Benzoquinone toxicity is not prevented by sulforaphane in CD‐1 mouse fetal liver cells
Bibliographic record
Abstract
Benzene is an environmental pollutant known to cause leukemia in adults, and may be associated with childhood leukemia. While the mechanisms of benzene-mediated carcinogenicity have not been fully elucidated, increased reactive oxygen species (ROS) and DNA damage are implicated. Sulforaphane (SFN) induces nuclear factor erythroid 2-related factor 2 (Nrf2), which contributes to SFN-mediated protection against carcinogenesis. We exposed cultured CD-1 mouse fetal liver cells to the benzene metabolite, benzoquinone, to determine its potential to cause DNA damage and alter DNA repair. Cells were also exposed to SFN to determine potential protective effects. Initially, cells were exposed to benzoquinone to confirm increased ROS and SFN to confirm Nrf2 induction. Subsequently, cells were treated with benzoquinone (with or without SFN) and levels of ROS, 8-hydroxy-2-deoxyguanosine (8-OHdG; marker of oxidative DNA damage), gamma histone 2A variant X (γH2AX; marker of DNA double-stranded breaks; DSBs) and transcript levels of genes involved in DNA repair were measured. Benzoquinone exposure led to a significant increase in ROS, which was not prevented by pretreatment with SFN or the antioxidative enzyme, catalase. DNA damage was increased after benzoquinone exposure, which was not prevented by SFN. Benzoquinone exposure significantly decreased the transcript levels of the critical base excision repair gene, 8-oxoguanine glycosylase (Ogg1), which was not prevented by SFN. The findings of this study demonstrate for the first time that DNA damage and altered DNA repair are a consequence of benzoquinone exposure in CD-1 mouse fetal liver cells and that SFN conferred little protection in this model. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".