Procurement and Perfusion-Decellularization of Porcine Vascularized Flaps in a Customized Perfusion Bioreactor
Bibliographic record
Abstract
Large volume soft tissue defects lead to functional deficits and can greatly impact the patient's quality of life. Although surgical reconstruction can be performed using autologous free flap transfer or vascularized composite allotransplantation (VCA), such methods also have disadvantages. Issues such as donor site morbidity and tissue availability limit autologous free flap transfer, while immunosuppression is a significant limitation of VCA. Engineered tissues in reconstructive surgery using decellularization/recellularization methods represent a possible solution. Decellularized tissues are generated using methods that remove native cellular material while preserving the underlying extracellular matrix (ECM) microarchitecture. These acellular scaffolds can then be subsequently recellularized with recipient-specific cells. This protocol details the procurement and decellularization methods used to achieve acellular scaffolds in a pig model. In addition, it also provides a description of the perfusion bioreactor design and setup. The flaps include the porcine omentum, tensor fascia lata, and the radial forearm. Decellularization is performed via ex vivo perfusion of low concentration sodium dodecyl sulfate (SDS) detergent followed by DNase enzyme treatment and peracetic acid sterilization in a customized perfusion bioreactor. Successful tissue decellularization is characterized by a white-opaque appearance of flaps macroscopically. Acellular flaps show the absence of nuclei on histological staining and a significant reduction in DNA content. This protocol can be used efficiently to generate decellularized soft tissue scaffolds with preserved ECM and vascular microarchitecture. Such scaffolds can be used in subsequent recellularization studies and have the potential for clinical translation in reconstructive surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".