Longitudinal Follow-up of Cardiac Structure and Functional Changes in an Infarct Mouse Model Using Retrospectively Gated Micro-Computed Tomography
Bibliographic record
Abstract
OBJECTIVES: Mouse models of myocardial infarction are valuable in studying the effect of genetic modifications on structural and functional remodeling of the heart. Our group recently developed a method for acquiring three-dimensional images of the beating mouse heart using micro-computed tomography (micro-CT) and retrospective gating. In this study, we evaluated cardiac function in sham and infarcted mice longitudinally, using this novel technique. MATERIALS AND METHODS: Thirteen mice (7 sham-operated, 6 infarcted; male, C57BL/6) were imaged at baseline and at weeks 1, 2, 3, and 4 postligation of the left anterior descending coronary artery. Animals were anesthetized with 1.5% isoflurane; mechanical ventilation was not used. Contrast between blood and tissue was provided by an iodinated blood-pool contrast agent (0.01 mL/g Fenestra VC). The cardiac and respiratory waveforms were recorded during the 50-second scan time, to enable retrospective gating. Once scanning was completed on week 4 postsurgery, hemodynamic measurements were performed using a Millar pressure conductance catheter. RESULTS: There were significant differences in systolic and diastolic volumes, and ejection fraction, between sham and myocardial infarction groups (P < 0.0001). A comparison of ejection fraction derived from both CT and hemodynamic measurements was not significantly different (P > 0.1). CONCLUSIONS: We have demonstrated the first use of dynamic micro-CT for monitoring cardiac remodeling, resulting from myocardial infarction, over time. The fast scan times (<1 minute) and ability to track individual animals over an entire study make this quantitative noninvasive technique a promising tool for in vivo studies of cardiac disease in mouse models.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".