Docosahexaenoic Acid Monoacylglyceride Decreases Endothelin-1 Induced Ca2+ Sensitivity and Proliferation in Human Pulmonary Arteries
Bibliographic record
Abstract
BACKGROUND: Pulmonary artery vasoconstriction and vascular remodeling contribute to a sustained elevation of pulmonary vascular resistance and pressure in patients with pulmonary arterial hypertension (PH), an often fatal hemodynamic disease. The effect of docosahexaenoic acid monoacylglyceride (MAG-DHA) and the role of the 17 kDa protein kinase C-potentiated inhibitor protein (CPI-17) were determined on vasoconstriction and smooth muscle cell proliferation of human pulmonary arteries (HPA). METHODS: HPA were obtained from 16 patients undergoing lung resection for carcinoma. The mechanical tension and Ca(2+) sensitivity were measured on arterial rings treated with endothelin-1 (ET-1) in the absence or presence of MAG-DHA. The effect of MAG-DHA on the level of proliferation of smooth muscle cells isolated from HPA was evaluated in order to determine the role of CPI-17 protein. RESULTS: MAG-DHA treatment decreased the reactivity and Ca(2+) sensitivity induced by ET-1 in HPA. MAG-DHA treatment also decreased the expression of vascular endothelial growth factor (VEGF) induced by ET-1. Moreover, both VEGF inhibitor and MAG-DHA treatments reduced Ca(2+) hypersensitivity induced by ET-1, which was associated to a reduction in CPI-17 and myosin-binding subunit of the myosin light chain phosphatase (MYPT-1) phosphorylation levels. Proliferation of ET-1-stimulated HPA smooth muscle cells (PASMc) was also decreased following CPI-17 small interfering RNA transfection and MAG-DHA treatments. Western blot analyses revealed that MAG-DHA treatment resulted in decreased phosphorylation levels of CPI-17 and extracellular signal-regulated kinases (ERK) in PASMc treated with ET-1. CONCLUSIONS: We have demonstrated that VEGF interacts with CPI-17 signaling pathway resulting in an increase in Ca(2+) sensitivity and proliferation of PASMc, whereas MAG-DHA treatment reversed these effects.
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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".