Enzyme-Sensitive Magnetic Resonance Imaging Targeting Myeloperoxidase Identifies Active Inflammation in Experimental Rabbit Atherosclerotic Plaques
Bibliographic record
Abstract
BACKGROUND: Inflammation undermines the stability of atherosclerotic plaques, rendering them susceptible to acute rupture, the cataclysmic event that underlies clinical expression of this disease. Myeloperoxidase is a central inflammatory enzyme secreted by activated macrophages and is involved in multiple stages of plaque destabilization and patient outcome. We report here that a unique functional in vivo magnetic resonance agent can visualize myeloperoxidase activity in atherosclerotic plaques in a rabbit model. METHODS AND RESULTS: We performed magnetic resonance imaging of the thoracic aorta of New Zealand White rabbits fed a cholesterol (n=14) or normal (n=4) diet up to 2 hours after injection of the myeloperoxidase sensor bis-5HT-DTPA(Gd) [MPO(Gd)], the conventional agent DTPA(Gd), or an MPO(Gd) analog, bis-tyr-DTPA(Gd), as controls. Delayed MPO(Gd) images (2 hours after injection) showed focal areas of increased contrast (>2-fold) in diseased wall but not in normal wall (P=0.84) compared with both DTPA(Gd) (n=11; P<0.001) and bis-tyr-DTPA(Gd) (n=3; P<0.05). Biochemical assays confirmed that diseased wall possessed 3-fold elevated myeloperoxidase activity compared with normal wall (P<0.01). Areas detected by MPO(Gd) imaging colocalized and correlated with myeloperoxidase-rich areas infiltrated by macrophages on histopathological evaluations (r=0.91, P<0.0001). Although macrophages were the main source of myeloperoxidase, not all macrophages secreted myeloperoxidase, which suggests that distinct subpopulations contribute differently to atherogenesis and supports our functional approach. CONCLUSIONS: The present study represents a unique approach in the detection of inflammation in atherosclerotic plaques by examining macrophage function and the activity of an effector enzyme to noninvasively provide both anatomic and functional information in vivo.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".