New approaches in small animal echocardiography: imaging the sounds of silence
Bibliographic record
Abstract
Systolic and diastolic dysfunction of the left ventricle (LV) is a hallmark of most cardiac diseases. In vivo assessment of heart function in animal models, particularly mice, is essential to refining our understanding of cardiovascular disease processes. Ultrasound echocardiography has emerged as a powerful, noninvasive tool to serially monitor cardiac performance and map the progression of heart dysfunction in murine injury models. This review covers current applications of small animal echocardiography, as well as emerging technologies that improve evaluation of LV function. In particular, we describe speckle-tracking imaging-based regional LV analysis, a recent advancement in murine echocardiography with proven clinical utility. This sensitive measure enables an early detection of subtle myocardial defects before global dysfunction in genetically engineered and rodent surgical injury models. Novel visualization technologies that allow in-depth phenotypic assessment of small animal models, including perfusion imaging and fetal echocardiography, are also discussed. As imaging capabilities continue to improve, murine echocardiography will remain a critical component of the investigator's armamentarium in translating animal data to enhanced clinical treatment of cardiovascular diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".