Oral cannabis disrupts uterine NK cell function leading to impaired decidual vessel remodeling and fetal growth in mice 3373
Bibliographic record
Abstract
Abstract Description The consumption of cannabis during pregnancy has increased in recent years following the widespread legalization of recreational cannabis. However, accumulating evidence uncovers the detrimental harm of cannabis and specifically the psychoactive component Δ9-tetrahydrocannabinol (THC) on pregnancy complications and fetal growth. Despite growing concern, the mechanism underlying cannabis-induced pregnancy complications is largely unknown. Given the substantial role of uterine Natural Killer (uNK) cells in maternal tissue remodeling during early pregnancy, we sought to determine if cannabis impacts uNK cell function and ultimately pregnancy outcome. Using a mouse model, we directly compared the effect of oral THC and cannabidiol (CBD) consumption on uNK cell function, decidual remodeling, and fetal growth. We also assessed how THC and CBD impact human NK cell angiogenic capabilities. Strikingly, we observed that both THC and CBD disrupted mouse and human NK cell angiogenic abilities which corresponded to impaired remodeling of vessels at the maternal-fetal interface in cannabis-exposed pregnant mice. Alarmingly, this disruption led to impaired fetal growth in both the CBD and THC-exposed mice. Altogether, our work pinpoints that oral exposure to either THC or CBD leads to compromised uNK cell function and ultimately impaired fetal growth and development in mice. Funding Sources This work is funded by the Michael G. DeGroote Centre for Medicinal Cancer Research and the Canadian Institutes of Health Research. Topic Categories Mucosal and Regional Immunology (MUC)
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".