An update on inhibitors targeting RNA-dependent RNA polymerase for COVID-19 treatment: Promises and challenges.

The highly transmissible variants of SARS-CoV-2, the causative pathogen of the COVID-19 pandemic, bring new waves of infection worldwide. Identification of effective therapeutic drugs to combat the COVID-19 pandemic is an urgent global need. RNA-dependent RNA polymerase (RdRp), an essential enzyme for viral RNA replication, is the most promising target for antiviral drug research since it has no counterpart in human cells and shows the highest conservation across coronaviruses. This review summarizes recent progress in studies of RdRp inhibitors, focusing on interactions between these inhibitors and the enzyme complex, based on structural analysis, and their effectiveness. In addition, we propose new possible strategies to address the shortcomings of current inhibitors, which may guide the development of novel efficient inhibitors to combat COVID-19.

Correlation coefficient-directed label-free characterization of native proteins by surface-enhanced Raman spectroscopy.

Investigation of proteins in their native state is the core of proteomics towards better understanding of their structures and functions. Surface-enhanced Raman spectroscopy (SERS) has shown its unique advantages in protein characterization with fingerprint information and high sensitivity, which makes it a promising tool for proteomics. It is still challenging to obtain SERS spectra of proteins in the native state and evaluate the native degree. Here, we constructed 3D physiological hotspots for a label-free dynamic SERS characterization of a native protein with iodide-modified 140 nm Au nanoparticles. We further introduced the correlation coefficient to quantitatively evaluate the variation of the native degree, whose quantitative nature allows us to explicitly investigate the Hofmeister effect on the protein structure. We realized the classification of a protein of SARS-CoV-2 variants in 15 min, which has not been achieved before. This study offers an effective tool for tracking the dynamic structure of proteins and biomedical research.

Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling.

Hourly PM2.5 speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM2.5 components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM2.5 water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwhole). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwhole solution. After removing event data, PMF modeling was conducted for individual months (PMFmonth) and the 4-year period (PMF4-year), respectively. PMFmonth solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF4-year solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Government dissemination of epidemic information as a policy instrument during COVID-19 pandemic: Evidence from Chinese cities.

COVID-19 has given rise to a surge in the number of policy instruments used to deal with the pandemic at different levels of governments globally. While much attention has been placed on travel bans, lockdown, social distancing, and economic stimulus packages, government dissemination of epidemic information as a policy instrument has received less attention. Based on 14,637 news items collected from the portals of 79 municipal governments in China, this study aims to 1) conduct a content analysis of news items and construct three key attributes of governments' practices of epidemic information dissemination, namely, comprehensiveness, responsiveness, and the protection of privacy, and 2) study the patterns and determinants of the dissemination of epidemic information. Our results show that these cities vary substantially in how they disseminate statistical data and information on individual cases of COVID-19 infections within their localities, which are shaped by government performance in open data, severity of the pandemic, cities' administrative level, population, and health sector capacities. The findings generate theoretical and policy implications for government dissemination of epidemic information.