Transplantation of a human induced pluripotent stem cell-derived airway epithelial cell sheet into the middle ear of rats
Bibliographic record
Abstract
INTRODUCTION: Early postoperative regeneration of the middle ear mucosa is essential for the prevention of postoperative refractory otitis media and recurrent cholesteatoma. As a means for intractable otitis media management, we focused on human induced pluripotent stem cell (hiPSC)-derived airway epithelial cells (AECs), which have been used in upper airway mucosal regeneration and transplantation therapy. In this study, we transplanted hiPSC-derived AECs into the middle ear of immunodeficient rats. METHODS: Following the preparation of AEC sheets from hiPSCs, the bilateral middle ear mucosa of X-linked severe combined immunodeficient rats was scraped, and the AEC sheets were transplanted in the ears unilaterally. RESULTS: Human nuclear antigen (HNA)-positive ciliated cells were observed on the transplanted side of the middle ear cavity surface in three of six rats in the 1-week postoperative group and in three of eight rats in the 2-week postoperative group. No HNA-positive cells were found on the control side. The percentage of HNA-positive ciliated cells in the transplanted areas increased in the 2-week postoperative group compared with the 1-week group, suggesting survival of hiPSC-derived AECs. Additionally, HNA-positive ciliated cells were mainly located at sites where the original ciliated cells were localized. Immunohistochemical analysis showed that the transplanted AECs contained cytokeratin 5- and mucin 5AC-positive cells, indicating that both basal cells and goblet cells had regenerated within the middle ear cavity. CONCLUSIONS: The results of this study are an important first step in the establishment of a novel transplantation therapy for chronic otitis media.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".