Local Activation of Cannabinoid CB <sub>1</sub> Receptors in the Urinary Bladder Reduces the Inflammation-Induced Sensitization of Bladder Afferents
Bibliographic record
Abstract
BACKGROUND: Systemic administration of cannabinoid agonists is known to reduce pain induced by bladder inflammation and to modulate cystometric parameters in vivo. We have previously reported that intravesical administration of a cannabinoid agonist reduces the electrical activity of bladder afferents under normal conditions. However, the effects of local activation of bladder cannabinoid receptors on afferent activity during inflammation are unknown. This study was aimed to assess the effects of intravesical administration of a cannabinoid agonist on the discharges of afferent fibers in inflamed bladders ex vivo. We also characterized the expression of CB1 receptors in the bladder and their localization and co-expression with TRPV1, a marker of nociceptive afferents. RESULTS: Compared to untreated animals, afferent fiber activity in inflamed bladders was increased for intravesical pressures between 10 and 40 mmHg. Local treatment with a non selective cannabinoid agonist (AZ12646915) significantly reduced the afferent activity at intravesical pressures above 20 mmHg. This effect was blocked by AM251 but not by AM630 (selective for CB1 and CB2 respectively). Finally, CB1 was co-expressed with TRPV1 in control and inflamed bladders. CONCLUSION: These results demonstrate that sensitization of bladder afferents induced by inflammation is partly suppressed by intravesical activation of cannabinoid receptors, an effect that appears to be mediated by CB1 receptors. Also, TRPV1 positive fibers were found to co-express CB1, supporting the hypothesis of a direct action of the cannabinoid agonist on nociceptive afferents. Taken together, these results indicate a peripheral modulation by the cannabinoid system of bladder hypersensitivity during inflammation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".