Aluminum induces oxidative damage in <i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
The mechanism of aluminum toxicity was studied in the model cells of Saccharomyces cerevisiae. Cell growth of yeast was inhibited by aluminum. The spot assay showed that the mechanism of aluminum detoxification in yeast cells was different from that of heavy metal cadmium. After treatment with aluminum, intracellular levels of reactive oxygen species, protein carbonyl, and thiobarbituric acid reactive substances were dramatically increased. Meanwhile, the percentage of aluminum-treated cells permeable to propidium iodide was augmented significantly. These data demonstrated that aluminum toxicity was attributed to oxidative stress in yeast, and it induced oxidative damage by causing lipid peroxidation, injuring cell membrane integrity. Moreover, aluminum triggered the antioxidant defense system in the cells. Glutathione levels were found to be decreased, while activities of superoxide dismutase and catalase were increased after treatment with aluminum. Additionally, an oxidative-stress-related mutation sensitivity assay showed that aluminum-induced yeast oxidative stress was closely related to glutathione. These data demonstrated that the oxidative damage caused by aluminum was different from that of hydrogen peroxide, in yeast. Aluminum could cause DNA damage, and aluminum toxicity was associated with sulfhydryl groups, such as glutathione, while it was independent of YAP1.
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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".