Regulation of plasminogen activator inhibitor-1 secretion by growth factors in smooth muscle cells
Bibliographic record
Abstract
Epithelioid-type vascular smooth muscle cells are metabolically active and secrete many proteases and protease inhibitors. We have previously cloned epithelioid-type smooth muscle cells from rat carotid arteries, and showed that polypeptide growth factors basic fibroblast growth factor (bFGF) and platelet-derived growth factor (PDGF) could dose-dependently induce plasminogen activator inhibitor-1 (PAI-1) secretion from these cells. In the present study, we have used these cells to investigate the growth factor-induced signal transduction pathways leading to PAI-1 secretion. We report here that PAI-1 induction was dependent on protein kinase C (PKC) and tyrosine kinase but not on protein kinase A (PKA), ras and phosphoinositol-3-kinase inhibitor. Induction of PAI-1 by bFGF and PDGF was also accompanied by activation of a mitogen-activated protein kinase pathway involving Raf/Mek/Erk1/2, and the family non-receptor tyrosine kinases., another non-receptor tyrosine kinase, on the contrary, behaved differently from in that it was part of a pathway leading to PAI-1 induction by bFGF, but not when PDGF was used as the stimulating reagent. Activation of a PKA-dependent pathway(s) opposed PAI-1 induction. One mechanism for PKA activators to inhibit PAI-1 secretion was that they markedly inhibited the phosphorylations of Mek and mitogen-activated protein kinase that were up-regulated in the presence of bFGF and PDGF.
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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) | 0.000 | 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".