This study aimed to evaluate the clinical efficacy and safety of using the traditional Chinese herbal medicine Scutellaria baicalensis for the treatment of severe HFMD in 725 patients aged >1 year in a multicenter, retrospective analysis. The patients were divided into the S. baicalensis and ribavirin groups, and the temperatures, presence or absence of skin rashes and oral lesions, nervous system (NS) involvement, and viral loads of the patients, as well as the safety of the treatments, were evaluated. The median duration of fever, median time to NS involvement, and the number of patients with oral ulcers and/or vesicles, as well as skin rashes, were decreased in the S. baicalensis group compared with the ribavirin group. In addition, the EV71 viral loads were decreased in the S. baicalensis group, suggesting that S. baicalensis exerted more potent antiviral effects compared with ribavirin. The present study demonstrated that S. baicalensis was suitable for the treatment of severe HFMD in patients aged >1 year, since it was shown to rapidly relieve fever, attenuate oral lesions and rashes, and improve NS involvement. Furthermore, it was demonstrated to be relatively safe for topical application.

Identifying biomarkers of Multiple Sclerosis is important for the diagnosis and treatment of Multiple Sclerosis. The existing study has shown that miRNA is one of the most important biomarkers for diseases. However, few existing methods are designed for predicting Multiple Sclerosis-related miRNAs. To fill this gap, we proposed a novel computation framework for predicting Multiple Sclerosis-associated miRNAs. The proposed framework uses a network representation model to learn the feature representation of miRNA and uses a deep learning-based model to predict the miRNAs associated with Multiple Sclerosis. The evaluation result shows that the proposed model can predict the miRNAs associated with Multiple Sclerosis precisely. In addition, the proposed model can outperform several existing methods in a large margin.