Prevascular Structures Promote Vascularization in Engineered Human Adipose Tissue Constructs upon Implantation
Bibliographic record
Abstract
Vascularization is still one of the most important limitations for the survival of engineered tissues after implantation. In this study, we aim to improve the in vivo vascularization of engineered adipose tissue by preforming vascular structures within in vitro-engineered adipose tissue constructs that can integrate with the host vascular system upon implantation. Different cell culture media were tested and different amounts of human adipose tissue-derived mesenchymal stromal cells (ASC) and human umbilical vein endothelial cells (HUVEC) were combined in spheroid cocultures to obtain optimal conditions for the generation of prevascularized adipose tissue constructs. Immunohistochemistry revealed that prevascular structures were formed in the constructs only when 20% ASC and 80% HUVEC were combined and cultured in a 1:1 mixture of endothelial cell medium and adipogenic medium. Moreover, the ASC in these constructs accumulated lipid and expressed the adipocyte-specific gene fatty acid binding protein-4. Implantation of prevascularized ASC/HUVEC constructs in nude mice resulted in a significantly higher amount of vessels (37 ± 17 vessels/mm(2)) within the constructs compared to non-prevascularized constructs composed only of ASC (3 ± 4 vessels/mm(2)). Moreover, a subset of the preformed human vascular structures (3.6 ± 4.2 structures/mm(2)) anastomosed with the mouse vasculature as indicated by the presence of intravascular red blood cells. Our results indicate that preformed vascular structures within in vitro-engineered adipose tissue constructs can integrate with the host vascular system and improve the vascularization upon implantation.
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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".