Modeling Parkinson's disease‐related symptoms in alpha‐synuclein overexpressing mice
Bibliographic record
Abstract
BACKGROUND: Intracellular deposition of alpha-synuclein (α-syn) as Lewy bodies and Lewy neurites is a central event in the pathogenesis of Parkinson's disease (PD) and other α-synucleinopathies. Transgenic mouse models overexpressing human α-syn, are useful research tools in preclinical studies of pathogenetic mechanisms. Such mice develop α-syn inclusions as well as neurodegeneration with a topographical distribution that varies depending on the choice of promoter and which form of α-syn that is overexpressed. Moreover, they display motor symptoms and cognitive disturbances that to some extent resemble the human conditions. PURPOSE: One of the main motives for assessing behavior in these mouse models is to evaluate the potential of new treatment strategies, including their impact on motor and cognitive symptoms. However, due to a high within-group variability with respect to such features, the behavioral studies need to be applied with caution. In this review, we discuss how to make appropriate choices in the experimental design and which tests that are most suitable for the evaluation of PD-related symptoms in such studies. METHODS: We have evaluated published results on two selected transgenic mouse models overexpressing wild type (L61) and mutated (A30P) α-syn in the context of their validity and utility for different types of behavioral studies. CONCLUSIONS: By applying appropriate behavioral tests, α-syn transgenic mouse models provide an appropriate experimental platform for studies of symptoms related to PD and other α-synucleinopathies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".