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Genome sequencing is pivotal to SARS-CoV-2 surveillance, elucidating the emergence and global dissemination of acquired genetic mutations. Amplicon sequencing has proven very effective for sequencing SARS-CoV-2, but prevalent mutations disrupting primer binding sites have necessitated the revision of sequencing protocols in order to maintain performance for emerging virus lineages. We compared the performance of Oxford Nanopore Technologies (ONT) Midnight and ARTIC tiling amplicon protocols using 196 Delta lineage SARS-CoV-2 clinical specimens, and 71 mostly Omicron lineage samples with S gene target failure (SGTF), reflecting circulating lineages in the United Kingdom during December 2021. 96-plexed nanopore sequencing was used. For Delta lineage samples, ARTIC v4 recovered the greatest proportion of [≥]90% complete genomes (81.1%; 159/193), followed by Midnight (71.5%; 138/193) and ARTIC v3 (34.1%; 14/41). Midnight protocol however yielded higher average genome recovery (mean 98.8%) than ARTIC v4 (98.1%) and ARTIC v3 (75.4%), resulting in less ambiguous final consensus assemblies overall. Explaining these observations were ARTIC v4s superior genome recovery in low viral titre/high cycle threshold (Ct) samples and inferior performance in high titre/low Ct samples, where Midnight excelled. We evaluated Omicron sequencing performance using a revised Midnight primer mix alongside prototype ARTIC v4.1 primers, head-to-head with the existing commercially available Midnight and ARTIC v4 protocols. The revised protocols both improved considerably the recovery of Omicron genomes and exhibited similar overall performance to one another. Revised Midnight protocol recovered [≥]90% complete genomes for 85.9% (61/71) of Omicron samples vs. 88.7% (63/71) for ARTIC v4.1. Approximate cost per sample for Midnight ({pound}12) is lower than ARTIC ({pound}16) while hands-on time is considerably lower for Midnight ([~]7 hours) than ARTIC protocols ([~]9.5 hours).
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ObjectiveTo describe the impact of the SARS-CoV-2 pandemic on the incidence of paediatric viral respiratory tract infection in Oxfordshire, UK. MethodsData on paediatric Emergency Department (ED) attendances (0-15 years inclusive), respiratory virus testing, vital signs and mortality at Oxford University Hospitals were summarised using descriptive statistics. ResultsBetween 1-March-2016 and 30-July-2021, 155,056 ED attendances occurred and 7,195 respiratory virus PCRs were performed. Detection of all pathogens was suppressed during the first national lockdown. Rhinovirus and adenovirus rates increased when schools reopened September-December 2020, then fell, before rising in March-May 2021. The usual winter RSV peak did not occur in 2020/21, with an inter-seasonal rise (32/1,000 attendances in 0-3yr olds) in July 2021. Influenza remained suppressed throughout. A higher Paediatric Early Warning Score (PEWS) was seen for attendees with adenovirus during the pandemic compared to pre-pandemic (p=0.04, Mann-Witney U test), no other differences in PEWS were seen. ConclusionsSARS-CoV-2 caused major changes in the incidence of paediatric respiratory viral infection in Oxfordshire, with implications for clinical service demand, testing strategies, timing of palivizumab RSV prophylaxis, and highlighting the need to understand which public health interventions are most effective for preventing respiratory virus infections.
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The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. Additionally, we were able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods, and has the potential for significant impact.
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BackgroundLaboratory diagnosis of SARS-CoV-2 infection (the cause of COVID-19) uses PCR to detect viral RNA (vRNA) in respiratory samples. SARS-CoV-2 RNA has also been detected in other sample types, but there is limited understanding of the clinical or laboratory significance of its detection in blood. MethodsWe undertook a systematic literature review to assimilate the evidence for the frequency of vRNA in blood, and to identify associated clinical characteristics. We performed RT-PCR in serum samples from a UK clinical cohort of acute and convalescent COVID-19 cases (n=212), together with convalescent plasma samples collected by NHS Blood and Transplant (NHSBT) (n=111 additional samples). To determine whether PCR-positive blood samples could pose an infection risk, we attempted virus isolation from a subset of RNA-positive samples. ResultsWe identified 28 relevant studies, reporting SARS-CoV-2 RNA in 0-76% of blood samples; pooled estimate 10% (95%CI 5-18%). Among serum samples from our clinical cohort, 27/212 (12.7%) had SARS-CoV-2 RNA detected by RT-PCR. RNA detection occurred in samples up to day 20 post symptom onset, and was associated with more severe disease (multivariable odds ratio 7.5). Across all samples collected [≥]28 days post symptom onset, 0/143 (0%, 95%CI 0.0-2.5%) had vRNA detected. Among our PCR-positive samples, cycle threshold (ct) values were high (range 33.5-44.8), suggesting low vRNA copy numbers. PCR-positive sera inoculated into cell culture did not produce any cytopathic effect or yield an increase in detectable SARS-CoV-2 RNA. ConclusionsvRNA was detectable at low viral loads in a minority of serum samples collected in acute infection, but was not associated with infectious SARS-CoV-2 (within the limitations of the assays used). This work helps to inform biosafety precautions for handling blood products from patients with current or previous COVID-19.
