Cannabidiol Exposure During Gestation Leads to Adverse Cardiac Outcomes Early in Postnatal Life in Male Rat Offspring
Bibliographic record
Abstract
Introduction: Studies indicate that ∼7% of pregnant individuals in North America consume cannabis in pregnancy. Pre-clinical studies have established that maternal exposure to Δ 9 -tetrahydrocannabinol (THC; major psychoactive component in cannabis) leads to fetal growth restriction and impaired cardiac function in offspring. However, the effects of maternal exposure to cannabidiol (CBD; major non-euphoric constituent) on cardiac outcomes in offspring remain unknown. Therefore, our objective is to investigate the functional and underlying molecular impacts in the hearts of offspring exposed to CBD in pregnancy. Methods: Pregnant Wistar rats were exposed to either 3 or 30 mg/kg CBD or vehicle control i.p. daily from gestational day 6 to term. Echocardiography was used to assess cardiac function in male and female offspring at postnatal day (PND) 21. Furthermore, quantitative polymerase chain reaction (qPCR), immunoblotting, and bulk RNA-sequencing (RNA-seq) were performed on PND21 offspring hearts. Results: Despite no differences in the heart-to-body weight ratio, both doses of CBD led to reduced cardiac function exclusively in male offspring at 3 weeks of age. Underlying this, significant alterations in the expression of the endocannabinoid system (ECS; e.g., decreased cannabinoid receptor 2) were observed. In addition, bulk RNA-seq data demonstrated transcriptional pathways significantly enriched in mitochondrial function/metabolism as well as development. Conclusion: Collectively, we demonstrated for the first time that gestational exposure to CBD, a constituent perceived as safe, leads to early sex-specific postnatal cardiac deficits and alterations in the cardiac ECS in offspring.
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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) | 0.001 | 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) | 0.003 | 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".