Background Asthma is a chronic disease that seriously harms the health of patients. Oxidative stress is involved in asthma. As an oxidative stress-inducible protein, sestrin2 is elevated in oxidative stress-related diseases. We aimed to explore whether sestrin2 was involved in asthma. Methods Seventy-six subjects (44 in the asthma group, 32 in the control group) were recruited in this study. Plasma sestrin2 levels, peak expiratory flow (PEF), forced expiratory volume in 1 s (FEV 1 ) % predicted, forced vital capacity (FVC) % predicted and FEV 1 /FVC ratio were measured in controls and in asthmatics both during an exacerbation and when controlled after the exacerbation. Results The asthma group had a significant higher sestrin2 level than the control group (asthmatics during exacerbation, 1.75 ± 0.53 ng/mL vs. 1.32 ± 0.48 ng/mL, p < 0.001; asthmatics when controlled after the exacerbation, 1.56 ± 0.46 ng/mL vs. 1.32 ± 0.48 ng/mL, p = 0.021, respectively). In addition, sestrin2 was negatively correlated with FEV 1 % predicted and FEV 1 /FVC ratio in asthmatics during exacerbation (r = − 0.393, p = 0.008; r = − 0.379, p = 0.011; respectively). Moreover, negative correlations between sestrin2 and FEV 1 % predicted and FEV 1 /FVC ratio also existed in asthmatics when controlled after the exacerbation (r = − 0.543, p < 0.001; r = − 0.433, p = 0.003 respectively). More importantly, multiple linear regression analysis demonstrated that FEV 1 % predicted was independently associated with sestrin2 in asthmatics both during exacerbation and when controlled after the exacerbation. Conclusions Sestrin2 is involved in asthma. Sestrin2 levels increase in asthmatics both during exacerbation and when controlled after the exacerbation. In addition, sestrin2 is independently associated with FEV 1 % predicted.
Background Obstructive sleep apnea (OSA) is a disease seriously threatening individual health, which results in serious complications such as hypertension and stroke. These complications are associated with oxidative stress triggered by intermittent hypoxia in OSA. Sestrin2 is a crucial factor involved in oxidative stress. The goal of this study was to investigate if a relationship exists between OSA and Sestrin2. Methods We prospectively enrolled 71 subjects, and 16 patients of them with severe OSA completed 4 weeks of nasal continuous positive airway pressure (nCPAP) therapy. We measured and compared the concentration of Sestrin2 in the urine of all subjects, as well as the changes between before and after nCPAP treatment. Additionally, the correlation between Sestrin2 and sleep parameters was analyzed, and the multiple linear regression analysis with stepwise selection was performed to explore the relationship between Sestrin2 and various factors. Results A total of 71 subjects were enrolled and divided into two groups: OSA group ( n = 41), control group ( n = 30). The level of urinary Sestrin2 in OSA patients was significantly higher than that of the control group, and increased with the severity of OSA, while it reduced after nCPAP treatment. Additionally, Sestrin2 was positively correlated with apnea/hypopnea index (AHI), oxygen desaturation index, oxygen saturation < 90% percentage of recording time spent (PRTS) and high-density lipoprotein (HDL), while negatively correlated with the lowest oxygen saturation. Importantly, Sestrin2 was independently associated with AHI, oxygen saturation < 90% PRTS and HDL. Conclusions Urinary Sestrin2 is involved in OSA, and is a paramount marker of OSA severity.
Urban land-use patterns change has become one of hot topics on land use/ land cover change (LUCC) studies in the worldwide area. As urban land change is influenced by both topography and human behavior driving factors, it is a complex dynamic and spatio-temporal progress. To simulate and analyze urban land use patterns and its driving factors, the logistic regression model is implemented as the statistical tool combined with spatial data by GIS.Because of China coastal development strategy along the Yangtze River, Nantong is one of the most fast-developing cities in the area. Meanwhile, Tongzhou District is the transitional area of Nantong main city, which is the fourth economic city of Jiangsu province located at the eastern coastal area. Based on Tongzhou land-use actual map, DEM from the SRTM and social-economic census data in the 2006 statistical year book, the land use types were mainly redivided into construction land and cultivated land and all driving factors of topography elements, the distances to the main rivers and roads, and the densities of population and economy were implemented to each own layer with ArcGIS spatial analysis tools. And then each land-use pattern's weight coefficient is calculated by SPSS with the logistic model. Furthermore, those incorporating components of the spatial auto-correlation were also considered.As to the simulation results of Tongzhou, the area under ROC curves of construction land and cultivated land were 0.613 and 0.565 respectively, which was used to compare the land-use probability simulation with the actual map. Due to this study on the plain area, the topography driving factors played a less role than in other related studies and depressed the simulation impression, such as the study on Yongding County, which is one of the typical Karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. To a certain extent, the social-economic factors mainly determined construction land distribution, and the cultivated land more depended on the surrounding main rivers and roads like Tongzhou, which is located at the eastern coastal plain. From the causes, locations and consequences of urban transitional land-use change, this study could help various local governments with land use planning and policy making.
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