Morus nigra leaf extract improves glycemic response and redox profile in the liver of diabetic rats
Bibliographic record
Abstract
Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemia and alterations in the carbohydrate, lipid, and protein metabolism. DM is associated with increased oxidative stress and pancreatic beta cell damage, which impair the production of insulin and the maintenance of normoglycemia. Inhibiting oxidative damage and controlling hyperglycemia are two important strategies for the prevention of diabetes. The pulp and leaf extracts of mulberry (Morus nigra L.) have abundant total phenolics and flavonoids, and its antioxidant potential may be an important factor for modulating oxidative stress induced by diabetes. In this study, DM was induced by intraperitoneal injection of alloxan monohydrate (135 mg kg(-1)). Female Fischer rats were divided into four groups: control, diabetic, diabetic pulp, and diabetic leaf extract. Animals in the diabetic pulp and diabetic leaf extract groups were treated for 30 days with M. nigra L. pulp or leaf extracts, respectively. At the end of treatment, animals were euthanized and, liver and blood samples were collected for analysis of biochemical and metabolic parameters. Our study demonstrated that treatment of diabetic rats with leaf extracts decreased the superoxide dismutase (SOD)/catalase (CAT) ratio and carbonylated protein levels by reducing oxidative stress. Moreover, the leaf extract of M. nigra L. decreased the matrix metalloproteinase (MMP)-2 activity, increased insulinemia, and alleviated hyperglycemia-induced diabetes. In conclusion, our study found that the leaf extract of M. nigra L. improved oxidative stress and complications in diabetic rats, suggesting the utility of this herbal remedy in the prevention and treatment of DM.
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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".