Acute stress of the typical disinfectant glutaraldehyde-didecyldimethylammonium bromide (GD) on sludge microecology in livestock wastewater treatment plants: Effect and its mechanisms.

Glutaraldehyde and didecyldimethylammonium bromide (GD) is a disinfectant widely used to prevent African swine fever (ASF) in livestock farms. However, the effect of residual GD on the activated sludge microbial ecology of receiving wastewater treatment plants (WWTPs) remains largely unknown. In this study, seven simulated systems were established to research the effects of GD on WWTPs and reveal the underlying mechanisms of microecological responses to GD at different concentrations. Both the nitrogen and carbon removal rates decreased with increasing GD concentrations, and nitrogen metabolism was inhibited more obviously, but the inhibition weakened with increasing stress duration. Microorganisms activated their SoxRS systems to promote ATP synthesis and electron transfer to support the hydrolysis and efflux of GD by producing a small number of ROS when exposed to GD at less than 1 mg/L. The overproduction of ROS led to a decrease of antioxidant and nitrogen removal enzyme activities, and upregulation of the porin gene increased the risk of GD entering the intracellular space upon exposure to GD at concentrations higher than 1 mg/L. Some denitrifiers survived via resistance and their basic capabilities of sugar metabolism and nitrogen assimilation. Notably, low concentrations of disinfectants could promote vertical and horizontal transfer of multiple resistance genes, especially aminoglycosides, among microorganisms, which might increase not only the adaptation capability of denitrifiers but also the risk to ecological systems. Therefore, the risks of disinfectants targeting ASF on ecology and health as well as the effects of disinfectant residuals from the COVID-19 epidemic should receive more attention.

A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients.

BACKGROUND: COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. METHODS: COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ2-test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. RESULTS: A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). CONCLUSIONS: A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.