Long-term improvement of air quality associated with lung function benefits in Chinese young adults: A quasi-experiment cohort study.

INTRODUCTION: Long-term exposure to air pollution is associated with lung function impairment. However, whether long-term improvements in air quality could improve lung function is unclear. OBJECTIVES: To examine whether the reduction of long-term air pollution was associated with lung function improvement among Chinese young adults. METHODS: We conducted a prospective quasi-experiment cohort study with 1731 college students in Shandong, China from September 2019 to September 2020, covering COVID-19 lockdown period. Data on air pollution concentrations were obtained from China Environmental Monitoring Station. Lung function indicators included forced vital capacity (FVC), forced expiratory volume in 1st second (FEV1) and forced expiratory flow at 50 % of FVC (FEF50%). We used linear mixed-effects model to examine the associations between the change of air pollutants concentrations and the change of lung function, and additional adjustments for indoor air pollution (IAP) source. We also conducted stratified analysis by sex. RESULTS: Compared with 2019, the mean FVC, FEV1 and FEF50% were elevated by 414.4 ml, 321.5 ml, and 28.4 ml/s respectively in 2020. Every 5 µg/m3 decrease in annual average PM2.5 concentrations was associated with 36.0 ml [95 % confidence interval (CI):6.0, 66.0 ml], 46.1 ml (95 % CI:16.7, 75.5 ml), and 124.2 ml/s (95 % CI:69.5, 178.9 ml/s) increment in the FVC, FEV1, and FEF50%, respectively. Similar associations were found for PM10. The estimated impact was almost unchanged after adjusting for IAP source. There was no significant effect difference between males and females. CONCLUSION: Long-term improvement of air quality can improve lung function among young adults. Stricter policies on improving air quality are needed to protect human health.

Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks.

In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying the undetected infections in local transmission will be of great help in COVID-19 control. In this work, we propose an RNA virus transmission network representation model based on graph attention networks (RVTR); this model is constructed using the principle of natural language processing to learn the information of gene sequence and using a graph attention network to catch the topological character of COVID-19 transmission networks. Since SARS-CoV-2 will mutate when it spreads, our approach makes use of graph context loss function, which can reflect that the genetic sequence of infections with close spreading relation will be more similar than those with a long distance, to train our model. Our approach shows its ability to find asymptomatic spreaders both on simulated and real COVID-19 datasets and performs better when compared with other network representation and feature extraction methods.

Potential mechanism study of herbal pair schizonepetae herba and saposhnikoviae radix against coronavirus pneumonia via network pharmacology and molecular docking

This study aims to predict potential targets and molecular mechanisms of herbal pair Schizonepetae Herba(SH) and Saposhnikoviae Radix(SR) against coronavirus pneumonia based on network pharmacology and molecular docking.At first, the active compounds and potential targets of SH and SR were collected from TCMSP,ETCM,BATMAN-TCM, and the related targets of coronavirus pneumonia were collected from GeneCards,OMIM, NCBI Gene. And then, PPI of common targets was analyzed by STRING,GO and KEGG enrichment analysis was performed by DAVID.At last, Autodock was used for molecular docking of potential pharmacodynamic compounds and key targets.A total of 28 active compounds and 56 key targets were collected from SH and SR.GO enrichment analysis collected 176 biological processes,47 molecular functions and 36 cell compounds (P < 0.05). The results of molecular docking showed that the binding energy of potential pharmacological compounds with key targets, angiotensin converting enzyme II (ACE2) and COVID-19 main protease was lower than -5 kcal/mol. This study demonstrated the potential pharmacodynamic compounds and targets of SH and SR on treatment of coronavirus pneumonia, which laid a foundation for the development and follow-up research of SH and SR.