<i>Dioscorea zingiberensis</i> ameliorates diabetic nephropathy by inhibiting NLRP3 inflammasome and curbing the expression of p66Shc in high-fat diet/streptozotocin-induced diabetic mice
Bibliographic record
Abstract
OBJECTIVES: Diabetic nephropathy (DN) is a severe diabetic complication. Dioscorea zingiberensis (DZ) possesses excellent pharmacological properties with lower toxicity. The purpose of this study was to investigate the efficacy and mechanism of DZ in DN. METHODS: DN was established by the high-fat diet combining intraperitoneal injection of streptozotocin in mice. The DZ (125 and 250 mg/kg/day) were intragastrical administered for 8 consecutive weeks. After treatment, blood, urine and kidney tissue were collected for biological detection, renal morphology, fibrosis and molecular mechanism research, respectively. KEY FINDINGS: This study has shown that DZ significantly ameliorated kidney hypertrophy, renal structural damage and abnormal function of the kidney indicators (creatinine, urinary protein and blood urea nitrogen). Further molecular mechanism data suggested that the NLRP3/Cleaved-caspase-1 signal pathway was remarkably activated in DN, and DZ treatment reversed these changes, which indicated that it effectively attenuated inflammatory response caused by hyperglycaemia. In addition, DN inhibits hyperglycaemia-induced activation of oxidative stress by suppressing the expression of p66Shc proteins. CONCLUSIONS: DZ could efficiently suppress oxidative stress and inflammatory responses to postpone the development of DN, and its mechanism might be related to inhibition of NLRP3 and p66Shc activities. Thus, DZ could be developed into a new therapeutic agent for DN.
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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".