Pre-Clinical Application of Functional Human Induced Pluripotent Stem Cell-Derived Airway Epithelial Grafts
Bibliographic record
Abstract
Abstract Airway pathologies including cancer, trauma and stenosis lack effective treatments, meanwhile airway transplantation and available tissue engineering approaches fail due to epithelial dysfunction. Autologous progenitors do not meet the clinical need for regeneration due to their insufficient expansion and differentiation, for which human induced pluripotent stem cells (hiPSCs) are promising alternatives. Airway epithelial grafts are engineered by differentiating hiPSC-derived airway progenitors into physiological proportions of ciliated (73.9±5.5%) and goblet (2.1±1.4%) cells on a Silk Fibroin-Collagen Vitrigel Membrane (SF-CVM) composite biomaterial for transplantation in porcine tracheal defects ex vivo and in vivo . Evaluation of ex vivo tracheal repair using hiPSC-derived SF-CVM grafts demonstrate native-like tracheal epithelial metabolism and maintenance of mucociliary epithelium to day 3. In vivo studies reveal SF-CVM integration, maintenance of airway patency, showing 80.8±3.6% graft coverage with an hiPSC-derived pseudostratified epithelium and 70.7±2.3% coverage with viable cells, 3 days post-operatively. We demonstrate the utility of bioengineered, hiPSC-derived epithelial grafts for airway repair in a pre-clinical survival model, providing a significant leap for airway reconstruction approaches.
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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) | 0.000 | 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) | 0.005 | 0.001 |
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".