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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.)

With the depressive psychiatric disorders becoming more common, people are gradually starting to take it seriously. Somatisation disorders, as a general mental disorder, are rarely accurately identified in clinical diagnosis for its specific nature. In the previous work, speech recognition technology has been successfully applied to the task of identifying somatisation disorders on the Shenzhen Somatisation Speech Corpus. Nevertheless, there is still a scarcity of labels for somatisation disorder speech database. The current mainstream approaches in the speech recognition heavily rely on the well labelled data. Compared to supervised learning, self-supervised learning is able to achieve the same or even better recognition results while reducing the reliance on labelled samples. Moreover, self-supervised learning can generate general representations without the need for human hand-crafted features depending on the different recognition tasks. To this end, we apply self-supervised learning pre-trained models to solve few-labelled somatisation disorder speech recognition. In this study, we compare and analyse the results of three self-supervised learning models (contrastive predictive coding, wav2vec and wav2vec 2.0). The best result of wav2vec 2.0 model achieves 77.0 % unweighted average recall and is significantly better than CPC (p < .005), performing better than the benchmark of the supervised learning model.Clinical relevance- This work proposed a self-supervised learning model to resolve the few-labelled SD speech data, which can be well used for helping psychiatrists with clinical assistant to diagnosis. With this model, psychiatrists no longer need to spend a lot of time labelling SD speech data.

Cardiovascular diseases (CVDs) are the leading cause of death globally. Heart sound signal analysis plays an important role in clinical detection and physical examination of CVDs. In recent years, auxiliary diagnosis technology of CVDs based on the detection of heart sound signals has become a research hotspot. The detection of abnormal heart sounds can provide important clinical information to help doctors diagnose and treat heart disease. We propose a new set of fractal features - fractal dimension (FD) - as the representation for classification and a Support Vector Machine (SVM) as the classification model. The whole process of the method includes cutting heart sounds, feature extraction, and classification of abnormal heart sounds. We compare the classification results of the heart sound waveform (time domain) and the spectrum (frequency domain) based on fractal features. Finally, according to the better classification results, we choose the fractal features that are most conducive for classification to obtain better classification performance. The features we propose outperform the widely used features significantly (p < .05 by one-tailed z-test) with a much lower dimension.Clinical relevance-The heart sound classification model based on fractal provides a new time-frequency analysis method for heart sound signals. A new effective mechanism is proposed to explore the relationship between the heart sound acoustic properties and the pathology of CVDs. As a non-invasive diagnostic method, this work could supply an idea for the preliminary screening of cardiac abnormalities through heart sounds.