Dynamic contrast‐enhanced MRI to quantify VEGF‐enhanced tissue‐engineered bladder graft neovascularization: Pilot study
Bibliographic record
Abstract
Tissue engineered organs require an immediately perfused vascular tree. Currently, neovascularization assessment requires animal sacrifice and graft harvest. In this pilot study we assess whether neovascularization of an engineered urinary bladder construct is enhanced with vascular-derived endothelial growth factor (VEGF) and assessable non-invasively with dynamic contrast-enhanced MRI (DCE-MRI). Rabbit bladder acellular matrix was hybridized with hyaluronic acid (ACM-HA), fortified with one of three concentrations (0 ng, 10 ng, or 20 ng per gram of tissue) of vascular-derived endothelial growth factor (VEGF), and grafted onto bladders in nine rabbits (3 per VEGF concentration). At 1, 2 and 3 weeks, one rabbit from each VEGF group underwent DCE-MRI and graft harvest. Microvasculature was quantified with scrial optical transverse sectioning of CD31 stained whole mounts using PCl software. Masson trichrome and H&E staining were used to assess cellular proliferation and fibrosis. There was a significant difference in mean microvascular area (MVA) between the 10 and 20 ng VEGF groups (230187 vs 477894 microm2, P=0.014) but not between the 0 and 10 ng groups (210497 microm2, P=0.21). Gadolinium uptake increased with MVA and correlated with it on linear regression analysis (Pearson's correlation coefficient r=0.71). At three weeks, stromal cellularity was greatest, and fibrosis was least, in the high VEGF group. This preliminary work demonstrates improved neovascularization of bladder constructs with VEGF fortification of ACM-HA and the feasibility of MRI as a non-invasive method to assume angiogenesis in tissue engineered constructs.
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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 | 0.000 | 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".