Microultrasound Molecular Imaging of Vascular Endothelial Growth Factor Receptor 2 in a Mouse Model of Tumor Angiogenesis
Bibliographic record
Abstract
High-frequency microultrasound imaging of tumor progression in mice enables noninvasive anatomic and functional imaging at excellent spatial and temporal resolution, although microultrasonography alone does not offer molecular scale data. In the current study, we investigated the use of microbubble ultrasound contrast agents bearing targeting ligands specific for molecular markers of tumor angiogenesis using high-frequency microultrasound imaging. A xenograft tumor model in the mouse was used to image vascular endothelial growth factor receptor 2 (VEGFR-2) expression with microbubbles conjugated to an anti-VEGFR-2 monoclonal antibody or an isotype control. Microultrasound imaging was accomplished at a center frequency of 40 MHz, which provided lateral and axial resolutions of 40 and 90 Im, respectively. The B-mode (two-dimensional mode) acoustic signal from microbubbles bound to the molecular target was determined by an ultrasound-based destruction-subtraction scheme. Quantification of the adherent microbubble fraction in nine tumor-bearing mice revealed significant retention of VEGFR-2-targeted microbubbles relative to control-targeted microbubbles. These data demonstrate that contrast-enhanced microultrasound imaging is a useful method for assessing molecular expression of tumor angiogenesis in mice at high resolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".