Chemoprotective and Carcinogenic Effects of tert-Butylhydroquinone and Its Metabolites
Bibliographic record
Abstract
Tert-butylhydroquinone (tBHQ) has been commonly used as a synthetic food antioxidant to prevent oils and fats from oxidative deterioration and rancidity due to its potent anti-lipid peroxidation activity. In North America, the maximum level of tBHQ allowed in fat products is 0.02% with an acceptable daily intake of 0-0.7 mg/kg body weight. Extensive studies have demonstrated that tBHQ exhibit anti-carcinogenic effect. The ability of tBHQ to induce phase II xenobiotic metabolizing enzymes through an Nrf2-dependent pathway is thought to be responsible for the observed protective effect of tBHQ. It has been proposed that tBHQ enhances Nrf2-mediated transcription by promoting reactive oxygen species-mediated dissociation of Nrf2-Keap1, Nrf2 stabilization, phosphatidylinositol 3-kinase (PI3K)/Akt activity, and MAPK pathway activation. In contrast to the beneficial effects of tBHQ, a number of studies have shown that chronic exposure to tBHQ may induce carcinogenicity. However, the precise mechanisms of tBHQ carcinogenicity are not well understood. The toxicity or carcinogenicity of tBHQ has been attributed to the formation of reactive GSH-conjugates, generation of reactive species, CYP1A1 induction, caspase activation and reduced GSH/ATP levels. This review provides an account of recent mechanisms proposed for both chemoprotective and carcinogenic effect of tBHQ.
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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 | 0.001 | 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) | 0.002 | 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".