Del-1 Overexpression in Endothelial Cells Increases Vascular Density in Tissue-Engineered Implants Containing Endothelial Cells and Adipose-Derived Mesenchymal Stromal Cells
Bibliographic record
Abstract
We used a combination of strategies to stimulate the vascularization of tissue-engineered constructs in vivo including a modular approach to build larger tissues from individual building blocks ("modules") mixed together. Each building block included vascular cells by design; modules were submillimeter-sized collagen gels with an outer layer of endothelial cells (ECs), and with embedded adipose-derived mesenchymal stromal cells (adMSCs) to support EC survival and blood vessel maturation in vivo. We transduced the ECs that coat the modules with a lentiviral construct to overexpress the angiogenic extracellular matrix (ECM) protein Developmental endothelial locus-1 (Del-1). Upon injection of modules in a subcutaneous SCID/Bg mouse model, there was an increase in the number of blood vessels for implants with ECs transduced to overexpress Del-1 compared with control implants (with enhanced green fluorescent protein [eGFP]-transduced ECs) over the 21-day duration of the study. The greatest difference between Del-1 and eGFP implants and the highest number of blood vessels were observed 7 days after transplantation. The day-7 Del-1 implants also had increased SMA+ staining compared with control, suggesting increased blood vessel maturation through recruitment of SMA+ smooth muscle cells or pericytes to stabilize the newly formed blood vessels. Perfusion studies (microcomputed tomography, ultrasound imaging, and systemic injection of fluorescent UEA-1 or dextran) showed that some of the newly formed blood vessels (both donor derived and host derived, in both Del-1 and eGFP implants) were perfused and connected to the host vasculature as early as 7 days after transplantation, and at later time points as well. Nevertheless, perfusion of the implants was limited in some cases, suggesting that further improvements are necessary to normalize the vasculature at the implant site.
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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".