De-epithelialization of porcine tracheal allografts as an approach for tracheal tissue engineering
Bibliographic record
Abstract
Replacement of large tracheal defects remains an unmet clinical need. While recellularization of acellular tracheal grafts appeared to be a viable pathway, evidence from the clinic suggests otherwise. In hindsight, complete removal of chondrocytes and repopulation of the tracheal chondroid matrix to achieve functional tracheal cartilage may have been unrealistic. In contrast, the concept of a hybrid graft whereby the epithelium is removed and the immune-privileged cartilage is preserved is a radically different path with initial reports indicating potential clinical success. Here, we present a novel approach using a double-chamber bioreactor to de-epithelialize tracheal grafts and subsequently repopulate the grafts with exogenous cells. A 3 h treatment with sodium dodecyl sulfate perfused through the inner chamber efficiently removes the majority of the tracheal epithelium while the outer chamber, perfused with growth media, keeps most (68.6 ± 7.3%) of the chondrocyte population viable. De-epithelialized grafts support human bronchial epithelial cell (BEAS-2B) attachment, viability and growth over 7 days. While not without limitations, our approach suggests value in the ultimate use of a chimeric allograft with intact donor cartilage re-epithelialized with recipient-derived epithelium. By adopting a brief and partial decellularization approach, specifically removing the epithelium, we avoid the need for cartilage regeneration.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".