Noninvasive Imaging of Myocardial Reperfusion Injury Using Leukocyte-Targeted Contrast Echocardiography
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: We hypothesized that myocardial contrast echocardiography (MCE) with leukocyte-targeted microbubbles could temporally and spatially characterize the severity of postischemic myocardial inflammation. METHODS AND RESULTS: In 9 open-chest dogs, either the left anterior descending or left circumflex coronary artery was occluded for 90 minutes (n=6), while the remaining dogs served as non-ischemic controls. During occlusion, MCE was performed to determine the risk area (RA) and regions supplied by collateral flow. Myocardial inflammation was assessed 5, 60, and 120 minutes after reflow by MCE imaging of leukocyte-targeted (phosphatidylserine-containing) lipid microbubbles. The spatial extent and severity of inflammation were also assessed by radionuclide imaging of the neutrophil-avid tracer 99mTcRP517 and tissue myeloperoxidase activity. Early after reflow, MCE detected inflammation throughout the entire risk area, the extent of which decreased over time due to reduced signal in collateral-supplied regions. The spatial extent of inflammation late after reflow was similar for MCE and radionuclide imaging. The severity of inflammation in the infarct zone, the noninfarcted risk area, and collateral-supplied territories determined by quantitative MCE correlated well with myeloperoxidase activity (r=0.81). CONCLUSIONS: MCE with leukocyte-targeted microbubbles can temporally assess the severity and extent of postischemic myocardial inflammation and could be used to evaluate new treatment strategies designed to limit inflammation in acute coronary syndromes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it