Delta-9 tetrahydrocannabinol (THC) effects on the cortisol stress response in bovine granulosa cells
Bibliographic record
Abstract
Maternal stress can result in changes in the hypothalamic-pituitary-adrenal (HPA) axis and lead to stress-related behaviours in offspring. Under physiological conditions, delta-9 tetrahydrocannabinol (THC) appears to be detrimental for fertility. However, cannabis is also commonly used for stress-relief. THC acts on the endocannabinoid receptors in granulosa cells (GCs), which affect oocyte competency. The objective of this study was to evaluate the effects of THC on in vitro bovine granulosa cell viability, apoptosis, and stress response pathway. GCs were cultured in vitro in the presence of clinically relevant therapeutic and recreational plasma doses of THC. Cortisol doses reflecting normal and elevated plasma levels were used to evaluate the effects of THC under induced stress in vitro. No effect of THC was observed on cell viability or apoptosis. High and low cortisol concentrations caused significant increases in 11β-HSD1 mRNA expression (n = 6, p < 0.0001). Interestingly, when combined with high [THC], there was a significant decrease in 11β-HSD1 expression compared to high and low cortisol treatments alone (p < 0.001, p < 0.05). GR expression was unaffected by cortisol treatments, and low [THC] treatment maintained increased expression in the presence of high and low cortisol treatments (n = 6, p < 0.01, p < 0.0001). Our findings represent a foundation to obtain useful data for evaluating THC potential therapeutic benefit.
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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 | 0.000 | 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 | 0.001 |
| 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".