Intranigral injection of Alpha-Synuclein pre-formed fibrils leads to BBB compromise and Bilateral Dopaminergic Neurodegeneration in A53T Alpha-Synuclein transgenic mice
Bibliographic record
Abstract
ABSTRACT Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by alpha-(α)-Synuclein neuronal aggregation and loss of dopaminergic (DA) neurons. Developing animal models that replicate PD’s neuropathological phenotypes is critical for understanding its pathophysiology and evaluating potential therapeutic targets. In this study, we show that direct unilateral injection of human α-Synuclein PFFs into the Substantia Nigra (SN) of mutant A53T α-synuclein overexpressing mice induce bilateral phosphorylated α-Synuclein (pS129) pathology in the SN. This pathology spreads to the striatum, cerebral cortex, and midbrain within 60 days and is accompanied by neuroinflammation in the midbrain and cerebral cortex. Additionally, we observed synuclein-dependent neurodegeneration, with a 50% reduction in Tyrosine Hydroxylase (TH) intensity in the SN and a 40% reduction in Striatum, both bilaterally. The model also revealed a compromised blood-brain barrier (BBB) and T-cell infiltration in the PFF injected animals, correlating with pS129 pathology and neuroinflammation. Taken together, we developed a mouse model that recapitulates multiple PD phenotypes, providing a valuable platform for testing therapeutic strategies targeting human α-Synuclein pathology and for exploring CNS-peripheral immune interactions in PD. Graphical Abstract
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".