Anti-allodynic effects of peripheral delta opioid receptors in neuropathic pain
Bibliographic record
Abstract
The analgesic effects of local administration of opioid agonists into peripheral tissues in alleviating pain have been well documented in both clinical and preclinical studies, although few studies have examined their effects in neuropathic pain. In this study, we investigated the anti-allodynic effects of peripherally acting delta opioid receptor (DOR) agonists in a rat model of neuropathic pain. Peripheral nerve injury (PNI) produced a time-dependent decrease in mechanical withdrawal thresholds that was attenuated by local administration into the hind paw of either morphine or the DOR agonist deltorphin II. Using Western blotting techniques, no change in DOR protein expression was detected in DRG ipsilateral to the site of injury compared to contralateral. However, an up-regulation of DOR protein was found in neuropathic DRG compared to sham, suggesting that there may be a bilateral increase in the expression of DOR following PNI. Results obtained from immunohistochemical studies confirmed up-regulation in small and large DRG neurons in neuropathic compared to sham animals. Additionally, there was an increase in DOR protein within the ipsilateral sciatic nerve of neuropathic animals compared to sham and contralateral neuropathic conditions indicating the occurrence of receptor trafficking to the site of injury. Taken together, our findings suggest that functional peripheral DORs are present in sensory neurons following PNI and validate the development of selective DOR agonists for alleviating 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 | 0.001 |
| 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".