Prenatal and early postnatal cannabis exposure interactions with adolescent chronic stress on anxiety-like, depression-like, and risk-taking behaviour
Bibliographic record
Abstract
RATIONALE: Low socioeconomic status people make up a majority of those who use cannabis during pregnancy. Both developmental cannabis exposure and developmental stress increase the risk of developing psychiatric disorders; however, the interaction of these factors has not been studied. OBJECTIVES: This study examined whether prenatal and early postnatal cannabis exposure (PPCE) impacted susceptibility to chronic adolescent stress in a dose- and environment-controlled animal model. METHODS: Mouse dams orally consumed 5 mg/kg THC in whole cannabis oil daily from GD1-PD10. Offspring were exposed to chronic mild unpredictable stress throughout adolescence (PD28-56). From PD58, mice were challenged with a battery of tests to measure anxiety-like (elevated plus maze, open field test), stress coping (forced swim test, tail suspension test), anhedonia-like (sucrose preference), risk-taking behaviour (wire beam bridge), and social motivation (3 chamber sociability and social novelty task). Brain slices were taken 90 min after forced swim test to analyze c-Fos expression. RESULTS: PPCE did not interact with chronic adolescent stress to impact anxiety-like, acute stress coping, or social motivation. However, co-exposed mice showed a significantly increased incidence of bridge crossing in the wire beam bridge task, whereas stress-only exposed animals did not. There were sex differences in c-FOS expression in the prefrontal cortex (PFC) in response to stress and PPCE. CONCLUSIONS: These data indicate that PPCE, when combined with adolescent stress, increases risk-taking behaviour.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".