Recellularization of Bioengineered Scaffolds for Vascular Composite Allotransplantation
Bibliographic record
Abstract
Traumatic injuries or cancer resection resulting in large volumetric soft tissue loss requires surgical reconstruction. Vascular composite allotransplantation (VCA) is an emerging reconstructive option that transfers multiple, complex tissues as a whole subunit from donor to recipient. Although promising, VCA is limited due to side effects of immunosuppression. Tissue-engineered scaffolds obtained by decellularization and recellularization hold great promise. Decellularization is a process that removes cellular materials while preserving the extracellular matrix architecture. Subsequent recellularization of these acellular scaffolds with recipient-specific cells can help circumvent adverse immune-mediated host responses and allow transplantation of allografts by reducing and possibly eliminating the need for immunosuppression. Recellularization of acellular tissue scaffolds is a technique that was first investigated and reported in whole organs. More recently, work has been performed to apply this technique to VCA. Additional work is needed to address barriers associated with tissue recellularization such as: cell type selection, cell distribution, and functionalization of the vasculature and musculature. These factors ultimately contribute to achieving tissue integration and viability following allotransplantation. The present work will review the current state-of-the-art in soft tissue scaffolds with specific emphasis on recellularization techniques. We will discuss biological and engineering process considerations, technical and scientific challenges, and the potential clinical impact of this technology to advance the field of VCA and 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".