Using Del-1 to Tip the Angiogenic Balance in Endothelial Cells in Modular Constructs
Bibliographic record
Abstract
Modular tissue engineering is a method of building vascularized tissue-engineered constructs. Submillimeter-sized collagen pieces (modules) coated with a layer of endothelial cells (EC; vascular component), and with embedded functional cells, are self-assembled into a larger, three-dimensional tissue. In this study, we examined the use of developmental endothelial locus-1 (Del-1), an extracellular matrix protein with proangiogenic properties, as a means of tipping the angiogenic balance in human umbilical vein endothelial cells incorporated in modular tissue-engineered constructs. The motivation was to enhance the vascularization of these constructs upon transplantation in vivo, in this case, without the use of exogenous mesenchymal stromal cells. EC were transduced using a lentiviral construct to overexpress Del-1. The Del-1 EC formed more sprouts in a fibrin gel sprouting assay in vitro compared with eGFP (control) transduced EC, as expected. Del-1 EC had a distinct profile of gene expression (upregulation of matrix metalloproteinase-9 [MMP-9], urokinase-type plasminogen activator [uPA/PLAU], vascular endothelial growth factor [VEGF-A], and intercellular adhesion molecule-1 [ICAM-1]; downregulation of angiopoietin-2 [Ang2]), also supporting the notion of "tipping the angiogenic balance". On the other hand, contrary to our expectations, when Del-1 EC-coated modules were implanted subcutaneously in a severe combined immunodeficient/beige animal model, the proangiogenic effect of Del-1 was less remarkable. There was only a small increase in the number of blood vessels formed in Del-1 implants compared with the eGFP implants, and only few blood vessels formed at the implant site in both cases. This was presumed due to limited EC survival after transplantation. We speculate that if we could improve EC survival in our study (for example, by adding other prosurvival factors or supporting cells), we would see a greater Del-1-induced angiogenic benefit in vivo as a consequence of increased Del-1 secretion by a higher number of surviving cells.
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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) | 0.000 | 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".