eng Research Support, Non-U.S. Gov't 2023/02/28 ACS Sens. 2023 Mar 24; 8(3):1339-1347. doi: 10.1021/acssensors.3c00011. Epub 2023 Feb 27"