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Enterovirus 71 (EV71) is one of the most common intestinal virus that causes hand, foot, and mouth disease (HFMD) in infants and young children (mostly ≤5 years of age). Generally, children with EV71-infected HFMD have mild symptoms that resolve spontaneously within 7-14 days without complications. However, some EV71-infected HFMD cases lead to severe complications such as aseptic meningitis, encephalitis, acute flaccid paralysis, pulmonary edema, cardiorespiratory complication, circulatory disorders, poliomyelitis-like paralysis, myocarditis, meningoencephalitis, neonatal sepsis, and even death. The mechanism of EV71 pathogenesis has been studied extensively, and the regulation of host immune responses is suspected to aggravate EV71-induced severe complications. Recently, several cytokines or chemokines such as TNF-α, IFN-γ, IL-1β, IL-18, IL-33, IL-37, IL-4, IL-13, IL-6, IL-12, IL-23, IL-27, IL-35, IL-10, IL-22, IL-17F, IL-8, IP-10, MCP-1, G-CSF, and HMGB1 have been reported to be associated with severe EV71 infection by numerous research teams, including our own. This review is aimed at summarizing the pathophysiology of the cytokines and chemokines with severe EV71 infection. [ABSTRACT FROM AUTHOR]

Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance. [ABSTRACT FROM AUTHOR]

Background. Kaempferol is a natural polyphenol in lots of Chinese herbs, which has shown promising treatment for gastric cancer (GC). However, the molecular mechanisms of its action have not been systematically revealed yet. In this work, a network pharmacology approach was used to elucidate the potential mechanisms of kaempferol in the treatment of GC. Methods. The kaempferol was input into the PharmMapper and SwissTargetPrediction database to get its targets, and the targets of GC were obtained by retrieving the Online Mendelian Inheritance in Man (OMIM) database, MalaCards database, Therapeutic Target Database (TTD), and Coolgen database. The molecular docking was performed to assess the interactions between kaempferol and these targets. Next, the overlap targets of kaempferol and GC were identified for GO and KEGG enrichment analyses. Afterward, a protein-protein interaction (PPI) network was constructed to get the hub targets, and the expression and overall survival analysis of the hub target were investigated. Finally, the overall survival (OS) analysis of hub targets was performed using the Kaplan-Meier Plotter online tool. Results. A total of 990 genes related to GC and 10 overlapping genes were determined through matching the 24 potential targets of kaempferol with disease-associated genes. The result of molecular docking indicated that kaempferol can bind with these hub targets with good binding scores. These targets were further mapped to 140 GO biological process terms and 11 remarkable pathways. In the PPI network analysis, 3 key targets were identified, including ESR1, EGFR, and SRC. The mRNA and protein expression levels of EGFR and SRC were obviously higher in GC tissues. High expression of these targets was related to poor OS in GC patients. Conclusions. This study provided a novel approach to reveal the therapeutic mechanisms of kaempferol on GC, which will ease the future clinical application of kaempferol in the treatment of GC. [ABSTRACT FROM AUTHOR]