Comparative Mask Protection against Inhaling Wildfire Smoke, Allergenic Bioaerosols, and Infectious Particles.

This work compares relative mask inhalation protection against a range of airborne particle sizes that the general public may encounter, including infectious particles, wildfire smoke and ash, and allergenic fungal and plant particles. Several mask types available to the public were modeled with respirable fraction deposition. Best-case collection efficiencies for cloth, surgical, and respirator masks were predicted to be lowest (0.3, 0.6, and 0.8, respectively) for particle types with dominant sub-micrometer modes (wildfire smoke and human-emitted bronchial particles). Conversely, all mask types were predicted to achieve good collection efficiency (up to ~1.0) for the largest-sized particle types, including pollen grains, some fungal spores, and wildfire ash. Polydisperse infectious particles were predicted to be captured by masks with efficiencies of 0.3-1.0 depending on the pathogen size distribution and the type of mask used. Viruses aerosolized orally are predicted to be captured efficiently by all mask types, while those aerosolized from bronchiolar or laryngeal-tracheal sites are captured with much lower efficiency by surgical and cloth masks. The predicted efficiencies changed very little when extrathoracic deposition was included (inhalable rather than respirable fraction) or when very large (100 µm) particles were neglected. Actual mask fit and usage will determine protection levels in practice, but the relative comparisons in this work can inform mask guidance for different inhalation hazards, including particles generated by yard work, wildfires, and infections.

Delineating COVID-19 immunological features using single-cell RNA sequencing.

Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response toward COVID-19 in multiple tissues and organs. Here, we review the applications and findings from over 80 original COVID-19 single-cell RNA sequencing studies as well as many secondary analysis studies. We describe that single-cell RNA sequencing reveals multiple features of COVID-19 patients with different severity, including cell populations with proportional alteration, COVID-19-induced genes and pathways, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in single cells, and adaptation of immune repertoire. We also collect published single-cell RNA sequencing datasets from original studies. Finally, we discuss the limitations in current studies and perspectives for future advance.

Modeling the impacts of physical distancing and other exposure determinants on aerosol transmission.

Minimization of airborne virus transmission has become increasingly important due to pandemic and endemic infectious respiratory diseases. Physical distancing is a frequently advocated control measure, but the proximity-based transmission it is intended to control is challenging to incorporate into generalized, ventilation-based models. We utilize a size-dependent aerosol release model with turbulent dispersion to assess the impact of direct, near-field transport in conjunction with changes in ventilation, exposure duration, exhalation/inhalation rates, and masks. We demonstrate this model on indoor and outdoor scenarios to estimate the relative impacts on infection risk. The model can be expressed as a product of six multiplicative factors that may be used to identify opportunities for risk reduction. The additive nature of the short-range (proximity) and long-range (background) transmission components of the aerosol transport factor implies that they must be minimized simultaneously. Indoor simulations showed that close physical distances attenuated the impact of most other risk reduction factors. Increasing ventilation resulted in a 17-fold risk decrease at further physical distances but only a 6-fold decrease at shorter distances. Distance, emission rate, and duration also had large impacts on risk (11-65-fold), while air direction and inhalation rate had lower risk impacts (3-4-fold range). Surgical mask and respirator models predicted higher maximum risk impacts (33- and 280-fold, respectively) than cloth masks (4-fold). Most simulations showed decreasing risk at distances > 1-2 m (3-6 ft). The risk benefit of maintaining 2-m distance vs. 1 m depended substantially on the environmental turbulence and ventilation rate. Outdoors, long-range transmission was negligible and short-range transmission was the primary determinant of risk. Temporary passing events increased risk by up to 50 times at very slow walking speeds and close passing distances, but the relative risks outdoors were still much lower than indoors. The current model assumes turbulent dispersion typical of a given room size and ventilation rate. However, calm environments or confined airflows may increase transmission risks beyond levels predicted with this turbulent model.

Association of dyslipidemia with the severity and mortality of coronavirus disease 2019 (COVID-19): a meta-analysis.

BACKGROUND: The numbers of confirmed cases of coronavirus disease 2019 (COVID-19) and COVID-19 related deaths are still increasing, so it is very important to determine the risk factors of COVID-19. Dyslipidemia is a common complication in patients with COVID-19, but the association of dyslipidemia with the severity and mortality of COVID-19 is still unclear. The aim of this study is to analyze the potential association of dyslipidemia with the severity and mortality of COVID-19. METHODS: We searched the PubMed, Embase, MEDLINE, and Cochrane Library databases for all relevant studies up to August 24, 2020. All the articles published were retrieved without language restriction. All analysis was performed using Stata 13.1 software and Mantel-Haenszel formula with fixed effects models was used to compare the differences between studies. The Newcastle Ottawa scale was used to assess the quality of the included studies. RESULTS: Twenty-eight studies involving 12,995 COVID-19 patients were included in the meta-analysis, which was consisted of 26 cohort studies and 2 case-control studies. Dyslipidemia was associated with the severity of COVID-19 (odds ratio [OR] = 1.27, 95% confidence interval [CI] 1.11-1.44, P = 0.038, I2 = 39.8%). Further, patients with dyslipidemia had a 2.13-fold increased risk of death compared to patients without dyslipidemia (95% CI 1.84-2.47, P = 0.001, I2 = 66.4%). CONCLUSIONS: The results proved that dyslipidemia is associated with increased severity and mortality of COVID-19. Therefore, we should monitor blood lipids and administer active treatments in COVID-19 patients with dyslipidemia to reduce the severity and mortality.

Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation.

The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~ 80% asymptomatic or mild cases and ~ 5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis, and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single-cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on lung, and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung-resident memory CD8+ T (TRM) cells, we found a 2.24-fold decrease of cell proportion among CD8+ T cells and lower expression of CXCR6 in the severe patients than moderate patients. Pro-inflammatory transcriptional programs were highlighted in the TRM cellular trajectory from moderate to severe patients. CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis.