Effects of vascular endothelial growth factor on endothelin-1 production by human lung microvascular endothelial cells in vitro
Bibliographic record
Abstract
AIMS: Increased endothelin-1 (ET-1) is a hallmark of pulmonary arterial hypertension (PAH), and contributes to its pathogenesis. The factors controlling ET-1 in PAH are poorly understood. Combined with other stimuli, vascular endothelial growth factor (VEGF) blockade results in PAH-like lesions in animal models, and has been associated with PAH in humans. The effects of VEGF on ET-1 production by human lung blood microvascular endothelial cells (HMVEC-LBl) are unknown. MAIN METHODS: We exposed HMVEC-LBl in-vitro to human VEGF-121 (40 ng/mL) in serum-free medium for 7h, in the absence or presence of the VEGF receptor antagonist, SU5416 (3 and 10 μM). ET-1 production was measured in the supernatant. Phosphorylation of VEGF receptor 2 (VEGFR2) was measured by Western blotting after exposure to VEGF without or with SU5416 for 5 and 10 min. KEY FINDINGS: VEGF effectively caused VEGFR2 phosphorylation, which was blocked by SU5416. VEGF decreased ET-1 production by at least 29%. In the absence of VEGF, SU5416 increased ET-1 production, by 16% at 10 μM, and SU5416 was able to completely abolish the VEGF effect on ET-1 production. SIGNIFICANCE: VEGF may promote vascular health by decreasing ET-1 production in HVMEC-LBl. Blockade of VEGF signaling by SU5416 increases ET-1 levels. The role of VEGF in modulating endothelin production in PAH deserves further study.
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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 | 0.001 | 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) | 0.002 | 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".