Circuits Regulating Superoxide and Nitric Oxide Production and Neutralization in Different Cell Types: Expression of Participating Genes and Changes Induced by Ionizing Radiation
Bibliographic record
Abstract
Superoxide radicals, together with nitric oxide (NO), determine the oxidative status of cells, which use different pathways to control their levels in response to stressing conditions. Using gene expression data available in the Cancer Cell Line Encyclopedia and microarray results, we compared the expression of genes engaged in pathways controlling reactive oxygen species and NO production, neutralization, and changes in response to the exposure of cells to ionizing radiation (IR) in human cancer cell lines originating from different tissues. The expression of NADPH oxidases and NO synthases that participate in superoxide radical and NO production was low in all cell types. Superoxide dismutase, glutathione peroxidase, thioredoxin, and peroxiredoxins participating in radical neutralization showed high expression in nearly all cell types. Some enzymes that may indirectly influence superoxide radical and NO levels showed tissue-specific expression and differences in response to IR. Using fluorescence microscopy and specific dyes, we followed the levels and the distribution of superoxide and NO radicals in living melanoma cells at different times after exposure to IR. Directly after irradiation, we observed an increase of superoxide radicals and NO coexistent in the same subcellular locations, suggesting a switch of NO synthase to the production of superoxide radicals.
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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".