Inhibition of nucleotide excision repair by arsenic
Bibliographic record
Abstract
Inhibition of DNA repair is one proposed mechanism for the co-mutagenicity/co-carcinogenicity of arsenic. This review summarizes the current literature on the effects of arsenic compounds on nucleotide excision repair (NER). Several possible mechanisms for the observed NER inhibition have been proposed. Modulation of the expression of NER proteins has been considered to be one possibility of impairing the NER process. However, data on the effects of arsenic on the expression of NER proteins remain inconsistent. It is more likely that arsenic inhibits the induction of accessory or other key proteins involved in cellular control of DNA repair pathways, such as p53. For example, arsenic affects p53 phosphorylation and p53 DNA binding activity, which could regulate NER through transcriptional activation of downstream NER genes. Although it is important to study possible direct inactivation of NER proteins by arsenic binding, indirect inactivation of proteins having thiol residues critical to their function or zinc finger proteins cannot be negated. For example, nitric oxide (NO) induced in arsenic-treated cells serves as a specific inhibitor of NER, possibly through NO-induced S-nitrosylation of proteins related to DNA repair. Poly(ADP-ribose) polymerase-1, a zinc finger protein implicated in both NER and base excision repair (BER), deserves special attention because of its involvement in NO production and its broad range of protein substrates including many repair enzymes.
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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) | 0.002 | 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".