Sodium Selenite Induces Superoxide-Mediated Mitochondrial Damage and Subsequent Autophagic Cell Death in Malignant Glioma Cells
Bibliographic record
Abstract
Malignant gliomas are resistant to various proapoptotic therapies, such as radiotherapy and conventional chemotherapy. In this study, we show that selenite is preferentially cytotoxic to various human glioma cells over normal astrocytes via autophagic cell death. Overexpression of Akt, survivin, XIAP, Bcl-2, or Bcl-xL failed to block selenite-induced cell death, suggesting that selenite treatment may offer a potential therapeutic strategy against malignant gliomas with apoptotic defects. Before selenite-induced cell death in glioma cells, disruption of the mitochondrial cristae, loss of mitochondrial membrane potential, and subsequent entrapment of disorganized mitochondria within autophagosomes or autophagolysosomes along with degradation of mitochondrial proteins were noted, showing that selenite induces autophagy in which mitochondria serve as the main target. At the early phase of selenite treatment, high levels of superoxide anion were generated and overexpression of copper/zinc superoxide dismutase or manganese superoxide dismutase, but not catalase, significantly blocked selenite-induced mitochondrial damage and subsequent autophagic cell death. Furthermore, treatment with diquat, a superoxide generator, induced autophagic cell death in glioma cells. Taken together, our study clearly shows that superoxide anion generated by selenite triggers mitochondrial damage and subsequent mitophagy, leading to irreversible cell death in glioma cells.
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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) | 0.000 | 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".