Genetic predisposition to the cytotoxicity of arsenic: the role of DNA damage and ATM
Bibliographic record
Abstract
Arsenic is a pervasive cytotoxin and carcinogen in the environment. Although its mode of action has yet to be fully elucidated, oxidative DNA damage has been suggested. A series of DNA repair-defective human and hamster cell lines associated with sensitivity to oxidative agents were examined for their response to arsenic-induced cytotoxicity. Only the Ataxia telangiectasia (AT) cells displayed a marked hypersensitive response (greater than twofold). The protective role of the ATM protein was confirmed by the normal response to arsenic displayed by AT cells expressing wild-type ATM. Although the ATM protein plays a pivotal role in response to DNA double-strand breakage, none of the other cell lines with defects in double-strand break repair displayed a similar hypersensitivity. Further examination indicated that concentrations of sodium arsenite as high as 1 mg/l do not generate significant levels of double-strand breaks. Our data suggest that the ATM protein functions in an important but different capacity in the cellular response to arsenic toxicity than it does in response to agents that generate double-strand breaks, such as ionizing radiation. Furthermore, the lack of hypersensitivity to arsenic displayed by the other cell lines calls into question the hypothesis that DNA damage is a significant factor in arsenic cytotoxicity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".