Changes in primary and secondary aerosols during a controlled Chinese New Year.

Large reductions in anthropogenic emissions during the Chinese New Year (CNY) holiday in Beijing have been well reported. However, the changes during the CNY of 2021 are different because most people stayed in Beijing to control the spread of coronavirus disease (COVID-19). Here a high-resolution aerosol mass spectrometer (HR-AMS) was deployed for characterization of the changes in size-resolved aerosol composition and sources during the CNY. We found that the reductions in traffic-related NOx and fossil fuel-related organic aerosol (OA), and cooking OA (1.3-12.7%) during the CNY of 2021 were much smaller than those in previous CNY holidays of 2013, 2015, and 2020. In contrast, the mass concentrations of secondary aerosol species except nitrate showed ubiquitous increases (17.6-30.4%) during the CNY of 2021 mainly due to a 4-day severe haze episode. OA composition also changed substantially during the CNY of 2021. In particular, we observed a large increase by nearly a factor of 2 in oxidized primary OA likely from biomass burning, and a decrease of 50.1% in aqueous-phase secondary OA. A further analysis of the severe haze episode during the CNY illustrated a rapid transition of secondary formation from photochemical to aqueous-phase processing followed by a scavenging process, leading to significant changes in aerosol composition, size distributions, and oxidation degree of OA. A parameterization relationship between oxygen-to-carbon (O/C) and f44 (fraction of m/z 44 in OA) from a collocated capture vaporizer aerosol chemical speciation monitor (CV-ACSM) was developed, which has a significant implication for characterization of OA evolution and the impacts on hygroscopicity due to the rapidly increased deployments of CV-ACSM worldwide.

Safety, Tolerability, Pharmacokinetics, and Immunogenicity of a Monoclonal Antibody (SCTA01) Targeting SARS-CoV-2 in Healthy Adults: a Randomized, Double-Blind, Placebo-Controlled, Phase I Study.

SCTA01 is a novel monoclonal antibody with promising prophylactic and therapeutic potential for COVID-19. This study aimed to evaluate the safety, tolerability, pharmacokinetics (PK) and immunogenicity of SCTA01 in healthy adults. This was a randomized, double-blind, placebo-controlled, dose escalation phase I clinical trial. Healthy adults were randomly assigned to cohort 1 (n = 5; 3:2), cohort 2 (n = 8; 6:2), cohort 3, or cohort 4 (both n = 10; 8:2) to receive SCTA01 (5, 15, 30, and 50 mg/kg, respectively) versus placebo. All participants were followed up for clinical, laboratory, PK, and immunogenicity assessments for 84 days. The primary outcomes were the dose-limiting toxicity (DLT) and maximal tolerable dose (MTD), and the secondary outcomes included PK parameters, immunogenicity, and adverse events (AE). Of the 33 participants, 18 experienced treatment-related AEs; the frequency was 52.0% (13/25) in participants receiving SCTA01 and 62.5% (5/8) in those receiving placebo. All AEs were mild. There was no serious AE or death. No DLT was reported, and the MTD of SCTA01 was not reached. SCTA01 with a dose range of 5 to 50 mg/kg had nearly linear dose-proportional increases in Cmax and AUC parameters. An antidrug antibody response was detected in four (16.0%) participants receiving SCTA01, with low titers, between the baseline and day 28, but all became negative later. In conclusion, SCTA01 up to 50 mg/kg was safe and well-tolerated in healthy participants. Its PK parameters were nearly linear dose-proportional. (This study has been registered at ClinicalTrials.gov under identifier NCT04483375.).

Habitual use of vitamin D supplements and risk of coronavirus disease 2019 (COVID-19) infection: a prospective study in UK Biobank.

BACKGROUND: Previous studies have related vitamin D supplementation to a lower risk of acute respiratory tract infection. Emerging evidence suggests that vitamin D insufficiency is related to a higher risk of coronavirus disease 2019 (COVID-19) infection. OBJECTIVES: We aimed to investigate the prospective association between habitual use of vitamin D supplements and risk of COVID-19 infection, and assess whether such an association differed according to the different levels of circulating and genetically predicted vitamin D. METHODS: This study included 8297 adults who have records of COVID-19 test results from UK Biobank (from 16 March 2020 to 29 June 2020). The use of vitamin D supplements, circulating vitamin D levels, and main covariates were measured at baseline (2006-2010). Genetically predicted vitamin D levels were evaluated by genetic risk score. RESULTS: After adjustment for covariates, the habitual use of vitamin D supplements was significantly associated with a 34% lower risk of COVID-19 infection (OR, 0.66; 95% CI, 0.45-0.97; P = 0.034). Circulating vitamin D levels at baseline or genetically predicted vitamin D levels were not associated with the risk of COVID-19 infection. The association between the use of vitamin D supplements and the risk of COVID-19 infection did not vary according to the different levels of circulating or genetically predicted vitamin D (P-interactions = 0.75 and 0.74, respectively). CONCLUSIONS: Our findings suggest that habitual use of vitamin D supplements is related to a lower risk of COVID-19 infection, although we cannot rule out the possibility that the inverse association is due to residual confounding or selection bias. Further clinical trials are needed to verify these results.

An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long -Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data. 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.