Skin responses to topical dehydroepiandrosterone: implications in antiageing treatment?
Bibliographic record
Abstract
BACKGROUND: Although low dehydroepiandrosterone (DHEA) is suspected to have a role in skin ageing, little information is available on the mechanisms potentially involved. OBJECTIVES: To obtain information on androgen receptor (AR) and procollagen expression in ageing skin during DHEA treatment. METHODS: A placebo-controlled, randomized, prospective study was performed with 75 postmenopausal women aged 60-65 years. The women were treated twice daily for 13 weeks with 3·0 mL of placebo or 0·1%, 0·3%, 1% or 2% DHEA cream applied on the face, arms, back of hands, upper chest and right thigh where 2-mm biopsies were collected before and after treatment. RESULTS: Although the overall structure of the epidermis was not significantly affected at the light microscopy level, AR expression examined by immunocytochemistry was markedly increased by DHEA treatment. In the dermis, the expression levels of procollagen 1 and 3 mRNA estimated by in situ hybridization were increased by DHEA treatment. In addition, the expression of heat shock protein (HSP) 47, a molecule believed to have chaperone-like functions potentially affecting procollagen biosynthesis, was also found by immunocytochemistry evaluation to be increased, especially at the two highest DHEA doses. CONCLUSION: These data suggest the possibility that topical DHEA could be used as an efficient and physiological antiageing skin agent.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".