Pronociceptive role of spinal Ca<sub>v</sub>2.3 (R‐type) calcium channels in a mouse model of postoperative pain
Bibliographic record
Abstract
Abstract Background More than 80% of patients may experience acute pain after a surgical procedure, and this is often refractory to pharmacological intervention. The identification of new targets to treat postoperative pain is necessary. There is an association of polymorphisms in the Cav2.3 gene with postoperative pain and opioid consumption. Our study aimed to identify Cav2.3 as a potential target to treat postoperative pain and to reduce opioid‐related side effects. Experimental Approach A plantar incision model was established in adult male and female C57BL/6 mice. Cav2.3 expression was detected by qPCR and suppressed by siRNA treatment. The antinociceptive efficacy and safety of a Cav2.3 blocker—alone or together with morphine—was also assessed after surgery. Key Results Paw incision in female and male mice caused acute nociception and increased Cav2.3 mRNA expression in the spinal cord but not in the incised tissue. Intrathecal treatment with siRNA against Cav2.3, but not with a scrambled siRNA, prevented the development of surgery‐induced nociception in both male and female mice, with female mice experiencing long‐lasting effects. High doses of i.t. SNX‐482, a Cav2.3 channel blocker, or morphine injected alone, reversed postoperative nociception but also induced side effects. A combination of lower doses of morphine and SNX‐482 mediated a long‐lasting reversal of postsurgical pain in female and male mice. Conclusion Our results demonstrate that Cav2.3 has a pronociceptive role in the induction of postoperative pain, indicating that it is a potential target for the development of therapeutic approaches for the treatment of postoperative pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".