Production of an Optimized Tissue-Engineered Pig Connective Tissue for the Reconstruction of the Urinary Tract
Bibliographic record
Abstract
Nonurological autologous tissues are used for urethral reconstruction to correct urinary tract disorders but are still leading to complications. Other substitutes have been studied on small animal models without great success. For preclinical tests, we selected the porcine model for its similarity to the human urinary tract. Up to now, porcine skin fibroblasts were not able to synthesize enough extracellular matrix under standard conditions to sustain the formation of an adequate tissue for transplantation purposes. Therefore, our goal was to optimize the harvesting site and culture conditions to obtain a thick and easy to handle porcine fibroblast tissue. The oral mucosa was found to be the ideal harvesting site, and a culture temperature of 39°C enabled the formation of a good porcine fibroblast sheet. We successfully superimpose three fibroblast sheets that merged into a thick and resistant tissue where physiological extracellular matrix was produced. Mechanical resistance evaluation by uniaxial traction on the three-layer fibroblast constructs also demonstrated its suitable properties. The production of this porcine connective tissue offers an interesting option in the field of urological tissue engineering. Autologous experiments on a larger animal model are now possible and accessible, allowing the performance of long-term in vivo studies.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".