Cannabidiol attenuates methamphetamine-induced conditioned place preference in male rats and viability in PC12 cells through the Sigma1R/AKT/GSK3β/CREB signaling pathway
Bibliographic record
Abstract
Background: Methamphetamine use is associated with several negative consequences, including neurotoxicity and greater probability of exhibiting a substance use disorder. Sigma1 receptor is involved in the neurobiological basis of several drug use disorders. Cannabidiol has received attention in the treatment of drug use disorders and neurotoxicity. Objectives: To investigate the effects of cannabidiol on methamphetamine-induced conditioned place preference (CPP) and the viability of PC12 cells. Methods: Adult male rats (n = 70) underwent methamphetamine (2 mg/kg, IP) induced CPP, and were administered cannabidiol (10, 20, 40, or 80 mg/kg, IP) during the methamphetamine withdrawal period for five consecutive days. Methamphetamine (0.5 mg/kg) was then injected to reactivate CPP. Four brain regions (ventral tegmental area, nucleus accumbens, prefrontal cortex, and hippocampus) were extracted after the last test. PC12 cells were treated with cannabidiol, Sigma1R-siRNA, or BD1047 before methamphetamine exposure. Results: Administration of 20, 40, or 80 mg/kg cannabidiol facilitated CPP extinction (80 mg/kg, p < .001) and prevented CPP development (80 mg/kg, p < .0001). This was associated with changes in the expression of Sigma1R (ventral tegmental area, 80 mg/kg, p < .0001) in the four brain regions. Cannabidiol protected the PC12 cell’s viability (10 μM, p = .0008) and inhibited the methamphetamine-induced activation of the AKT/GSK3β/CREB signaling pathway by mediating Sigma1R (10 μM, p < .0001). Conclusions: Cannabidiol seems to inhibit the rewarding effects of methamphetamine and the effects of this drug on cell viability. Sigma1R should be given further consideration as a potential target for cannabidiol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".