Cannabinoid receptor CB2 localisation and agonist-mediated inhibition of capsaicin responses in human sensory neurons
Bibliographic record
Abstract
Cannabinoid receptor 2 (CB2) agonists provide the potential for treating chronic pain states without CNS effects associated with CB1 receptor activation. Animal models suggest that they act mainly via non-neuronal cells, possibly inhibition of inflammatory cells in the periphery or CNS, or via release of beta-endorphin; however, the clinical relevance and mechanism of analgesic action is uncertain. Here, we demonstrate colocalisation of CB2 with CB1 and the capsaicin receptor TRPV1 in human dorsal root ganglion (DRG) sensory neurons and increased levels of CB2 receptors in human peripheral nerves after injury, particularly painful neuromas. In primary cultures of human DRG neurons, selective CB2 agonists blocked activation of inward cation currents and elevation of cytoplasmic Ca2+ in response to capsaicin. These inhibitory effects were reversed by GW818646X a CB2 antagonist, and 8-bromo cAMP, but not by SR141716 a CB1 antagonist, or naloxone. Thus CB2 receptor agonists functionally inhibited nociceptive signalling in human primary sensory neurons via a mechanism shared with opioids, of adenylyl cyclase inhibition, but not via mu-opioid receptors. We conclude that CB2 agonists deserve imminent clinical trials for nociceptive, inflammatory and neuropathic chronic pain, in which capsaicin or heat-activated responses via TRPV1 may provide a clinical marker.
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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) | 0.003 | 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".