Neuropsychiatric Behaviors in the MPTP Marmoset Model of Parkinson’s Disease
Bibliographic record
Abstract
OBJECTIVES: Neuropsychiatric symptoms are increasingly recognised as a significant problem in patients with Parkinson's disease (PD). These symptoms may be due to 'sensitisation' following repeated levodopa treatment or a direct effect of dopamine on the disease state. The levodopa-treated MPTP-lesioned marmoset was used as a model of neuropsychiatric symptoms in PD patients. Here we compare the time course of levodopa-induced motor fluctuations and neuropsychiatric-like behaviors to determine the relationship between duration of treatment and onset of symptoms. METHODS: Marmosets were administered 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (2.0 mg/kg s.c.) for five days, resulting in stable parkinsonism. Levodopa (15 mg/kg and benserazide, 3.75 mg/kg) p.o. b.i.d, was administered for 30 days. Animals were evaluated for parkinsonian disability, dyskinesia and on-time (motor fluctuations) and neuropsychiatric-like behaviors on Day 0 (prior to levodopa) and on Days 1, 7, 13, 27 and 30 of treatment using post hoc DVD analysis by a trained rater, blind to the treatment day. RESULTS: The neuropsychiatric-like behavior rating scale demonstrated high interrater reliability between three trained raters of differing professional backgrounds. As anticipated, animals exhibited a progressive increase in levodopa-induced motor fluctuations, dyskinesia and wearing-off, that correlated with the duration of levodopa therapy. In contrast, levodopa-induced neuropsychiatric-like behaviors were present on Day 1 of levodopa treatment and their severity did not correlate with duration of treatment. CONCLUSIONS: The data suggest that neuropsychiatric disorders in PD are more likely an interaction between levodopa and the disease state than a consequence of sensitisation to repeated dopaminergic therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".