DNA-breaking versus DNA-protecting activity of four phenolic compounds<i>in vitro</i>
Bibliographic record
Abstract
Given the paradoxical effects of phenolics in oxidative stress, we evaluated the relative pro-oxidant and antioxidant properties of four natural phenolic compounds in DNA nicking. The phenolic compounds differed dramatically in their ability to nick purified supercoiled DNA, with the relative DNA nicking activity in the order: 1,2,4-benzenetriol (100% nicking) > gallic acid > caffeic acid > gossypol (20% nicking). Desferrioxamine (0.02 mM) decreased DNA strand breakage by each phenolic, most markedly with gallate (85% protection) and least with caffeic acid (26% protection). Addition of metals accelerated DNA nicking, with copper more effective (approximately 5-fold increase in damage) than iron with all four phenolics. Scavengers revealed the participation of specific oxygen-derived active species in DNA breakage. Hydrogen peroxide participated in all cases (23-90%). Hydroxyl radicals were involved (32-85%), except with 1,2,4-benzenetriol. Superoxide participated (81-86%) with gallic acid and gossypol, but not with caffeic acid or 1,2,4-benzenetriol. With 1,2,4-benzenetriol, scavengers failed to protect significantly except in combination. Thus, in the presence of desferrioxamine, catalase or superoxide dismutase inhibited almost completely. When DNA breakage was induced by Fenton's reagent (ascorbate plus iron) the two catechols (caffeic acid and gossypol) were protective, whereas the two triols (1,2,4-benzenetriol and gallic acid) exacerbated damage.
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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) | 0.001 | 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".