This is the first report to provide evidence that miR-449a could modulate cell cycle and apoptosis through regulating cyclin D1 and BCL2 expression in SGC7901 cells.
BackgroundNumerous studies have shown that long noncoding RNA (lncRNA) is involved in gastric cancer (GC). A relevant microarray containing gastric cancer-related lncRNAs was downloaded from The Cancer Genome Atlas database.MethodsqRT-PCR was used to analyze LINC00565 and AKT3 expression in tumor tissues and cell lines. Proliferative, colony formation and apoptotic abilities of GC cells after transfection of sh-LINC00565 were determined by CCK-8, colony formation assay and flow cytometry, respectively. RIP was enrolled to detect the interaction between LINC00565, AKT3 and miR-665. Dual luciferase assay was used to confirm the relation between miR-665 and LINC00565 and AKT3.ResultsExpression level of LINC00565 in GC tissue was highly expressed in GC, which was negatively correlated to prognosis of GC patients. The results showed that knockdown of LINC00565 decreased proliferative and colony formation abilities, and induced apoptosis of GC cells. Pearson analysis showed that LINC00565 was positively correlated with AKT3. Besides, AKT3 was significantly up-regulated in GC. In addition, knockdown of LINC00565 down-regulated AKT3. In order to explore the mechanism, we found that miR-665 could bind to LINC00565 by bioinformatics. Dual-luciferase reporter gene assay and RIP assay both verified the binding relationship between miR-665 and AKT3. Finally, rescue experiments were carried out to explore whether AKT3 could reverse the anti-cancer effect of low-level LINC00565 on GC development.ConclusionIn summary, the expression of LINC00565 is upregulated in GC. LINC00565 can be used as the sponge of miR-665 to up-regulate the expression of AKT3, thus promoting the progression of GC.
Objectives One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19. Methods A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19. Results Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16+/CD56+ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value. Conclusions The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.
The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in China is achieving under control in China. We develop a mathematical model based on epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and close contacts. We calculate the basic reproduction numbers 2.5 in China (excluding Hubei province) and 2.9 in Hubei province with the initial time on January 30 which show the severe infectivity of COVID-19, and verify that the current isolation method effectively contains the transmission of COVID-19. Under the isolation of healthy people, confirmed cases and close contacts, we find a noteworthy phenomenon that is the potential second epidemic of COVID-19, and estimate the peak time and value and the cumulative number of cases. Simulations show that the isolation of close contacts tracked measure can efficiently contain the transmission of the potential second epidemic of COVID-19. With isolation of all susceptible people or all infected people or both, there is no potential second epidemic of COVID-19. Furthermore, resumption of work and study can increase the transmission risk of the potential second epidemic of COVID-19.
African swine fever first broke out in mainland China in August 2018 and has caused a substantial loss to China’s pig industry. Numerous investigations have confirmed that trades and movements of infected pigs and pork products, feeding pigs with contaminative swills, employees, and vehicles carrying the virus are the main transmission routes of the African swine fever virus (ASFV) in mainland China. However, which transmission route is more risky and what is the specific transmission map are still not clear enough. In this study, we crawl the data related to pig farms and slaughterhouses from Baidu Map by writing the Python language and then construct the pig transport network. Following this, we establish an ASFV transmission model over the network based on probabilistic discrete-time Markov chains. Furthermore, we propose spatiotemporal backward detection and forward transmission algorithms in semi-directed weighted networks. Through the simulation and calculation, the risk of transmission routes is analyzed, and the results reveal that the infection risk for employees and vehicles with the virus is the highest, followed by contaminative swills, and the transportation of pigs and pork products is the lowest; the most likely transmission map is deduced, and it is found that ASFV spreads from northeast China to southwest China and then to west; in addition, the infection risk in each province at different times is assessed, which can provide effective suggestions for the prevention and control of ASFV.
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