Enzymatic characterization and dominant sites of foot-and-mouth disease virus 2C protein.

Foot-and-mouth disease virus (FMDV) 2C protein is a conserved non-structural protein and crucial for replication of the virus. In this study, FMDV 2C protein was prepared and the enzymatic activities were investigated in detail. The protein could digest ssDNA or ssRNA into a small fragment at about 10 nt, indicating that the protein has nuclease activity. But it did not show digestion to blunt-end dsDNA or dsRNA. The nuclease activity of 2C protein could be inhibited in 2 mM Zn2+ or Ca2+ while enhanced by Mg2+ or Mn2+. FMDV 2C protein exhibited unwinding activity to all the three kinds of dsDNA and dsRNA (5' protruded, 3' protruded, and blunt-end). The unwinding velocity to 5' protruded dsRNA was higher than to the blunt-end dsRNA. 2C protein only showed unwinding activity in high concentration of Mg2+, but no unwinding activity in physiological concentrations of Mg2+ and Ca2+, as well as in cell lysate. The 2C protein could catalyze two structured ssRNA to form double strand, thus it was proved to have RNA chaperone activity. The Mg2+ and ATP in different concentrations did not show promotion to the RNA chaperone activity. Finally, six mutant proteins (K116A, D160A, D170A, N207A, R226A, and F316A) were constructed and the enzymatic activities were analyzed. All the six mutations reduced the ATPase activity, D170A and F361A could inactivate the nuclease activity, while the N207A and F316A could inactivate the helicase activity. Our study provides a comprehensive understanding of the enzymatic activities of FMDV 2C protein.

Heavy metals and metalloids exposure and liver function in Chinese adults - A nationally representative cross-sectional study.

BACKGROUND: In China, the effects of heavy metals and metalloids (HMMs) on liver health are not consistently documented, despite their prevalent environmental presence. OBJECTIVE: Our research assessed the association between HMMs and liver function biomarkers in a comprehensive sample of Chinese adults. METHODS: We analyzed data from 9445 participants in the China National Human Biomonitoring survey. Blood and urine were evaluated for HMM concentrations, and liver health was gauged using serum albumin (ALB), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) metrics. Various statistical methods were employed to understand the relationship between 11 HMMs and liver function, adjusting for multiple factors. We also explored interactions with alcohol intake, gender, and age. RESULTS: Among HMMs, selenium in blood [weighted geometric mean (GM) = 95.56 µg/L] and molybdenum in urine (GM = 46.44 µg/L) showed the highest concentrations, while lead in blood (GM = 21.92 µg/L) and arsenic in urine (GM = 19.80 µg/L) had the highest levels among risk HMMs. Manganese and thallium consistently indicated potential risk factor to liver in both sample types, while selenium displayed potential liver protection. Blood HMM mixtures were negatively associated with ALB (ß = -0.614, 95% CI: -0.809, -0.418) and positively with AST (ß = 0.701, 95% CI: 0.290, 1.111). No significant associations were found in urine HMM mixtures. Manganese, tin, nickel, and selenium were notable in blood mixture associations, with selenium and cobalt being significant in urine. The relationship of certain HMMs varied based on alcohol consumption. CONCLUSION: This research highlights the complex relationship between HMM exposure and liver health in Chinese adults, particularly emphasizing metals like manganese, thallium, and selenium. The results suggest a need for public health attention to low dose HMM exposure and underscore the potential benefits of selenium for liver health. Further studies are essential to establish causality.

Modeling Cross-Regional Transmission and Assessing the Effectiveness of Restricting Inter-Regional Population Movements in Controlling COVID-19 - Xi'an City, Shaanxi Province, China, 2021.

Introduction: The aim of this study was to construct an assessment method for cross-regional transmission of coronavirus disease 2019 (COVID-19) and to provide recommendations for optimizing measures such as interregional population movements. Methods: Taking Xi'an City as the example subject of this study's analysis, a Cross-Regional-Gravitational-Dynamic model was constructed to simulate the epidemic in each district of Xi'an under three scenarios of controlled population movement (Scenario 1: no intensive intervention; Scenario 2: blocking Yanta District on December 18 and blocking the whole region on December 23; and Scenario 3: blocking the whole region on December 23). This study then evaluated the effects of such simulated population control measures. Results: The cumulative number of cases for the three scenarios was 8,901,425, 178, and 474, respectively, and the duration of the epidemic was 175, 18, and 22 days, respectively. The real world prevention and control measures in Xi'an reduced the cumulative number of cases for its outbreak by 99.98% in comparison to the simulated response in Scenario 1; in contrast, the simulated prevention and control strategies set in Scenarios 2 (91.26%) and 3 (76.73%) reduced cases even further than the real world measures used in Xi'an. Discussion: The constructed model can effectively simulate an outbreak across regions. Timely implementation of two-way containment and control measures in areas where spillover is likely to occur is key to stopping cross-regional transmission.

Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model - China, December 2019-April 2020.

Introduction: To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic. Methods: The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2, 2019 to April 8, 2020. The goodness of fit of the two models was compared using the coefficient of determination (R2). Results: The compartment model predicts that the number of new cases without a cordon sanitaire, i.e., a restriction of mobility to prevent spread of disease, will increase exponentially over 10 days starting from January 23, 2020, while the ARIMA model shows a linear increase. The calculated R2 values of the two models without cordon sanitaire were 0.990 and 0.981. The prediction results of the ARIMA model after February 2, 2020 have a large deviation. The R2 values of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933, respectively. Discussion: The two models fit well at different stages of the epidemic. The predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19. The accuracy of recent historical data had a large impact on the predictions of the ARIMA model as compared to those of the compartment model.