Inhibition of SREBP With Fatostatin Does Not Attenuate Early Diabetic Nephropathy in Male Mice
Bibliographic record
Abstract
Sterol regulatory element binding protein (SREBP) is an important potential mediator of kidney fibrosis and is known to be upregulated in diabetic nephropathy. We evaluated the effectiveness of SREBP inhibition as treatment of diabetic nephropathy. Type 1 diabetes was induced in uninephrectomized male CD1 mice with streptozotocin. The mice were treated with the SREBP inhibitor fatostatin for 12 weeks. At the endpoint, kidney function and pathologic findings were assessed. Fatostatin inhibited the increase of both isoforms of SREBP (types 1 and 2) in diabetic kidneys. Treatment attenuated basement membrane thickening but did not improve hyperfiltration, albuminuria, or kidney fibrosis in diabetic mice. The treatment of nondiabetic mice with fatostatin led to hyperfiltration and increased the glomerular volume to levels seen in diabetic mice. This was associated with increased renal inflammation and a trend toward increased renal fibrosis. Both in vivo and in cultured renal proximal tubular epithelial cells, fatostatin increased the expression of the proinflammatory cytokine monocyte chemoattractant protein-1. Thus, SREBP inhibition with fatostatin not only is ineffective in preventing diabetic nephropathy but also leads to kidney injury in nondiabetic mice. Further research on the efficacy of other SREBP inhibitors and the specific roles of SREBP-1 and SREBP-2 in the treatment and pathogenesis of diabetic nephropathy is needed.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".