Differences between subacute and chronic MPTP mice models: investigation of dopaminergic neuronal degeneration and α‐synuclein inclusions
Bibliographic record
Abstract
Animal models are invaluable tools to study neurodegenerative disorders but a general consensus on the most accurate rodent model of Parkinson's disease has not been reached. Here, we examined how different methods of MPTP administration influence the degeneration of the dopaminergic (DA) system. Adult male C57BL/6 mice were treated with the same cumulative dose of MPTP following four distinct procedures: (i) subacute i.p. injections; (ii) 28-day chronic s.c. infusion; (iii) 28-day chronic i.p. infusion; and (iv) 14-day chronic i.p. infusion. Subacute MPTP treatment significantly affected all aspects of the DA system within the nigral and striatal territories. In contrast, the 28-day chronic s.c. infusion did not significantly alter any components of the DA system. The 28- and 14-day chronic i.p. infusions induced loss of tyrosine hydroxylase (TH)-positive cells correlated with a decrease in Nurr1 mRNA levels, but no significant decrease in the density of TH striatal fibers. Importantly, however, only the 14-day chronic MPTP i.p. infusion protocol promoted the formation of neuronal inclusions as noted by the expression of alpha-synuclein protein within the cytoplasm of TH nigral neurons. Overall, we found that the 14-day chronic MPTP i.p. infusion reproduces more accurately the pathological characteristics of early stage 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".