Tissue-Engineered Vascular Adventitia with <i>Vasa Vasorum</i> Improves Graft Integration and Vascularization Through Inosculation
Bibliographic record
Abstract
Tissue-engineered blood vessel is one of the most promising living substitutes for coronary and peripheral artery bypass graft surgery. However, one of the main limitations in tissue engineering is vascularization of the construct before implantation. Such a vascularization could play an important role in graft perfusion and host integration of tissue-engineered vascular adventitia. Using our self-assembly approach, we developed a method to vascularize tissue-engineered blood vessel constructs by coculturing endothelial cells in a fibroblast-laden tissue sheet. After subcutaneous implantation, enhancement of graft integration within the surrounding environment was noted after 48 h and an important improvement in blood circulation of the grafted tissue at 1 week postimplantation. The distinctive branching structure of end arteries characterizing the in vivo adventitial vasa vasorum has also been observed in long-term postimplantation follow-up. After a 90-day implantation period, hybrid vessels containing human and mouse endothelial cells were still perfused. Characterization of the mechanical properties of both control and vascularized adventitia demonstrated that the ultimate tensile strength, modulus, and failure strain were in the same order of magnitude of a pig coronary artery. The addition of a vasa vasorum to the tissue-engineered adventitia did not influence the burst pressure of these constructs. Hence, the present results indicate a promising answer to the many challenges associated with the in vitro vascularization and in vivo integration of many different tissue-engineered substitutes.
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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".