Urinary Bladder Tissue Engineering Using Natural Scaffolds in a Porcine Model: Role of Toll-Like Receptors and Impact of Biomimetic Molecules
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Bibliographic record
Abstract
INTRODUCTION: Natural scaffolds have been shown to induce T helper 2 (TH2)-specific immune responses in host tissues; however, the precise mechanisms that underlie this immune response are unknown. Using a porcine animal model, we evaluated the role of Toll-like receptors (TLRs) and matrix remodelling in the implantation of bladder acellular matrix (ACM) grafts and ACMs fortified with biomimetic materials. MATERIALS AND METHODS: Bladders were decellularized with detergent and treated in 3 different ways prior to implantation: ACM alone, hyaluronic acid (HA)-ACM and HA-vascular endothelial growth factor (VEGF)-ACM. Animals were sacrificed at 4 or 10 weeks post-implantation and total gene expressions for TH2 (IL-4), TH1 (IFN-γ), TLR2, TLR4, and TGF-β1 were analyzed using real-time RT-PCR. Using histology (H&E and Masson's trichrome) and immunohistochemistry (uroplakin, α-smooth muscle actin, CD31 and factor VIII) the regenerative capacity was correlated with the gene expression of different proteins. RESULTS: IL-4, TLR2, and TLR4 gene expression were markedly decreased at 4 and 10 weeks in both the HA-ACM group and the HA-VEGF-ACM group compared to ACM alone. IFN-γ expression was negligible in all groups and time periods. TGF-β1 expression was highest in the HA- and VEGF-treated grafts. Recellularization was inversely proportional to TLR and TH2 expression but proportional to TGF-β1. CONCLUSION: ACM alone grafts demonstrated stronger TLR4 expression which may promote a distinct TH2 immune response and a reduced regenerative capacity in grafts. Treatment of grafts with HA and VEGF may help regulate host immune responses by reducing TLR4 and IL-4 and increasing TGF-β1.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it