医学
可穿戴计算机
间隙
药效学
左旋多巴
血压
物理医学与康复
汗水
疾病
血流动力学
可穿戴技术
劳累
光容积图
生物利用度
持续监测
麻醉
物理疗法
药理学
重症监护医学
作者
Tamoghna Saha,Muhammad Inam Khan,Katherine Longardner,Barak Sabbagh,Kaiwen Zheng,Hugo Mendoza,Gaoyuan Ji,Bumsik Choi,Zongnan Wang,Rosie Pham,Michael Skipworth,Eshita Shah,Maria Reynoso,Chochanon Moonla,Abdulhameed Abdal,Debika Datta,Samar S. Sandhu,Ponnusamy Nandhakumar,Artur Jędrzak,Shichao Ding
标识
DOI:10.1073/pnas.2610453123
摘要
Precision management of Parkinson's disease (PD) requires frequent levodopa (L-dopa) dose adjustments, yet current monitoring relies on subjective symptom reporting and infrequent blood testing. Here, we present a soft, fingertip-mounted wearable platform for continuous, noninvasive L-dopa monitoring. By combining osmotically harvested passive sweat with soft hydrogels, a potentiometric sensing strategy, and individualized calibration, the platform estimates blood L-dopa information from sweat without external power or iontophoresis. Strong correlations between sweat and high-performance liquid chromatography (HPLC)-measured blood L-dopa concentrations were observed in healthy ([Formula: see text]) and PD subjects ([Formula: see text]) following a single immediate-release L-dopa/carbidopa dose. Low motor symptom scores aligned with peak L-dopa levels, confirming pharmacodynamic relevance. L-dopa cleared faster in PD patients despite similar bioavailability to healthy subjects, while recorded hemodynamic responses showed short hypotensive trends for both groups. Machine learning identified sweat and blood pressure as key contributors toward accurate estimation of blood L-dopa levels (mean absolute error = 2.02 µM vs. ground truth). Overall, our easy-to-use, energy-efficient wearable supports real-time, stimulation-free monitoring, potentially enabling at-home dosage adjustments and paving the way for future autonomous closed-loop L-dopa therapeutic system development.
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