Antioxidant interactions among hydrophilic and lipophilic dietary phytochemicals based on inhibition of low‐density lipoprotein and <scp>DNA</scp> damage
Bibliographic record
Abstract
Antioxidant interaction among hydrophilic phytochemicals (caffeic acid, p-coumaric acid) and lipophilic phytochemicals (β-carotene, lycopene) in different mole ratios (n/n, 1:9, 3:7, 5:5, 7:3, 9:1) was evaluated. Assays performed were based on the scavenging activity of hydrogen peroxide (H2O2), the inhibition of low-density lipoprotein oxidation (ox-LDL) and DNA damage in vitro, using isobological analysis, synergistic rate (SR), and combination index (CI). Results showed that groups containing higher ratios of hydrophilic phytochemicals exhibited synergism while those containing higher ratios of lipophilic phytochemicals showed antagonism. Meanwhile, groups containing caffeic acid (e.g., caffeic acid:β-carotene, 9:1) with more hydroxyl groups showed higher synergism (SR = 0.76 ± 0.02, CI = 0.77 ± 0.03) than groups containing p-coumaric acid (e.g., p-coumaric acid:β-carotene, 9:1, SR = 0.88 ± 0.04, CI = 0.82 ± 0.05) on the scavenging activity of H2O2. Groups that contained lycopene (caffeic acid: lycopene, 9:1) with a higher ability of regeneration by phenolic acids showed more significant synergism (SR = 0.70 ± 0.02, CI = 0.79 ± 0.03) than groups containing β-carotene (e.g., caffeic acid:β-carotene, 9:1, SR = 1.00 ± 0.03, CI = 0.98 ± 0.04) on the inhibition of DNA damage. This study provided a basis for antioxidant interactions among phytochemicals against ox-LDL and DNA damage in vivo. In addition, the choice of appropriate ratios and structures of hydrophilic and lipophilic phytochemicals should be considered in the diet and formulation of functional foods.
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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".