Bladder tissue engineering: Tissue regeneration and neovascularization of HA‐VEGF‐incorporated bladder acellular constructs in mouse and porcine animal models
Bibliographic record
Abstract
Successful tissue engineering requires appropriate recellularization and vascularization. Herein, we assessed the regenerative and angiogenic effects of porcine bladder acellular matrix (ACM) incorporated with hyaluronic acid (HA) and vascular endothelial growth factor (VEGF) in mouse and porcine models. Prepared HA-ACMs were rehydrated in different concentrations of VEGF (1, 2, 3, 10, and 50 ng/g ACM). Grafts were implanted in mice peritoneum in situ for 1 week. Angiogenesis was quantified with CD31 and Factor VIII immunostaining using Simple PCI. Selected optimal VEGF concentration that induced maximum vascularization was then used in porcine bladder augmentation model. Implants were left in for 4 and 10 weeks. Three groups of six pigs each were implanted with ACM alone, HA-ACM, and HA-VEGF-ACM. Histological, immunohistochemical (Uroplakin III, alpha-SMA, Factor VIII), and immunofluorescence (CD31) analysis were performed to assess graft regenerative capacity and angiogenesis. In mouse model, statistically significant increase in microvascular density was demonstrated in the 2 ng/g ACM group. When this concentration was used in porcine model, recellularization increased significantly from weeks 4 to 10 in HA-VEGF-ACM, with progressive decrease in fibrosis. Significantly increased vascularization, coupled with increased urothelium and smooth muscle cell (SMC) regeneration, was observed in HA-VEGF grafts at week 10 in the center and periphery, compared with week 4. HA-VEGF grafts displayed highest in vivo epithelialization, neovascularization, and SMCs regeneration. A total of 2 ng/g tissue VEGF when incorporated with HA proved effective in stimulating robust graft recellularization and vascularization, coordinated with increased urothelial bladder development and SMC augmentation into bundles by week 10.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".