Background The Qingcaosha Reservoir is facing issues of algal blooms and eutrophication, and the resulting increase in the level of chlorination disinfection by-products in the water has been a major concern. Objective To evaluate the impact of "Algae Monitoring and Control Program in Qingcaosha Reservoir" (hereinafter referred to as the program) on the control of trihalomethanes (THMs) in conventional finished water. Methods From 2011 to 2019, water samples were collected from the Lujiazui Water Plant once per season, one sample each time, and the concentrations of four THMs (trichloromethane, dichlorobromomethane, monochlorodibromomethane, and tribromomethane) were measured in the samples. Using 2014 when the program was implemented as a cut-off point, the entire study period was divided into two phases: pre-implementation (2011–2013) and post-implementation(2014–2019). Segmented linear regression with interrupted time series analysis was applied to assess the concentrations and trends of THMs in the finished water before and after the program launch. Results The concentration of total THMs in finished water increased by 1.561 µg·L⁻¹ (P=0.010) for each season of time extension before launching the program. The change in the concentration of total THMs in finished water was not statistically significant after the program launch, but the THMs concentration showed a decreasing trend as the slope was −0.626 (P=0.001). From 2017 until the end of 2019, the average concentration of THMs in finished water of Lujiazui Water Plant dropped to 10 μg·L⁻¹ or less. Conclusions The algae and eutrophication control measures in Qingcaosha Reservoir have achieved good results, controlling THMs in finished water at a low level, and the trend of THMs has changed from a yearly increase pattern before the program to a yearly decrease pattern after the program.

OBJECTIVETo investigate the effects of a recombinant adenovirus-mediated triple mutant hypoxia-inducible factor-1α (HIF-1α) on the proliferation and vascular endothelial growth factor (VEGF) expression in human microvascular endothelial cells (hMVECs).

METHODSThe adenovirus vector of the triple mutant HIF-1α (Ad-HIF-1α(564/402/803)), adenovirus vector of wild-type HIF-1α (Ad-HIF-1α(nature)), Ad-lacZ and Ad-Null were amplified in HEK293A cells, and the adenoviruses were purified and titrated. Dual luciferase reporter assay system was employed to detect the transcriptional activities of wild-type and triple mutant HIF-1α. After infection of the hMVECs with the adenoviruses, the cellular protein expressions of HIF-1α and VEGF were detected using Western blotting, and the cell proliferation was assessed by MTS assay.

RESULTSThe transcriptional activity of the triple mutant HIF-1α was significantly higher than that of wildtype HIF-1α in the infected hMVECs (P<0.001). The protein levels of HIF-1α and VEGF in cells infected with Ad-HIF-1α(564/402/803) were significantly higher than those in cells infected with other adenoviruses, and HIF-1α dose-dependently up-regulated VEGF protein expression. The absorbance was significantly higher in Ad-HIF-1α(564/402/803) group than in the other groups (P<0.01) on the third and fifth days after infection.

CONCLUSIONThe recombinant adenovirus-mediated triple mutant HIF-1α expression is stable under normoxic condition. The triple mutant HIF-1α can up-regulate the expression of VEGF protein in hMVECs to promote the cell proliferation.

Adenoviridae ; genetics ; Cell Proliferation ; Endothelial Cells ; cytology ; metabolism ; Endothelium, Vascular ; cytology ; Genetic Vectors ; HEK293 Cells ; Humans ; Hypoxia-Inducible Factor 1, alpha Subunit ; genetics ; pharmacology ; Microvessels ; cytology ; Recombinant Proteins ; genetics ; Transfection ; Vascular Endothelial Growth Factor A ; metabolism

Patients with chronic kidney disease (CKD) have a relatively high risk of cardiovascular disease (CVD). Risk stratification guided by CVD risk prediction models is essential for managing CKD populations. We reviewed the outcome events, predictive variables, modeling methods, and predictive performance of CVD risk prediction models in CKD populations. We found a large variability in predictive outcomes, number of predictors, and sample sizes across studies. The models tended to overestimate the CVD risk of CKD populations. There are few independently validated or constructed CVD risk prediction models for CKD populations in developing countries, and in particular, there is a lack of independent external validation studies of model calibration. Future studies should comply with the reporting standards of risk prediction models to better support the application of CVD risk prediction models for CKD populations.