生物标志物
可穿戴计算机
无线
分子生物标志物
压力(语言学)
计算机科学
纳米技术
材料科学
嵌入式系统
电信
生物
医学
遗传学
语言学
哲学
肿瘤科
作者
Shingirirai M. Chakoma,Xiaochang M. Pei,Huiting Qin,Anita Ghandehari,Sahar Najafikhoshnoo,Jerome Rajendran,Rahim Esfandyarpour
出处
期刊:Nano Research
[Springer Science+Business Media]
日期:2024-06-20
卷期号:17 (8): 7542-7556
被引量:26
标识
DOI:10.1007/s12274-024-6738-7
摘要
The significant impact of stress on health necessitates accurate assessment methods, where traditional questionnaires lack reliability and objectivity. Current advancements like wearables with electrocardiogram (ECG) and galvanic skin response (GSR) sensors face accuracy and artifact challenges. Molecular biosensors detecting cortisol, a critical stress hormone, present a promising solution. However, existing cortisol assays, requiring saliva, urine, or blood, are complex, expensive, and unsuitable for continuous monitoring. Our study introduces a passive, molecularly imprinted polymer-radio-frequency (MIP-RF) wearable sensing system for real-time, non-invasive sweat cortisol assessment. This system is wireless, flexible, battery-free, reusable, environmentally stable, and designed for long-term monitoring, using an inductance-capacitance transducer. The transducer translates cortisol concentrations into resonant frequency shifts with high sensitivity (∼ 160 kHz/(log (µM))) across a physiological range of 0.025–1 µM. Integrated with near-field communication (NFC) for wireless and battery-free operation, and three-dimensional (3D)-printed microfluidic channel for in-situ sweat collection, it enables daily activity cortisol level tracking. Validation of cortisol circadian rhythm through morning and evening measurements demonstrates its effectiveness in tracking and monitoring sweat cortisol levels. A 28-day stability test and the use of cost-effective 3D nanomaterials printing enhance its economic viability and reusability. This innovation paves the way for a new era in realistic, on-demand health monitoring outside the laboratory, leveraging wearable technology for molecular stress biomarker detection.
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