Forebrain delta opioid receptors regulate the response of delta agonist in models of migraine and opioid-induced hyperalgesia
Bibliographic record
Abstract
Delta opioid receptor (DOR) agonists have been identified as a promising novel therapy for headache disorders. DORs are broadly expressed in several peripheral and central regions important for pain processing and mood regulation; and it is unclear which receptors regulate headache associated symptoms. In a model of chronic migraine-associated pain using the human migraine trigger, nitroglycerin, we observed increased expression of DOR in cortex, hippocampus, and striatum; suggesting a role for these forebrain regions in the regulation of migraine. To test this hypothesis, we used conditional knockout mice with DORs deleted from forebrain GABAergic neurons (Dlx-DOR), and investigated the outcome of this knockout on the effectiveness of the DOR agonist SNC80 in multiple headache models. In DOR loxP controls SNC80 blocked the development of acute and chronic cephalic allodynia in the chronic nitroglycerin model, an effect that was lost in Dlx-DOR mice. In addition, the anti-allodynic effects of SNC80 were lost in a model of opioid induced hyperalgesia/medication overuse headache in Dlx-DOR conditional knockouts. In a model reflecting negative affect associated with migraine, SNC80 was only effective in loxP controls and not Dlx-DOR mice. Similarly, SNC80 was ineffective in the cortical spreading depression model of migraine aura in conditional knockout mice. Taken together, these data indicate that forebrain DORs are necessary for the action of DOR agonists in relieving headache-related symptoms and suggest that forebrain regions may play an important role in migraine modulation.
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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) | 0.000 | 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".