Chemical processing and shampooing impact cortisol measured in human hair
Bibliographic record
Abstract
PURPOSE: The assessment of cortisol in hair has gained popularity as a means to measure retrospective hypothalamic-pituitary-adrenal activity in a number of species; however, cortisol levels from human hair subjected to typical chemicals for cosmetic or hygienic purposes may be altered by the chemicals used. The purposed of this study was to determine if exposure of hair to chemical processing or shampooing impacts cortisol values. METHODS: Human hair not exposed to prior chemical processing was cut from the posterior vertex region of the head of 106 human subjects as close to the scalp as possible. The hair sample was divided into 4-6 full-length clusters depending on quantity of hair available. Each hair sample was processed for baseline (native) cortisol and remaining clusters were exposed to five standard chemical hair treatments (Experiment 1) or were shampooed 15 or 30 times (Experiment 2). Hair was ground and cortisol levels were determined by enzyme immunoassay (EIA). Comparisons were made between native hair and processed hair using paired t-tests and Pearson correlation. RESULTS: Hair cortisol as assessed by EIA was significantly altered by chemical processing but in somewhat different ways. Exposure to bleach (harshest exposure), demi-perm (least exposure) or 15-30 shampoos resulted in a significant decrease in cortisol level while exposure to varying percentages of peroxides increased cortisol measured. There were no differences in cortisol levels associated with sex, age or tobacco use in the native hair for this particular group. CONCLUSION: Chemical processing and frequent shampooing affect cortisol levels measured in hair. Chemically processed or excessively shampooed hair should be avoided when recruiting subjects for hair cortisol studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".