The Impacts of Adolescent Cannabinoid Exposure on Striatal Anxiety- and Depressive-Like Pathophysiology Are Prevented by the Antioxidant N-Acetylcysteine
Bibliographic record
Abstract
Exposure to Δ-9-tetrahydrocannabinol (THC) is an established risk factor for later life neuropsychiatric vulnerability, including mood and anxiety-related symptoms. The psychotropic effects of THC on affect and anxiogenic behavioural phenomena are known to target the striatal network, particularly the nucleus accumbens (Acb), a neural region linked to mood and anxiety disorder pathophysiology. THC may increase neuroinflammatory responses via the redox system as well as dysregulate inhibitory and excitatory neural balance in various brain circuits, including the striatum. Thus, interventions that can induce antioxidant effects may counteract the neurodevelopmental impacts of THC exposure. In the present study, we used an established preclinical adolescent rat model to examine the impacts of adolescent THC exposure on various behavioural, molecular, and neuronal biomarkers associated with increased mood and anxiety disorder vulnerability. Moreover, we investigated the protective properties of the antioxidant, N-Acetylcysteine (NAC), against THC-related pathology. We demonstrated that adolescent THC exposure induced long-lasting anxiety and depressive-like phenotypes concomitant with differential neuronal and molecular abnormalities in the two sub-regions of Acb, the Shell (AcbSh) and Core (AcbC). In addition, we report for the first time that NAC can prevent THC-induced accumbal pathophysiology and associated behavioural abnormalities. The preventative effects of this antioxidant intervention highlight the critical role of redox mechanisms underlying cannabinoid-induced neurodevelopmental pathology and identify a potential intervention strategy for the prevention and/or reversal of these pathophysiological sequelae.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".