Inflammasome inhibition protects dopaminergic neurons from α-synuclein pathology in a model of progressive Parkinson’s disease
Bibliographic record
Abstract
Abstract Neuroinflammation has been suggested as a pathogenetic mechanism contributing to Parkinson’s disease (PD). However, anti-inflammatory treatment strategies have not yet been established as a therapeutic option for PD patients. We have used a human α-synuclein mouse model of progressive PD to examine the anti-inflammatory and neuroprotective effects of inflammasome inhibition on dopaminergic (DA) neurons in the substantia nigra (SN). As the NLRP3 (NOD-, LRR- and pyrin domain-containing 3)-inflammasome is a core interface for both adaptive and innate inflammation and is also highly druggable, we investigated the implications of its inhibition. Repeat administration of MCC950, an inhibitor of NLRP3, in a PD model with ongoing pathology reduced CD4 + and CD8 + T cell infiltration into the SN. Furthermore, the anti-inflammasome treatment mitigated microglial activation and modified the aggregation of α-synuclein protein in DA neurons. MCC950-treated mice showed significantly less neurodegeneration of DA neurons and a reduction in PD-related motor behavior. In summary, early inflammasome inhibition can reduce neuroinflammation and prevent DA cell death in an α-synuclein mouse model for progressive 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) | 0.001 | 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) | 0.008 | 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".