Loss of Apelin Exacerbates Myocardial Infarction Adverse Remodeling and Ischemia‐reperfusion Injury: Therapeutic Potential of Synthetic Apelin Analogues
Bibliographic record
Abstract
BACKGROUND: Coronary artery disease leading to myocardial ischemia is the most common cause of heart failure. Apelin (APLN), the endogenous peptide ligand of the APJ receptor, has emerged as a novel regulator of the cardiovascular system. METHODS AND RESULTS: Here we show a critical role of APLN in myocardial infarction (MI) and ischemia-reperfusion (IR) injury in patients and animal models. Myocardial APLN levels were reduced in patients with ischemic heart failure. Loss of APLN increased MI-related mortality, infarct size, and inflammation with drastic reductions in prosurvival pathways resulting in greater systolic dysfunction and heart failure. APLN deficiency decreased vascular sprouting, impaired sprouting of human endothelial progenitor cells, and compromised in vivo myocardial angiogenesis. Lack of APLN enhanced susceptibility to ischemic injury and compromised functional recovery following ex vivo and in vivo IR injury. We designed and synthesized two novel APLN analogues resistant to angiotensin converting enzyme 2 cleavage and identified one analogue, which mimicked the function of APLN, to be markedly protective against ex vivo and in vivo myocardial IR injury linked to greater activation of survival pathways and promotion of angiogenesis. CONCLUSIONS: APLN is a critical regulator of the myocardial response to infarction and ischemia and pharmacologically targeting this pathway is feasible and represents a new class of potential therapeutic agents.
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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 | 0.001 |
| 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".