Genistein and Procyanidin B2 Reduce Carcinogen-Induced Reactive Oxygen Species and DNA Damage through the Activation of Nrf2/ARE Cell Signaling in Bronchial Epithelial Cells In Vitro
Bibliographic record
Abstract
Cancer is one of the leading causes of death worldwide. Chemotherapy and radiation therapy are currently providing the basis for cancer therapies, although both are associated with significant side effects. Thus, cancer prevention through dietary modifications has been receiving growing interest. The potential of selected flavonoids in reducing carcinogen-induced reactive oxygen species (ROS) and DNA damage through the activation of nuclear factor erythroid 2 p45 (NF-E2)-related factor (Nrf2)/antioxidant response element (ARE) pathway was studied in vitro. Dose-dependent effects of pre-incubated flavonoids on pro-carcinogen 4-[(acetoxymethyl)nitrosamino]-1-(3-pyridyl)-1-butanone (NNKAc)-induced ROS and DNA damage in human bronchial epithelial cells were studied in comparison to non-flavonoids. The most effective flavonoids were assessed for the activation of Nrf2/ARE pathway. Genistein, procyanidin B2 (PCB2), and quercetin significantly suppressed the NNKAc-induced ROS and DNA damage. Quercetin significantly upregulated the phosphorylated protein kinase B/Akt. PCB2 significantly upregulated the activation of Nrf2 and Akt through phosphorylation. Genistein and PCB2 significantly upregulated the phospho-Nrf2 nuclear translocation and catalase activity. In summary, genistein and PCB2 reduced the NNKAc-induced ROS and DNA damage through the activation of Nrf2. Further studies are required to understand the role of dietary flavonoids on the regulation of the Nrf2/ARE pathway in relation to carcinogenesis.
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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".