Dose and chemical species-specific effects of selenium against arsenite toxicity in cultured hepatocytes of rainbow trout (Oncorhynchus mykiss)
Bibliographic record
Abstract
The present study evaluated the mechanistic underpinnings of the interactive effects of selenium (Se), both inorganic (selenite) and organic (selenomethionine (SeMet)), against arsenite (As-III) cytotoxicity using rainbow trout (Oncorhynchus mykiss) hepatocytes in primary culture. Arsenite is known to induce cytotoxic effects by disrupting cellular redox homeostasis. In contrast, Se is essential for the maintenance of cellular anti-oxidative machinery, but when present above a threshold concentration, can also induce reactive oxygen species (ROS) generation and cause oxidative damage. In this study, hepatocytes were exposed to 100 μM arsenite independently or in combination with selenite or SeMet (5-40 μM) for 24 h. Exposure to arsenite alone reduced cell viability by inducing intracellular ROS generation, which also corresponded with a concomitant decrease in cellular thiol (GSH : GSSG) ratio and the activities of enzymatic antioxidants (GPx and SOD). Both selenite and SeMet were found to ameliorate the arsenite-induced loss of cell viability and thiol balance significantly, but only at low-intermediate exposure levels (5-20 μM), with selenite being more effective than SeMet. Further analyses of cellular antioxidative pathways, using specific pharmacological treatments, revealed that selenite and SeMet mediate their protective effects against arsenite toxicity via different mechanisms. Selenite ameliorates arsenite-induced oxidative stress primarily by augmenting enzymatic antioxidants (especially SOD), whereas SeMet elicits its protective response essentially by upregulating the non-enzymatic antioxidative pathway that involves GSH. Overall, our study demonstrated that the antagonistic interactions of arsenite and Se at the cellular level are influenced by the exposure dose as well as the chemical speciation of Se.
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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".