Delta 9-Tetrahydrocannabinol Signaling Through Cannabinoid Receptor 1 Alters Trophoblast Differentiation
Bibliographic record
Abstract
Cannabis use during pregnancy is increasing. In rodent models of delta-9 tetrahydrocannabinol (Δ9-THC) exposure during pregnancy, placental pathology, including compromised labyrinth development, is reported. Cannabinoid receptor 1 (CB1/ Cnr1 ) is the primary mediator of Δ9-THC action, with its expression reportedly limited to the placental junctional zone in the rodent. Given a Δ9-THC-induced labyrinth-specific pathology, we predicted more diverse expression. This study aimed to elucidate the spatiotemporal expression of CB1/ Cnr1 in the rodent and assess whether it mediates Δ9-THC-induced alterations in trophoblast differentiation. Results revealed CB1 expression in all maternal blood-facing trophoblast cells. Furthermore, Δ9-THC exposure (at levels matching those reported in maternal serum) had a more significant effect on the expression of markers associated with differentiating trophoblast cells than on proliferating trophoblast stem (TS) cells. Δ9-THC impacted mouse (m) TS cell differentiation in a CB1-dependent manner, reducing the expression of syncytiotrophoblast (SynT) markers, driving differentiation along the junctional zone/trophoblast giant cell pathway. mTS cells without Cnr1/ CB1 (mTS Cnr1 KO ) did not express markers of SynT cells or the differentiated junctional zone cell types. However, at a higher than physiologically relevant concentration, Δ9-THC (15 μM) induced Gcm1 (SynT) expression in mTS Cnr1 KO cells. This study reveals a mechanism by which Δ9-THC may impact placental growth.
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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".