The Detection of Cortisol in Human Sweat
Bibliographic record
Abstract
BACKGROUND: Hair cortisol analysis has been shown to be an effective measure of chronic stress. Cortisol is assumed to incorporate into hair via serum, sebum, and sweat sources; however, the extent to which sweat contributes to hair cortisol content is unknown. METHODS: Sweat and saliva samples were collected from 17 subjects after a period of intensive exercise and analyzed by salivary enzyme-linked immunosorbent assay (ELISA). Subsequently, an in vitro test on exposure of hair to hydrocortisone was conducted. Residual hair samples were immersed in a 50-ng/mL hydrocortisone solution for periods lasting 15 minutes to 24 hours, followed by a wash or no-wash condition. Hair cortisol content was determined using our modified protocol for a salivary ELISA. RESULTS: Postexercise control sweat cortisol concentrations ranged from 8.16 to 141.7 ng/mL and correlated significantly with the log-transformed time of day. Sweat cortisol levels significantly correlated with salivary cortisol concentrations. In vitro hair exposure to a 50-ng/mL hydrocortisone solution (mimicking sweat) for 60 minutes or more resulted in significantly increased hair cortisol concentrations. Washing with isopropanol did not affect immersion-increased hair cortisol concentrations. CONCLUSIONS: Human sweat contains cortisol in concentrations comparable with salivary cortisol levels. This study suggests that perfuse sweating after intense exercise may increase cortisol concentrations detected in hair. This increase likely cannot be effectively decreased with conventional washing procedures and should be considered carefully in studies using hair cortisol as a biomarker of chronic stress.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".