Oxidatively Generated Damage to Cellular <scp>DNA</scp> by <scp>UVB</scp> and <scp>UVA</scp> Radiation<sup>,</sup>
Bibliographic record
Abstract
This review article focuses on a critical survey of the main available information on the UVB and UVA oxidative reactions to cellular DNA as the result of direct interactions of UV photons, photosensitized pathways and biochemical responses including inflammation and bystander effects. UVA radiation appears to be much more efficient than UVB in inducing oxidatively generated damage to the bases and 2-deoxyribose moieties of DNA in isolated cells and skin. The UVA-induced generation of 8-oxo-7,8-dihydroguanine is mostly rationalized in terms of selective guanine oxidation by singlet oxygen generated through type II photosensitization mechanism. In addition, hydroxyl radical whose formation may be accounted for by metal-catalyzed Haber-Weiss reactions subsequent to the initial generation of superoxide anion radical contributes in a minor way to the DNA degradation. This leads to the formation of both oxidized purine and pyrimidine bases together with DNA single-strand breaks at the exclusion, however, of direct double-strand breaks. No evidence has been provided so far for the implication of delayed oxidative degradation pathways of cellular DNA. In that respect putative characteristic UVA-induced DNA damage could include single and more complex lesions arising from one-electron oxidation of the guanine base together with aldehyde adducts to amino-substituted nucleobases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".