Comparison of adenoma detection in different colorectal segments between deep-sedated and unsedated colonoscopy.

To investigate if deep-sedated colonoscopy affects adenoma detection in certain colorectal segment. Review of colonoscopy reports, electronic images and medical records of individuals underwent screening colonoscopy with or without propofol sedation between October 2020 and March 2021 from seven hospitals in China. A total of 4500 individuals were analyzed. There was no significant difference in ADR between deep-sedated colonoscopy and unsedated colonoscopy [45.4% vs. 46.3%, P > 0.05]. The APP of deep-sedated colonoscopy was lower than unsedated colonoscopy (1.76 ± 0.81 vs. 2.00 ± 1.30, P < 0.05). Both average number of adenomas and luminal distention score of splenic flexure and descending colon were lower in deep-sedated colonoscopy (P < 0.05), and average number of adenomas was positively correlated with an improved distension score in splenic flexure and descending colon (splenic flexure r = 0.031, P < 0.05; descending colon r = 0.312, P < 0.05). Linear regression model showed deep-sedated colonoscopy significantly affected luminal distention of splenic flexure and descending colon as well as average number of adenomas detected in splenic flexure (P < 0.05). Deep-sedated colonoscopy decreased adenoma detection in splenic flexure and the luminal distention of splenic flexure and descending colon compared with unsedated colonoscopy.

[Spatial distribution of human activity intensity in Yunnan-Guizhou Plateau Wetland scenic area: A case study of Lashihai watershed in Yunnan Province, China]. / 云贵高原湿地景区人类活动强度的空间分布­­ä»¥äº‘南省拉市海流域为例.

Human activity intensity is mostly used to quantify the degree of human influence on natural systems, with obvious spatial variability. With Lashihai watershed in Yunnan Province as an example, we used SPOT remote sensing images to update land use data, and obtained a comprehensive index of land use intensity after gridding by assigning weights to different land types, which was considered as the basic human activity intensity. The local tourism activities (horseback riding and boating) were also included. The horseback riding and boating were spatially quantified according to the location of horse farms and the abundance of horses and boats which were superimposed with the basic human activity intensity on the spatial scale of 100 m×100 m to obtain a more accurate comprehensive human activity intensity and to analyze the spatial variations. The results showed that the gridding and the kernel density analysis improved the accuracy of spatial analysis and reflected the spatial superposition and diffusion effects. In the comprehensive human activity intensity map of Lashihai watershed, the highest intensity value of water area was at the mouth of the sea, the lowest intensity value was at the center of the sea, and the overall trend of intensity gradually decreased from the surrounding to the middle. The land settlement had the highest intensity, the intensity value of the agricultural land gathering area was at the middle level, and the intensity of human activities in the forestry area of higher altitude was lower. The comprehensive human activity intensity in the water area of the Lashihai watershed varied most obviously, and differed greatly from the basic human activity intensity. Although there were many local characteristic tourism activities in Yunnan-Guizhou Plateau Wetland scenic area, but their land use types did not change. We need to take them into account when quantifying the intensity of human activities.

[Estimation of the SOA Formation Potential of the National Trunk Highway in Central Plains Urban Agglomeration].

Secondary organic aerosols (SOAs) are among the main components of air pollution. Accurately estimating SOAs formed from automobile exhaust is crucial for controlling and mitigating traffic air pollution. Sufficient monitoring data is difficult for regional analysis owing to limited monitoring data over a small area or few observation stations. Indirect methods may be used to estimate SOA using data on the number and types of vehicles. A linear reference system of Central Plains urban agglomeration was built from the national trunk line network system and the traffic survey data of transportation. The numbers of different types of vehicles were assigned to road segments as the traffic flow according to the represented length between monitoring stations. Then, VOCs emissions were calculated through the emission coefficient method based on the previous traffic flow data. Moreover, further estimations of the SOA formation potential were made by the fractional aerosol coefficient approach. Through kernel density analysis, discrete point data of the observer station were transformed into line segments and expanded to a continuous spatial distribution for quantitative and spatial variation analysis of the SOA in the study area. The results show that ① toluene has the highest SOA generation potential, 1,4-diethylbenzene has the strongest ability to form SOA, and aromatic hydrocarbons exhibit higher SOA formation capacity than alkanes; ② small and medium gasoline passenger cars generate the most SOA and account for about 1/3 of the total SOA, but small gasoline trucks exhibit the strongest capacity for SOA formation; ③ regarding the capacity of SOA formation for vehicles using different fuel types, gasoline vehicles have a higher capacity than diesel vehicles, and passenger cars have a slightly higher capacity than trucks; ④ in Central Plains urban agglomeration, the spatial distribution of SOA intensity data shows a trend of convergence to the center of Zhengzhou city. Gasoline vehicles exhibit a similar pattern overall, but diesel vehicles exhibit a weaker trend that decreases distinctly. SOA intensity along the north-south direction is much higher than that of the east-west direction around the center of Zhengzhou crossing.