Environmental toxicant exposure and Parkinson’s disease: LRRK2 and inflammatory processes
Bibliographic record
Abstract
Parkinson's disease (PD) results from the progressive loss of dopamine producing neurons in the Substantia Nigra pars comapcta (SNc).This loss is thought to occur over several years to decades and current evidence suggests that neuroinflammation may play a central role in this loss.Numerous epidemiological studies have implicated chronic exposure to environmental toxicants such as heavy metals and pesticides to risk of developing PD.Indeed, many of these risk factors have been validated as rodent models of PD, able to induce dopaminergic cell loss in the SNc as well as motor dysfunction.While research strongly supports a role for environmental toxicants and inflammation in PD relatively little work has examined the interactions between these toxicants and other risk factors including senescence, genetic vulnerabilities, the gut microbiota or immunogens.Thus, the present dissertation set out to investigate the interactions between these factors in PD to determine which factors may be of greatest relevance in further animal models of the disease.We presently demonstrated that interaction of diverse environmental and genetic factors contributed to the neuroinflammatory and PD-like neurodegeneration.We found that exposure to paraquat led to neuroinflammatory consequences persisting over six months and provide evidence that at this time point further inflammatory processes arise.We also found that LPS and paraquat treatment did not significantly alter the gut microbiome or gut inflammasome; however, in combination with dextran sodium sulphate (DSS) we found greatly increased pro-inflammatory factors.Using mice overexpressing the LRRK2 gene, G2019S, we found no changes in sickness, inflammation or neurodegeneration following paraquat treatment.However, the G2019S overexpressing mice did display augmented stressor effects.Further experiments revealed that G2019S knock-in mice showed increased signs of inflammation and that this was reversed by the CSF-1 iii
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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 | 0.001 | 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) | 0.017 | 0.004 |
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".