μ Opioid receptors and analgesia at the site of a peripheral nerve injury
Bibliographic record
Abstract
Opioid ligands may exert antinociception through receptors expressed on peripheral afferent axons. Whether local opioid receptors might attenuate neuropathic pain is uncertain. In this work, we examined the function and expression of local mu opioid receptors (MORs) associated with the chronic constriction injury (CCI) model of sciatic neuropathic pain in rats. Low-dose morphine or its carrier were percutaneously superfused over the CCI site with the injector blinded to the identity of the injectate. Morphine, but not its carrier, and not equimolar systemic doses of morphine reversed thermal hyperalgesia in a dose-related, naloxone-sensitive fashion. Moreover, analgesia was conferred at both 48 hours and 14 days after CCI, times associated with very different stages of nerve repair. Equimolar local DAGO ([D-Ala2, N-Me-Phe4, Gly5-(ol)] enkephalin), a selective MOR ligand, provided similar analgesia. Local morphine also attenuated mechanical allodynia. MOR protein was expressed in axonal endbulbs of Cajal just proximal to the injury site, in aberrantly regenerating small axons in the epineurial sheath around the CCI site and in residual small axons distal to the CCI lesion. Sensory neurons ipsilateral to CCI had an increase in the proportion of neurons expressing MOR. We suggest that local MOR expressed in axons may be exploited to modulate some forms of neuropathic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".