Vitamin-Containing Antioxidant Formulation Reduces Carcinogen-Induced DNA Damage through ATR/Chk1 Signaling in Bronchial Epithelial Cells In Vitro
Bibliographic record
Abstract
Lung cancer has the highest mortality rate worldwide and is often diagnosed at late stages, requiring genotoxic chemotherapy with significant side effects. Cancer prevention has become a major focus, including the use of dietary and supplemental antioxidants. Thus, we investigated the ability of an antioxidant formulation (AOX1) to reduce DNA damage in human bronchial epithelial cells (BEAS-2B) with and without the combination of apple peel flavonoid fraction (AF4), or its major constituent quercetin (Q), or Q-3-O-d-glucoside (Q3G) in vitro. To model smoke-related genotoxicity, we used cigarette-smoke hydrocarbon 4-[(acetoxymethyl)nitrosamino]-1-(3-pyridyl)-1-butanone (NNKOAc) as well as methotrexate (MTX) to induce DNA damage in BEAS-2B cells. DNA fragmentation, γ-H2AX immunofluorescence, and comet assays were used as indicators of DNA damage. Pre-exposure to AOX1 alone or in combination with AF4, Q, or Q3G before challenging with NNKOAc and MTX significantly reduced intracellular reactive oxygen species (ROS) levels and DNA damage in BEAS-2B cells. Although NNKOAc-induced DNA damage activated ATM-Rad3-related (ATR) and Chk1 kinase in BEAS-2B cells, pre-exposure of the cells with tested antioxidants prior to carcinogen challenge significantly reduced their activation and levels of γ-H2AX (p ≤ 0.05). Therefore, AOX1 alone or combined with flavonoids holds promise as a chemoprotectant by reducing ROS and DNA damage to attenuate activation of ATR kinase following carcinogen exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".