Vascularization of prevascularized and non-prevascularized fibrin-based human adipose tissue constructs after implantation in nude mice
Bibliographic record
Abstract
Adipose regeneration strategies have been hampered by the inability to supply an adequate vascular supply following implantation. Vascularization in vitro, also called prevascularization, is a promising method that could promote the vascularization of engineered adipose tissue constructs upon implantation. In this study we compared the ability of prevascularized-to-non-prevascularized fibrin-based human adipose tissue to promote vascularization. Human adipose tissue-derived stromal cells (ASCs) and different mixtures (1:1, 1:2 and 1:5) of ASCs with human umbilical vein endothelial cells (HUVECs) were cultured in fibrin at two different densities (1.0 × 10(6) and 10 × 10(6) cells/ml) for 7 days. Histological analysis revealed that prevascular structures formed in 1:5 ASC/HUVEC fibrin-based constructs seeded with a total of 10 × 10(6) cells/ml. These constructs and ASC-only constructs were implanted subcutaneously in athymic mice for 7 days and generated lipid-containing grafts. The numbers and densities of blood vessels within the ASC/HUVEC constructs were similar to those of ASC-only constructs. Furthermore, immunostaining studies demonstrated human-derived vasculature within a few of the ASC/HUVEC and ASC-only constructs. A subset of this human-derived vasculature contained erythrocytes, indicating integration with the host vasculature. In conclusion, our study indicated no difference in the rate of vascularization of prevascularized ASC/HUVEC and non-prevascularized ASC-only fibrin-based constructs, suggesting that prevascularization of these fibrin-based constructs does not promote vascularization. Our results further indicated that not only endothelial cells, but also ASCs may contribute to the formation of vascular lumina upon implantation. This finding is interesting, since it demonstrates the possibility of vascularized adipose tissue engineering from a single cell source.
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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".