Targeting Amino Acid Metabolism for Molecular Imaging of Inflammation Early After Myocardial Infarction
Bibliographic record
Abstract
Acute tissue inflammation after myocardial infarction influences healing and remodeling and has been identified as a target for novel therapies. Molecular imaging holds promise for guidance of such therapies. The amino acid (11)C-methionine is a clinically approved agent which is thought to accumulate in macrophages, but not in healthy myocytes. We assessed the suitability of positron emission tomography (PET) with (11)C-methionine for imaging post-MI inflammation, from cell to mouse to man. Uptake assays demonstrated 7-fold higher (11)C-methionine uptake by polarized pro-inflammatory M1 macrophages over anti-inflammatory M2 subtypes (p<0.001). C57Bl/6 mice (n=27) underwent coronary artery ligation or no surgery. Serial (11)C-methionine PET was performed 3, 5 and 7d later. MI mice exhibited a perfusion defect in 32-50% of the left ventricle (LV). PET detected increased (11)C-methionine accumulation in the infarct territory at 3d (5.9±0.9%ID/g vs 4.7±0.9 in remote myocardium, and 2.6±0.5 in healthy mice; p<0.05 and <0.01 respectively), which declined by d7 post-MI (4.3±0.6 in infarct, 3.4±0.8 in remote; p=0.03 vs 3d, p=0.08 vs healthy). Increased (11)C-methionine uptake was associated with macrophage infiltration of damaged myocardium. Treatment with anti-integrin antibodies (anti-CD11a, -CD11b, -CD49d; 100µg) lowered macrophage content by 56% and (11)C-methionine uptake by 46% at 3d post-MI. A patient study at 3d after ST-elevation MI and early reperfusion confirmed elevated (11)C-methionine uptake in the hypoperfused myocardial region. Targeting of elevated amino acid metabolism in pro-inflammatory M1 macrophages enables PET imaging-derived demarcation of tissue inflammation after MI. (11)C-methionine-based molecular imaging may assist in the translation of novel image-guided, inflammation-targeted regenerative therapies.
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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) | 0.001 | 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) | 0.002 | 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".