Prenatal tetrahydrocannabinol and cannabidiol exposure produce sex-specific pathophysiological phenotypes in the adolescent prefrontal cortex and hippocampus
Bibliographic record
Abstract
Clinical and preclinical evidence has demonstrated an increased risk for neuropsychiatric disorders following prenatal cannabinoid exposure. However, given the phytochemical complexity of cannabis, there is a need to understand how specific components of cannabis may contribute to these neurodevelopmental risks later in life. To investigate this, a rat model of prenatal cannabinoid exposure was utilized to examine the impacts of specific cannabis constituents (Δ9-tetrahydrocannabinol [THC]; cannabidiol [CBD]) alone and in combination on future neuropsychiatric liability in male and female offspring. Prenatal THC and CBD exposure were associated with low birth weight. At adolescence, offspring displayed sex-specific behavioural changes in anxiety, temporal order and social cognition, and sensorimotor gating. These phenotypes were associated with sex and treatment-specific neuronal and gene transcriptional alterations in the prefrontal cortex, and ventral hippocampus, regions where the endocannabinoid system is implicated in affective and cognitive development. Electrophysiology and RT-qPCR analysis in these regions implicated dysregulation of the endocannabinoid system and balance of excitatory and inhibitory signalling in the developmental consequences of prenatal cannabinoids. These findings reveal critical insights into how specific cannabinoids can differentially impact the developing fetal brains of males and females to enhance subsequent neuropsychiatric risk.
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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 | 0.001 | 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 | 0.000 |
| 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".