Vascular permeability induced by VEGF family members in vivo: Role of endogenous PAF and NO synthesis
Bibliographic record
Abstract
We previously reported that vascular endothelial growth factor (VEGF) increases vascular permeability through the synthesis of endothelial platelet-activating factor (PAF), while others reported the contribution of nitric oxide (NO). Herein, we addressed the contribution of VEGF receptors and the role played by PAF and NO in VEGF-induced plasma protein extravasation. Using a modified Miles assay, intradermal injection in mice ears of VEGF-A(165), VEGF-A(121), and VEGF-C (1 microM) which activate VEGFR-2 (Flk-1) receptor increased vascular permeability, whereas a treatment with VEGFR-1 (Flt-1) analogs; PlGF and VEGF-B (1 microM) had no such effect. Pretreatment of mice with PAF receptor antagonist (LAU8080) or endothelial nitric oxide synthase (eNOS) inhibitor (L-NAME) abrogated protein extravasation mediated by VEGF-A(165). As opposed to PAF (0.01-1 microM), treatment with acetylcholine (ACh; up to 100 microM; inducer of NO synthesis) or sodium nitroprusside (SNP; up to 1 microM; NO donor) did not induce protein leakage. Simultaneous pretreatment of mice with eNOS and protein kinase A (PKA) inhibitors restored VEGF-A(165) vascular hyperpermeability suggesting that endogenous NO synthesis leads to PKA inhibition, which support maintenance of vascular integrity. Our data demonstrate that VEGF analogs increase vascular permeability through VEGFR-2 activation, and that both endogenous PAF and NO synthesis contribute to VEGF-A(165)-mediated vascular permeability. However, PAF but not NO directly increases vascular permeability per se, thereby, suggesting that PAF is a direct inflammatory mediator, whereas NO serves as a cofactor in VEGF-A(165) proinflammatory activities.
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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) | 0.001 | 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 | 0.001 |
| 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".