Bibliographic record
Abstract
Phenols are a class of compounds that can create beneficial effects in vivo owing to their antioxidant properties (through radical scavenging), or they can display hazardous effects owing to their pro-oxidant properties. The mechanism by which phenols act as pro-oxidants stems from their one-electron oxidation into reactive phenoxyl radicals by peroxidase enzymes or redox-active transition metals. In the presence of thiols and molecular oxygen, these reactive phenoxyl radicals stimulate an oxidative stress and cause oxidative damage to biomolecules, which is proposed to contribute to the occurrence of cancer in peroxidase rich tissues. Recent results from our laboratory show that certain phenoxyl radicals can also react directly with the C-8 site of deoxyguanosine to afford oxygen and carbon bonded adducts. This reactivity is consistent with the ambident (oxygen vs. C attachment) electrophilicity of phenoxyl radicals coupled with the susceptibility of the C-8 site of deoxyguanosine to radical attachment. Given that formation of covalent DNA adducts is regarded as the initiation event in the carcinogenic process, C-8 deoxyguanosine adducts of phenolic toxins are expected to contribute greatly to peroxidase driven toxic effects of phenolic xenobiotics. The focus of this review is the role of phenoxyl radicals in direct reactions with DNA and the use of Brown σ+ values to predict their reactivity.Key words: DNA adduction, phenoxyl radicals, chlorophenols, ochratoxin A, deoxyguanosine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".