Attenuation of erythrocyte membrane oxidative stress by <i>Sesbania grandiflora</i> in streptozotocin-induced diabetic rats
Bibliographic record
Abstract
The aim of the present study was to investigate the protective effects of Sesbania grandiflora flower (SGF) extract on erythrocyte membrane in Streptozotocin (STZ)-induced diabetic rats. Adult male albino rats of Wistar strain, weighing 190-220 g, were made diabetic by an intraperitonial administration of STZ (45 mg/kg). Normal and diabetic rats were treated with SGF, and diabetic rats were also treated with glibenclamide as drug control, for 45 days. In this study plasma insulin and haemoglobin levels were decreased and blood glucose, glycosylated haemoglobin, protein oxidation, lipid peroxidation markers, and osmotic fragility levels were increased in diabetic rats. Moreover, erythrocytes antioxidant enzymes such as superoxide dismutase, catalase, glutathione peroxide, glutathione reductase, glutathione-S-transferase, and glucose-6-phosphate dehydrogenase activities and non-enzymatic antioxidants such as vitamin C, vitamin E, reduced glutathione (GSH), and oxidized glutathione (GSSG) levels were altered. Similarly, the activities of total ATPases, Na(+)/K(+)-ATPase, Ca(2+)-ATPase, and Mg(2+)-ATPase were also decreased in the erythrocytes of diabetic rats. Administration of SGF to STZ-induced diabetic rats reduced blood glucose and glycosylated haemoglobin levels with increased levels of insulin and haemoglobin. Moreover, SGF reversed the protein and lipid peroxidation markers, osmotic fragility, membrane-bound ATPases activities, and antioxidant status in STZ-induced diabetic rats. These results suggest that SGF could provide a protective effect on diabetes by decreasing oxidative stress-associated diabetic 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) | 0.000 | 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 | 0.001 |
| 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".