Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO2 and PM2.5 over the Beijing area using Generalized Additive Models (GAM).

Unprecedented travel restrictions due to the COVID-19 pandemic caused remarkable reductions in anthropogenic emissions, however, the Beijing area still experienced extreme haze pollution even under the strict COVID-19 controls. Generalized Additive Models (GAM) were developed with respect to inter-annual variations, seasonal cycles, holiday effects, diurnal profile, and the non-linear influences of meteorological factors to quantitatively differentiate the lockdown effects and meteorology impacts on concentrations of nitrogen dioxide (NO2) and fine particulate matters (PM2.5) at 34 sites in the Beijing area. The results revealed that lockdown measures caused large reductions while meteorology offset a large fraction of the decrease in surface concentrations. GAM estimates showed that in February, the control measures led to average NO2 reductions of 19 µg/m3 and average PM2.5 reductions of 12 µg/m3. At the same time, meteorology was estimated to contribute about 12 µg/m3 increase in NO2, thereby offsetting most of the reductions as well as an increase of 30 µg/m3 in PM2.5, thereby resulting in concentrations higher than the average PM2.5 concentrations during the lockdown. At the beginning of the lockdown period, the boundary layer height was the dominant factor contributing to a 17% increase in NO2 while humid condition was the dominant factor for PM2.5 concentrations leading to an increase of 65% relative to the baseline level. Estimated NO2 emissions declined by 42% at the start of the lockdown, after which the emissions gradually increased with the increase of traffic volumes. The diurnal patterns from the models showed that the peak of vehicular traffic occurred from about 12pm to 5pm daily during the strictest control periods. This study provides insights for quantifying the changes in air quality due to the lockdowns by accounting for meteorological variability and providing a reference in evaluating the effectiveness of control measures, thereby contributing to air quality mitigation policies.

Mental workload of frontline nurses aiding in the COVID-19 pandemic: A latent profile analysis.

AIMS: To investigate the mental workload level of nurses aiding the most affected area during the Coronavirus disease 2019 (COVID-19) pandemic and explore the subtypes of nurses regarding their mental workload. DESIGN: Cross-sectional study. METHODS: A sample of 446 frontline nurses participated from March 8 to 19, 2020. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of the National Aeronautics and Space Administration Task Load Index. The differences among the classes and the variables including sociodemographic characteristics, psychological capital and coping style were explored. RESULTS: The level of mental workload indicates that the nurses had high self-evaluations of their performance while under extremely intensive task loads. The following three latent subtypes were identified: 'low workload & low self-evaluation' (8.6%); 'medium workload & medium self-evaluation' (35.3%) and 'high workload & high self-evaluation' (56.1%) (Classes 1, 2, and 3, respectively). Nurses with shared accommodations, fewer years of practice, junior professional titles, lower incomes, nonmanagement working positions, lower psychological capital levels and negative coping styles had a higher likelihood of belonging to Class 1. In contrast, senior nurses with higher psychological capital and positive coping styles were more likely to belong to Classes 2 and 3. CONCLUSION: The characteristics of the 'low workload & low self-evaluation' subtype suggest that attention should be paid to the work pressure and psychological well-being of junior nurses. Further research on regular training program of public health emergency especially for novices is needed. Personnel management during public health events should be focused on the allocation between novice and senior frontline nurses. IMPACT: This study addresses the level of mental workload of frontline nurses who aid in the most severe area of the COVID-19 pandemic in China and delineates the characteristics of the subtypes of these nurses.

Traditional Chinese medicine network pharmacology study on exploring the mechanism of Xuebijing Injection in the treatment of coronavirus disease 2019.

As a representative drug for the treatment of severe community-acquired pneumonia and sepsis, Xuebijing (XBJ) injection is also one of the recommended drugs for the prevention and treatment of coronavirus disease 2019 (COVID-19), but its treatment mechanism for COVID-19 is still unclear. Therefore, this study aims to explore the potential mechanism of XBJ injection in the treatment of COVID-19 employing network pharmacology and molecular docking methods. The corresponding target genes of 45 main active ingredients in XBJ injection and COVID-19 were obtained by using multiple database retrieval and literature mining. 102 overlapping targets of them were screened as the core targets for analysis. Then built the PPI network, TCM-compound-target-disease, and disease-target-pathway networks with the help of Cytoscape 3.6.1 software. After that, utilized DAVID to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to predict the action mechanism of overlapping targets. Finally, by applying molecular docking technology, all compounds were docked with COVID-19 3 CL protease(3CLpro), spike protein (S protein), and angiotensin-converting enzyme II (ACE2). The results indicated that quercetin, luteolin, apigenin and other compounds in XBJ injection could affect TNF, MAPK1, IL6 and other overlapping targets. Meanwhile, anhydrosafflor yellow B (AHSYB), salvianolic acid B (SAB), and rutin could combine with COVID-19 crucial proteins, and then played the role of anti-inflammatory, antiviral and immune response to treat COVID-19. This study revealed the multiple active components, multiple targets, and multiple pathways of XBJ injection in the treatment of COVID-19, which provided a new perspective for the study of the mechanism of traditional Chinese medicine (TCM) in the treatment of COVID-19.