Antioxidant Actions of Phenolic Compounds Found in Dietary Plants on Low-Density Lipoprotein and Erythrocytes in Vitro
Bibliographic record
Abstract
OBJECTIVE: There is increasing interest in the study of the antioxidant actions of plant phenolic compounds as evidence shows that consumption of plant products rich in these compounds contributes to protection from a number of ailments including cardiovascular diseases. In the present study, the antioxidant effects of selected phenolic compounds from dietary sources, namely barbaloin, 6-gingerol and rhapontin, were investigated. METHODS: Low-density lipoprotein (LDL), erythrocytes and erythrocyte membranes were subjected to several in vitro oxidative systems. The antioxidant effects of the phenolic compounds were assessed by their abilities in inhibiting hemolysis and lipid peroxidation of LDL and erythrocyte membranes, and in protecting ATPase activities and protein sulfhydryl groups of erythrocyte membranes. RESULTS: 6-Gingerol and rhapontin were found to exhibit strong inhibition against lipid peroxidation in LDL induced by 2,2'-azobis(2-amidinopropane) hydrochloride (AAPH) and hemin while barbaloin possessed weaker effects. A similar order of antioxidant potencies among the three compounds was observed on the lipid peroxidation of erythrocyte membranes in a tert-butylhydroperoxide (tBHP)/hemin oxidation system. On the other hand, barbaloin and rhapontin were comparatively stronger antioxidants than 6-gingerol in preventing AAPH-induced hemolysis of erythrocytes. Among the three compounds, only barbaloin protected Ca2+-ATPase and protein sulfhydryl groups on erythrocyte membranes against oxidative attack by tBHP/hemin. Interestingly, rhapontin demonstrated protective actions on Na+/K+-ATPase in a sulfhydryl group-independent manner under the same experimental conditions. CONCLUSIONS: In view of their protective effects on LDL and erythrocytes against oxidative damage, these phenolic compounds might have potential applications in prooxidant state-related cardiovascular disorders.
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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".