The Dietary Flavones Apigenin and Luteolin Impair Smooth Muscle Cell Migration and VEGF Expression through Inhibition of PDGFR-β Phosphorylation
Bibliographic record
Abstract
Platelet-derived growth factor (PDGF)-dependent recruitment of mural cells such as pericytes and smooth muscle cells plays a central role in the maturation and stabilization of newly formed vasculature during angiogenesis. In this work, we show that the dietary flavones apigenin and luteolin may interfere with this event through their inhibitory effect on PDGF-dependent phosphorylation of PDGF receptor beta (PDGFR-beta) in smooth muscle cells. Inhibition of PDGFR-beta activity by apigenin and luteolin occurred at low concentrations of the molecules and resulted in the inhibition of extracellular signal-regulated kinase and Akt phosphorylation triggered by PDGF, as well as in a marked reduction of the migratory and invasive properties of these cells. Apigenin and luteolin also strongly inhibit the PDGF-dependent increase in vascular endothelial growth factor (VEGF) mRNA levels and the secretion of VEGF by smooth muscle cells as well as vessel formation in the mouse Matrigel plug assay, suggesting that the inhibitory effects of both molecules on smooth muscle cell function result in impaired angiogenesis. Overall, these results identify apigenin and luteolin as dietary-derived inhibitors of PDGFR-beta activity and suggest that this inhibitory effect may contribute to the chemopreventive properties of these molecules.
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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".