Cytotoxicity and Oxidative Damage Induced by Halobenzoquinones to T24 Bladder Cancer Cells
Bibliographic record
Abstract
Four halobenzoquinones (HBQs), 2,6-dichloro-1,4-benzoquinone (DCBQ), 2,6-dichloro-3-methyl-1,4-benzoquinone (DCMBQ), 2,3,6-trichloro-1,4-benzoquinone (TCBQ), and 2,6-dibromobenzoquinone (DBBQ), have been recently confirmed as disinfection byproducts (DBPs) in drinking water; however, their toxicological information is scarce. Here, we report that HBQs are cytotoxic to T24 bladder cancer cells and that the IC50 values are 95 μM for DCBQ, 110 μM for DCMBQ, 151 μM for TCBQ, and 142 μM for DBBQ, after a 24-h exposure. The antioxidant N-acetyl-l-cysteine (NAC) significantly reduces the cytotoxicity induced by the four HBQs, supporting the hypothesis that oxidative stress contributes to the cytotoxicity of HBQs. To further explore the oxidative mechanisms of cytotoxicity, we examined HBQ-induced production of reactive oxygen species (ROS) in T24 cells, and measured 8-hydroxydeoxyguanosine (8-OHdG), protein carbonyls, and malondialdehyde (MDA) adducts of proteins, markers of oxidative damage to DNA, proteins, and lipids, respectively. All four HBQs generated intracellular ROS in T24 cells in a concentration-dependent manner. HBQs also produced 8-OHdG in genomic DNA of T24 cells, with the highest levels of 8-OHdG induced by DCMBQ. Protein carbonylation was significantly increased in T24 cells that were incubated with each of the four HBQs for 24 h. However, MDA adduct formation, a marker of lipid peroxidation, was not affected by any of the four HBQs tested. These results suggest that the ROS-induced oxidative damage to DNA and protein carbonylation are involved in the observed toxicity of HBQs in T24 cells.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".