Differential Expression of Tie2 Receptor and VEGFR2 by Endothelial Clones Derived from Isolated Bovine Mononuclear Cells
Bibliographic record
Abstract
The purpose of these experiments was to evaluate the expression of endothelial markers, such as Tie2 and VEGFR2 in endothelial cells derived from blood mononuclear endothelial progenitor cells. Bovine mononuclear cells were isolated using separation by centrifugation and were grown in endothelial specific media supplemented with growth factors. Isolation of the whole cell population of mononuclear cells (MNC) from bovine peripheral blood gave rise to progenitor-like cells (CD45(-)) that, although morphologically similar, have different phenotypes revealed by expression of endothelial specific markers Tie2 and VEGFR2. Plating of MNCs on collagen and fibronectin gave rise to more colonies than non-coated dishes. Occasional colonies from MNC isolations had a mural cell phenotype, negative for Tie2 and VEGFR2 but positive for smooth muscle actin and PDGFRβ. Although cells expressing high levels of VEGFR2 and low levels of Tie2, and vice versa were both able to form cords on Matrigel, cells with higher expression of Tie2 migrate faster in a scratch assay than ones with lower expression of Tie2. When these different clones of cells were introduced in mice through tail vein injections, they retained an ability to home to angiogenesis occurring in a subcutaneous Matrigel plug, regardless of their Tie2/VEGFR2 receptor expression patterns, but cells with high VEGFR2/low Tie2 were more likely to be CD31 positive. Therefore, we suggest that active sites of angiogenesis (such as wounds, tumors, etc.) can attract a variety of endothelial cell precursors that may differentially express Tie2 and VEGFR2 receptors, and thus affect our interpretation of EPCs as biomarkers or therapies for vascular disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".