Dietary soy isoflavones decrease methylglyoxal formation and prevent the diabetic cataracts in streptozotocin-induced severe diabetic rats
Bibliographic record
Abstract
AIM:Methylglyoxal (MG) is considered as a very active free radical,which is an important contributor to diabetic complications such as cataracts. The purpose of this study was to investigate the serum MG levels and protective effects of soybean isoflavones on streptozotocin induced diabetes. METHODS:Diabetes was induced in male Sprague -Dawley rats by intraperitoneal injection of 100 mg/kg streptozotocin (STZ). Diabetic rats were then randomly di-vided into 3 groups and received a special diet supplemented with casein (diabetes),low -isoflavone soy protein (diabetes + LIS),or high-isoflavone soy protein (diabetes + HIS) for 8 weeks,respectively. RESULTS:Compared to the diabetes or diabetes + LIS groups,diabetes + HIS diet significantly increased serum insulin levels,and reduced serum glucose,HbA1c and methylglyoxal levels (P 0. 05 or P 0. 01). Serum GSH levels were also increased in diabetes + HIS-fed rats as compared to the diabetes or diabetes + LIS rats (P 0. 01). Significant insulin production by the β-cells of the islets was observed in diabetic rats treated with diabetes + HIS protein as compared to that in diabetes group or diabetes + LIS group. More importantly,the incidence of cataracts in the diabetic rats was markedly decreased in diabetes + HIS group. No difference of above parameters between the rats in diabetes + LIS group and diabetes group was observed.CONCLUSION:Injection of high isoflavones soy protein not only lowers the glucose levels but also reduce the incidence of cataracts in diabetic rats. The underlying mechanism of soy isoflavones are attributed to the increase in insulin secretion,the decrease in MG formation,and the antioxidant effect.
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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 | 0.001 | 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".