Exploring the impact of adolescent exposure to cannabinoids and nicotine on psychiatric risk: insights from translational animal models
Bibliographic record
Abstract
Adolescence represents a highly sensitive period of mammalian neurodevelopment wherein critical synaptic and structural changes are taking place in brain regions involved in cognition, self-regulation and emotional processing. Importantly, neural circuits such as the mesocorticolimbic pathway, comprising the prefrontal cortex, sub-cortical mesolimbic dopamine system and their associated input/output centres, are particularly vulnerable to drug-related insults. Human adolescence represents a life-period wherein many individuals first begin to experiment with recreational drugs such as nicotine and cannabis, both of which are known to profoundly modulate neurochemical signalling within the mesocorticolimbic pathway and to influence both long-term and acute neuropsychiatric symptoms. While a vast body of epidemiological clinical research has highlighted the effects of adolescent exposure to drugs such as nicotine and cannabis on the developing adolescent brain, many of these studies are limited to correlative analyses and rely on retrospective self-reports from subjects, making causal interpretations difficult to discern. The use of pre-clinical animal studies can avoid these issues by allowing for precise temporal and dose-related experimental control over drug exposure during adolescence. In addition, such animal-based research has the added advantage of allowing for in-depth molecular, pharmacological, genetic and neuronal analyses of how recreational drug exposure may set up the brain for neuropsychiatric risk. This review will explore some of the advantages and disadvantages of these models, with a focus on the common, divergent and synergistic effects of adolescent nicotine and cannabis exposure on 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".