Striatal delta opioid receptor binding in experimental models of Parkinson's disease and dyskinesia
Bibliographic record
Abstract
Enhanced delta opioid receptor transmission may represent an endogenous compensatory mechanism in parkinsonism to reduce the activity of the indirect striatopallidal pathway following dopamine depletion. Furthermore, increased delta opioid receptor transmission may be causative in the production of dyskinesia following repeated dopaminergic treatment in Parkinson's disease. The present study employed radioligand receptor autoradiography, using [3H]naltrindole, a ligand selective for the delta opioid receptor, to assess delta opioid receptor binding sites in forebrain regions of reserpine-treated rats, and in parkinsonian nondyskinetic, and dyskinetic MPTP-lesioned macaques. In reserpine-treated animals, specific delta opioid binding was increased in premotor cortex (+30%), sensorimotor striatum (+20%), and associative striatum (+17%) rostrally, but was not changed in caudal forebrain. In contrast, delta opioid receptor binding was not significantly altered at any region analyzed, in either nondyskinetic or dyskinetic, MPTP-lesioned macaques, compared to normal. These results suggest that transient changes in delta opioid receptor binding may occur in motor circuits following acute dopamine depletion. However, in the more chronic MPTP-lesioned macaque model, simple changes in delta opioid receptor number or affinity are unlikely to contribute to mechanisms for abnormal opioid transmission in Parkinson's disease and dyskinesia.
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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 | 0.001 | 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) | 0.001 | 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".