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Objective Breast cancer is a malignant tumor in the epithelial tissue of the breast gland. This study aimed to unveil the expression and clinical diagnostic value of lncRNA cervical cancer high-expressed 1 (CCHE1) in breast cancer. Methods CCHE1 expression in breast cancer tissues was evaluated by RT-qPCR. The relationship between the CCHE1 expression and clinicopathological features of breast cancer was analyzed with the chi-square test, and the survival of breast cancer patients was evaluated with the Kaplan–Meier method. The diagnostic value of CCHE1 expression for breast cancer was evaluated by using the receiver operating characteristics (ROC) curve. Breast cancer cell lines (SKBR3, T47D, BT474, and MCF-7) were cultured for detecting CCHE1 expression in the cells. MCF-7 cells were selected for the subsequent experiments, and the small interfering RNA of CCHE1 (si-CCHE1) and CCHE1 overexpression vector (pcDNA-CCHE1) were transfected into MCF-7 cells. The proliferation, migration, and invasive ability were assessed by CCK-8 and Transwell assays. The influence of CCHE1 on the growth of tumors was validated by nude mice xenograft assay. Results CCHE1 was up-regulated in breast cancer tissues and breast cancer cells. The high expression level of CCHE1 in cancer tissues of breast cancer patients was correlated with larger tumor size, advanced TNM stage, Ki-67 status, and lymph node metastasis. The area under the ROC curve for CCHE1 in the diagnosis of breast cancer was 0.983 (95% CI: 0.966–1.000), with a sensitivity of 95.00% and a specificity of 91.70%. The 5-year survival rate was higher in patients with low CCHE1 expression than those with high CCHE1 expression. Furthermore, restrained CCHE1 impeded proliferation, invasion, and migration of MCF-7 cells, as well as tumor growth in mice. Conclusion Our study highlights that elevated expression of CCHE1 in breast cancer tissues, which is closely related to clinicopathologic features, has some clinical value in the diagnosis of the disease. [ABSTRACT FROM AUTHOR]

Fine-tuned interaction between reacting molecules and metal surface is of great importance in heterogeneous catalysis for the design of highly selective processes. Conventional strategies based on adjusting the intrinsic catalyst properties such as particle size, metal-support interaction and morphology suffer from complicated and time-consuming synthetic procedures. Recently, the modification of metal nanoparticles with non-metallic promoters has been demonstrated as an effective tool to regulate the interfacial environment of metallic catalysis. Herein, we will give an overview of recent progress in this area. A general analysis will be proposed about the electronic, steric effects and new functionalities imposed by non-metallic promoters of metal catalysts. The potential for the selectivity enhancement of the promoted metal catalysts in various industrially important reactions is discussed. [ABSTRACT FROM AUTHOR]

Global climate change has resulted in an increase in the frequency, intensity, and duration of extreme events. However, we know very little about the associations between large temperature changes lasting for several days (LTCD) and CVD, particularly in less-developed, rural areas. We collected daily data on cardiovascular-related diseases and weather conditions from 119 villages in seven counties between 2010 and 2016. A distributed lag non-linear model was used to investigate the association between LTCD and relative risk (RR) of hospital admissions for CVD. Analyses were stratified by the duration and intensity of LTCD, age, and sex. The RR of CVD increases with the intensity and duration of LTCD. Men and young people were more vulnerable to LTCD of longer duration and higher intensity than women and old. Our findings may help local public health authorities develop adaptive preventive strategies targeting inhabitants of villages. [ABSTRACT FROM AUTHOR]

Current research on group decision making (GDM) focuses on how to quickly reach a consensus, in which decision makers' opinions are mainly changed by feedback suggestion, not by changing circumstances over time. In a long-term GDM problem, decision-makers may be affected by the environment to change their views. For example, real estate market prices will affect residents' opinions in time-consuming demolition negotiations. We introduce time uncertainty to study the long-term GDM problem with random opinions following this idea. This article constructs the minimum cost and maximum utility-based consensus models in GDM with random opinion based on the time uncertainty. The optimal time of decision-making can be determined. Specifically, the real options theory is employed to study time uncertainty. Furthermore, the feasibility of the models is verified by case analysis of China's demolition negotiation for old communities' renewal projects, and sensitivity analyses are developed to analyse the robustness of our models. In the case analysis and sensitivity analysis, we obtain the optimal solution of models by using a genetic algorithm based on MATLABR2018b. [ABSTRACT FROM AUTHOR]