Investigating the cumulative effects of Δ9-tetrahydrocannabinol and repetitive mild traumatic brain injury on adolescent rats
Bibliographic record
Abstract
Abstract The prevalence of mild traumatic brain injury is highest amongst the adolescent population and can lead to complications including neuroinflammation and excitotoxicity. Also pervasive in adolescents is recreational cannabis use. Δ9-Tetrahydrocannabinol, the main psychoactive component of cannabis, is known to have anti-inflammatory properties and serves as a neuroprotective agent against excitotoxicity. Thus, we investigated the effects of Δ9-tetrahydrocannabinol on recovery when administered either prior to or following repeated mild brain injuries. Male and female Sprague-Dawley rats were randomly assigned to receive Δ9-tetrahydrocannabinol or vehicle either prior to or following the repeated injuries. Rats were then tested on a behavioural test battery designed to measure post-concussive symptomology. The hippocampus, nucleus accumbens and prefrontal cortex were extracted from all animals to examine mRNA expression changes (Bdnf, Cnr1, Comt, GR, Iba-1 and Vegf-2R). We hypothesized that, in both experiments, Δ9-tetrahydrocannabinol administration would provide neuroprotection against mild injury outcomes and confer therapeutic benefit. Δ9-Tetrahydrocannabinol administration following repeated mild traumatic brain injury was beneficial to three of the six behavioural outcomes affected by injury (reducing anxiety and depressive-like behaviours while also mitigating injury-induced deficits in short-term working memory). Δ9-Tetrahydrocannabinol administration following injury also showed beneficial effects on the expression of Cnr1, Comt and Vegf-2R in the hippocampus, nucleus accumbens and prefrontal cortex. There were no notable benefits of Δ9-tetrahydrocannabinol when administered prior to injury, suggesting that Δ9-tetrahydrocannabinol may have potential therapeutic benefit on post-concussive symptomology when administered post-injury, but not pre-injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".