The Yangtze River Basin is the largest river basin in China and has the most complex trans-boundary problems. The water quality monitoring system of the provincial boundary sections in the basin is the typical go-to system to show the interaction between administrative regions and basins. In this article, we discuss the water quality monitoring system in the basin from a legal perspective, explore the achievements and deficiencies of the system, and identify the main elements that constrain the effective operation of the system in the basin, including the fragmented competencies of monitoring institutions, the different monitoring techniques, the overlapping monitoring contents and scopes, the different data releasing channels, and the different applications of the data. We provide legislative suggestions to implement the newly enacted Yangtze River Protection Law and valuable lessons for the design of monitoring systems in other countries or (trans-boundary) basins that face a similar situation.
Fumin Xu,1,* Xiao Chen,2,* Xinru Yin,1 Qiu Qiu,3 Jingjing Xiao,4 Liang Qiao,5 Mi He,5 Liang Tang,6 Xiawei Li,1 Qiao Zhang,1 Yanling Lv,1 Shili Xiao,1 Rong Zhao,1 Yan Guo,1 Mingsheng Chen,5 Dongfeng Chen,1 Liangzhi Wen,1,7 Bin Wang,1,8 Yongjian Nian,5 Kaijun Liu1,8 1Department of Gastroenterology, Daping Hospital, Army Medical University, Chongqing, People's Republic of China; 2Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, People's Republic of China; 3Department of Gastroenterology, People's Hospital of Chongqing Hechuan, Chongqing, People's Republic of China; 4Department of Medical Engineering, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China; 5College of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, People's Republic of China; 6Department of Internal Medicine, 63790 Military Hospital of the Chinese People's Liberation Army, Xichang, People's Republic of China; 7Department of Infectious Disease, Taikang Tongji Hospital, Wuhan, People's Republic of China; 8Department of Infectious Disease, Wuhan Huoshenshan Hospital, Wuhan, People's Republic of China*These authors contributed equally to this workCorrespondence: Kaijun LiuDepartment of Gastroenterology, Daping Hospital, Army Medical University, 10 Changjiang Branch Road, Yuzhong District, Chongqing, 400038, People's Republic of ChinaTel +86 135-9406-7813Email kliu_tmmu@126.comBackground: Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression.Methods: In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models.Results: A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k–nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector–machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed.Conclusion: The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.Keywords: COVID-19, disease progression, machine-learning models
