Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in mainland China. HFRS is particularly endemic in Changsha, the capital city of Hunan Province, with one of the highest incidences in China. The occurrence of HFRS is influenced by environmental factors. However, few studies have examined the relationship between environmental variation (such as land use changes and climate variations), rodents and HFRS occurrence. The purpose of this study is to predict the distribution of HFRS and identify the risk factors and relationship between HFRS occurrence and rodent hosts, combining ecological modeling with the Markov chain Monte Carlo approach. Ecological niche models (ENMs) were used to evaluate potential geographic distributions of rodent species by reconstructing details of their ecological niches in ecological dimensions, and projecting the results onto geography. The Genetic Algorithm for Rule-set Production was used to produce ENMs. Data were collected on HFRS cases in Changsha from 2005 to 2009, as well as national land survey data, surveillance data of rodents, meteorological data and normalized difference vegetation index (NDVI). The highest occurrence of HFRS was in districts with strong temperature seasonality, where elevation is below 200 m, mean annual temperature is around 17.5°C, and annual precipitation is below 1600 mm. Cultivated and urban lands in particular are associated with HFRS occurrence. Monthly NDVI values of areas predicted present is lower than areas predicted absent, with high seasonal variation. The number of HFRS cases was correlated with rodent density, and the incidence of HFRS cases in urban and forest areas was mainly associated with the density of Rattus norvegicus and Apodemus agrarius, respectively. Heterogeneity between different areas shows that HFRS occurrence is affected by the intensity of human activity, climate conditions, and landscape elements. Rodent density and species composition have significant impacts on the number of HFRS cases and their distribution.

Objective: To provide evidence of the association between CLTA-4 gene polymorphisms and Alopecia Areata (AA). Methods: PubMed, EMBASE, Web of Science, Cochrane, Wanfang and CNKI databases were searched until April 30, 2021.The selection was completed according to the inclusion and exclusion criteria. The study quality assessment was based on Newcastle-Ottawa Scale. The assessment of the association was measured by ORs and 95%CIs. Results: Nine studies, containing 2858 AA cases and 5444 disease-free control subjects were included. For rs231775 polymorphism, no significant association with AA was found, which was A vs a, OR =1.02[0.81,1.30], P = 0.85; AA vs aa, OR =1.26[0.81,1.97], P = 0.31; Aa vs aa, OR =1.04[0.54,2.01], P = 0.91; AA + Aa vs aa, OR =1.04[0.71,1.53], P = 0.82; AA vs Aa + aa, OR =1.31[0.97,1.78], P = 0.08. For rs3087243 polymorphism, also no significant association was found, which was A vs a, OR =0.93[0.78,1.11]; P = 0.40, AA vs aa, OR =0.68[0.44,1.06]; P = 0.09; Aa vs aa, OR =0.87[0.45,1.68], P = 0.68; AA + Aa vs aa, OR =0.93[0.68,1.28], P = 0.66; AA vs Aa + aa, OR =0.78[0.34,1.81], P = 0.57. For rs231726 polymorphism, a significant correlation was found, which was A vs a, OR =0.76[0.70,0.82], P < 0.05. Conclusions: A significant correlation between CTLA-4 rs231726 polymorphism and AA susceptibility was found, but no significant association of CTLA-4 gene rs231775 and rs3087243 polymorphisms and AA susceptibility was found.