Ameliorative effect of polyphenols from <i>Padina boergesenii</i> against ferric nitrilotriacetate induced renal oxidative damage: With inhibition of oxidative hemolysis and <i>in vitro</i> free radicals
Bibliographic record
Abstract
The aim of this study was to evaluate the antioxidant activities of diethyl ether (DEE) and methanol (M) extracts from brown alga Padina boergesenii using in vitro and in vivo antioxidant assay, which may help to relate the antioxidant properties with the possible outline of its ameliorative effect. M extract showed higher radical scavenging activity through ferric reducing antioxidant power 139.11 µmol tannic acid equivalent/g; DPPH 71.32 ± 0.56%; deoxyribose radical 88.31 ± 0.47%, and total antioxidant activity 0.47 ± 0.02 mg ascorbic acid equivalents/g. Oxidative red blood cell (RBC) hemolysis inhibition rate was significantly higher in M extract (150 mg/kg body weight) in reference to total phenolic content (r = 0.935). Rats administered with DEE and M extracts (150 mg/kg body weight) for seven days before the administration of ferric nitrilotriacetate (9 mg of Fe/mg/kg bodyweight). Rats pretreated with extracts significantly changed the level of renal microsomal lipid peroxidation, glutathione, and antioxidant enzymes in post-mitochondrial supernatant (P < 0.05). Ameliorative effect of extracts against renal oxidative damage was evident in rat kidney through changes in necrotic and epithelial cells. HPTLC technique has identified the presence of rutin with reference to retardation factor (Rf ) in both the extracts. These findings support the source of polyphenols (rutin) from P. boergesenii had potent antioxidant activity; further work on isolation of bioactive compounds can be channeled to develop as a natural antioxidant.
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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) | 0.001 | 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".