Echocardiographic Speckle-Tracking Based Strain Imaging for Rapid Cardiovascular Phenotyping in Mice
Bibliographic record
Abstract
RATIONALE: High-sensitivity in vivo phenotyping of cardiac function is essential for evaluating genes of interest and novel therapies in small animal models of cardiovascular disease. Transthoracic echocardiography is the principal method currently used for assessing cardiac structure and function; however, standard echocardiographic techniques are relatively insensitive to early or subtle changes in cardiac performance, particularly in mice. OBJECTIVE: To develop and validate an echocardiographic strain imaging methodology for sensitive and rapid cardiac phenotyping in small animal models. METHODS AND RESULTS: Herein, we describe a modified echocardiographic technique that uses speckle-tracking based strain analysis for the noninvasive evaluation of cardiac performance in adult mice. This method is found to be rapid, reproducible, and highly sensitive in assessing both regional and global left ventricular (LV) function. Compared with conventional echocardiographic measures of LV structure and function, peak longitudinal strain and strain rate were able to detect changes in adult mouse hearts at an earlier time point following myocardial infarction and predicted the later development of adverse LV remodeling. Moreover, speckle-tracking based strain analysis was able to clearly identify subtle improvement in LV function that occurred early in response to standard post-myocardial infarction cardiac therapy. CONCLUSIONS: Our results highlight the utility of speckle-tracking based strain imaging for detecting discrete functional alterations in mouse models of cardiovascular disease in an efficient and comprehensive manner. Echocardiography speckle-tracking based strain analysis represents a method for relatively high-throughput and sensitive cardiac phenotyping, particularly in evaluating emerging cardiac agents and therapies in mice.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".