A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

The high dimensionality of ion mobility (IM)-resolved metabolomics data presents a great challenge to data processing. Here, authors develop a mass spectrum-oriented bottom-up assembly algorithm and the end-to-end computational framework Met4DX for IM-resolved metabolomics

Association Between Air Pollutants and Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Time Stratified Case-Crossover Design With a Distributed Lag Nonlinear Model

Acute exacerbation of chronic obstruction pulmonary disease (AECOPD) as a respiratory disease, is considered to be related to air pollution by more and more studies. However, the evidence on how air pollution affect the incidence of AECOPD and whether there are population differences is still insufficient. Therefore, we select PM10, PM2.5, SO2, NO2, CO, and O3 as representatives combined with daily AECOPD admission data from 1 January 2015 to 26 June 2016 in the rural areas of Qingyang, northwestern China to explore the associations of air pollution with AECOPD. Based on a time-stratified case-crossover design, we constructed a distributed lag nonlinear model to qualify the single and cumulative lagged effects of air pollution on AECOPD. Stratified related risks by sex and age were also reported. The cumulative exposure-response curves were approximately linear for PM2.5, “V”-shaped for PM10, “U”-shaped for NO2 and inverted-“V” for SO2, CO and O3. Exposure to high-PM2.5 (42 μg/m3), high-PM10 (91 μg/m3), high-SO2 (58 μg/m3), low-NO2 (12 μg/m3), and high-CO (1.55 mg/m3) increased the risk of AECOPD. Females aged 15–64 were more susceptible under extreme concentrations of PM2.5, SO2, CO, and low-PM10 than other subgroups. In addition, adults aged 15–64 were more sensitive to extreme concentrations of NO2 compared with the elderly ≥65 years old, while the latter were more sensitive to high-PM10. High-SO2, high-NO2, and extreme concentrations of PM2.5 had the greatest effects on the day of exposure, while low-SO2 and low-CO had lagged effects on AECOPD. Precautionary measures should be taken with a focus on vulnerable subgroups, to control hospitalization for AECOPD associated with air pollutants

Immediate and long-term changes in infectious diseases in China at the “First-level-response”, “Normalized-control” and “Dynamic-COVID-zero” stages from 2020 to 2022: a multistage interrupted-time-series-analysis

Abstract Background From January 2020 to December 2022, China implemented “First-level-response”, “Normalized-control” and “Dynamic-COVID-zero” to block the COVID-19 epidemic; however, the immediate and long-term impact of three strategies on other infectious diseases and the difference in their impact is currently unknown. We aim to provide a more comprehensive understanding of the impact of non-pharmacological interventions (NPIs) on infectious diseases in China. Methods We collected data on the monthly case count of infectious diseases in China from January 2015 to July 2022. After considering long-term trends using the Cox-Stuart test, we performed the two ratio Z tests to preliminary analyze the impact of three strategies on infectious diseases. Next, we used a multistage interrupted-time-series analysis fitted by the Poisson regression to evaluate and compare the immediate and long-term impact of three strategies on infectious diseases in China. Results Compared to before COVID-19, the incidence of almost all infectious diseases decreased immediately at stages 1, 2, and 3; meanwhile, the slope in the incidence of many infectious diseases also decreased at the three stages. However, the slope in the incidence of all sexually transmitted diseases increased at stage 1, the slope in the incidence of all gastrointestinal infectious diseases increased at stage 2, and the slope in the incidence of some diseases such as pertussis, influenza, and brucellosis increased at stage 3. The immediate and long-term limiting effects of “Normalized-control” on respiratory-transmitted diseases were weaker than “First-level-response” and the long-term limiting effects of “Dynamic-COVID-zero” on pertussis, influenza, and hydatid disease were weaker than “Normalized-control”. Conclusions Three COVID-19 control strategies in China have immediate and long-term limiting effects on many infectious diseases, but there are differences in their limiting effects. Evidence from this study shows that pertussis, influenza, brucellosis, and hydatid disease began to recover at stage 3, and relaxation of NPIs may lead to the resurgence of respiratory-transmitted diseases and vector-borne diseases

Flavivirus prM interacts with MDA5 and MAVS to inhibit RLR antiviral signaling

Abstract Background Vector-borne flaviviruses, including tick-borne encephalitis virus (TBEV), Zika virus (ZIKV), West Nile virus (WNV), yellow fever virus (YFV), dengue virus (DENV), and Japanese encephalitis virus (JEV), pose a growing threat to public health worldwide, and have evolved complex mechanisms to overcome host antiviral innate immunity. However, the underlying mechanisms of flavivirus structural proteins to evade host immune response remain elusive. Results We showed that TBEV structural protein, pre-membrane (prM) protein, could inhibit type I interferon (IFN-I) production. Mechanically, TBEV prM interacted with both MDA5 and MAVS and interfered with the formation of MDA5-MAVS complex, thereby impeding the nuclear translocation and dimerization of IRF3 to inhibit RLR antiviral signaling. ZIKV and WNV prM was also demonstrated to interact with both MDA5 and MAVS, while dengue virus serotype 2 (DENV2) and YFV prM associated only with MDA5 or MAVS to suppress IFN-I production. In contrast, JEV prM could not suppress IFN-I production. Overexpression of TBEV and ZIKV prM significantly promoted the replication of TBEV and Sendai virus. Conclusion Our findings reveal the immune evasion mechanisms of flavivirus prM, which may contribute to understanding flavivirus pathogenicity, therapeutic intervention and vaccine development

Additional file 2 of Immediate and long-term changes in infectious diseases in China at the “First-level-response”, “Normalized-control” and “Dynamic-COVID-zero” stages from 2020 to 2022: a multistage interrupted-time-series-analysis

Supplementary Material