Evaluation of a decellularized bronchial patch transplant in a porcine model
Bibliographic record
Abstract
Biological scaffolds for airway reconstruction are an important clinical need and have been extensively investigated experimentally and clinically, but without uniform success. In this study, we evaluated the use of a decellularized bronchus graft for airway reconstruction. Decellularized left bronchi were procured from decellularized porcine lungs and utilized as grafts for airway patch transplantation. A tracheal window was created and the decellularized bronchus was transplanted into the defect in a porcine model. Animals were euthanized at 7 days, 1 month, and 2 months post-operatively. Histological analysis, immunohistochemistry, scanning electron microscopy, and strength tests were conducted in order to evaluate epithelialization, inflammation, and physical strength of the graft. All pigs recovered from general anesthesia and survived without airway obstruction until the planned euthanasia timepoint. Histological and electron microscopy analyses revealed that the decellularized bronchus graft was well integrated with native tissue and covered by an epithelial layer at 1 month. Immunostaining of the decellularized bronchus graft was positive for CD31 and no difference was observed with immune markers (CD3, CD11b, myeloperoxidase) at two months. Although not significant, tensile strength was decreased after one month, but recovered by two months. Decellularized bronchial grafts show promising results for airway patch reconstruction in a porcine model. Revascularization and re-epithelialization were observed and the immunological reaction was comparable with the autografts. This approach is clinically relevant and could potentially be utilized for future applications for tracheal replacement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".