Motivation: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data. Results: In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA–disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction by utilizing multichannel attention, which adaptively learns the importance of different features. Empirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.

Chronic use of antipsychotic medications entails a dilemma between the benefit of alleviating psychotic symptoms and the risk of troubling, sometimes life-shortening adverse effects. Antipsychotic-induced cardiotoxicity is one of the most life-threatening adverse effects that raises widespread concerns. These cardiotoxic effects range from arrhythmia to heart failure in the clinic, with myocarditis/cardiomyopathy, ischemic injuries, and unexplained cardiac lesions as the pathological bases. Multiple mechanisms have been proposed to underlie antipsychotic cardiotoxicity. This review aims to summarize the clinical signs and pathological changes of antipsychotic cardiotoxicity and introduce recent progress in understanding the underlying mechanisms at both the subcellular organelle level and the molecular level. We also provide an up-to-date perspective on future clinical monitoring and therapeutic strategies for antipsychotic cardiotoxicity. We propose that third-generation antipsychotics or drug adjuvant therapy, such as cannabinoid receptor modulators that confer dual benefits — i.e. , alleviating cardiotoxicity and improving metabolic disorders — deserve further clinical evaluation and marketing.

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The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon termed electroporation is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. Additionally, by analysing poration kinetics with Bayesian survival analysis we show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.