Combined inhibition of PDGF and VEGF receptors by ellagic acid, a dietary-derived phenolic compound
Bibliographic record
Abstract
The vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) receptors play essential and complementary roles in angiogenesis and combined inhibition of these receptors has been shown to result in potent antitumor activity in vivo. In this study, we report that ellagic acid (EA), a natural polyphenol found in fruits and nuts, inhibits VEGF-induced phosphorylation of VEGFR-2 in endothelial cell (EC) as well as PDGF-induced phosphorylation of PDGFR in smooth muscle cells, leading to the inhibition of downstream signaling triggered by these receptors. EA also specifically inhibited VEGF-induced migration of ECs as well as their differentiation into capillary-like tubular structures and abolished PDGF-dependent smooth muscle cell migration. Interestingly, EA presents a greater selectivity for normal cells than for tumor cells since the migration of the U87 and HT1080 cell lines were much less affected by this molecule. The identification of EA as a naturally occurring dual inhibitor of VEGF and PDGF receptors suggests that this molecule possesses important antiangiogenic properties that may be helpful for the prevention and treatment of cancer.
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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".