Murine echocardiography and ultrasound imaging
Bibliographic record
Abstract
Rodent models of cardiac pathophysiology represent a valuable research tool to investigate mechanism of disease as well as test new therapeutics. Echocardiography provides a powerful, non-invasive tool to serially assess cardiac morphometry and function in a living animal. However, using this technique on mice poses unique challenges owing to the small size and rapid heart rate of these animals. Until recently, few ultrasound systems were capable of performing quality echocardiography on mice, and those generally lacked the image resolution and frame rate necessary to obtain truly quantitative measurements. Newly released systems such as the VisualSonics Vevo2100 provide new tools for researchers to carefully and non-invasively investigate cardiac function in mice. This system generates high resolution images and provides analysis capabilities similar to those used with human patients. Although color Doppler has been available for over 30 years in humans, this valuable technology has only recently been possible in rodent ultrasound. Color Doppler has broad applications for echocardiography, including the ability to quickly assess flow directionality in vessels and through valves, and to rapidly identify valve regurgitation. Strain analysis is a critical advance that is utilized to quantitatively measure regional myocardial function. This technique has the potential to detect changes in pathology, or resolution of pathology, earlier than conventional techniques. Coupled with the addition of three-dimensional image reconstruction, volumetric assessment of whole-organs is possible, including visualization and assessment of cardiac and vascular structures. Murine-compatible contrast imaging can also allow for volumetric measurements and tissue perfusion assessment.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".