An in vitro examination of selenium–cadmium antagonism using primary cultures of rainbow trout (Oncorhynchus mykiss) hepatocytes
Bibliographic record
Abstract
The present study evaluated the ameliorative properties of selenium (Se) against cadmium (Cd)-induced oxidative stress, using isolated rainbow trout (Oncorhynchus mykiss) hepatocytes in primary culture as the model experimental system. Cadmium (Cd) is known to induce cytotoxic effects by disrupting cellular oxidative homeostasis. On the other hand, selenium (Se) is an essential component of biological antioxidative machinery, and thus may provide protection against the toxic insults of Cd by augmenting the cellular antioxidant response. However, Se, when present above the threshold concentration, can also induce reactive oxygen species (ROS) generation and cause oxidative damage. In this experiment, trout hepatocytes in primary culture were exposed to 100 µM Cd, alone or in combination with different concentrations (25-500 µM) of selenite (SeO3(2-)) or selenomethionine (SeMet) for 48 h. Our findings indicated that both chemical forms of Se, at the lowest concentration used (25 µM), significantly reduced Cd-induced cytotoxicity (measured as cell viability). In contrast, Se at higher concentrations (≥ 50 µM) did not offer any protection against a Cd induced decrease in cell viability. The reduced cytotoxicity of Cd in the presence of 25 µM selenite or SeMet was associated with reduced intracellular ROS production, recovery of the cellular thiol status (ratio of reduced and oxidized glutathione), and amelioration in the activities of major enzymatic antioxidants (superoxide dismutase, catalase, and glutathione peroxidase). Co-treatment of hepatocytes with Cd and pharmacological antioxidants (TEMPO and NAC) also reduced Cd-induced oxidative stress in trout hepatocytes. This provided further evidence that Se likely ameliorates Cd toxicity via different antioxidative mechanisms.
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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) | 0.001 | 0.001 |
| 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".