L-745,870 reduces the expression of abnormal involuntary movements in the 6-OHDA-lesioned rat
Bibliographic record
Abstract
L-3,4-Dihydroxyphenylalanine (L-DOPA) is the most effective treatment for Parkinson's disease, but chronic administration is complicated by the development of dyskinesia. We have previously demonstrated that the dopamine D4 receptor antagonist L-745,870 reduces the severity of L-DOPA-induced dyskinesia in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned macaque without compromising L-DOPA antiparkinsonian benefits. In the current study, we have addressed the effects of L-745,870 on the expression of L-DOPA-induced abnormal involuntary movements (AIMs) in the 6-hydroxydopamine-lesioned rat. Rats were primed with repeated L-DOPA administration, after which acute challenges of L-DOPA/L-745,870 (vehicle, 0.1, 0.3 and 1 mg/kg) were administered, and AIMs were assessed. Rotarod performance and AIMs were assessed. In L-DOPA-primed rats, L-745,870 (1 mg/kg, but not lower doses) alleviated previously established AIMs (by 84%, P<0.001). Whereas rotarod performance was significantly improved by L-DOPA/vehicle treatment, L-DOPA/L-745,870 failed to improve rotarod performance (P>0.05), suggesting that, in contrast to the MPTP-lesioned macaque, L-745,870 reduces L-DOPA antiparkinsonian benefit in the rat model. Overall, these data suggest that L-745,870 may have a narrow therapeutic window as an antidyskinetic agent in advanced Parkinson's disease.
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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 | 0.000 |
| 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) | 0.002 | 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".