Attenuation of myocardial reperfusion injury in pigs by Mirococept, a membrane-targeted complement inhibitor derived from human CR1
Bibliographic record
Abstract
OBJECTIVES: Membrane-targeted application of complement inhibitors may ameliorate ischemia/reperfusion (I/R) injury by directly targeting damaged cells. We investigated whether Mirococept, a membrane-targeted, myristoylated peptidyl construct derived from complement receptor 1 (CR1) could attenuate I/R injury following acute myocardial infarction in pigs. METHODS: In a closed-chest pig model of acute myocardial infarction, Mirococept, the non-tailed derivative APT154, or vehicle was administered intracoronarily into the area at risk 5 min pre-reperfusion. Infarct size, cardiac function and inflammatory status were evaluated. RESULTS: Mirococept targeted damaged vasculature and myocardium, significantly decreasing infarct size compared to vehicle, whereas APT154 had no effect. Cardioprotection correlated with reduced serum troponin I and was paralleled by attenuated local myocardial complement deposition and tissue factor expression. Myocardial apoptosis (TUNEL-positivity) was also reduced with the use of Mirococept. Local modulation of the pro-inflammatory and pro-coagulant phenotype translated to improved left ventricular end-diastolic pressure, ejection fraction and regional wall motion post-reperfusion. CONCLUSIONS: Local modification of a pro-inflammatory and pro-coagulant environment after regional I/R injury by site-specific application of a membrane-targeted complement regulatory protein may offer novel possibilities and insights into potential treatment strategies of reperfusion-induced injury.
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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) | 0.001 | 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".