The acquisition of epithelial–mesenchymal transition (EMT) and/or existence of a sub-population of cancer stem-like cells (CSC) are associated with malignant behavior and chemoresistance. To identify which factor could promote EMT and CSC formation and uncover the mechanistic role of such factor is important for novel and targeted therapies. In the present study, we found that the long intergenic non-coding RNA linc-DYNC2H1-4 was upregulated in pancreatic cancer cell line BxPC-3-Gem with acquired gemcitabine resistance. Knockdown of linc-DYNC2H1-4 decreased the invasive behavior of BxPC-3-Gem cells while ectopic expression of linc-DYNC2H1-4 promoted the acquisition of EMT and stemness of the parental sensitive cells. Linc-DYNC2H1-4 upregulated ZEB1, the EMT key player, which led to upregulation and downregulation of its targets vimentin and E-cadherin respectively, as well as enhanced the expressions of CSC makers Lin28, Nanog, Sox2 and Oct4. Linc-DYNC2H1-4 is mainly located in the cytosol. Mechanically, it could sponge miR-145 that targets ZEB1, Lin28, Nanog, Sox2, Oct4 to restore these EMT and CSC-associated genes expressions. We proved that MMP3, the nearby gene of linc-DYNC2H1-4 in the sense strand, was also a target of miR-145. Downregulation of MMP3 by miR-145 was reverted by linc-DYNC2H1-4, indicating that competing with miR-145 is one of the mechanisms for linc-DYNC2H1-4 to regulate MMP3. In summary, our results explore the important role of linc-DYNC2H1-4 in the acquisition of EMT and CSC, and the impact it has on gemcitabine resistance in pancreatic cancer cells.

Background Despite of the established effectiveness, the acceptance and adherence of cardiac rehabilitation (CR) remains sub-optimal. Mobile technologies are increasingly used in promoting CR without any firm evidence of their safety and efficacy. This systematic review and meta-analysis were aimed to assess the effect of mobile applications as an intervention for improving adherence to CR. Methods Relevant studies were searched in PubMed, the Cochrane Library, Embase and Web of Science from inception to 29th December 2018. Eligible studies were the ones which used mobile applications as a stand-alone intervention or as the primary component for the intervention directed at improving CR adherence, without any limitations on outpatient or home-based CR. Results Eight studies were eligible for the systematic review including four randomized controlled trials (RCTs) as well as four before-after studies of which only one had control group. Four RCTs and 185 patients in experimental group were included in meta-analysis, which had evaluated the effect of mobile health applications on CR completion and had reported that the adherence of patients using mobile applications was 1.4 times higher than the control group (RR = 1.38; CI 1.16 to 1.65; P = 0.0003). Moreover, we also found mixed results in exercise capacity, mental health and quality of life. Conclusion The use of mobile applications for improving the adherence of the CR might be effective. However, it appears to be in the initial stage of implementing mobile applications in CR and more research is essential to validate their effectiveness.

25Background: Understanding clinical progression of COVID-19 is a key public health priority that 26 informs resource allocation during an emergency. We characterized clinical progression of 27 COVID-19 and determined important predictors for faster clinical progression to key clinical 28 events and longer use of medical resources. 29 Methods and Findings:The study is a single-center, observational study with prospectively 30 collected data from all 420 patients diagnosed with COVID-19 and hospitalized in Shenzhen 31 between January 11 th and March 10 th , 2020 regardless of clinical severity. Using competing risk 32 regressions according to the methods of Fine and Gray, we found that males had faster clinical 33 progression than females in the older age group and the difference could not be explained by 34 difference in baseline conditions or smoking history. We estimated the proportion of cases in 35 each severity stage over 80 days following symptom onset using a nonparametric method built 36 upon estimated cumulative incidence of key clinical events. Based on random survival forest 37 models, we stratified cases into risk sets with very different clinical trajectories. Those who 38 progressed to the severe stage (22%,93/420), developed acute respiratory distress syndrome 39 (9%,39/420), and were admitted to the intensive care unit (5%,19/420) progressed on average 40 9.5 days (95%CI 8.7,10.3), 11.0 days (95%CI 9.7,12.3), and 10.5 days (95%CI 8.2,13.3), 41 respectively, after symptom onset. We estimated that patients who were admitted to ICUs 42 remained there for an average of 34.4 days (95%CI 24.1,43.2). The median length of hospital 43 stay was 21.3 days (95%CI, 20.5,22.2) for cases who did not progress to the severe stage, but 44 increased to 52.1 days (95%CI, 43.3,59.5) for those who required critical care. 45 Conclusions: Our analyses provide insights into clinical progression of cases starting early in 46the course of infection. Patient characteristics near symptom onset both with and without lab 47