“Redox Imaging” to Distinguish Cells with Different Proliferative Indexes: Superoxide, Hydroperoxides, and Their Ratio as Potential Biomarkers
Bibliographic record
Abstract
The present study was directed to the development of EPR methodology for distinguishing cells with different proliferative activities, using "redox imaging." Three nitroxide radicals were used as redox sensors: (a) mito-TEMPO-cell-penetrating and localized mainly in the mitochondria; (b) methoxy-TEMPO-cell-penetrating and randomly distributed between the cytoplasm and the intracellular organelles; and (c) carboxy-PROXYL-nonpenetrating in living cells and evenly distributed in the extracellular environment. The experiments were conducted on eleven cell lines with different proliferative activities and oxidative capacities, confirmed by conventional analytical tests. The data suggest that cancer cells and noncancer cells are characterized by a completely different redox status. This can be analyzed by EPR spectroscopy using mito-TEMPO and methoxy-TEMPO, but not carboxy-PROXYL. The correlation analysis shows that the EPR signal intensity of mito-TEMPO in cell suspensions is closely related to the superoxide level. The described methodology allows the detection of overproduction of superoxide in living cells and their identification based on the intracellular redox status. The experimental data provide evidences about the role of superoxide and hydroperoxides in cell proliferation and malignancy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".