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Managing stress is essential for mental and physical health, yet current methods rely on subjective self-assessments or indirect physiological measurements, often lacking accuracy. Existing wearable sensors primarily target a single stress hormone, cortisol, using single-point measurements that fail to capture real-time changes and distinguish between acute and chronic stress. To address this, we present Stressomic, a wearable multiplexed microfluidic biosensor for noninvasive monitoring of cortisol, epinephrine, and norepinephrine in sweat. Stressomic integrates iontophoresis-driven sweat extraction with bursting valve-regulated microfluidic channels for continuous sampling and analysis. Gold nanodendrite–decorated laser-engraved graphene electrodes achieve picomolar-level sensitivity, enabling simultaneous detection of multiple stress hormones. Electrochemical assays and human studies demonstrate that Stressomic reliably tracks hormone fluctuations in response to physical, psychological, and pharmacological stressors. Distinct temporal patterns reveal the dynamic interplay between the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. This platform enables continuous, multiplexed stress profiling, offering opportunities for early detection of maladaptive responses, personalized stress management, and deeper insights into stress biology. 

Abstract Recent advancements in wearable sensor technologies have enabled real-time monitoring of physiological and biochemical signals, opening new opportunities for personalized healthcare applications. However, conventional wearable devices often depend on rigid electronics components for signal transduction, processing, and wireless communications, leading to compromised signal quality due to the mechanical mismatches with the soft, flexible nature of human skin. Additionally, current computing technologies face substantial challenges in efficiently processing these vast datasets, with limitations in scalability, high power consumption, and a heavy reliance on external internet resources, which also poses security risks. To address these challenges, we have developed a miniaturized, standalone, chip-less wearable neuromorphic system capable of simultaneously monitoring, processing, and analyzing multimodal physicochemical biomarker data (i.e., metabolites, cardiac activities, and core body temperature). By leveraging scalable printing technology, we fabricated artificial synapses that function as both sensors and analog processing units, integrating them alongside printed synaptic nodes into a compact wearable system embedded with a medical diagnostic algorithm for multimodal data processing and decision making. The feasibility of this flexible wearable neuromorphic system was demonstrated in sepsis diagnosis and patient data classification, highlighting the potential of this wearable technology for real-time medical diagnostics. 

Abstract The rapid advancement in personalized healthcare has driven the development of wearable biomedical devices for real-time biomarker monitoring and diagnosis. Traditional invasive blood-based diagnostics are painful and limited to sporadic health snapshots. To address these limitations, microneedle-based sensing platforms have emerged, utilizing interstitial fluid (ISF) as an alternative biofluid for continuous health monitoring in a minimally invasive and painless manner. This review aims to provide a comprehensive overview of microneedle sensor technology, covering microneedle design, fabrication methods, and sensing strategy. Additionally, it explores the integration of monitoring electronics for continuous on-body monitoring. Representative applications of microneedle sensing platforms for both monitoring and therapeutic purposes are introduced, highlighting their potential to revolutionize personalized healthcare. Finally, the review discusses the remaining challenges and future prospects of microneedle technology. Graphical Abstract 

The amalgamation of wearable technologies with physiochemical sensing capabilities promises to create powerful interpretive and predictive platforms for real-time health surveillance. However, the construction of such multimodal devices is difficult to be implemented wholly by traditional manufacturing techniques for at-home personalized applications. Here, we present a universal semisolid extrusion–based three-dimensional printing technology to fabricate an epifluidic elastic electronic skin (e³-skin) with high-performance multimodal physiochemical sensing capabilities. We demonstrate that the e³-skin can serve as a sustainable surveillance platform to capture the real-time physiological state of individuals during regular daily activities. We also show that by coupling the information collected from the e³-skin with machine learning, we were able to predict an individual’s degree of behavior impairments (i.e., reaction time and inhibitory control) after alcohol consumption. The e³-skin paves the path for future autonomous manufacturing of customizable wearable systems that will enable widespread utility for regular health monitoring and clinical applications.