Tenovin-1 Ameliorates Renal Fibrosis in High-Fat-Diet-Induced Diabetic Nephropathy via Antioxidant and Anti-Inflammatory Pathways
Bibliographic record
Abstract
High-fat diet (HFD)-induced obesity has been involved in the development of diabetic nephropathy (DN). Tenovin-1, a potent selective SIRT1/2 inhibitor, regulates various target proteins. The present study evaluated the protective effect of Tenovin-1 against renal fibrosis in HFD-induced Zucker diabetic fatty (ZDF) rats. Rats were fed a normal chow diet or HFD. Tenovin-1 (45 mg/kg) administered to HFD-fed rats decreased inflammatory cytokine expression in the serum of the rats. The antioxidant status and oxidative damage to lipids or DNA were significantly restored by Tenovin-1. Additionally, Tenovin-1 reduced the levels of blood urea nitrogen (BUN), serum creatinine (sCr), microalbumin, and urinary protein-based biomarkers in the urine of HFD-fed rats. The abnormal architecture of the kidney and pancreas was restored by Tenovin-1 administration. Tenovin-1 also reduced apoptosis in the kidneys of the HFD-fed rats and HG-treated NRK-52E cells. It significantly lowered the levels of ECM proteins in the kidneys of HFD-fed rats and HG-treated NRK-52E cells. Additionally, Tenovin-1 markedly reduced claudin-1, SIRT1, and SIRT2, but increased SIRT3 and SIRT4 in HFD-fed rats and NRK-52E cells treated with HG. Furthermore, Tenovin-1 altered epidermal growth factor receptor (EGFR), platelet-derived growth factor receptor-β (PDGFR-β), and signal transducer and activator of transcription 3 (STAT3) levels in the kidneys of HFD-fed rats. Conclusively, this study shows that Tenovin-1 can be a potential candidate drug for the treatment of HFD-induced renal fibrosis, in vivo and in vitro models.
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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) | 0.001 | 0.001 |
| 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".