Effects of methylmercury on dopamine release in MN9D neuronal cells
Bibliographic record
Abstract
Epidemiological evidence has shown associations between prevalence of Parkinson's disease (PD) and exposure to environmental pollutants, but the mechanisms of pathogensis are still unclear. The objective of this study is to investigate effects of methylmercury (MeHg) on a dopaminergic neuronal cell line, MN9D and compare that to 1-methyl-4-phenylpyridinium (MPP+), a well-established agent associated with pathogenesis of PD. MN9D cells were exposed to MeHg (1-10 µM) and MPP+ (10-400 µM) for 24 or 48 h. Our results showed that MeHg induced cell death dose-dependently. MeHg also decreased the release of dopamine (DA), 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA) similar to the effects of MPP+. There was an increase in DOPAC + HVA/DA ratio. At the same time, both MeHg and MPP+ decreased the synthesis of tyrosine hydroxylase and dopamine transporter at the mRNA and protein levels. Expression of the α-Synuclein (α-Syn), a hallmark neuropathological indicator of PD, was also up-regulated at the mRNA level but not at the protein level after both MeHg and MPP+ dosing. Monoamine oxidase-B activity was suppressed in all MeHg treatments and MPP+ (1 µM)-treated cells. These findings suggest that MeHg can disrupt the synthesis, the uptake of DA and the metabolism as well as alter the biology of α-Syn similar to MPP+. Exposure to MeHg may potentially be a risk factor for the development 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) | 0.000 | 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 | 0.000 |
| 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".