Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis.
 
  Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related lncRNAs. The accuracy of model was evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) analysis, and the independent prognostic indicator was identified with Cox analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to detect the accuracy of our results.
 
  A network of 29 transcription factors and 17 immune-related genes was constructed. Furthermore, four immune-pro of EC.
  Compelling evidences reported the role of microRNAs (miRNAs) in ovarian cancer. However, little was known regarding the molecular mechanism of miR-367 in ovarian cancer. This study intended to investigate the role and regulatory mechanism of miR-367 in ovarian cancer involving lysophosphatidic acid receptor-1 (LPA1).
 
  Potentially regulatory miRNAs in ovarian cancer were obtained from bioinformatics analysis. RT-qPCR was used to detect miR-367 expression in both ovarian cancer tissues and relevant adjacent normal tissues. Relationship between miR-367 and LPA1 was predicted by miRNA database and further verified using dual luciferase reporter gene assay and RIP. EdU and Transwell assay were used to measure the proliferation and invasion ability of cells. Moreover, tube formation and chick chorioallantois membrane (CAM) assay were performed to determine angiogenesis of human umbilical vein endothelial cells (HUVECs). Finally, the roles of LPA1 in tumor growth was also studied using nude mice xenograft assay.
 
  High expression of LPA1 and low expression of miR-367 were observed in ovarian cancer tissues and cells. Overexpressed miR-367 downregulated LPA1 expression to inhibit proliferation, invasion, and angiogenesis of cancer cells. Low expression of LPA1 suppressed tumor formation and repressed angiogenesis in ovarian in vivo.
 
  All in all, overexpression of miR-367 downregulated LPA1 expression to inhibit ovarian cancer progression, which provided a target for the cancer treatment.
 All in all, overexpression of miR-367 downregulated LPA1 expression to inhibit ovarian cancer progression, which provided a target for the cancer treatment.
  Vaccination is an effective intervention against epidemics. Previous work has demonstrated that psychological cognition affects individual behavior. However, perceptual differences between individuals, as well as the dynamics of perceptual evolution, are not taken into account. In order to explore how these realistic characteristics of psychological cognition influence collective vaccination behavior, we propose a prospect theory based evolutionary vaccination game model, where the evolution of reference points is used to characterize changes in perception. We compare the fractions of vaccinated individuals and infected individuals under variable reference points with those under the expected utility theory and the fixed reference point, and highlight the role of evolving perception in promoting vaccination and contributing to epidemic control. We find that the epidemic size under variable reference point is always less than that under the expected utility theory. Finding that there exists a vaccination cost threshold for the cognitive effect, we develop a novel mixed-reference-point mechanism by combining individual psychological characteristics with network topological feature. The effectiveness of this mechanism in controlling the network epidemics is verified with numerical simulations. Compared with pure reference points, the mixed-reference-point mechanism can effectively reduce the final epidemic size, especially at a large vaccination cost.
 The control of spreading of COVID-19 in emergency situation the entire world is a challenge, and therefore, the aim of this study was to propose a spherical intelligent fuzzy decision model for control and diagnosis of COVID-19. The emergency event is known to have aspects of short time and data, harmfulness, and ambiguity, and policy makers are often rationally bounded under uncertainty and threat. There are some classic approaches for representing and explaining the complexity and vagueness of the information. The effective tool to describe and reduce the uncertainty in data information is fuzzy set and their extension. Therefore, we used fuzzy logic to develop fuzzy mathematical model for control of transmission and spreading of COVID19. The fuzzy control of early transmission and spreading of coronavirus by fuzzy mathematical model will be very effective. The proposed research work is on fuzzy mathematical model of intelligent decision systems under the spherical fuzzy information.  https://www.selleckchem.com/products/VX-770.html  In the proposed work, we will develop a newly and generalized technique for COVID19 based on the technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under spherical fuzzy environment. Finally, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested method, along with a sensitivity analysis and comparative analysis, showing the feasibility and reliability of its results.The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated.