Chronic neuropathic pain reduces opioid receptor availability with associated anhedonia in rat
Bibliographic record
Abstract
The opioid system plays a critical role in both the experience and management of pain. Although acute activation of the opioid system can lead to pain relief, the effects of chronic pain on the opioid system remain opaque. Cross-sectional positron emission tomography (PET) studies show reduced availability of brain opioid receptors in patients with chronic pain but are unable to (1) determine whether these changes are due to the chronic pain itself or due to preexisting or medication-induced differences in the endogenous opioid system, and (2) identify the neurobiological substrate of reduced opioid receptor availability. We investigated these possibilities using a well-controlled longitudinal study design in rat. Using [F]-FDPN-PET in either sham rats (n = 17) or spared nerve injury rats (n = 17), we confirmed reduced opioid receptor availability in the insula, caudate-putamen, and motor cortex of nerve injured rats 3 months after surgery, indicating that painful neuropathy altered the endogenous opioid system. Immunohistochemistry showed reduced expression of the mu-opioid receptor, MOR1, in the caudate-putamen and insula. Neither the opioid peptide enkephalin nor the neuronal marker NeuN differed between groups. In nerve-injured animals, sucrose preference, a measure of anhedonia/depression-like behavior, positively correlated with PET opioid receptor availability and MOR1-immunoreactivity in the caudate-putamen. These findings provide new evidence that the altered supraspinal opioid receptor availability observed in human patients with chronic pain may be a direct result of chronic pain. Moreover, reduced opioid receptor availability seems to reflect decreased receptor expression, which may contribute to pain-induced depression.
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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) | 0.000 | 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) | 0.002 | 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".