To mask or not to mask: Debunking the myths of mask-wearing during COVID-19 across cultures.

Mask-wearing is the simplest yet most effective preventive behavior during COVID-19. However, it has sparked great controversy, particularly in America. Little is known about what psychosocial factors predict people's decision to mask. This research challenges three myths about mask-wearing. First, does mask-wearing provide a false sense of security? Second, is knowledge of COVID-19 a more robust predictor than political ideology of mask-wearing behavior? Third, does resistance to masks reflect anti-authoritarianism or a lack of trust in government? With nationally representative samples across two cultures (N = 1,121), findings reveal a significant positive correlation between mask-wearing and other preventive behaviors. Moreover, knowledge of COVID-19 and trust in government significantly predicted mask-wearing. Implications of the results are also discussed in the cross-cultural context. Critically, findings could provide practical implications for public education and policymaking by uncovering how to more effectively promote compliance with recommended preventive behaviors during our ongoing struggle with COVID-19.

Bifunctional molecules targeting SARS-CoV-2 spike and the polymeric Ig receptor display neutralization activity and mucosal enrichment.

The global health crisis and economic tolls of COVID-19 necessitate a panoply of strategies to treat SARS-CoV-2 infection. To date, few treatment options exist, although neutralizing antibodies against the spike glycoprotein have proven to be effective. Because infection is initiated at the mucosa and propagates mainly at this site throughout the course of the disease, blocking the virus at the mucosal milieu should be effective. However, administration of biologics to the mucosa presents a substantial challenge. Here, we describe bifunctional molecules combining single-domain variable regions that bind to the polymeric Ig receptor (pIgR) and to the SARS-CoV-2 spike protein via addition of the ACE2 extracellular domain (ECD). The hypothesis behind this design is that pIgR will transport the molecule from the circulation to the mucosal surface where the ACE ECD would act as a decoy receptor for the nCoV2. The bifunctional molecules bind SARS-Cov-2 spike glycoprotein in vitro and efficiently transcytose across the lung epithelium in human tissue-based analyses. Designs featuring ACE2 tethered to the C-terminus of the Fc do not induce antibody-dependent cytotoxicity against pIgR-expressing cells. These molecules thus represent a potential therapeutic modality for systemic administration of neutralizing anti-SARS-CoV-2 molecules to the mucosa.

Improving the Energy Efficiency of Buildings Based on Fluid Dynamics Models: A Critical Review

The built environment is the global sector with the greatest energy use and greenhouse gas emissions. As a result, building energy savings can make a major contribution to tackling the current energy and climate change crises. Fluid dynamics models have long supported the understanding and optimization of building energy systems and have been responsible for many important technological breakthroughs. As Covid-19 is continuing to spread around the world, fluid dynamics models are proving to be more essential than ever for exploring airborne transmission of the coronavirus indoors in order to develop energy-efficient and healthy ventilation actions against Covid-19 risks. The purpose of this paper is to review the most important and influential fluid dynamics models that have contributed to improving building energy efficiency. A detailed, yet understandable description of each model’s background, physical setup, and equations is provided. The main ingredients, theoretical interpretations, assumptions, application ranges, and robustness of the models are discussed. Models are reviewed with comprehensive, although not exhaustive, publications in the literature. The review concludes by outlining open questions and future perspectives of simulation models in building energy research.

Neurological manifestations in COVID-19 patients and their application in predicting fatal disease: A retrospective cohort study.

BACKGROUND: To explore the development of central nervous system (CNS) symptoms and clinical application in predicting the clinical outcomes of SARS-COV-2 patients. METHODS: A retrospective cohort study was performed on the hospitalized patients with SARS-COV-2 recruited from four hospitals in Hubei Province, China from 18 January to 10 March 2020. The patients with CNS symptoms were determined. Data regarding clinical symptoms and laboratory tests were collected from medical records. RESULTS: Of 1268 patients studied, 162 (12.8%) had CNS symptoms, manifested as unconsciousness (71, 5.6%), coma (69, 5.4%), dysphoria (50, 3.9%), somnolence (34, 2.7%) and convulsion (3, 0.2%), which were observed at median of 14 (interquartile range 9-18) days after symptom onset and significantly associated with older age (OR = 5.71, 95% confidence interval [CI] 2.78-11.73), male (OR = 1.73, 95% CI 1.22-2.47) and preexisting hypertension (OR = 1.78, 95% CI 1.23-2.57). The presence of CNS symptoms could be predicted by abnormal laboratory tests across various clinical stages, including by lymphocyte counts of <0.93 × 109/L, LDH≥435 U/L and IL-6≥28.83 pg/L at 0-10 days post disease; by lymphocyte count<0.86 × 109/L, IL-2R ≥ 949 U/L, LDH≥382 U/L and WBC≥8.06 × 109/L at 11-20 days post disease. More patients with CNS symptoms developed fatal outcome compared with patients without CNS symptoms (HR = 33.96, 95% CI 20.87-55.16). CONCLUSION: Neurological symptoms of COVID-19 were related to increased odds of developing poor prognosis and even fatal infection.

A systematic meta-analysis of immune signatures in patients with COVID-19.

Currently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been on the rise worldwide. Predicting outcome in COVID-19 remains challenging, and the search for more robust predictors continues. We made a systematic meta-analysis on the current literature from 1 January 2020 to 15 August 2020 that independently evaluated 32 circulatory immunological signatures that were compared between patients with different disease severity was made. Their roles as predictors of disease severity were determined as well. A total of 149 distinct studies that evaluated ten cytokines, four antibodies, four T cells, B cells, NK cells, neutrophils, monocytes, eosinophils and basophils were included. Compared with the non-severe patients of COVID-19, serum levels of Interleukins (IL)-2, IL-2R, IL-4, IL-6, IL-8, IL-10 and tumor necrosis factor α were significantly up-regulated in severe patients, with the largest inter-group differences observed for IL-6 and IL-10. In contrast, IL-5, IL-1ß and Interferon (IFN)-γ did not show significant inter-group difference. Four mediators of T cells count, including CD3+ T, CD4+ T, CD8+ T, CD4+ CD25+ CD127- Treg, together with CD19+ B cells count and CD16+ CD56+ NK cells were all consistently and significantly depressed in severe group than in non-severe group. SARS-CoV-2 specific IgA and IgG antibodies were significantly higher in severe group than in non-severe group, while IgM antibody in the severe patients was slightly lower than those in the non-severe patients, and IgE antibody showed no significant inter-group differences. The combination of cytokines, especially IL-6 and IL-10, and T cell related immune signatures can be used as robust biomarkers to predict disease severity following SARS-CoV-2 infection.

Epidemiological parameters of COVID-19 and its implication for infectivity among patients in China, 1 January to 11 February 2020.

BackgroundThe natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.AimOur objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.MethodsWe estimated the distributions of four epidemiological parameters of SARS-CoV-2 transmission using a large database of COVID-19 cases and potential transmission pairs of cases, and assessed their heterogeneity by demographics, epidemic phase and geographical region. We further calculated the time of peak infectivity and quantified the proportion of secondary infections during the incubation period.ResultsThe median incubation period was 7.2 (95% confidence interval (CI): 6.9‒7.5) days. The median serial and generation intervals were similar, 4.7 (95% CI: 4.2‒5.3) and 4.6 (95% CI: 4.2‒5.1) days, respectively. Paediatric cases < 18 years had a longer incubation period than adult age groups (p = 0.007). The median incubation period increased from 4.4 days before 25 January to 11.5 days after 31 January (p < 0.001), whereas the median serial (generation) interval contracted from 5.9 (4.8) days before 25 January to 3.4 (3.7) days after. The median time from symptom onset to discharge was also shortened from 18.3 before 22 January to 14.1 days after. Peak infectivity occurred 1 day before symptom onset on average, and the incubation period accounted for 70% of transmission.ConclusionThe high infectivity during the incubation period led to short generation and serial intervals, necessitating aggressive control measures such as early case finding and quarantine of close contacts.