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BackgroundIncreasing evidence indicates that Epithelial–mesenchymal transition (EMT) can be regulated by microRNAs (miRNAs). MiR-449a is a liver abundant miRNA. However, the role of miR-449a in the metastasis of hepatocellular carcinoma (HCC) remains largely unknown.MethodsThe expression levels of miR-449a were first examined in HCC cell lines and tumour tissues by real-time PCR. The in vitro and in vivo functional effect and underlying molecular mechanisms of miR-449a were examined further.ResultsIn the present study, we found that miR-449a was significantly decreased in HCC cells and tissues, especially in those with the portal vein tumor thrombus. In HCC cell lines, stable overexpression of miR-449a was sufficient to inhibit cell motility in vitro, and pulmonary metastasis in vivo. In addition, ectopic overexpression of miR-449a in HCC cells promoted the expression of epithelial markers and reduced the levels of mesenchymal markers. Further studies revealed that the reintroduction of miR-449a attenuated the downstream signaling of Met, and consequently reduced the accumulation of Snail in cell nucleus by targeting the 3’-untranslated regions (3’-UTR) of FOS and Met.ConclusionsOur data highlight an important role of miR-449a in the molecular etiology of HCC, and implicate the potential application of miR-449a in cancer therapy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1738-3) contains supplementary material, which is available to authorized users.

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<abstract> <p>Since December 2019, an outbreak of a novel coronavirus pneumonia (WHO named COVID-19) swept across China. In Shanxi Province, the cumulative confirmed cases finally reached 133 since the first confirmed case appeared on January 22 2020, and most of which were imported cases from Hubei Province. Reasons for this ongoing surge in Shanxi province, both imported and autochthonous infected cases, are currently unclear and demand urgent investigation. In this paper, we developed a SEIQR difference-equation model of COVID-19 that took into account the transmission with discrete time imported cases, to perform assessment and risk analysis. Our findings suggest that if the lock-down date in Wuhan is earlier, the infectious cases are fewer. Moreover, we reveal the effects of city lock-down date on the final scale of cases: if the date is advanced two days, the cases may decrease one half (67, 95% CI: 66–68); if the date is delayed for two days, the cases may reach about 196 (95% CI: 193–199). Our investigation model could be potentially helpful to study the transmission of COVID-19, in other provinces of China except Hubei. Especially, the method may also be used in countries with the first confirmed case is imported.</p> </abstract>

This study develops three temporal multivariate random parameters Tobit models to analyze crash rate by injury severity; these models simultaneously accommodate temporal correlation and unobserved heterogeneity across observations and correlations across injury severity. The three models are estimated and compared in the Bayesian context with a crash dataset collected from Hong Kong's Traffic Information System, which contains crash, road geometry, traffic, and environmental information on 194 directional road segments over a five-year period (2002 to 2006). Significant temporal effects are found in all of the temporal models, and the inclusion of temporal correlation considerably improves the goodness-of-fit of the multivariate random parameters Tobit regression, according to the results of deviance information criteria (DIC) and Bayesian R 2 , indicating the strength of considering cross-period temporal correlation. Moreover, after accounting for temporal effects, the magnitude of the correlation between the crash rates at various injury degrees decreases, probably because a portion of the correlation may be attributed to unobserved or unobservable factors with time-dependent or autoregressive safety effects. Among the three candidate temporal models, the one with independent temporal effects has lower DIC and R 2 values, which suggests better model fit performance than the two with constant or correlated temporal effects. This finding supports the model with independent temporal effects as a good alternative for traffic safety analysis.

In this study, a multivariate random-parameters Tobit model is proposed for the analysis of crash rates by injury severity. In the model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed model is compared with a multivariate (fixed-parameters) Tobit model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002-2006). The multivariate random-parameters Tobit model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R, while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters model. Thus, the random-parameters Tobit model, which provides a more comprehensive understanding of the factors' effects on crash rates by injury severity, is superior to the multivariate Tobit model and should be considered a good alternative for traffic safety analysis.