Characterizing the Role of Endocannabinoid Receptor Cnr1 in Mouse Ovarian Granulosa Cells
Bibliographic record
Abstract
The endocannabinoid receptors Cnr1 and Cnr2 have been found in reproductive organs such as the oviduct and uterus. These receptors bind to endocannabinoids, the arachinodoylethanolamine (AEA) and arachinodoylglycerol (2-AG), respectively. Both cannbinoid receptors have been investigated for their role in implantation and fertilization. However, not much is explored in terms of their role in ovarian granulosa cells. As these two receptors (especially Cnr1) have affinity towards the major component of Cannabis, tetrahydrocannabinol (THC), its usage raises concerns about the potential effects of THC on ovarian functions. Hence, it is important to characterize the role of endocannabinoid system in the ovarian granulosa cells. The objectives of this study were to use the mouse model to: (1) profile the expression pattern of the Cnr1 and Cnr2 and the endocannabinoid metabolizing enzymes (Faah and Mgll) in granulosa cells and (2) to determine the effect of the Cnr1 antagonist, AM251 on ovarian functions. We found that Cnr1 transcript abundance was higher (p < 0.05) at 4 h hCG than 24 h and 48 h eCG timepoints, whereas Cnr2 transcript decreased (p < 0.05) with follicular development. Conversely, Faah and Mgll transcripts were higher at 14 h hCG (p < 0.05) suggesting their upregulation after ovulation. The ovulation rate was lower in AM251 than vehicle-treated mice (p < 0.05), indicating that Cnr1 signaling may regulate ovulation. Further investigating the effect of AM251, we found that it significantly downregulated Ptgs2 and Pappa (p < 0.05). Overall, these data suggest that Cnr1, an important player in the endocannabinoid system, is important for ovulation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".