G Proteins and Phospholipase C Mediate Thrombin-lnduced Generation of Plasminogen Activator Inhibitor-1 from Vascular Smooth Muscle Cells
Bibliographic record
Abstract
The present study investigated transcellular signalling mechanism involved in thrombin-induced production of plasminogen activator inhibitor-1 (PAI-1) in cultured vascular baboon aortic smooth muscle cells (BASMC). Treatments with thrombin dose-dependently increased the steady state levels of PAI-1 mRNA and the generation of PAI-1 antigen from BASMC. Thrombin receptor-activating peptide mimicked the effect of thrombin on the generation of PAI-1. Sodium fluoride (1 mM) stimulated PAI-1 generation from BASMC. Pertussis toxin dose-dependently suppressed thrombin-induced increase of PAI-1 generation. Treatment with 5 mM neomycin, 10 microM U73122 or 1 microM calphostin C blocked thrombin-induced PAI-1 generation. Phorbol myristate acetate at 10 nM for 3 h strongly stimulated the generation of PAI-1 from BASMC. Forskolin (100 microM) or 8-bromo-cAMP (100 microM) suppressed thrombin-induced PAI-1 generation. The responses of quiescent BASMC to thrombin or the inhibitors on PAI-1 generation were comparable to that of growing cells. The results of the present study suggest that pertussis toxin-sensitive G proteins and a phospholipase C are involved in thrombin-induced generation of PAI-1 in BASMC, which may transmit signals from occupied thrombin receptor to protein kinase C and thereby increase the generation of PAI-1. Elevated levels of intracellular cAMP may negatively regulate the generation of PAI-1 from vascular SMC.
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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".