Bioengineering of vascularized porcine flaps using perfusion-recellularization
Bibliographic record
Abstract
Large volume soft tissue defects greatly impact patient quality of life and function while suitable repair options remain a challenge in reconstructive surgery. Engineered flaps could represent a clinically translatable option that may circumvent issues related to donor site morbidity and tissue availability. Herein, we describe the regeneration of vascularized porcine flaps, specifically of the omentum and tensor fascia lata (TFL) flaps, using a tissue engineering perfusion-decellularization and recellularization approach. Flaps were decellularized using a low concentration sodium dodecyl sulfate (SDS) detergent perfusion to generate an acellular scaffold with retained extracellular matrix (ECM) components while removing underlying cellular and nuclear contents. A perfusion-recellularization strategy allowed for seeding of acellular flaps with a co-culture of human umbilical vein endothelial cell (HUVEC) and mesenchymal stromal cells (MSC) onto the decellularized omentum and TFL flaps. Our recellularization technique demonstrated evidence of intravascular cell attachment, as well as markers of endothelial and mesenchymal phenotype. Altogether, our findings support the potential of using bioengineered porcine flaps as a novel, clinically-translatable strategy for future application in reconstructive surgery.
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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) | 0.001 | 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".