Microultrasound and Its Application to Longitudinal Studies of Mouse Eye Development and Disease
Bibliographic record
Abstract
Microultrasound imaging is a flexible high-resolution real-time in vivo imaging modality based on the transmission and the reception of ultrasound waves. Because of its high temporal (>250 Hz) and spatial (30-150 µm) resolutions and the noninvasive nature of ultrasound, microultrasound is used extensively in preclinical research to monitor functional and dynamic phenotypic changes in small animal models. Its ability to perform in vivo longitudinal monitoring of development, pathology, and therapeutic effectiveness is particularly advantageous. This article reviews the technology and the applications of high-frequency microultrasound for the study of mouse eye development from embryonic day E11.5 to postnatal day P16. Procedures for animal handling and scanning are given, and applications are described in the context of ocular development and disease. Quantitative analysis of the growth kinetics of the lens and the orbit is discussed. In addition, mouse models of retinoblastoma and glaucoma are followed as a function of disease progression to reveal their associated morphological and functional traits. Microultrasound is performed with high-frequency imaging equipment (from VisualSonics) operating at center frequencies between 15 and 50 MHz. These instruments provide both anatomical imaging as well as functional and molecular analyses of the living mouse.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".