Beneficial influence of fungal metabolite nigerloxin on diabetes-induced oxidative stress in experimental rats
Bibliographic record
Abstract
Oxidative stress plays a key role in the progression of diabetes and its complications. In this study, the beneficial influence of the fungal metabolite nigerloxin, a new aldose reductase inhibitor and a free-radical scavenger, was investigated on oxidative stress in streptozotocin-induced diabetic rats. Groups of diabetic rats were orally administered nigerloxin for 30 days at a dose of 25 and 100 mg·(kg body mass)(-1)·day(-1). Diabetic rats showed significantly increased lipid peroxide levels in blood and liver, which was accompanied by lowered concentrations of antioxidant molecules and activities of antioxidant enzymes in blood and liver. Administration of nigerloxin for 30 days at a daily dose of 100 mg∙(kg body mass)(-1) to diabetic rats significantly decreased plasma and liver lipid peroxides, elevated the nonenzymatic antioxidants ascorbic acid, reduced glutathione, and total thiols, and elevated the activities of antioxidant enzymes in blood and liver. Nigerloxin showed a tendency to counter lipid abnormalities in diabetic animals, while fasting glucose and body mass were unaffected by nigerloxin treatment. Thus, this animal study has indicated the beneficial influence of nigerloxin on oxidative stress associated with diabetes that may have an implication in delaying or ameliorating the secondary complications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".