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Journal articleZhou J, Otter JA, Price JR, et al., 2020, , Clinical Infectious Diseases, Vol: 2020, Pages: 1-1, ISSN: 1058-4838
BACKGROUND: Evaluation of SARS-CoV-2 surface and air contamination during the COVID-19 pandemic in London. METHODS: We performed this prospective cross-sectional observational study in a multi-site London hospital. Air and surface samples were collected from seven clinical areas, occupied by patients with COVID-19, and a public area of the hospital. Three or four 1.0 m3 air samples were collected in each area using an active air sampler. Surface samples were collected by swabbing items in the immediate vicinity of each air sample. SARS-CoV-2 was detected by RT-qPCR and viral culture; the limit of detection for culturing SARS-CoV-2 from surfaces was determined. RESULTS: Viral RNA was detected on 114/218 (52.3%) of surfaces and 14/31 (38.7%) air samples but no virus was cultured. The proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area. Viral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients than in other areas (67/105 (63.8%) vs. 29/64 (45.3%) (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Chi squared test)). The high PCR Ct value for all samples (>30) indicated that the virus would not be culturable. CONCLUSIONS: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from environmental contamination in managing COVID-19, and the need for effective use of PPE, physical distancing, and hand/surface hygiene.
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Journal articleYang B, Lessler J, Zhu H, et al., 2020, , PLOS PATHOGENS, Vol: 16, ISSN: 1553-7366
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- Citations: 14
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Journal articleBruni T, Lalvani A, Richeldi L, 2020, , American Journal of Respiratory and Critical Care Medicine, Vol: 202, Pages: 508-510, ISSN: 1073-449X
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Journal articlePeng Y, Mentzer AJ, Liu G, et al., 2020, , bioRxiv
COVID-19 is an ongoing global crisis in which the development of effective vaccines and therapeutics will depend critically on understanding the natural immunity to the virus, including the role of SARS-CoV-2-specific T cells. We have conducted a study of 42 patients following recovery from COVID-19, including 28 mild and 14 severe cases, comparing their T cell responses to those of 16 control donors. We assessed the immune memory of T cell responses using IFNγ based assays with overlapping peptides spanning SARS-CoV-2 apart from ORF1. We found the breadth, magnitude and frequency of memory T cell responses from COVID-19 were significantly higher in severe compared to mild COVID-19 cases, and this effect was most marked in response to spike, membrane, and ORF3a proteins. Total and spike-specific T cell responses correlated with the anti-Spike, anti-Receptor Binding Domain (RBD) as well as anti-Nucleoprotein (NP) endpoint antibody titre (p<0.001, <0.001 and =0.002). We identified 39 separate peptides containing CD4 + and/or CD8 + epitopes, which strikingly included six immunodominant epitope clusters targeted by T cells in many donors, including 3 clusters in spike (recognised by 29%, 24%, 18% donors), two in the membrane protein (M, 32%, 47%) and one in the nucleoprotein (Np, 35%). CD8+ responses were further defined for their HLA restriction, including B*4001-restricted T cells showing central memory and effector memory phenotype. In mild cases, higher frequencies of multi-cytokine producing M- and NP-specific CD8 + T cells than spike-specific CD8 + T cells were observed. They furthermore showed a higher ratio of SARS-CoV-2-specific CD8 + to CD4 + T cell responses. Immunodominant epitope clusters and peptides containing T cell epitopes identified in this study will provide critical tools to study the role of virus-specific T cells in control and resolution of SARS-CoV-2 infections. The identification of T cell specificity and functionality associated wi
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Journal articlePalmieri C, Palmer D, Openshaw PJM, et al., 2020, , ESMO Open, Vol: 5, Pages: 1-3, ISSN: 2059-7029
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Journal articleDocherty AB, Harrison EM, Green CA, et al., 2020, , BMJ, Vol: 369, Pages: 1-12, ISSN: 1759-2151
Objective To characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital.Design Prospective observational cohort study with rapid data gathering and near real time analysis.Setting 208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission.Participants 20 133 hospital inpatients with covid-19.Main outcome measures Admission to critical care (high dependency unit or intensive care unit) and mortality in hospital.Results The median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive
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Journal articlede Lusignan S, Carlyon T, Lalvani A, 2020, , BMJ-BRITISH MEDICAL JOURNAL, Vol: 369, Pages: 1-1, ISSN: 1756-1833
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Journal articleGoya S, Galiano M, Nauwelaers I, et al., 2020, , Influenza and Other Respiratory Viruses, Vol: 14, Pages: 274-285, ISSN: 1750-2640
BackgroundHuman respiratory syncytial virus (RSV) is classified into antigenic subgroups A and B. Thirteen genotypes have been defined for RSV鈥怉 and 20 for RSV鈥怋, without any consensus on genotype definition.MethodsWe evaluated clustering of RSV sequences published in GenBank until February 2018 to define genotypes by using maximum likelihood and Bayesian phylogenetic analyses and average p鈥恉istances.ResultsWe compared the patterns of sequence clustering of complete genomes; the three surface glycoproteins genes (SH, G, and F, single and concatenated); the ectodomain and the 2nd hypervariable region of G gene. Although complete genome analysis achieved the best resolution, the F, G, and G鈥恊ctodomain phylogenies showed similar topologies with statistical support comparable to complete genome. Based on the widespread geographic representation and large number of available G鈥恊ctodomain sequences, this region was chosen as the minimum region suitable for RSV genotyping. A genotype was defined as a monophyletic cluster of sequences with high statistical support (≥80% bootstrap and ≥0.8 posterior probability), with an intragenotype p鈥恉istance ≤0.03 for both subgroups and an intergenotype p鈥恉istance ≥0.09 for RSV鈥怉 and ≥0.05 for RSV鈥怋. In this work, the number of genotypes was reduced from 13 to three for RSV鈥怉 (GA1鈥怗A3) and from 20 to seven for RSV鈥怋 (GB1鈥怗B7). Within these, two additional levels of classification were defined: subgenotypes and lineages. Signature amino acid substitutions to complement this classification were also identified.ConclusionsWe propose an objective protocol for RSV genotyping suitable for adoption as an international standard to support the global expansion of RSV molecular surveillance.
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Journal articleHay JA, Minter A, Ainslie KEC, et al., 2020, , PLOS COMPUTATIONAL BIOLOGY, Vol: 16, ISSN: 1553-734X
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- Citations: 17
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Journal articleAinslie KEC, Walters CE, Fu H, et al., 2020, , Wellcome Open Res, Vol: 5, ISSN: 2398-502X
Background: The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. By 24 March 2020, over 440,000 cases and almost 20,000 deaths had been reported worldwide. In response to the fast-growing epidemic, which began in the Chinese city of Wuhan, Hubei, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. These interventions have impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic was not clear. Methods: Using daily reported cases from mainland China and Hong Kong SAR, we estimated transmissibility over time and compared it to daily within-city movement, as a proxy for economic activity. Results: Initially, within-city movement and transmission were very strongly correlated in the five mainland provinces most affected by the epidemic and Beijing. However, that correlation decreased rapidly after the initial sharp fall in transmissibility. In general, towards the end of the study period, the correlation was no longer apparent, despite substantial increases in within-city movement. A similar analysis for Hong Kong shows that intermediate levels of local activity were maintained while avoiding a large outbreak. At the very end of the study period, when China began to experience the re-introduction of a small number of cases from Europe and the United States, there is an apparent up-tick in transmission. Conclusions: Although these results do not preclude future substantial increases in incidence, they suggest that after very intense social distancing (which resulted in containment), China successfully exited its lockdown to some degree. Elsewhere, movement data are being used as proxies for economic activity to assess the impact of interventions. The results presented here illustrate how the eventual decorrelation between transmission and movement is likely a key feature of successful COVID-19 exit strategies.
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