Search

Background: Myocardial infarction (MI) is characterized by irreversible cardiomyocyte death resulting from an inadequate supply of oxygenated blood to the myocardium. Recent studies have indicated that ferroptosis, a form of regulated cell death, exacerbates myocardial injury during MI. Concurrently, the upregulation of CD47 on the surface of damaged myocardium following MI impairs the clearance of dead cells by macrophages, thereby hindering efferocytosis. In this context, simultaneously inhibiting ferroptosis and enhancing efferocytosis may represent a promising strategy to mitigate myocardial damage post-MI., Methods: In this study, we engineered platelet membrane-coated hollow mesoporous silicon nanoparticles (HMSN) to serve as a drug delivery system, encapsulating ferroptosis inhibitor, Ferrostatin-1, along with an anti-CD47 antibody. We aimed to assess the potential of these nanoparticles (designated as Fer-aCD47@PHMSN) to specifically target the site of MI and evaluate their efficacy in reducing cardiomyocyte death and inflammation., Results: The platelet membrane coating on the nanoparticles significantly enhanced their ability to successfully target the site of myocardial infarction (MI). Our findings demonstrate that treatment with Fer-aCD47@PHMSN resulted in a 38.5% reduction in cardiomyocyte ferroptosis under hypoxia, indicated by decreased lipid peroxidation and increased in vitro. Additionally, Fer-aCD47@PHMSN improved cardiomyocyte efferocytosis by approximately 15% in vitro. In MI mice treated with Fer-aCD47@PHMSN, we observed a substantial reduction in cardiomyocyte death (nearly 30%), decreased inflammation, and significant improvement in cardiac function., Conclusion: Our results demonstrated that the cooperation between the two agents induced anti-ferroptosis effects and enhanced dead cardiomyocyte clearance by macrophage as well as anti-inflammation effects. Thus, our nanoparticle Fer-aCD47@PHMSN provides a new therapeutic strategy for targeted therapy of MI., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Song et al.)

Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade. Nevertheless, lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset. However, inconsistent standards on data collection, annotation, and partition are still restraining a fair and efficient comparison between different works. To this line, we introduced and benchmarked a first version of the Heart Sounds Shenzhen (HSS) corpus. Motivated and inspired by the previous works based on HSS, we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study. First, we segmented the heart sound recording into shorter recordings (10 s), which makes it more similar to the human auscultation case. Second, we redefined the classification tasks. Besides using the 3 class categories (normal, moderate, and mild/severe) adopted in HSS, we added a binary classification task in this study, i.e., normal and abnormal. In this work, we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies, which are reproducible by using open-source toolkits. Last but not least, we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable., Competing Interests: Author contributions: K.Q., Z.B., Z.Z., Q.D., and B.S. collectively wrote and revised the manuscript. K.Q., Z.B., T.K., F.D., W.J., and B.H. participated in the design and interpretation of the reported experiments or results. K.Q., Z.Z., F.D., Y.J., B.D., Z.D., and B.H. participated in the acquisition and analysis of data. K.Q., Z.B., F.D., M.S., J.S., and B.H. were primarily responsible for a particular, specialized role in the research. K.Q., B.H., and Y.Y. provided administrative, technical or supervisory support. Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Kun Qian et al.)