Gingerols synergize with anthocyanins to induce antioxidant activity in vitro
Bibliographic record
Abstract
Oxidative stress caused by free radicals contributes to the pathogenesis of multiple chronic health conditions. Phytochemicals protect against oxidative stress; however, low bioavailability from dietary sources limits their health benefits. This study aimed to assess the effects of anthocyanins and gingerols' combination on the cellular antioxidant response of Caco-2 cells against oxidative stress. A strong synergism was observed for anthocyanin-gingerol (Ac-G) w/w combined ratios of 8:1 and 2:1 (dosages of (1 + 0.125) and (1 + 0.5) μg/mL) in the cellular antioxidant activity (CAA) and cytoprotective effects, with synergistic effect indicator (SE) values of 1.41 and 1.61, respectively. The synergism of Ac-G combinations promoted cellular antioxidant defense systems and cytoprotective effects by reducing the induced GPx enzyme activity, protecting SOD enzyme activity, reducing cellular ROS generation, increasing glutathione content, and inhibiting lipid peroxidation. Thus, Ac-G combinations showed potential in supporting the endogenous antioxidant systems to protect cells from oxidation and restore physiological redox status. The Ac-G formulation is a promising healthy option that can be developed into functional foods or nutraceutical products. Furthermore, it could help address the low bioavailability of these phenolics, as higher effects were achieved when combining the same doses.
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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) | 0.001 | 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".