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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".