文献类型：期刊文献

英文题名：Modularly-Assembled Smart Microneedle Platform for Machine Learning-Driven Personalized Health Monitoring

作者：Sun, Hongyi[1,2];Chen, Lechen[3];Wang, Tao[4];Li, Zhuoheng[3];Shi, Yi[1];Lv, Wen[3];Yang, Zhi[3];Xuan, Fuzhen[4];Zhang, Min[1];Shi, Guoyue[1,2]

机构：[1]East China Normal Univ, Sch Chem & Mol Engn, Shanghai 200241, Peoples R China;[2]East China Normal Univ, WuHu Hosp, Peoples Hosp WuHu 2, Wuhu 241000, Peoples R China;[3]Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Natl Key Lab Adv Micro & Nano Manufacture Technol, Shanghai 200240, Peoples R China;[4]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect, Shanghai 200237, Peoples R China

年份：2026

卷号：18

期号：1

外文期刊名：NANO-MICRO LETTERS

收录：;EI(收录号：20260720051518);WOS:【SCI-EXPANDED(收录号:WOS:001686330800004)】；

基金：This work was supported by the National Natural Science Foundation of China (No. 22274051, No. 22274053, No. 62301314, and No. 52321002) and the Science and Technology Commission of Shanghai Municipality (24140711700). Additional support was provided by the In Situ Devices Research Center of East China Normal University.

语种：英文

外文关键词：Microneedle; Multiplexed sensing; Flexible patch; Machine learning; Personalized health

摘要：Given the inherent complexity of metabolic pathways and disease-associated agents, next-generation healthcare necessitates wearable, non-invasive, and customized approaches to continuously monitor a broad spectrum of physiologically relevant biomarkers for personalized health management. Moreover, existing data-based analytical strategies remain inadequate for delivering quantitative and predictive evaluations of health status in real-life settings. Here, we report an electronic multiplexed microneedle-based biosensor patch (eMPatch) that enables real-time, minimally invasive monitoring of key metabolic biomarkers in interstitial fluid, including glucose, uric acid, cholesterol, sodium, potassium, and pH. By integrating modular microneedle (MN) sensors into a skin-interfaced flexible platform, the eMPatch achieves robust mechanical stability and seamless skin conformity, thereby ensuring reliable and continuous sensing within the dermal space. In vivo validation in animal models under metabolic intervention highlights the strong capability of the eMPatch for real-time physiological tracking across diverse daily activities. Implemented with a machine learning algorithm, the eMPatch enables automatic feature extraction and multi-task health assessment, achieving a classification accuracy of 0.996 in distinguishing normal and diet-induced metabolic disorder for health condition identification and an R2 score of 0.977 for the corresponding degree evaluation. This study highlights the potential of the MN-integrated, machine learning-enhanced biosensing platform toward personalized health management.