Delphinidin, a dietary anthocyanidin, inhibits vascular endothelial growth factor receptor-2 phosphorylation
Bibliographic record
Abstract
Epidemiological studies have shown that a diet rich in fruits and vegetables has a beneficial preventive effect on cardiovascular diseases and cancer by mechanisms that have not yet been elucidated. In this work, we investigated the antiangiogenic activities of anthocyanidins, a class of polyphenols present at high levels in fruits. Among the tested anthocyanidins (cyanidin, delphinidin, malvidin, pelargonidin, peonidin and petunidin), delphinidin was the most potent angiogenic inhibitor. In vitro, low concentrations of delphinidin inhibited vascular endothelial growth factor (VEGF)-induced tyrosine phosphorylation of VEGF receptor (VEGFR)-2, leading to the inhibition of downstream signaling triggered by VEGFR-2. Inhibition of VEGFR-2 by delphinidin inhibited the VEGF-induced activation of ERK-1/2 signaling and the chemotactic motility of human EC as well as their differentiation into capillary-like tubular structures in Matrigel and within fibrin gels. In vivo, delphinidin was able to suppress basic fibroblast growth factor-induced vessel formation in the mouse Matrigel plug assay. The identification of delphinidin as a naturally occurring inhibitor of VEGF 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".