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Cardiovascular diseases have become the leading cause of death in urban and rural areas. Myocardial fibrosis is a common pathological manifestation at the adaptive and repair stage of cardiovascular diseases, easily predisposing to cardiac death. Non-coding RNAs (ncRNAs), RNA molecules with no coding potential, can regulate gene expression in the occurrence and development of myocardial fibrosis. Recent studies have suggested that Chinese herbal medicine can relieve myocardial fibrosis through targeting various ncRNAs, mainly including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Thus, ncRNAs are novel drug targets for Chinese herbal medicine. Herein, we summarized the current understanding of ncRNAs in the pathogenesis of myocardial fibrosis, and highlighted the contribution of ncRNAs to the therapeutic effect of Chinese herbal medicine on myocardial fibrosis. Further, we discussed the future directions regarding the potential applications of ncRNA-based drug screening platform to screen drugs for myocardial fibrosis.

Higher standards have been proposed for detection systems since camouflaged objects are not distinct enough, making it possible to ignore the difference between their background and foreground. In this paper, we present a new framework for Camouflaged Object Detection (COD) named FSANet, which consists mainly of three operations: spatial detail mining (SDM), cross-scale feature combination (CFC), and hierarchical feature aggregation decoder (HFAD). The framework simulates the three-stage detection process of the human visual mechanism when observing a camouflaged scene. Specifically, we have extracted five feature layers using the backbone and divided them into two parts with the second layer as the boundary. The SDM module simulates the human cursory inspection of the camouflaged objects to gather spatial details (such as edge, texture, etc.) and fuses the features to create a cursory impression. The CFC module is used to observe high-level features from various viewing angles and extracts the same features by thoroughly filtering features of various levels. We also design side-join multiplication in the CFC module to avoid detail distortion and use feature element-wise multiplication to filter out noise. Finally, we construct an HFAD module to deeply mine effective features from these two stages, direct the fusion of low-level features using high-level semantic knowledge, and improve the camouflage map using hierarchical cascade technology. Compared to the nineteen deep-learning-based methods in terms of seven widely used metrics, our proposed framework has clear advantages on four public COD datasets, demonstrating the effectiveness and superiority of our model.