Time course and magnitude of alpha-synuclein inclusion formation and nigrostriatal degeneration in the rat model of synucleinopathy triggered by intrastriatal α-synuclein preformed fibrils
Bibliographic record
Abstract
Animal models that accurately recapitulate the accumulation of alpha-synuclein (α-syn) inclusions, progressive neurodegeneration of the nigrostriatal system and motor deficits can be useful tools for Parkinson's disease (PD) research. The preformed fibril (PFF) synucleinopathy model in rodents generally displays these PD-relevant features, however, the magnitude and predictability of these events is far from established. We therefore sought to optimize the magnitude of α-syn accumulation and nigrostriatal degeneration, and to understand the time course of both. Rats were injected unilaterally with different quantities of α-syn PFFs (8 or 16 μg of total protein) into striatal sites selected to concentrate α-syn inclusion formation in the substantia nigra pars compacta (SNpc). Rats displayed an α-syn PFF quantity-dependent increase in the magnitude of ipsilateral SNpc inclusion formation at 2 months and bilateral loss of nigral dopamine neurons at 6 months. Unilateral 16 μg PFF injection also resulted in modest sensorimotor deficits in forelimb adjusting steps associated with degeneration at 6 months. Bilateral injection of 16 μg α-syn PFFs resulted in symmetric bilateral degeneration equivalent to the ipsilateral nigral degeneration observed following unilateral 16 μg PFF injection (~50% loss). Bilateral PFF injections additionally resulted in alterations in several gait analysis parameters. These α-syn PFF parameters can be applied to generate a reproducible synucleinopathy model in rats with which to study pathogenic mechanisms and vet potential disease-modifying therapies.
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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".