Vascular tissue engineering from human adipose tissue: fundamental phenotype of its resident microvascular endothelial cells and stromal/stem cells
Bibliographic record
Abstract
Adipose tissue is an abundant, accessible, and uniquely dispensable source of cells for vascular tissue engineering. Despite its intrinsic endothelial cells, considerable effort is directed at deriving endothelium from its resident stem and progenitor cells. Here, we investigate the composition of human adipose tissue and characterize the phenotypes of its constituent cells in order to help ascertain their potential utility for vascular tissue engineering. Unsupervised clustering based on cell-surface protein signatures failed to detect CD45 – CD31 – VEGFR2 + endothelial progenitor cells within adipose tissue, but supported further investigation of its resident CD45 – CD31 + microvascular endothelial cells (HAMVECs) and CD45 – CD31 – stromal/stem cells (ASCs). The endothelial differentiation of ASCs altered their proteome, but it remained distinct from that of primary endothelial cell controls – as well as HAMVECs – regardless of their arterial-venous specification or macrovascular-microvascular origin. Rather, ASCs retained a proteome indicative of a perivascular phenotype, which was supported by their ability to facilitate the capillary morphogenesis of HAMVECs. This study supports the use of HAMVECs for the generation of endothelium. It suggests that the utility of ASCs for vascular tissue engineering lies in their capacity to remodel the extracellular matrix and to function as mural cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".