Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics

Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR

BackgroundThe ongoing outbreak of the recently emerged novel coronavirus (2019-nCoV) poses a challenge for public health laboratories as virus isolates are unavailable while there is growing evidence that the outbreak is more widespread than initially thought, and international spread through travellers does already occur.AimWe aimed to develop and deploy robust diagnostic methodology for use in public health laboratory settings without having virus material available.MethodsHere we present a validated diagnostic workflow for 2019-nCoV, its design relying on close genetic relatedness of 2019-nCoV with SARS coronavirus, making use of synthetic nucleic acid technology.ResultsThe workflow reliably detects 2019-nCoV, and further discriminates 2019-nCoV from SARS-CoV. Through coordination between academic and public laboratories, we confirmed assay exclusivity based on 297 original clinical specimens containing a full spectrum of human respiratory viruses. Control material is made available through European Virus Archive - Global (EVAg), a European Union infrastructure project.ConclusionThe present study demonstrates the enormous response capacity achieved through coordination of academic and public laboratories in national and European research networks

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics

Standardization of SARS-CoV-2 Nucleic Acid Amplification Techniques by Calibration and Quantification to the First WHO International Standard for SARS-CoV-2 RNA

Clinical decision-making regarding isolation of SARS-CoV-2 patients is usually based on semiquantitative cycle-threshold (Ct) values without standardization. However, not all molecular assays produce Ct values, and there is ongoing discussion about whether Ct values can be safely used for decision-making. In this study, we standardized two molecular assays which use different nucleic acid amplification techniques (NAAT): the Hologic Aptima SARS-CoV-2/Flu (TMA) and Roche Cobas 6800 SARS-CoV-2 assays. We calibrated these assays against the first WHO international standard for SARS-CoV-2 RNA by using linear regression of log10 dilution series. These calibration curves were used to calculate viral loads for clinical samples. Clinical performance was assessed retrospectively using samples collected between January 2020 and November 2021, including known positives of the wild-type SARS-CoV-2 virus, the VOCs (alpha, beta, gamma, delta, and omicron) and quality control panels. Linear regression and Bland-Altman analysis showed good correlations for SARS-CoV-2 between Panther TMA and Cobas 6800 when standardized viral loads were used. These standardized quantitative results can benefit clinical decision-making and standardization of infection control guidelines

Mpox outbreak among men who have sex with men in Amsterdam and Rotterdam, the Netherlands: no evidence for undetected transmission prior to May 2022, a retrospective study

Since May 2022, over 21,000 mpox cases have been reported from 29 EU/EEA countries, predominantly among men who have sex with men (MSM). The Netherlands was the fourth most affected country in Europe, with more than 1,200 cases and a crude notification rate of 70.7 per million population. The first national case was reported on 10 May, yet potential prior transmission remains unknown. Insight into prolonged undetected transmission can help to understand the current outbreak dynamics and aid future public health interventions. We performed a retrospective study and phylogenetic analysis to elucidate whether undetected transmission of human mpox virus (hMPXV) occurred before the first reported cases in Amsterdam and Rotterdam. In 401 anorectal and ulcer samples from visitors to centres for sexual health in Amsterdam or Rotterdam dating back to 14 February 2022, we identified two new cases, the earliest from 6 May. This coincides with the first cases reported in the United Kingdom, Spain and Portugal. We found no evidence of widespread hMPXV transmission in Dutch sexual networks of MSM before May 2022. Likely, the mpox outbreak expanded across Europe within a short period in the spring of 2022 through an international highly intertwined network of sexually active MSM