Cannabivarin and tetrahydrocannabivarin modulate nociception via vanilloid channels and cannabinoid-like receptors in <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
Cannabis has attracted growing interest for its therapeutic potential, especially in pain management. This study explores the antinociceptive effects of two promising non-psychoactive cannabinoids, cannabivarin (CBV) and tetrahydrocannabivarin (THCV), using Caenorhabditis elegans, a nematode model that expresses homologs of mammalian cannabinoid and vanilloid receptors. Thermotaxis assays were employed to quantify the antinociceptive effects of CBV and THCV in C. elegans. Wild-type animals were exposed to increasing concentrations of each compound to establish dose–response relationships. To investigate potential molecular targets, additional experiments were performed using mutant strains deficient in vanilloid receptor homologs (OCR-2 and OSM-9) and cannabinoid receptor homologs (NPR-19 and NPR-32). Mass spectrometry-based proteomics combined with network biology analyses were used to identify the biological pathways associated with drug response. Results confirmed that both compounds elicit dose-dependent antinociceptive effects. Mutant analyses support the involvement of vanilloid and cannabinoid signaling pathways in mediating these responses. These findings highlight the potential of CBV and THCV as non-psychoactive analgesic agents and support further research into their mechanisms of action and translational relevance for mammalian pain management.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".