Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published May 2, 2017 | Published + Supplemental Material
Journal Article Open

Autonomous sweat extraction and analysis applied to cystic fibrosis and glucose monitoring using a fully integrated wearable platform

Abstract

Perspiration-based wearable biosensors facilitate continuous monitoring of individuals' health states with real-time and molecular-level insight. The inherent inaccessibility of sweat in sedentary individuals in large volume (≥10 µL) for on-demand and in situ analysis has limited our ability to capitalize on this noninvasive and rich source of information. A wearable and miniaturized iontophoresis interface is an excellent solution to overcome this barrier. The iontophoresis process involves delivery of stimulating agonists to the sweat glands with the aid of an electrical current. The challenge remains in devising an iontophoresis interface that can extract sufficient amount of sweat for robust sensing, without electrode corrosion and burning/causing discomfort in subjects. Here, we overcame this challenge through realizing an electrochemically enhanced iontophoresis interface, integrated in a wearable sweat analysis platform. This interface can be programmed to induce sweat with various secretion profiles for real-time analysis, a capability which can be exploited to advance our knowledge of the sweat gland physiology and the secretion process. To demonstrate the clinical value of our platform, human subject studies were performed in the context of the cystic fibrosis diagnosis and preliminary investigation of the blood/sweat glucose correlation. With our platform, we detected the elevated sweat electrolyte content of cystic fibrosis patients compared with that of healthy control subjects. Furthermore, our results indicate that oral glucose consumption in the fasting state is followed by increased glucose levels in both sweat and blood. Our solution opens the possibility for a broad range of noninvasive diagnostic and general population health monitoring applications.

Additional Information

© 2017 National Academy of Sciences. Freely available online through the PNAS open access option. Contributed by Ronald W. Davis, February 14, 2017 (sent for review December 15, 2016; reviewed by Maneesh Jain and Andre Marziali. Author contributions: S.E., W.G., C.M., A.J., and R.W.D. designed research; S.E., W.G., E.W., Z.A.D., H.Y.Y.N., S.C., S.P.R., H.M.F., K.C., Z.S., S.T., C.M., A.J., and R.W.D. performed research; S.E., W.G., E.W., C.M., A.J., and R.W.D. contributed new reagents/analytic tools; S.E., W.G., E.W., C.M., A.J., and R.W.D. analyzed data; and S.E., W.G., E.W., C.M., A.J., and R.W.D. wrote the paper. Reviewers: M.J., Cirina; and A.M., University of British Columbia. The authors declare no conflict of interest. The work at Stanford University was supported by the National Institutes of Health Grant P01 HG000205; the work at University of California, Berkeley was supported by National Science Foundation Nanomanufacturing Systems for Mobile Computing and Energy Technologies Center. The sensor fabrication was performed in the Electronic Materials Laboratory (funded by the Director, Office of Science, Office of Basic Energy Sciences, Material Sciences and Engineering Division of the US Department of Energy under Contract DE-AC02-05CH11231) and Stanford Nanofabrication Facility. K.C. acknowledges support from the Robert N. Noyce Fellowship in Microelectronics.

Attached Files

Published - PNAS-2017-Emaminejad-4625-30Gao.pdf

Supplemental Material - pnas.201701740SI.pdf

Files

PNAS-2017-Emaminejad-4625-30Gao.pdf
Files (2.0 MB)
Name Size Download all
md5:837be321fa59a1c4a9a89fae0c39eea2
1.3 MB Preview Download
md5:aedd9c2b6034713298354b2077cf1288
615.5 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 17, 2023