Long-term NO2 exposure and mortality: A comprehensive meta-analysis.

In response to the World Health Organization's (WHO) revised annual mean nitrogen dioxide (NO2) standard from 40 µg/m3 to 10 µg/m3, reflecting the growing evidence linking long-term exposure to ambient NO2 and excess mortality, we conducted a comprehensive meta-analysis incorporating 11 new studies published since the WHO analysis. Our investigation involved a systematic search of three major databases (PubMed, Web of Science, and Scopus) for articles published until July 1, 2022. We employed random effects models to calculate summarized risk ratios (RR) along with 95% confidence intervals (CIs) for overall and subgroup analyses. Sensitivity analyses were conducted to assess result robustness, and publication bias was evaluated using funnel plots and Egger's linear regression. Out of 2799 identified articles, 56 were included in our meta-analysis. The findings indicate a heightened risk of all-cause, cardiovascular, and respiratory mortality associated with long-term exposure to ambient NO2, with pooled RR values of 1.03 (95% CI: 1.02, 1.05), 1.07 (95% CI: 1.04, 1.10), and 1.03 (95% CI: 1.02, 1.05) per 10 µg/m3 increase, respectively. Substantial heterogeneity (I2 = 84%-96%) among studies was observed. Subgroup analysis revealed significantly elevated RR values in Asia and Oceania (p-value <0.05). The aggregated values for all-cause and cardiovascular mortality were slightly larger than those reported in previous studies. Our study emphasizes the imperative to develop more patient cohorts and conduct age-refined analyses to explore the impact of existing chronic diseases on these associations. Further, additional cohorts in Asia and Oceania are essential to fortify evidence in these regions. Lastly, we recommend using fused multi-source data with higher spatiotemporal resolution for individual exposure representation to minimize heterogeneity among studies in future research.

Comprehensive evolution and molecular characteristics of a large number of SARS-CoV-2 genomes reveal its epidemic trends.

OBJECTIVES: To further reveal the phylogenetic evolution and molecular characteristics of the whole genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on a large number of genomes and provide a basis for the prevention and treatment of SARS-CoV-2. METHODS: Various evolution analysis methods were employed. RESULTS: The estimated ratio of the rates of non-synonymous to synonymous changes (Ka/Ks) of SARS-CoV-2 was 1.008 or 1.094 based on 622 or 3624 SARS-CoV-2 genomes and nine key specific sites of high linkage, and four major haplotypes were found: H1, H2, H3 and H4. The results of Ka/Ks, detected population size and development trends of each major haplotype showed that H3 and H4 subgroups were going through a purify evolution and almost disappeared after detection, indicating that they might have existed for a long time. The H1 and H2 subgroups were going through a near neutral or neutral evolution and globally increased with time, and the frequency of H1 was generally high in Europe and correlated with the death rate (r >0.37), suggesting that these two haplotypes might relate to the infectivity or pathogenicity of SARS-CoV-2. CONCLUSIONS: Several key specific sites and haplotypes related to the infectivity or pathogenicity of SARS-CoV-2, and the possible earlier origin time and place of SARS-CoV-2 were indicated based on the evolution and epidemiology of 16,373 SARS-CoV-2 genomes.

Systematic comparison of somatic variant calling performance among different sequencing depth and mutation frequency.

In the past decade, treatments for tumors have made remarkable progress, such as the successful clinical application of targeted therapies. Nowadays, targeted therapies are based primarily on the detection of mutations, and next-generation sequencing (NGS) plays an important role in relevant clinical research. The mutation frequency is a major problem in tumor mutation detection and increasing sequencing depth is a widely used method to improve mutation calling performance. Therefore, it is necessary to evaluate the effect of different sequencing depth and mutation frequency as well as mutation calling tools. In this study, Strelka2 and Mutect2 tools were used in detecting the performance of 30 combinations of sequencing depth and mutation frequency. Results showed that the precision rate kept greater than 95% in most of the samples. Generally, for higher mutation frequency (≥20%), sequencing depth ≥200X is sufficient for calling 95% mutations; for lower mutation frequency (≤10%), we recommend improving experimental method rather than increasing sequencing depth. Besides, according to our results, although Strelka2 and Mutect2 performed similarly, the former performed slightly better than the latter one at higher mutation frequency (≥20%), while Mutect2 performed better when the mutation frequency was lower than 10%. Besides, Strelka2 was 17 to 22 times faster than Mutect2 on average. Our research will provide a useful and comprehensive guideline for clinical genomic researches on somatic mutation identification through systematic performance comparison among different sequencing depths and mutation frequency.