Metal complexes of dietary flavonoids: evaluation of radical scavenger properties and protective activity against oxidative stress in vivo.
Bibliographic record
Abstract
Interaction of certain flavonoids with transition metals increases their water solubility and leads to the formation of flavonoid-metal complexes, which may act as superoxide dismutase mimics with high scavenger potencies toward superoxide. Effect of serum albumin on stability of flavonoid-metal complexes was studied and complex of rutin with iron (II) was found to be the most stable. The ability of flavonoid metal complexes to catalyze homolytic cleavage of hydrogen peroxide was also studied and rutin iron (II) complex was found to be relatively poor Fenton catalyst. The potential therapeutic benefits of this new antioxidant agent were studied using experimental model of pathological states associated with oxidative stress in vivo. Acute hepatic injury in MT-I/MT-II null transgenic mice induced by injections of thioacetamide was used for this purpose. It was found that pretreatment with rutin- iron complex protected against thioacetamide induced hepatotoxicity as observed by a significant reduction in the elevated levels of serum enzymes and partial normalization of GSH/GSSG ratio, glutathione peroxidase II and glutathione reductase activity in mice liver. The results demonstrate that flavonoid-metal complexes possess effective free radical scavenger ability and have potent therapeutic benefits for the treatment of oxidative stress-related diseases and dysfunction.
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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".