Terpenes as Modulators of Nociceptive Signaling: Behavioral and Molecular Insights from <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
Abstract Terpenes such as Limonene and β-Caryophyllene have demonstrated pain-modulating properties, potentially through interactions with TRPV1 receptors. This study examines the antinociceptive effects of four terpenes derived from Cannabis sativa : Limonene, β-Caryophyllene, α-Humulene, and α-Myrcene using Caenorhabditis elegans ( C. elegans ). The primary objective was to characterize terpene-induced modulation of nocifensive responses to noxious heat, and to elucidate their influence on molecular pathways via specific receptor targets. Thermotaxis assays quantified the antinociceptive activity of increasing terpene concentrations in wild-type nematodes. To assess receptor-specific mechanisms, assays were performed in mutant strains lacking functional OCR-2 and OSM-9 (TRPV-like vanilloid nociceptors), and NPR-19 and NPR-32 (encoding cannabinoid-like receptors). Proteomic profiling coupled with bioinformatics analysis identified terpene-induced alterations in signaling pathways and biological processes. All four terpenes exhibited significant antinociceptive activity in wild-type C. elegans , with impaired effects observed in vanilloid receptor mutants, implicating TRPV-like channels in their mechanism of action. Proteomic and pathway analyses revealed terpene-specific molecular signatures, highlighting differential modulation of neuronal and stress-responsive signaling cascades. By elucidating the molecular mechanisms underlying terpene-induced nociceptive modulation, this work strengthens the growing body of evidence supporting the therapeutic promise of terpenes in pain management outside the effect referred to as the “entourage effect.”
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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".