Natriuretic peptides have emerged as important candidates for development of diagnostic tools and therapeutic agents in cardiovascular disease. The family contains of three major peptides—ANP, BNP, CNP—that participate in cardiovascular and cardiorenal homeostasis. Each of these natriuretic peptides binds differentially to specific receptors that signal through different mechanisms. They are cleared enzymatically by neutral endopeptidase as well as by receptor-mediated endocytosis. Because of its fast induction and specific expression in overt heart failure, BNP seems the most promising natriuretic peptide. It is predominantly synthesized in the cardiac ventricles, released as pre-proBNP and then enzymatically cleaved to BNP and the N-terminal portion of BNP(NT-proBNP). Blood measurements of BNP and NT-proBNP have been shown to identify patients with LV dysfunction. This review focuses on the physiology of natriuretic peptides as a group and brain natriuretic peptide in more detail, its structure and regulation as well as its effects at the cellular level.

Aims: A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non–ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results: An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924–0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7–92.0), sensitivity 80.6% (95% CI: 76.8–84.0), and specificity 93.7 (95% CI: 92.6–94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1–85.1), sensitivity 32.5% (95% CI: 28.4–36.6), and specificity 97.7% (95% CI: 97.0–98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5–91.9), sensitivity 73.0% (95% CI: 68.7–77.0), and specificity 95.7% (95% CI: 94.7–96.6)]. Conclusion: The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.