Viral-toxin interactions and Parkinson’s disease: poly(I:C) priming enhanced the neurodegenerative effects of paraquat
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) has been linked with exposure to a variety of environmental and immunological insults (for example, infectious pathogens) in which inflammatory and oxidative processes seem to be involved. In particular, epidemiological studies have found that pesticide exposure and infections may be linked with the incidence of PD. The present study sought to determine whether exposure to a viral mimic prior to exposure to pesticides would exacerbate PD-like pathology. METHODS: Mice received a supra-nigral infusion of 5 μg of the double-stranded RNA viral analog, polyinosinic: polycytidylic acid (poly(I:C)), followed 2, 7 or 14 days later by administration of the pesticide, paraquat (nine 10 mg/kg injections over three weeks). RESULTS: As hypothesized, poly(I:C) pre-treatment enhanced dopamine (DA) neuron loss in the substantia nigra pars compacta elicited by subsequent paraquat treatment. The augmented neuronal loss was accompanied by robust signs of microglial activation, and by increased expression of the catalytic subunit (gp91) of the NADPH oxidase oxidative stress enzyme. However, the paraquat and poly(I:C) treatments did not appreciably affect home-cage activity, striatal DA terminals, or subventricular neurogenesis. CONCLUSIONS: These findings suggest that viral agents can sensitize microglial-dependent inflammatory responses, thereby rendering nigral DA neurons vulnerable to further environmental toxin exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".