Granisetron, a selective 5-HT3 antagonist, reduces L-3,4-dihydroxyphenylalanine-induced abnormal involuntary movements in the 6-hydroxydopamine-lesioned rat
Bibliographic record
Abstract
Administration of L-3,4-dihydroxyphenylalanine (L-DOPA) provides Parkinson's disease patients with effective symptomatic relief. However, long-term L-DOPA therapy is often marred by complications such as dyskinesia. We have previously demonstrated that serotonin type 3 (5-HT3) receptor blockade with the clinically available and highly selective antagonist ondansetron alleviates dyskinesia in the 6-hydroxydopamine (6-OHDA)-lesioned rat. Here, we sought to explore the antidyskinetic efficacy of granisetron, another clinically available 5-HT3 receptor antagonist. Rats were rendered hemi-parkinsonian by 6-OHDA injection in the medial forebrain bundle. Following induction of stable abnormal involuntary movements (AIMs), granisetron (0.0001, 0.001, 0.01, 0.1 and 1 mg/kg) or vehicle was acutely administered in combination with L-DOPA and the severity of AIMs, both duration and amplitude, was determined. We also assessed the effect of granisetron on L-DOPA antiparkinsonian action by performing the cylinder test. Adding granisetron (0.0001, 0.001, 0.01, 0.1 and 1 mg/kg) to L-DOPA resulted in a significant reduction of AIMs duration and amplitude, with certain parameters being reduced by as much as 38 and 45% (P < 0.05 and P < 0.001, respectively). The antidyskinetic effect of granisetron was not accompanied by a reduction of L-DOPA antiparkinsonian action. These results suggest that 5-HT3 blockade may reduce L-DOPA-induced dyskinesia without impairing the therapeutic efficacy of L-DOPA. However, a U-shaped dose-response curve obtained with certain parameters may limit the therapeutic potential of this strategy and require further investigation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".