Cannabinoid Signalling in Immune–Reproductive Crosstalk during Human Pregnancy
Bibliographic record
Abstract
Despite the intricate involvement of the endocannabinoid system in various physiological processes, it remains one of the most under-studied biological systems of the human body. The scope of endocannabinoid signalling is widespread, ranging from modulation of immune responses in innate and adaptive immunity to gestational processes in female physiology. Cannabinoid receptors are ubiquitously distributed in reproductive tissues and are thought to play a role in regulating the immune-reproductive interactions required for successful pregnancy, specifically among uterine natural killer cells and placental extravillous trophoblasts. The use of cannabis during pregnancy, however, can perturb endocannabinoid homeostasis through effects mediated by its major constituents, Δ-9-tetrahydrocannabinol and cannabidiol. Decidualization of the endometrium, invasion, and angiogenesis may be impaired as a consequence, leading to clinical complications such as miscarriage and preeclampsia. In this review, the crosstalk between endocannabinoid signalling in uterine natural killer cells and placental extravillous trophoblasts will be examined in healthy and complicated pregnancies. This lays a foundation for discussing the potential of targeting the endocannabinoid system for therapeutic benefit, particularly with regard to the emerging field of synthetic cannabinoids.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".