Cannabinoid type-2 receptors modulate terpene induced anxiety-reduction in zebrafish
Bibliographic record
Abstract
Terpenes are the most extensive and varied group of naturally occurring compounds mostly found in plants, including cannabis, and have an array of potential therapeutic benefits for pathological conditions. The endocannabinoid system can potently modulate anxiety in humans, rodents, and zebrafish. The ‘entourage effect’ suggests terpenes may target cannabinoid CB1 and CB2 receptors, among others, but this requires further investigation. In this study we first tested for anxiety-altering effects of the predominant ‘Super-Class’ terpenes, bisabolol (0.001%, 0.0015%, and 0.002%) and terpinolene (TPL; 0.01%, 0.05%, and 0.1%), in zebrafish with the open field test. Bisabolol did not have an effect on zebrafish behaviour or locomotion. However, TPL caused a significant increase in time spent in the inner zone and decrease in time spent in the outer zone of the arena indicating an anxiolytic (anxiety decreasing) effect. Next, we assessed whether CB1 and CB2 receptor antagonists, rimonabant and AM630 (6-Iodopravadoline) respectively, could eliminate or reduce the anxiolytic effects of TPL (0.1%) and β-caryophyllene (BCP; 4%), another super-class terpene previously shown to be anxiolytic in zebrafish. Rimonabant and AM630 were administered prior to terpene exposure and compared to controls and fish exposed to only the terpenes. AM630, but not rimonabant, eliminated the anxiolytic effects of both BCP and TPL. AM630 modulated locomotion on its own, which was potentiated by terpenes. These findings suggest the behavioural effects of TPL and BCP on zebrafish anxiety-like behaviour are mediated by a selective preference for CB2 receptor sites. Furthermore, the CB2 pathways mediating the anxiolytic response are likely different from those altering locomotion.
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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 | 0.001 |
| 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 | 0.000 |
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".