RESUMO
Previous studies have described RT-LAMP methodology for the rapid detection of SARS-CoV-2 in nasopharyngeal (NP) and oropharyngeal (OP) swab and saliva samples. This study describes the validation of an improved sample preparation method for extraction free RT-LAMP and defines the clinical performance of four different RT-LAMP assay formats for detection of SARS-CoV-2 within a multisite clinical evaluation. Direct RT-LAMP was performed on 559 swabs and 86,760 saliva samples and RNA RT-LAMP on extracted RNA from 12,619 swabs and 12,521 saliva from asymptomatic and symptomatic individuals across healthcare and community settings. For Direct RT-LAMP, overall diagnostic sensitivity (DSe) of 70.35% (95% CI 63.48-76.60%) on swabs and 84.62% (79.50-88.88%) on saliva was observed, with diagnostic specificity (DSp) of 100% (98.98-100.00%) on swabs and 100% (99.72-100.00%) on saliva when compared to RT-qPCR; analysing samples with RT-qPCR ORF1ab CT values of [≤]25 and [≤]33, DSe of 100% (96.34-100%) and 77.78% (70.99-83.62%) for swabs were observed, and 99.01% (94.61-99.97%) and 87.61% (82.69-91.54%) for saliva, respectively. For RNA RT-LAMP, overall DSe and DSp were 96.06% (92.88-98.12%) and 99.99% (99.95-100%) for swabs, and 80.65% (73.54-86.54%) and 99.99% (99.95-100%) for saliva, respectively. These findings demonstrate that RT-LAMP is applicable to a variety of use-cases, including frequent, interval-based testing of saliva with Direct RT-LAMP from asymptomatic individuals that may otherwise be missed using symptomatic testing alone.
RESUMO
BackgroundThe worldwide pandemic caused by SARS-CoV-2 has claimed millions of lives and has had a profound effect on global life. Understanding the pathogenicity of the virus and the bodys response to infection is crucial in improving patient management, prognosis, and therapeutic strategies. To address this, we performed functional transcriptomic profiling to better understand the generic and specific effects of SARS-CoV-2 infection. MethodsWhole blood RNA sequencing was used to profile a well characterised cohort of patients hospitalised with COVID-19, during the first wave of the pandemic prior to the availability of approved COVID-19 treatments and who went on to survive or die of COVID-19, and patients hospitalised with influenza virus infection between 2017 and 2019. Clinical parameters between patient groups were compared, and several bioinformatic tools were used to assess differences in transcript abundances and cellular composition. ResultsThe analyses revealed contrasting innate and adaptive immune programmes, with transcripts and cell subsets associated with the innate immune response elevated in patients with influenza, and those involved in the adaptive immune response elevated in patients with COVID-19. Topological analysis identified additional gene signatures that differentiated patients with COVID-19 from patients with influenza, including insulin resistance, mitochondrial oxidative stress and interferon signalling. An efficient adaptive immune response was furthermore associated with patient survival, while an inflammatory response predicted death in patients with COVID-19. A potential prognostic signature was found based on a selection of transcript abundances, associated with circulating immunoglobulins, nucleosome assembly, cytokine production and T cell activation, in the blood transcriptome of COVID-19 patients, upon admission to hospital, which can be used to stratify patients likely to survive or die. ConclusionsThe results identified distinct immunological signatures between SARS-CoV-2 and influenza, prognostic of disease progression and indicative of different targeted therapies. The altered transcript abundances associated with COVID-19 survivors can be used to predict more severe outcomes in patients with COVID-19.