Tissue Engineering of Urinary Bladder and Urethra: Advances from Bench to Patients
Bibliographic record
Abstract
Urinary tract is subjected to many varieties of pathologies since birth including congenital anomalies, trauma, inflammatory lesions, and malignancy. These diseases necessitate the replacement of involved organs and tissues. Shortage of organ donation, problems of immunosuppression, and complications associated with the use of nonnative tissues have urged clinicians and scientists to investigate new therapies, namely, tissue engineering. Tissue engineering follows principles of cell transplantation, materials science, and engineering. Epithelial and muscle cells can be harvested and used for reconstruction of the engineered grafts. These cells must be delivered in a well-organized and differentiated condition because water-seal epithelium and well-oriented muscle layer are needed for proper function of the substitute tissues. Synthetic or natural scaffolds have been used for engineering lower urinary tract. Harnessing autologous cells to produce their own matrix and form scaffolds is a new strategy for engineering bladder and urethra. This self-assembly technique avoids the biosafety and immunological reactions related to the use of biodegradable scaffolds. Autologous equivalents have already been produced for pigs (bladder) and human (urethra and bladder). The purpose of this paper is to present a review for the existing methods of engineering bladder and urethra and to point toward perspectives for their replacement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".