Noninvasive Imaging of Myocardial Reperfusion Injury Using Leukocyte-Targeted Contrast Echocardiography
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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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 | 0.000 | 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) | 0.001 | 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".