Effects of an acute cannabidiol treatment on cocaine self-administration and cue-induced cocaine seeking in male rats
Bibliographic record
Abstract
Cannabidiol is a non-psychoactive compound that is the second most abundant component of cannabis. It has been shown to have a potential therapeutic value for a wide range of disorders, including anxiety, psychosis, and depression. Recently, it was suggested that cannabidiol might be a potential treatment for heroin craving and relapse. Here we investigated the effects of an acute treatment with cannabidiol on cocaine self-administration and cue-induced cocaine seeking in rats. Rats were trained to press a lever to self-administer cocaine (0.5 mg/kg/infusion), first under a fixed interval 20 s (FI-20 s) and then under a progressive ratio (PR) schedule of reinforcement. Cocaine self-administration under a PR schedule of reinforcement was not attenuated by cannabidiol injections (5.0 mg/kg and 10.0 mg/kg; i.p.) when tested 30 min and 24 h after treatment. Cannabidiol treatment (5.0 mg/kg or 10.0 mg/kg) also did not attenuate cue-induced cocaine seeking in rats after a withdrawal period of 14 days. In contrast, treatment with cannabidiol (10.0 mg/kg; i.p.) resulted in a statistically significant anxiolytic effect in the elevated plus-maze. Our findings suggest that, under the conditions described here, an acute cannabidiol treatment has a minimal effect on a rat model of cocaine intake and relapse.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".