Azuki bean (<i>Vigna angularis</i>) extract reduces oxidative stress and stimulates autophagy in the kidneys of streptozotocin-induced early diabetic rats
Bibliographic record
Abstract
Diabetic kidney disease is associated with oxidative stress, inflammation, and autophagy. The aim of this study was to investigate the effect of azuki bean (Vigna angularis) extract (ABE) on oxidative stress and autophagy in the kidneys of diabetic rats. Streptozotocin (STZ)-induced diabetic rats received 0, 10, or 40 mg/kg of ABE orally for 4 weeks, whereas vehicle-injected control rats received distilled water. Level of plasma glutathione and expression of heme oxygenase-1 (HO-1), p47phox (NADPH oxidase subunit), and markers associated with autophagy were examined. The glutathione level in the 40 mg/kg ABE-treated diabetic group (ABE-40 group) was higher than that of the untreated diabetic group (ABE-0 group). The HO-1 and p47phox protein expression levels of the ABE-40 group were lower (47% and 33%, respectively) than those of the ABE-0 group. The level of light chain 3B II (LC3B-II) was higher in the ABE-40 group than in the ABE-0 group. Protein levels of p62/sequestosome 1 (p62) in the ABE-40 group were lower than those in the ABE-0 group. Our results suggest that ABE may attenuate STZ-induced diabetic kidney injury by suppressing oxidative stress and (or) by upregulating autophagy.
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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".