Heparanase upregulation in high glucose-treated endothelial cells is prevented by insulin and heparin.
Bibliographic record
Abstract
Heparan sulfate proteoglycans on the endothelial cell surface and extracellular matrix play an important role in vascular homeostasis. Previous studies have shown that the quantity of heparan sulfate is reduced in kidney and other organs in diabetes. The objectives of this study were to determine if heparanase is induced by high glucose in endothelial cells and if heparin and/or insulin or basic fibroblast growth factor (bFGF) affect this upregulation. Cultured porcine aortic endothelial cells in M199 medium were treated with high glucose (30 mM) and/or bFGF (1 or 10 ng/ml) or high glucose plus insulin (1 U/ml) and/or heparin (0.5 microg/ml) for 7 days. To help define the mechanism of endothelial damage, cells were also exposed to H(2)O(2) (0.1 mM) for 1 day or mannitol (30 mM) for 7 days. Heparanase mRNA was detected by reverse transcription polymerase chain reaction. Heparanase activity was measured by incubating cell lysates with [(35)S]labeled extracellular matrix of bovine corneal endothelial cells and analyzing released radioactive products by gel filtration and beta-scintillation. Heparanase mRNA was found in high-glucose- and H(2)O(2)-treated cells; however, it was not found in control cells, mannitol- or high glucose plus insulin- and/or heparin-treated cells, or fresh porcine tissue. Heparanase activity was only found in high-glucose- and H(2)O(2)-treated cells. As well, bFGF did not prevent heparanase mRNA upregulation by high glucose. From these observations, we concluded that heparanase upregulation by high glucose is prevented by insulin and/or heparin but not bFGF. Reactive oxygen species, but not changes in osmolarity, may be involved in the upregulation of heparanase.
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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 | 0.001 |
| 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".