Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Chang-Lin Mei"'
Publikováno v:
Journal of International Medical Research, Vol 50 (2022)
Whether pancreatic extracorporeal shock wave lithotripsy (ESWL) is safe for patients with autosomal dominant polycystic kidney disease (ADPKD) is unclear. A woman in her early 30s was admitted to our hospital because of intermittent upper abdominal p
Externí odkaz:
https://doaj.org/article/2aa2cf7da193499fa40eae0cec39c692
Publikováno v:
Entropy, Vol 25, Iss 2, p 320 (2023)
Multiscale estimation for geographically weighted regression (GWR) and the related models has attracted much attention due to their superiority. This kind of estimation method will not only improve the accuracy of the coefficient estimators but also
Externí odkaz:
https://doaj.org/article/429f7e2478104fd28a933e0f153c90fb
Publikováno v:
Journal of Inequalities and Applications, Vol 2020, Iss 1, Pp 1-19 (2020)
Abstract With the increasing availability of spatially extensive geo-referenced data, much attention has been paid to the use of local statistics to identify local patterns of spatial association, in which the null distributions of local statistics p
Externí odkaz:
https://doaj.org/article/39c5fcd2267e4acd830d805ff560627f
Publikováno v:
Kidney Diseases, Pp 1-7 (2020)
Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary nephropathy with few treatments to slow renal progression. The evidence on the effect of lipid-lowering agents (statins) on ADPKD progression remains incon
Externí odkaz:
https://doaj.org/article/7d3e23584ba64532b97e9aca99ee4b97
Autor:
Chang-Lin Mei, Cheng Xue, Sheng-Qiang Yu, Bing Dai, Jiang-Hua Chen, Ying Li, Li-Meng Chen, Zhang-Suo Liu, Yong-Gui Wu, Zhao Hu, Yan Zha, Hong Liu, Yong-Ze Zhuang, Chun Zhang, Xiang-Cheng Xiao, Yue Wang, Gui-Sen Li, Yi-Yi Ma, Lin Li
Publikováno v:
Kidney Diseases, Vol 6, Iss 3, Pp 143-148 (2020)
Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease, with a prevalence of 1/2,500–1/1,000, and it affects 1.25 million people in China. ADPKD is responsible for nearly 5% of end-stage renal disease case
Externí odkaz:
https://doaj.org/article/99fa78c52e7b4bb78d763a58b5b0a32d
Autor:
Qing Yao, Ming Wu, Jie Zhou, Meiyang Zhou, Dongping Chen, Lili Fu, Rongrong Bian, Xiaohong Xing, Lijun Sun, Xiaohong Hu, Lin Li, Bing Dai, Rudolf P. Wüthrich, Yiyi Ma, Chang-Lin Mei
Publikováno v:
Kidney & Blood Pressure Research, Vol 42, Iss 1, Pp 156-164 (2017)
Background/Aims: In this retrospective study we aimed to compare the effect of tranexamic acid (TXA) vs etamsylate, two hemostatic agents, on hematuria duration in autosomal dominant polycystic kidney disease (ADPKD) patients with persistent gross he
Externí odkaz:
https://doaj.org/article/1bdfbc2e34aa40faac3b545f17f6945d
Autor:
Jing Xu, Zhi-Guo Mao, Mei Kong, Liang-Hao Hu, Chao-Yang Ye, Cheng-Gang Xu, Shu Rong, Li-Jun Sun, Jun Wu, Bing Dai, Dong-Ping Chen, Yu-Xian Zhu, Yi-Xiang Zhang, Yu-Qiang Zhang, Xue-Zhi Zhao, Chang-Lin Mei
Publikováno v:
PLoS ONE, Vol 6, Iss 4, p e14781 (2011)
BACKGROUND: Diseases of the kidneys and genitourinary tract are common health problems that affect people of all ages and demographic backgrounds. In this study, we compared the quantity and quality of nephrological and urological articles published
Externí odkaz:
https://doaj.org/article/a29baa6b4373469cb1781136cbae6392
Publikováno v:
Geographical Analysis. 54:357-381
Autor:
Feng Chen, Chang-Lin Mei
Publikováno v:
Economic Modelling. 94:737-747
Mixed geographically weighted regression (GWR) models, a combination of linear and spatially varying coefficient models, have found their applications in a variety of disciplines including economic modelling for geo-referenced data analysis. Generall
Geographically and temporally weighted regression (GTWR) models have been widely used to explore spatiotemporal nonstationarity where all the regression coefficients are assumed to be varying over both space and time. In reality, however, constant, o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::883fb3be6743aa895d5d98f577b79cd2