Epithelial cadherin (E-cadherin) is a Ca(2+)-dependent cell-cell adhesion molecule that connects cells via homotypic interactions. Its function is critical in the induction and maintenance of cell polarity and differentiation, and its loss of downregulation is associated with an invasive and poorly differentiated phenotype in colon and other tumors. We have used an avidin-biotin immunoperoxidase technique to localize E-cadherin in microwave-treated, paraffin-embedded sections from 36 patients with pancreatic adenocarcinomas. E-cadherin was expressed by normal ductal and acinar cells with typical membranous staining at the intercellular junctions. Loss of normal surface E-cadherin expression was found in 19/36 (53 per cent) tumours compared to the adjacent normal ductal cells. Abnormal E-cadherin expression was found more frequently in poorly differentiated (grade III) (6/7, 86 per cent) than in well-differentiated tumors (grade I) (4/14, 28 per cent) (P = 0.012). Membranous E-cadherin expression was also lost more frequently in primary tumours with lymph node (stage III) (14/23, 61 per cent) and distant metastasis (stage IV) (2/2, 100 per cent) compared with 3/11 (27 per cent) lymph node-negative tumours (stage I) (P = 0.043). In conclusions, our data indicate that loss of membranous E-cadherin expression is associated with high grade and advanced stage in pancreatic cancer.
With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011–2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.
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