Effect of oxidative stress on the expression of t-PA, u-PA, u-PAR, and PAI-1 in endothelial cells
Bibliographic record
Abstract
In this study we examined the effects of exogenous nitric oxide (sodium nitroprusside, SNP) and hydrogen peroxide (H2O2) on the expression level of tissue-type plasminogen activator (t-PA), urokinase-type plasminogen activator (u-PA), urokinase-type plasminogen activator receptor (u-PAR), and plasminogen activator inhibitor type 1 (PAI-1) in human umbilical vein endothelial cells (HUVEC). The expression of selected genes involved in fibrynolysis under the influence of oxidative stress was analyzed at the levels of mRNA, protein, and promoter activity. The results of the conducted studies revealed that oxidative stress in endothelial cells causes a significant increase in PAI-1 and u-PAR expression and a moderate increase in t-PA and u-PA expression at all of the investigated levels. We attempted to elucidate the molecular signaling mechanisms by which SNP and H2O2 regulate expression of the respective fibrinolytic factors. Therefore, we tested the protein levels of AP-1, NF-kappaB, and HIF-1 and their DNA-binding activity in endothelial cells subjected to oxidative stress. We found strong correlation between AP-1, NF-kappaB, and HIF-1 in the contribution of regulation of selected genes. In addition, we also found that the inhibition of PAI-1 synthesis by antisense oligonucleotide to PAI-1 mRNA results in markedly increased u-PAR expression and that NF-kappaB and AP-1 are involved in this regulation.
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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".