The impact of oral cannabis consumption during pregnancy on maternal spiral artery remodelling, fetal growth and offspring behaviour in mice
Bibliographic record
Abstract
BACKGROUND: The use of cannabis during pregnancy is rising following its widespread legalization. Cannabidiol (CBD) is gaining popularity due to the public perception that it is safer than the psychoactive cannabis component Δ9-tetrahydrocannabinol (THC). However, while evidence underpins the harm of THC and cannabis smoke on fetal development, there is minimal research on the safety of CBD and oral cannabis. The current study aims to decipher the safety of oral CBD and THC use during pregnancy. METHODS: Using a mouse model, we directly compared the effects of oral CBD and THC oil exposure (20 mg/kg body weight) from early to mid-gestation on implantation site remodelling and fetal growth. We examined offspring behaviour and metabolic activity using both traditional and automated cage systems. Lastly, using human and mouse immune cells we assessed how CBD and THC influence angiogenic factor production. FINDINGS: We observed impaired maternal spiral artery remodelling in cannabis exposed mice and found that CBD and THC disrupt immune cell angiogenic factor production. Oral consumption of THC or CBD oil also resulted in significant fetal growth impairment and led to long-lasting sex-dependent consequences as male offspring exhibited altered aggression and metabolic activity while females had impaired spatial learning. INTERPRETATION: Our results show that oral consumption of either CBD or THC oil during pregnancy in mice results in harm to the developing fetus and causes behavioural changes after birth. FUNDING: The Michael G. DeGroote Centre for Medicinal Cancer Research, the Canadian Institutes of Health Research, and the Canadian Foundation for Innovation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".