Prevention of Diabetic Nephropathy by Modified Acidic Fibroblast Growth Factor
Bibliographic record
Abstract
BACKGROUND/AIMS: Oxidative stress (OS) contributes to all chronic diabetic complications, including diabetic nephropathy (DN). Acidic fibroblast growth factor (aFGF) has shown to confer protection from OS. However, it also has potent angiogenic activity. We hypothesized that a modified human aFGF (maFGF), with antioxidant properties but devoid of angiogenic activity, has preventative action in DN. METHODS: Streptozotocin-induced diabetic mice were treated with maFGF (intraperitoneally) daily for 1 or 6 months and were compared with untreated diabetic and non-diabetic controls. Microalbuminuria was assessed to determine functional damage. Renal cortical tissues were examined for multiple extracellular matrix proteins, vasoactive factors and OS markers. For mechanistic studies, immortalized mouse podocytes and human microvascular endothelial cells were exposed to high (25 mM) or low glucose (5 mM). OS, vasoactive factors, fibrosis and apoptosis-related gene expression were tested by real-time qPCR and Enzyme-Linked Immunosorbent Assay. Nitric oxide (NO) analyses were also performed. RESULTS: maFGF did not affect body weight and glycemia but prevented renal hypertrophy and functional changes in DN. It also prevented diabetes-induced DNA damage, nitrosative stress, vasoactive factors, angiotensinogen and endothelial NO synthase alterations. Although it failed to prevent transforming growth factor (TGF)-β1 mRNA upregulation, it prevented fibronectin production. Similar results were obtained in vitro. Decreased NO production in vivo and in vitro was also prevented by maFGF. CONCLUSIONS: maFGF treatment prevents DN. This prevention probably involves NO production.
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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".