Protective Effect of Modified Human Fibroblast Growth Factor on Diabetic Nephropathy
Bibliographic record
Abstract
Oxidative stress is a key mechanism causing Diabetic Nephropathy (DN). Acidic fibroblast growth factor (aFGF) is known to confer protection from oxidative stress. However, it also has significant angiogenic activity. Hence, we have generated a mutated human acidic FGF (maFGF), with intact antioxidant properties but devoid of angiogenic activities. Recent evidence shows that maFGF treatment prevented diabetic cardiomyopathy and further in vitro studies suggest that this prevention is mediated by suppression of cardiac oxidative stress, hypertrophy and fibrosis. We hypothesized that maFGF treatment has a protective effect in DN.\nWe show that maFGF treatment did not affect body weight and blood sugar levels in a type 1 diabetic mouse model. However maFGF prevented renal functional alterations in diabetes and decreased renal hypertrophy following long-term diabetes. maFGF also prevented diabetes–induced DNA damage, upregulation of angiotensinogen, oxidative stress marker heme oxygenase 1, and alteration of endothelial nitric oxide synthase (eNOS). Surprisingly, it failed to prevent upregulation of the fibrogenic cytokine transforming growth factor β1 mRNA expression. However, long term administration of maFGF partially prevented diabetes-induced extracellular matrix proteins accumulation. Further analyses showed similar results in high glucose-induced alterations in podocytes and microvascular endothelial cells. Likewise, maFGF showed prevention of diabetes- induced decreased nitric oxide (NO) production and apoptosis in vivo and in vitro. These results were consistent with the prevention of long term diabetes- induced down regulation of eNOS enzyme. Data from these experiments suggest that the preventative effects of maFGF treatment in DN are probably via alteration of NO production, and indicate a potential therapeutic role of maFGF in 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 | 0.000 |
| 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".