Lactoferricin, a peptide derived from bovine lactoferrin, has anti-angiogenic activity
Bibliographic record
Abstract
Proc Amer Assoc Cancer Res, Volume 46, 2005 3027 Angiogenesis is a complex process whereby new blood vessels form from pre-existing parental vessels. Interfering with angiogenesis is a promising anti-cancer therapy because angiogenesis is necessary for tumor growth beyond 1-2 mm in diameter due to the tumor’s need for oxygen, nutrients, and waste removal. Bovine lactoferricin (LfcinB) is a 25 amino acid peptide obtained by acid-pepsin hydrolysis of lactoferrin. Our research has shown that LfcinB selectively kills a variety of human tumor cells through the mitochondria-dependent pathway of apoptosis. Additionally, we have recently obtained evidence that supports an anti-angiogenic role for LfcinB. Basic fibroblast growth factor (bFGF)- and vascular endothelial growth factor (VEGF)-induced in vitro proliferation and migration of human umbilical vein endothelial cells (HUVECs) was inhibited by LfcinB. However, LfcinB was not cytotoxic to HUVECs. Furthermore, we have shown that LfcinB interacts with HUVECs by binding to heparin sulfate proteoglycans on the cell surface. This binding is competitively inhibited by the addition of heparan-binding growth factors, bFGF and VEGF, but not by the addition of non-heparin binding Epidermal Growth Factor (EGF). Importantly, LfcinB caused a 40% reduction in in vivo bFGF-associated angiogenesis using the matrigel model of angiogenesis. Together, these results support an inhibitory role for LfcinB in bFGF- and VEGF-induced angiogenesis through competition for heparin binding sites on the surface of HUVECs. The anti-angiogenic activity of LfcinB may therefore have possible application for the treatment of human cancers. Supported by the Nova Scotia Health Research Foundation and the Canadian Breast Cancer Foundation (Atlantic Chapter).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".