Differential effects of gestational Cannabis smoke and phytocannabinoid injections on male and female rat offspring behavior
Bibliographic record
Abstract
-tetrahydrocannabinol (THC) at relatively high doses. Few investigations of the effects of GCE to smoke from the whole Cannabis flower have been conducted, despite this being the most common mode of human consumption. Here, we compared the effects of repeated gestational exposure to high-THC or high-cannabidiol (CBD) Cannabis smoke to i.p. THC or i.p. CBD to those of GCE to high-THC or high-CBD Cannabis smoke on litter health and the offspring. We found that injecting phytocannabinoids generally had a more severe impact on measures of maternal and litter health and produced distinct behavioral phenotypes when compared to offspring from dams treated with high-THC and high-CBD smoke during gestation. GCE to high-THC smoke decreased prepulse inhibition (PPI) and MK-801-induced locomotor activity in female adolescent offspring, which normalized in adulthood. GCE to i.p. THC increased exploratory behavior in the open field test in adolescent offspring of both sexes. GCE had a negative impact on offspring performance in the Identical Stimuli Test and Different Stimuli Test with odors regardless of gestational treatment, sex, or age. CBD (i.p) impaired PPI in both male and female offspring in adulthood and increased time spent in proximity during social interaction for male offspring. There were no effects of GCE in the 5 Choice Serial Reaction Time Task. These data establish distinct behavioral phenotypes in the offspring between smoked and injected GCE, further demonstrating that route and specific phytocannabinoid dose produce differential outcomes across offspring lifespan. Smoked Cannabis is still the most common means of consumption, and more preclinical investigation is needed to determine the effects of smoked Cannabis on developmental trajectories.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".