Chronic Iron Deposition following Acute Hemorrhagic Myocardial Infarction: A Cardiovascular Magnetic Resonance Study
Bibliographic record
Abstract
Introduction - Intramyocardial hemorrhage frequently occurs in large reperfused myocardial infarctions (MI). However, its long-term fate remains unexplored. Hypothesis - We hypothesize that intramyocardial hemorrhage, secondary to reperfused MI, results in chronic iron deposition within infarcted territories. Methods - We studied 15 patients by Cardiovascular Magnetic Resonance (CMR) T2* mapping (1.5T) on day 3 and 6 months after successful percutaneous coronary intervention for first STEMI. Using the same CMR protocol, we also studied 20 canines, on days 3 and 56 post ischemia-reperfusion injury, of which 3 animals received sham procedures. Subsequently, canine hearts were explanted, imaged ex-vivo, and samples of hemorrhagic infarcts (Hemo+), non-hemorrhagic infarcts (Hemo-), remote and sham myocardium were isolated, sectioned and mass spectrometry was performed. Results - Eleven patients had Hemo+ (verified by T2* CMR on day 3) and their scar tissue T2* values remained significantly lower after 6 months, when compared to Hemo- and remote myocardium (Fig 1; p<0.001). In canines, Hemo+ territories showed a significant T2* reduction compared to the other groups (Fig 2; p<0.001). Mean iron content ([Fe]) of Hemo+ on day 56 was 10-fold greater than that observed in control groups (p<0.001), while no differences were observed among the control groups (p=0.14). A strong linear relationship was observed between log(T2*) and -log([Fe]) (R2 = 0.74; p<0.001) on day 56. Conclusion - Hemorrhagic MI leads to chronic iron depositions within the infarct zones. Consequences of chronic iron deposition within the scar tissue remain to be investigated.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".