Roles of Adipokines in Digestive Diseases: Markers of Inflammation, Metabolic Alteration and Disease Progression

Adipose tissue is a highly dynamic endocrine tissue and constitutes a central node in the interorgan crosstalk network through adipokines, which cause pleiotropic effects, including the modulation of angiogenesis, metabolism, and inflammation. Specifically, digestive cancers grow anatomically near adipose tissue. During their interaction with cancer cells, adipocytes are reprogrammed into cancer-associated adipocytes and secrete adipokines to affect tumor cells. Moreover, the liver is the central metabolic hub. Adipose tissue and the liver cooperatively regulate whole-body energy homeostasis via adipokines. Obesity, the excessive accumulation of adipose tissue due to hyperplasia and hypertrophy, is currently considered a global epidemic and is related to low-grade systemic inflammation characterized by altered adipokine regulation. Obesity-related digestive diseases, including gastroesophageal reflux disease, Barrett\u27s esophagus, esophageal cancer, colon polyps and cancer, non-alcoholic fatty liver disease, viral hepatitis-related diseases, cholelithiasis, gallbladder cancer, cholangiocarcinoma, pancreatic cancer, and diabetes, might cause specific alterations in adipokine profiles. These patterns and associated bases potentially contribute to the identification of prognostic biomarkers and therapeutic approaches for the associated digestive diseases. This review highlights important findings about altered adipokine profiles relevant to digestive diseases, including hepatic, pancreatic, gastrointestinal, and biliary tract diseases, with a perspective on clinical implications and mechanistic explorations

Application of geographic weighted regression to establish flood-damage functions reflecting spatial variation

Flood damage functions are necessary to ensure comprehensive flood-risk management. This study attempts to establish a residential flood-damage function through interviewing the residents living in the region where flood disasters occur frequently. Keelung River basin, near Taipei Metropolitan in Taiwan was selected as study area. Flood damages are related to the flood depths, which are the most commonly considered factor in previously published work. Ordinary least squares (OLS) regression was used to construct the flood-damage function at the beginning. Analytical results indicate that flood depth is the significant variable, but the spatial pattern of the residuals shows that residuals exhibit spatial autocorrelation. The Geographically Weighted Regression (GWR) Model was then applied to modify the traditional regression model, which cannot capture spatial variations, and to reduce the problem of spatial autocorrelation. The R-square value was found to increase from 0.15 to 0.24, and the spatial autocorrelation in the residuals was no longer evident. A modified OLS model with a dummy variable to capture the spatial autocorrelation pattern was also proposed for future applications. In conclusion, the residential flood damage is determined by flood depth and zone, and the GWR model not only captures the spatial variations of the affecting factors, but also helps to discover the independent variable to modify the traditional regression model.Keywords: flood damage, flood depth, OLS, GWR, spatial autocorrelatio