Modularly-Assembled Smart Microneedle Platform for Machine Learning-Driven Personalized Health MonitoringOA
Given the inherent complexity of metabolic pathways and disease-associated agents,next-generation healthcare necessitates wearable,noninvasive,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 realtime,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.
Hongyi Sun;Lechen Chen;Tao Wang;Zhuoheng Li;Yi Shi;Wen Lv;Zhi Yang;Fuzhen Xuan;Min Zhang;Guoyue Shi
School of Chemistry and Molecular Engineering,East China Normal University,Shanghai 200241,People''s Republic of China WuHu Hospital,East China Normal University(The Second People''s Hospital of WuHu),Wuhu 241000,People''s Republic of ChinaNational Key Laboratory of Advanced Micro and Nano Manufacture Technology,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People''s Republic of ChinaShanghai Key Laboratory of Intelligent Sensing and Detection Technology,School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai 200237,People''s Republic of ChinaNational Key Laboratory of Advanced Micro and Nano Manufacture Technology,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People''s Republic of ChinaSchool of Chemistry and Molecular Engineering,East China Normal University,Shanghai 200241,People''s Republic of ChinaNational Key Laboratory of Advanced Micro and Nano Manufacture Technology,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People''s Republic of ChinaNational Key Laboratory of Advanced Micro and Nano Manufacture Technology,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People''s Republic of ChinaShanghai Key Laboratory of Intelligent Sensing and Detection Technology,School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai 200237,People''s Republic of ChinaSchool of Chemistry and Molecular Engineering,East China Normal University,Shanghai 200241,People''s Republic of ChinaSchool of Chemistry and Molecular Engineering,East China Normal University,Shanghai 200241,People''s Republic of China WuHu Hospital,East China Normal University(The Second People''s Hospital of WuHu),Wuhu 241000,People''s Republic of China
医药卫生
MicroneedleMultiplexed sensingFlexible patchMachine learningPersonalized health
《Nano-Micro Letters》 2026 (7)
P.723-743,21
supported by the National Natural Science Foundation of China(No.22274051,No.22274053,No.62301314,and No.52321002)the Science and Technology Commission of Shanghai Municipality(24140711700).
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