Montelukast alleviates neuroinflammation and improves motor functions in the line 61 model of Parkinson's disease: An exploratory study
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative movement disorder of high global burden. Uncertainties regarding its exact etiology have been hindering the development of curative therapies. As microglia, the brain's immune cells, are suspected to contribute to neurodegeneration by instigating neuroinflammation, existing anti-inflammatory agents could potentially serve as disease-modifying treatments for PD. Here we evaluated the impact of montelukast, a leukotriene receptor antagonist and anti-inflammatory drug, on motor symptoms and neuropathology in an α-synuclein transgenic mouse model (Line 61) for early onset/genetic PD. Two -weeks -old male Line 61 mice and non-transgenic littermates received daily 10 mg/kg montelukast or vehicle orally for 10 weeks. Motor functions were assessed through behavioral tests. Brain tissue was analyzed via unbiased transcriptomics, biochemically, and histologically for various parameters, including microglial and inflammation mediators. Upon montelukast treatment, Line 61 mice significantly improved their beam walk performance compared to vehicle -treated mice. The striatum and cerebellum of the montelukast -treated group showed microglial changes toward a smaller but more ramified appearance. Transcriptomics analysis revealed SGK1, a serine/threonine kinase upstream of NFκB and known target in PD, as the most downregulated gene in the striatum of montelukast -treated animals. This downregulation correlated with reduced striatal protein levels of activated IκB kinase, suggesting a reduced NFκB pathway activity upon montelukast treatment. Thus, oral montelukast administration might be promising for the management of PD, with specific effects on motor coordination and balance.
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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 | 0.000 | 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 | 0.002 |
| 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".