Bibliographic record
Abstract
Parkinson's Disease (PD) is a neurodegenerative disorder characterized, in part, by the loss of dopaminergic neurons within the nigral-striatal pathway. Multiple lines of evidence support a role for reactive nitrogen species (RNS) in degeneration of this pathway, specifically nitric oxide (NO). This review will focus on how RNS leads to loss of dopaminergic neurons in PD and whether RNS accumulation represents a central signal in the degenerative cascade. Herein, we provide an overview of how RNS accumulates in PD by considering the various cellular sources of RNS including nNOS, iNOS, nitrate, and nitrite reduction and describe evidence that these sources are upregulating RNS in PD. We document that over 1/3 of the proteins that deposit in Lewy Bodies, are post-translationally modified (S-nitrosylated) by RNS and provide a broad description of how this elicits deleterious effects in neurons. In doing so, we identify specific proteins that are modified by RNS in neurons which are implicated in PD pathogenesis, with an emphasis on exacerbation of synucleinopathy. How nitration of alpha-synuclein (aSyn) leads to aSyn misfolding and toxicity in PD models is outlined. Furthermore, we delineate how RNS modulates known PD-related phenotypes including axo-dendritic-, mitochondrial-, and dopamine-dysfunctions. Finally, we discuss successful outcomes of therapeutics that target S-nitrosylation of proteins in Parkinson's Disease related clinical trials. In conclusion, we argue that targeting RNS may be of therapeutic benefit for people in early clinical stages of PD.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".