Quantifying angiogenesis in VEGF‐enhanced tissue‐engineered bladder constructs by dynamic contrast‐enhanced MRI using contrast agents of different molecular weights
Bibliographic record
Abstract
PURPOSE: To compare Gadomer, a macromolecular magnetic resonance (MR) contrast agent, and gadolinium diethylenetriamine pentaacetic acid (Gd-DTPA) for quantifying angiogenesis in tissue-engineered bladder constructs. MATERIALS AND METHODS: Constructs enhanced with vascular endothelial growth factor (VEGF) were grafted onto the bladder of 12 rabbits (N= 3/VEGF, VEGF = 0,10,15,20 ng/g tissue). After eight days dynamic contrast-enhanced MRI (DCE-MRI) was performed in each animal using Gadomer and Gd-DTPA, separated by a one-hour interval. DCE-MRI parameters were calculated from two-compartment pharmacokinetics (plasma volume fraction, v(p); transfer constant, K(trans)) and model-free analysis, area under the concentration-time curve (AUC). Histology assessment of microvessel density (MVD) and Evans blue permeability were compared to DCE-MRI. RESULTS: MVD was elevated (P < 0.05) at the highest VEGF but not among lower levels; permeability differences were absent. Contrast enhancement increased with VEGF and was better resolved with Gadomer than Gd-DTPA. Gadomer was the better assay for estimating plasma volume: v(p) provided the best distinction (P < 0.005), but both v(p) and AUC were correlated to MVD. With Gd-DTPA, only AUC distinguished MVD differences (P< 0.05). Changes in K(trans) were insignificant. CONCLUSION: Macromolecular contrast agents are valuable for monitoring angiogenesis in tissue-engineered bladder grafts. Compared to Gd-DTPA, Gadomer provides more accurate and precise quantification of microvessel function, and is better suited to pharmacokinetic analysis for accurate physiological quantification.
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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 | 0.001 |
| 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) | 0.000 | 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 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".