MicroSweat: A Wearable Microfluidic Patch for Noninvasive and Reliable Sweat Collection Enables Human Stress Monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Stress affects cognition, behavior, and physiology, leading to lasting physical and mental illness. The ability to detect and measure stress, however, is poor. Increased circulating cortisol during stress is mirrored by cortisol release from sweat glands, providing an opportunity to use it as an external biomarker for monitoring internal emotional state. Despite the attempts at using wearable sensors for monitoring sweat cortisol, there is a lack of reliable wearable sweat collection devices that preserve the concentration and integrity of sweat biomolecules corresponding to stress levels. Here, a flexible, self‐powered, evaporation‐free, bubble‐free, surfactant‐free, and scalable capillary microfluidic device, MicroSweat, is fabricated to reliably collect human sweat from different body locations. Cortisol levels are detected corresponding to severe stress ranging from 25 to 125 ng mL −1 averaged across multiple body regions and 100–1000 ng mL −1 from the axilla. A positive nonlinear correlation exists between cortisol concentration and stress levels quantified using the perceived stress scale (PSS). Moreover, owing to the sweat variation in response to environmental effects and physiological differences, the longitudinal and personalized profile of sweat cortisol is acquired, for the first time, for various body locations. The obtained sweat cortisol data is crucial for analyzing human stress in personalized and clinical healthcare sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it