Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound
Bibliographic record
Abstract
Ultrasound contrast-enhanced imaging can convey essential quantitative information regarding tissue vascularity and perfusion and, in targeted applications, facilitate the detection and measure of vascular biomarkers at the molecular level. Within the mouse embryo, this noninvasive technique may be used to uncover basic mechanisms underlying vascular development in the early mouse circulatory system and in genetic models of cardiovascular disease. The mouse embryo also presents as an excellent model for studying the adhesion of microbubbles to angiogenic targets (including vascular endothelial growth factor receptor 2 (VEGFR2) or αvβ3) and for assessing the quantitative nature of molecular ultrasound. We therefore developed a method to introduce ultrasound contrast agents into the vasculature of living, isolated embryos. This allows freedom in terms of injection control and positioning, reproducibility of the imaging plane without obstruction and motion, and simplified image analysis and quantification. Late gestational stage (embryonic day (E)16.6 and E17.5) murine embryos were isolated from the uterus, gently exteriorized from the yolk sac and microbubble contrast agents were injected into veins accessible on the chorionic surface of the placental disc. Nonlinear contrast ultrasound imaging was then employed to collect a number of basic perfusion parameters (peak enhancement, wash-in rate and time to peak) and quantify targeted microbubble binding in an endoglin mouse model. We show the successful circulation of microbubbles within living embryos and the utility of this approach in characterizing embryonic vasculature and microbubble behavior.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".