Interactions between Alpha-Synuclein Fibrils and Paraquat: Parallel Oxidative Stress and MHC-I Outcomes.
Bibliographic record
Abstract
Parkinson's Disease (PD) is characterized by a loss of midbrain dopamine neurons and the pathological accumulation of alpha-synuclein rich Lewy body inclusions.In particular, alphasynuclein inclusions readily accumulate in the motor cortex causing disability.Accumulating evidence indicates that misfolded alpha-synuclein fibrils can promote a neuroinflammatory environment.At the same time, oxidative stressors, such as the pesticide paraquat, have repeatedly been linked to PD.We sought to assess whether paraquat can augment the neuroinflammatory and toxic effects of exposure to alpha-synuclein preformed fibrils (PFFs).To this end, we exposed E17 primary cortical neurons to wild-type or mutant (A53T) PFFs and assessed oxidative stress (CellROX assay), total, S-129 phosphorylated, and aggregated conformations of alpha-synuclein, along with the expression of MHC-I (which is expressed on neurons and is an indicator of synaptic changes or vulnerability to inflammatory stress).Our findings demonstrated that the PFFs induced a robust increase in oxidative stress and MHC-I levels, coupled with increased alpha-synuclein accumulation.The A53T-PFFs generally had the most dramatic effects, and this was evident for all three forms of alpha-synuclein.The paraquat and alpha-synuclein PFF treatments induced additive effects on oxidative stress levels.These data suggest a potential mechanistic connection between oxidative stress, alpha-synuclein, and MHC-I, which might hold importance for the cumulative spread of PD pathology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".