Tissue Engineering of a Genitourinary Tubular Tissue Graft Resistant to Suturing and High Internal Pressures
Bibliographic record
Abstract
The aim of this study was to evaluate the possibility of constructing a fully autologous tissue-engineered tubular genitourinary graft (TTGG) and to determine its mechanical and physiological properties. Dermal fibroblasts (DFs) were expanded and cultured in vitro with sodium ascorbate to form fibroblast sheets. The sheets were then wrapped around a tubular support to form a cylinder. After maturation, urothelial cells (UCs) were seeded inside the DF tubes, and the constructs were placed in a bioreactor. The TTGGs were then characterized according to histology, immuno-histochemistry, Western blot, cell viability, resistance to suture, and burst pressure. Results obtained were encouraging on all levels. All layers of the TTGGs had merged, and a pluristratified urothelium coated the luminal surface of the tubes. The burst pressure of non-sutured TTGGs was measured and found to be, on average, three times as resistant as that of porcine urethras. Suturing was accomplished without difficulty. Results have shown that our construct can sustain an entire week of pulsatile stimulation without loss of mechanical or histological integrity. The tissue-engineering technique used to produce this model seems promising for bioengineering a urethra or ureter graft and could open a doorway to new possibilities for their reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".