A cell-free tissue-engineered tracheal substitute with sequential cytokine release maintained airway opening in a rabbit tracheal full circumferential defect model
Bibliographic record
Abstract
In this study, a cell-free tissue-engineered tracheal substitute was developed, which is based on a 3D-printed polycaprolactone scaffold coated with a gelatin-methacryloyl (GelMA) hydrogel, with transforming growth factor-β1 (TGF-β) and stromal cell-derived factor-1α (SDF-1) sequentially embedded, to facilitate cell recruitment and differentiation toward chondrocyte-phenotype. TGF-β was loaded onto polydopamine particles, and then encapsulated into the GelMA together with SDF-1, and called G/S/P@T, which was used to coat 3D-printed PCL scaffold to form the tracheal substitute. A rapid release of SDF-1 was observed during the first week, followed by a slow and sustained release of TGF-β for approximately four weeks. The tracheal substitute significantly promoted the recruitment of mesenchymal stromal cells (MSCs) or human bronchial epithelial cells in vitro, and enhanced the ability of MSCs to differentiate towards chondrocyte phenotype. Implantation of the tissue-engineered tracheal substitute with a rabbit tracheal anterior defect model improved regeneration of airway epithelium, recruitment of endogenous MSCs and expression of markers of chondrocytes at the tracheal defect site. Moreover, the tracheal substitute maintained airway opening for 4 weeks in a tracheal full circumferential defect model with airway epithelium coverage at the defect sites without granulation tissue accumulation in the tracheal lumen or underneath. The promising results suggest that this simple, cell-free tissue-engineered tracheal substitute can be used directly after tracheal defect removal and should be further developed towards clinical application.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".