Antioxidant activities of curcumin and combinations of this curcuminoid with other phytochemicals
Bibliographic record
Abstract
Biomedical investigations of curcumin (and curcuminoids) have provided evidence of a wide range of molecular and cellular activities, most related to redox reactions and signal transduction. The main goal of the present study was to compare antioxidant activities of curcumin with those of resveratrol, a polyphenol present in some dietary plants such as Vitis vinifera (L.) and Arachis hypogaea (L.) and many other, non-dietary plants. Combinations of the two were also examined for potential synergism in a heme-enhanced oxidation reaction. Curcumin exhibited antioxidant effects at all time points (1-5 min; 10 microM), e.g., 30.5 +/- 11.9% (SEM) oxidation relative to controls without phytochemicals (p < 0.01) at 3 min, a time chosen for comparisons. The same concentration of resveratrol exhibited about half of curcumin's activity. Curcumin and resveratrol together (5 microM each) resulted in a synergistic antioxidant effect: 15.5 +/- 1.7% greater than an average of individual activities. This synergy was significantly greater (p < 0.05; about 4-fold) than that of curcumin together with the flavonol quercetin. In conclusion, curcumin is a potent antioxidant in a reaction that may be relevant to in vivo toxicity. In relation to two other well-known antioxidants, curcumin shows significantly greater synergism with resveratrol than with quercetin.
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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".