Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Recent years, circular RNA (circRNA) have been shown to exert vital functions in the pathological progressions of many diseases. A growing number of evidences have identified the representative function of exosomal circRNAs in the physiological state of donor cells, which further induces cellular responses after captured by recipient cells. However, the contributions of circRNAs to HCC remain largely unknown. In vitro and in vivo regulatory roles of circRNA Cdr1as in proliferative and migratory abilities of HCC were evaluated by CCK8, EdU, Transwell and tumourigenicity assays, respectively. Results showed circRNA Cdr1as was highly expressed in HCC cell lines and tissues. Overexpression of circRNA Cdr1as greatly accelerated HCC cells to proliferate and migrate. Mechanistically, we found that Cdr1as could promote the expression of AFP, a well-known biomarker for HCC, by sponging miR-1270. Further studies showed exosomes extracted from HCC cells overexpressing circRNA Cdr1as accelerated the proliferative and migratory abilities of surrounding normal cells. In all, circRNA Cdr1as serves as a ceRNA to promote the progression of HCC. Meanwhile, it is directly transferred from HCC cells to surrounding normal cells via exosomes to further mediate the biological functions of surrounding cells.

our meta-analysis suggested that increased TV watching is associated with increased risk of childhood obesity. And restricting TV time and other sedentary behaviour of children may be an important public health strategy to prevent childhood obesity.

Viruses with double-stranded RNA genomes form isometric particles or are capsidless. Here we report a double-stranded RNA virus, Colletotrichum camelliae filamentous virus 1 (CcFV-1) isolated from a fungal pathogen, that forms filamentous particles. CcFV-1 has eight genomic double-stranded RNAs, ranging from 990 to 2444 bp, encoding 10 putative open reading frames, of which open reading frame 1 encodes an RNA-dependent RNA polymerase and open reading frame 4 a capsid protein. When inoculated, the naked CcFV-1 double-stranded RNAs are infectious and induce the accumulation of the filamentous particles in vivo. CcFV-1 is phylogenetically related to Aspergillus fumigatus tetramycovirus-1 and Beauveria bassiana polymycovirus-1, but differs in morphology and in the number of genomic components. CcFV-1 might be an intermediate virus related to truly capsidated viruses, or might represent a distinct encapsidating strategy. In terms of genome and particle architecture, our findings are a significant addition to the knowledge of the virosphere diversity.

CD4 + Th cells play an important role in the development of rheumatoid arthritis (RA) by regulating adaptive immune response. As major subsets of CD4 + Th cells, Th17 cells can produce a large number of hallmark cytokines such as IL-17A and IL-17F, which participate in host defense and immune homeostasis. However, increasing researches have shown that Th17 cells are unstable and exhibit a certain degree of plasticity, which aggravates their pathogenicity. Furthermore, the plasticity and pathogenicity of Th17 cells are closely related with the disease activity in RA. In this paper, the characteristics including phenotype, differentiation, plasticity, and pathogenicity of Th17 cells in RA will be systematically summarized. This will contribute to clarify the immunologic mechanism of RA and further provide a novel strategy for the clinical treatment of autoimmune diseases. K E Y W O R D S pathogenicity, plasticity, rheumatoid arthritis, Th17 cells 1

BackgroundHospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding.MethodsWe used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016.ResultsFor the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage.ConclusionsHybrid model does not necessarily outperform its constituents’ performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0616-8) contains supplementary material, which is available to authorized users.

BackgroundOutbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic.MethodIn this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012.ResultsThe best-fitted hybrid model was combined with seasonal ARIMA and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively −965.03, −1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend.ConclusionThe model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.

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