Hyperoside ameliorates glomerulosclerosis in diabetic nephropathy by downregulating miR-21
Bibliographic record
Abstract
The purpose of this study was to investigate the therapeutic effects of hyperoside (Hyp) on glomerulosclerosis in diabetic nephropathy and its underlying mechanisms. Blood glucose, kidney mass, and renal function of mice were measured. Renal morphology was observed using hematoxylin and eosin, periodic acid - Schiff's, and Masson's trichrome stain. Fibronectin (FN) and collagen IV (COL IV) in kidney were determined by Western blot and immunohistochemical studies. Matrix metalloproteinases (MMP)-2 and -9 and tissue inhibitors of metalloproteinase (TIMP)-1 in renal tissues were detected on both the mRNA and protein levels. miRNA expression and artificial alterations by miRNA agomir transfection were evaluated to investigate the protective mechanism of Hyp in mesangial cells. Hyp effectively improved renal function and physiologic features of db/db mice. Hyp also ameliorated glomerulosclerosis by suppressing FN, COL IV, and TIMP-1 expressions and promoting MMP-9 and MMP-2 expressions. The change in MMP-9 mRNA expression was inconsistent with that in protein levels in kidney, indicating that there was a post-transcriptional regulation. Further exploration in vitro showed that miR-21 was downregulated by Hyp, increasing expression of its target, MMP-9. These results suggest that Hyp can ameliorate glomerulosclerosis in diabetic nephropathy by downregulating miR-21 to increase expression of its target, MMP-9.
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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".