Activation of antioxidant response element (ARE)-dependent genes by roasted coffee extracts
Bibliographic record
Abstract
Coffee beans contain numerous bioactive components that exhibit antioxidant capacity when assessed using both chemical, cell free, and biological, cell-based model systems. However, the mechanisms underlying the antioxidant effects of coffee in biological systems are not totally understood and in some cases vary considerably from results obtained with simpler in vitro chemical assays. In the present study, the physicochemical characteristics and antioxidant activity of roasted and non-roasted coffee extracts were investigated in both cell free (ORAC(FL)) and cell-based systems. A profile of antioxidant gene expression in cultured human colon adenocarcinoma Caco-2 cells treated with both roasted and non-roasted coffee extracts, respectively, was investigated using Real-Time polymerase chain reaction (PCR) array technology. Results demonstrated that the mechanisms of the antioxidant activity associated with coffee constituents assessed by the ORAC(FL) assay were different to those observed using an intracellular oxidation assay with Caco-2 cells. Moreover, roasted coffee (both light and dark roasted) extracts produced both increased- and decreased-expressions of numerous genes that are involved in the management of oxidative stress via the antioxidant defence system. The selective and specific positive induction of antioxidant response element (ARE)-dependent genes, including gastrointestinal glutathione peroxidase (GPX2), sulfiredoxin (SRXN1), thioredoxin reductase 1 (TXNRD1), peroxiredoxin 1 (PRDX1), peroxiredoxin 4 (PDRX4) and peroxiredoxin 6 (PDRX6) were identified with the activation of the endogenous antioxidant defence system in Caco-2 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 | 0.001 | 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 | 0.000 |
| 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".