Amniotic Membrane Scaffolds Enable the Development of Tissue-Engineered Urothelium with Molecular and Ultrastructural Properties Comparable to that of Native Urothelium
Bibliographic record
Abstract
The amniotic membrane (AM) is a naturally derived biomaterial that possesses biological and mechanical properties of great importance for tissue engineering. The aim of our study was to determine whether the AM enables the formation of a normal urinary bladder epithelium-urothelium--and to reveal any differences in the urothelial cell (UC) growth and differentiation when using different AM scaffolds. Cryopreserved human AM was used as a scaffold in three different ways. Normal porcine UCs were seeded on the AM epithelium (eAM), denuded AM (dAM), and stromal AM (sAM) and were cultured for 3 weeks. UC growth on AM scaffolds was monitored daily. By using electron microscopy, histochemical and immunofluorescence techniques, we here provide evidence that all three AM scaffolds enable the development of the urothelium. The fastest growth and the highest differentiation of UCs were demonstrated on the sAM scaffold, which enables the development of tissue-engineered urothelium with molecular and ultrastructural properties comparable to that of the native urothelium. Most importantly, the highly differentiated urothelia on the sAM scaffolds provide important experimental models for future drug delivery studies and developing tissue engineering strategies considering that subtle differences are identified before translation to the clinical settings.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".