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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".