Noninvasive Assessment of Angiogenesis by Ultrasound and Microbubbles Targeted to α <sub>v</sub> -Integrins
Bibliographic record
Abstract
BACKGROUND: Noninvasive methods for characterizing neovessel formation during angiogenesis are currently lacking. We hypothesized that angiogenesis could be imaged with the use of contrast-enhanced ultrasound (CEU) with microbubbles targeted to alpha(v)-integrins. METHODS AND RESULTS: Microbubbles targeted to alpha(v)-integrins were prepared by conjugating echistatin (MB(E)) or monoclonal antibody against murine alpha(v) (MB(alpha)) to their surface. Control microbubbles (MB(c)) were also prepared. The microvascular behavior of these microbubbles was assessed by intravital microscopy of the cremaster muscle in mice treated for 4 days with sustained-release FGF-2. Microvascular retention was much greater (P<0.01) for MB(E) (11+/-6 mm(-3)) and MB(alpha) (10+/-7 mm(-3)) than that for MB(c) (1+/-1 mm(-3)). Retained MB(E) and MB(alpha) attached directly to the microvascular endothelial surface. Microbubble retention in 4 control mice was minimal. Subcutaneous matrigel plugs enriched with FGF-2 were created in 12 mice and studied 10 days later. Neovessels within the matrigel stained positive for alpha(v)-integrins. CEU demonstrated greater (P<0.01) acoustic intensity for MB(E) (16.0+/-5.9 U) and MB(alpha) (17.0+/-5.5 U) compared with MB(c) (5.8+/-2.6 U). The signal from targeted microbubbles (MB(E) and MB(alpha)) correlated well (r=0.90) with the matrigel blood volume determined by CEU perfusion imaging. CONCLUSIONS: CEU with microbubbles targeted for alpha(v)-integrins may provide a noninvasive method for assessing therapeutic angiogenesis.
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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 | 0.000 | 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".