Urinary Bladder Tissue Engineering Using Natural Scaffolds in a Porcine Model: Role of Toll-Like Receptors and Impact of Biomimetic Molecules
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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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".