Surgical Option for the Correction of Peyronie's Disease: An Autologous Tissue-Engineered Endothelialized Graft
Bibliographic record
Abstract
INTRODUCTION: Surgical treatment is indicated in severe cases of Peyronie's disease. Incision of the plaque with subsequent graft material implantation is the option of choice. Ideal graft tissue is not yet available. AIM: To evaluate the use of an autologous tissue-engineered endothelialized graft by the self-assembly method, for tunica albuginea (TA) reconstruction in Peyronie's disease. METHODS: Two TA models were created. Human fibroblasts were isolated from a skin biopsy and cultured in vitro until formation of fibroblast sheets. After 4 weeks of maturation, human umbilical vein endothelial cells (HUVEC) were seeded on fibroblasts sheets and wrapped around a tubular support to form a cylinder of about 10 layers. After 21 days of tube maturation, HUVEC were seeded into the lumen of the fibroblast tubes for the endothelialized tunica albuginea (ETA). No HUVEC were seeded into the lumen for the TA model. Both constructs were placed under perfusion in a bioreactor for 1 week. MAIN OUTCOME MEASURES: Histology, immunohistochemistry, and burst pressure were performed to characterize mature tubular graft. Animal manipulations were also performed to demonstrate the impact of endothelial cells in vivo. RESULTS: Histology showed uniform multilayered fibroblasts. Extracellular matrix, produced entirely by fibroblasts, presented a good staining for collagen 1. Some elastin fibers were also present. For the TA model, anti-human von Willebrand antibody revealed the endothelial cells forming capillary-like structures. TA model reached a burst pressure of 584 mm Hg and ETA model obtained a burst pressure of 719 mm Hg. CONCLUSIONS: This tissue-engineered endothelialized tubular graft is structurally similar to normal TA and presents an adequate mechanical resistance. The self-assembly method used and the autologous property of this model could represent an advantage comparatively to other available grafts. Further evaluation including functional testing will be necessary to characterize in vivo implantation and behavior of the graft.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".