Elevation of Cellular BPDE Uptake by Human Cells: A Possible Factor Contributing to Co-Carcinogenicity by Arsenite
Bibliographic record
Abstract
BACKGROUND: Arsenite (iAsIII) can promote mutagenicity and carcinogenicity of other carcinogens. Considerable attention has focused on interference with DNA repair by inorganic arsenic, especially the nucleotide excision repair (NER) pathway, whereas less is known about the effect of arsenic on the induction of DNA damage by other agents. OBJECTIVES: We examined how arsenic modulates DNA damage by other chemicals. METHODS: We used an NER-deficient cell line to dissect DNA damage induction from DNA repair and to examine the effects of iAsIII on the formation of benzo[a]pyrene diol epoxide (BPDE)-DNA adducts. RESULTS: We found that pretreatment with iAsIII at subtoxic concentrations (10 microM) led to enhanced formation of BPDE-DNA adducts. Reduced glutathione levels, glutathione S-transferase activity and chromatin accessibility were also measured after iAsIII treatment, but none of these factors appeared to account for the enhanced formation of DNA adducts. However, we found that pretreatment with iAsIII increased the cellular uptake of BPDE in a dose-dependent manner. CONCLUSIONS: Our results suggest that iAsIII enhanced the formation of BPDE-DNA adducts by increasing the cellular uptake of BPDE. Therefore, the ability of arsenic to increase the bioavailability of other carcinogens may contribute to arsenic co-carcinogenicity.
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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 | 0.000 |
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".