Cannabinoid exposure does not alter estradiol biosynthesis in human KGN granulosa cells
Bibliographic record
Abstract
Steroidogenesis is essential for ovarian physiology and reproductive health. Regulated by hormonal signals, it is susceptible to external modulators, notably environmental exposures. As cannabis becomes more accessible globally, its use among women of reproductive age has increased, yet the implications for reproductive endocrinology remain poorly understood and contradictory. In this study, we investigated whether cannabinoids modulate basal or stimulated estradiol secretion in the human granulosa cell line KGN. To characterize the endocannabinoid system (ECS) in these cells, we performed a meta-analysis of publicly available RNA sequencing datasets, revealing expression of key ECS components. KGN cells were cultured with or without cannabinoids in the presence of protein kinase activators, PKA (FSK), PKB (SC79), and PKC (PMA). Following cannabinoid and kinase stimulations, the media were collected and analyzed for estradiol concentrations via ELISA. We observed no significant changes in basal or activated estradiol secretion in response to THC or CBD. These findings were supported by RT-qPCR analysis showing no alteration in the expression of CYP19A1, the gene encoding aromatase, which catalyzes the conversion of androgens to estrogens in granulosa cells. Although cannabinoids have been shown to influence sex hormones in vivo, our data suggest that these effects are not mediated at the granulosa cell level. This study contributes to a better understanding of how cannabinoids may interact with ovarian steroidogenesis and reproductive function.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".