Regulation of plasminogen activator inhibitor‐1 secretion by urokinase and tissue plasminogen activator in rat epithelioid‐type smooth muscle cells
Bibliographic record
Abstract
Tissue plasminogen activator (tPA) and urokinase (uPA) are targets of plasminogen activator inhibitor-1 (PAI-1) inhibition. We have previously shown that both proteases can also induce PAI-1 secretion in rat smooth muscle cells (SMCs). We now report that both proteases appear to use very similar cellular mechanisms for signal transduction. They induced PAI-1 secretion using a pathway(s) involving protein kinase C (PKC). They also activated the Raf/Mek/mitogen-activated protein kinase (MAPK) pathway, which lies downstream of PKC activation. Activation of protein kinase A (PKA), however, lowered PAI-1 secretion induced by uPA and tPA, as a result of an inhibition of the PKC pathway and inhibition of Raf, Mek and MAPK phosphorylations. Src and syk family non-receptor tyrosine kinases (TK) were also involved in PAI-1 induction. The mechanisms of interaction of these tyrosine kinases with other pathways appeared to be quite different: src appeared to act within the PKC and PKA pathways, while syk operated independently of these pathways. Furthermore, whereas src inhibition resulted in inhibition of Raf/Mek/Erk phosphorylations, syk inhibition could only inhibit Mek and Erk phosphorylations but not the phosphorylation of Raf. These multiple pathways utilized by uPA and tPA to modulate PAI-1 secretion might be involved in determining the proteolytic or antiproteolytic potential of the SMCs under different pathophysiological conditions.
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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 | 0.001 |
| 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 | 0.001 |
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".