<scp>κ‐</scp> opioid receptors are not necessary for the antidepressant treatment of neuropathic pain
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Tricyclic antidepressants are used clinically as first-line treatments for neuropathic pain. Opioid receptors participate in this pain-relieving action, and preclinical studies in receptor-deficient mice have highlighted a critical role for δ-, but not μ-opioid receptors. In this study, we investigated whether κ-opioid (KOP) receptors have a role in the antiallodynic action of tricyclic antidepressants. EXPERIMENTAL APPROACH: We used a model of neuropathic pain induced by unilateral sciatic nerve cuffing. In this model, the mechanical allodynia was evaluated using von Frey filaments. Experiments were conducted in C57BL/6J mice, and in KOP receptor-deficient mice and their wild-type littermates. The tricyclic antidepressant nortriptyline (5 mg · kg(-1)) was delivered twice a day for over 2 weeks. Agonists and antagonists of opioid receptors were used to test the selectivity of the KOP receptor antagonist norbinaltorphimine (nor-BNI) in mice with neuropathic pain. KEY RESULTS: After 12 days of treatment, nortriptyline relieved neuropathic allodynia in both wild-type and KOP receptor-deficient mice. Surprisingly, acute nor-BNI reversed the effect of nortriptyline in both wild-type and KOP receptor-deficient mice. Further experiments showed that nor-BNI action was selective for KOP receptors at a late time-point after its administration (8 h), but not at an early time-point, when it may also interact with δ-opioid (DOP) receptors. CONCLUSIONS AND IMPLICATIONS: KOP receptors are not necessary for the effect of a tricyclic antidepressant against neuropathic allodynia. These findings together with previous data indicate that the DOP receptor is the only opioid receptor that is necessary for the antiallodynic action of antidepressants.
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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) | 0.004 | 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".