Analysis of the distribution of spinal NOP receptors in a chronic pain model using NOP‐eGFP knock‐in mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The nociceptin/orphanin FQ opioid peptide (NOP) receptor system plays a significant role in the regulation of pain. This system functions differently in the spinal cord and brain. The mechanism by which the NOP receptor agonists regulate pain transmission in these regions is not clearly understood. Here, we investigate the peripheral and spinal NOP receptor distribution and antinociceptive effects of intrathecal nociceptin/orphanin FQ (N/OFQ) in chronic neuropathic pain. EXPERIMENTAL APPROACH: We used immunohistochemistry to determine changes in NOP receptor distribution triggered by spinal nerve ligation (SNL) using NOP-eGFP knock-in mice. Antinociceptive effects of intrathecal N/OFQ on SNL-mediated allodynia and heat/cold hyperalgesia were assessed in wild-type mice. KEY RESULTS: NOP-eGFP immunoreactivity was decreased by SNL in the spinal laminae I and II outer, regions that mediate noxious heat stimuli. In contrast, immunoreactivity of NOP-eGFP was unchanged in the ventral border of lamina II inner, which is an important region for the development of allodynia. NOP-eGFP expression was also decreased in a large number of primary afferents in the L4 dorsal root ganglion (DRG) of SNL mice. However, SNL mice showed increased sensitivity, compared to sham animals to the effects of i.t administered N/OFQ with respect to mechanical as well as thermal stimuli. CONCLUSIONS AND IMPLICATIONS: Our findings suggest that the spinal NOP receptor system attenuates injury-induced hyperalgesia by direct inhibition of the projection neurons in the spinal cord that send nociceptive signals to the brain and not by inhibiting presynaptic terminals of DRG neurons in the superficial lamina.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".