Methylmercury in northern pike (Esox lucius) liver and hepatic mitochondria is linked to lipid peroxidation
Bibliographic record
Abstract
Methylmercury (MeHg) readily bioaccumulates and biomagnifies in aquatic food webs leading to elevated concentrations in fish and may thus induce toxicity. Oxidative stress is a suggested effect of MeHg bioaccumulation in fish. However, studies on how MeHg triggers oxidative stress in wild fish are scarce. The purpose of this study was to link the subcellular distribution of MeHg in the liver of northern pike from the St. Maurice River (Québec, Canada), affected by two run-of-river (RoR) dams, artificial wetlands, forest fires, and logging activity, to lipid peroxidation as an indicator of oxidative stress. We also evaluated the protective effects of the glutathione (GSH) system and selenium (Se), as they are known to alleviate MeHg toxicity. A customized subcellular partitioning protocol was used to separate the liver into metal-sensitive (mitochondria, microsome/lysosome and HDP – heat-denatured proteins) and metal-detoxified fractions (metal-rich granules and HSP – heat stable proteins). We examined the relation among THg, MeHg, and Se concentration in livers and subcellular fractions, and the hepatic ratio of total GSH (GSHt) to oxidized glutathione (GSSG) on lipid peroxidation levels, using the concentrations of malondialdehyde (MDA), a product of lipid peroxidation. Results showed that hepatic MDA concentration was positively correlated with the combined MeHg and Se concentration in northern pike liver (r2 = 0.88, p < 0.001) and that MDA concentrations were best predicted only by MeHg associated with the mitochondria (r2 = 0.71, p < 0.001). On the other hand, there was no significant relationship between the MDA concentration and the cellular thiol status, represented as the GSH/GSSG ratio. We also observed a positive significant relationship between MDA concentration and the Se:Hg molar ratio, suggesting that hepatic Se concentration might not be sufficient to confer a protective effect against MeHg induced oxidative stress.
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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".