Profiling mRNA encoding glucocorticoid receptor α in saliva: Relationship to hair cortisol levels in individuals aged 15–25 years
Bibliographic record
Abstract
OBJECTIVE: We assessed levels of mRNA encoding two glucocorticoid receptor (GR) isoforms (GRα and GRβ) in saliva and examined their relationship with hair cortisol levels and dental caries experience. DESIGN: Adolescents and young adults were assessed for dental caries experience, and hair cortisol was measured by ELISA. RNA was extracted from whole saliva using TRIzol, followed by quantitative real-time PCR analysis of GRα, GRβ, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH). RESULTS: GRβ mRNA was not detectable in most samples, whereas GRα mRNA was observed in all samples. There were significantly lower levels of GRα mRNA in individuals with elevated hair cortisol levels than in those with normal cortisol levels. Levels of GRα mRNA did not differ significantly in individuals with dental caries experience compared to individuals with no caries experience. CONCLUSIONS: We identified and quantified mRNA encoding GRα in saliva. Its levels were inversely associated with hair cortisol (a marker of chronic stress). Although caries experience was associated with hair cortisol levels, there was no significant association between GRα levels and caries experience. Chronic stress has been proposed to be associated with reduced expression of GRα and this association appears to hold for GRα mRNA levels in saliva.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".