汗水
囊性纤维化
可穿戴计算机
离子导入
背景(考古学)
原位
生物医学工程
汗腺
计算机科学
医学
生物
化学
内科学
神经科学
嵌入式系统
古生物学
有机化学
作者
Sam Emaminejad,Wei Gao,Eric Wu,Zoe Davies,Hnin Yin Yin Nyein,Samyuktha Challa,Sean P. Ryan,Hossain M. Fahad,Kevin Chen,Ziba Shahpar,Salmonn Talebi,Carlos Milla,Ali Javey,Ronald W. Davis
标识
DOI:10.1073/pnas.1701740114
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI