Mu and delta opioid receptors play opposite nociceptive and behavioural roles on nerve‐injured mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Mu and delta opioid receptors(MOP, DOP) contribution to the manifestations of pathological pain is not understood. We used genetic approaches to investigate the opioid mechanisms modulating neuropathic pain and its comorbid manifestations. EXPERIMENTAL APPROACH: We generated conditional knockout mice with MOP or DOP deletion in sensoryNav1.8-positive neurons (Nav1.8), in GABAergic forebrain neurons (DLX5/6) orconstitutively (CMV). Mutant mice and wild-type littermates were subjected topartial sciatic nerve ligation (PSNL) or sham surgery and their nociception wascompared. Anxiety-, depressivelike behaviour and cognitive performance were also measured. Opioid receptor mRNA expression, microgliosis and astrocytosis were assessed in the dorsalroot ganglia (DRG) and/or the spinal cord (SC). KEY RESULTS: Constitutive CMV-MOP knockouts after PSNL displayed reduced mechanical allodynia and enhanced heat hyperalgesia. This phenotype was accompanied by increased DOP expression in DRG and SC, and reduced microgliosis and astrocytosis in deep dorsal horn laminae. Conditional MOP knockouts and control mice developed similar hypersensitivity after PSNL, except for anenhanced heat hyperalgesia by DLX5/6-MOP male mice. Neuropathic pain-induced anxiety was aggravated in CMV-MOP and DLX5/6-MOP knockouts. Nerve-injured CMV-DOP mice showed increased mechanical allodynia, whereas Nav1.8-DOP and DLX5/8-DOP mice had partial nociceptive enhancement. CMV-DOP and DLX5/6-DOP mutants showed increased depressive-like behaviour after PSNL. CONCLUSIONS AND IMPLICATIONS: MOP activity after nerve injury increased anxiety-like responses involving forebrain GABAergic neurons and enhanced mechanical pain sensitivity along with repression of DOP expression and spinal cord gliosis. In contrast, DOP shows a protective function limiting nociceptive and affective manifestations of neuropathic 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".