B.1.1.7 became the dominant variant in Lebanon

Younes, Mahmoud; Hamze, Kassem; Osman, Karen L.; Carter, Dan; Pullan, Steven T.; Caroll, Miles; Vipond, Richard; Mohamad, N.; Nassar, Hassan; Ghaddar, Mayssa; makki, mohammad; Nguewa, Paul; Abdel-Sater, Fadi

doi:10.1101/2021.03.17.21253782

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Genome sequencing is the gold method to confirm the new variant, but observational studies provide also stronger evidence if similar models are observed in multiple countries, especially when randomized studies are not possible. In Lebanon, surveillance data from Beirut Medical Center and Bahman Hospital showed a rapid and dramatic increase in S-negative profile in PCR testing for SARS-CoV-2 in the first twelve days in January, reaching approximately 60% of positive cases and 95% in February [9,10].…”

Section: Introductionmentioning

confidence: 99%

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

Abdel-Sater

Younes

Nassar³

et al. 2021

Mol Biol Rep

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BackgroundThe new SARS-CoV-2 variant VOC (202012/01), identified recently in the United Kingdom (UK), exhibits a higher transmissibility rate compared to other variants, and a reproductive number 0.4 higher. In the UK, scientists were able to identify the increase of this new variant through the rise of false negative results for the spike (S) target using a three-target RT-PCR assay (TaqPath kit). Methods To control and study the current coronavirus pandemic, it is important to develop a rapid and low-cost molecular test to identify the aforementioned variant. In this work, we designed primer sets specific to the VOC (202012/01) to be used by SYBR Green-based RT-PCR. These primers were specifically designed to confirm the deletion mutations Δ69/Δ70 in the spike and the Δ106/Δ107/Δ108 in the NSP6 gene. We studied 20 samples from positive patients, detected by using the Applied Biosystems TaqPath RT-PCR COVID-19 kit (Thermo Fisher Scientific, Waltham, USA) that included the ORF1ab, S, and N gene targets. 16 samples displayed an S-negative profile (negative for S target and positive for N and ORF1ab targets) and four samples with S, N and ORF1ab positive profile. Results Our results emphasized that all S-negative samples harbored the mutations Δ69/Δ70 and Δ106/Δ107/Δ108. This protocol could be used as a second test to confirm the diagnosis in patients who were already positive to COVID-19 but showed false negative results for S-gene. Conclusions This technique may allow to identify patients carrying the VOC (202012/01) or a closely related variant, in case of shortage in sequencing.

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Section: Introductionmentioning

confidence: 99%

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

Abdel-Sater

Younes

Nassar³

et al. 2021

Mol Biol Rep

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“…On the other hand, the estimated sensitivity of the STANDARD M10 was higher for the E gene than for the ORF1ab gene; indeed, the latter was not detected in 4.5% of positive samples. A significant proportion of the ORF1ab dropout has been reported for both Alpha [24] and Delta [25] VOCs. Notably, among Alpha VOCs detected in Lebanon, the ORF1ab dropout was observed in all samples with S gene dropout [24].…”

Section: Discussionmentioning

confidence: 99%

“…A significant proportion of the ORF1ab dropout has been reported for both Alpha [24] and Delta [25] VOCs. Notably, among Alpha VOCs detected in Lebanon, the ORF1ab dropout was observed in all samples with S gene dropout [24]. Similarly, in our study, 6 of 9 samples negative for the ORF1ab gene had RdRp gene dropout in the Allplex 2019-nCoV assay.…”

Section: Discussionmentioning

confidence: 99%

Comparative Diagnostic Accuracy of the STANDARD M10 Assay for the Molecular Diagnosis of SARS-CoV-2 in the Point-of-Care and Critical Care Settings

Domnich

Orsi

Trombetta

et al. 2022

JCM

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Accurate and rapid molecular diagnosis of COVID-19 is a crucial step to tackle the ongoing pandemic. The primary objective of this study was to estimate the real-world performance of the novel RT-PCR STANDARD M10 SARS-CoV-2 assay in a large number of nasopharyngeal (NP) specimens eluted in universal transport medium. The secondary objective was to evaluate the compatibility of this kit in testing NP samples eluted in an inactivated transport medium (essential for point-of-care testing) and lower respiratory tract (LRT) specimens, which are commonly collected in critical care. A total of 591 samples were analyzed. Compared with the standard extraction-based RT-PCR Allplex 2019-nCoV (time-to-result of 270 min), the sensitivities of the STANDARD M10 were 100% (95% CI: 98.1–100%), 95.5% (95% CI: 91.7–97.6%), and 99.5% (95% CI: 97.2–99.9%) for ≥1 gene, the ORF1ab gene, and the E gene, respectively, while the specificity was 100% (95% CI: 98.7–100%). The diagnostic accuracy was 100% in testing both NP samples eluted in an inactivated transport medium and LRT specimens. STANDARD M10 reliably detects SARS-CoV-2 in 60 min, may be used as a POC tool, and is suitable for testing LRT specimens in the critical care setting.

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“…This variant was observed to spread in several countries including the Romania [40], East Germany [33], Lebanon [41], Czech Republic and Slovakia [42].…”

Section: Covid-19 Variantsmentioning

confidence: 99%

Classifying COVID-19 variants based on genetic sequences using deep learning models

Basu

Campbell

2021

Preprint

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The COrona VIrus Disease (COVID-19) pandemic led to the occurrence of several variants with time. This has led to an increased importance of understanding sequence data related to COVID-19. In this chapter, we propose an alignment-free k-mer based LSTM (Long Short-Term Memory) deep learning model that can classify 20 different variants of COVID-19. We handle the class imbalance problem by sampling a fixed number of sequences for each class label. We handle the vanishing gradient problem in LSTMs arising from long sequences by dividing the sequence into fixed lengths and obtaining results on individual runs. Our results show that one- vs-all classifiers have test accuracies as high as 92.5% with tuned hyperparameters compared to the multi-class classifier model. Our experiments show higher overall accuracies for B.1.1.214, B.1.177.21, B.1.1.7, B.1.526, and P.1 on the one-vs-all classifiers, suggesting the presence of distinct mutations in these variants. Our results show that embedding vector size and batch sizes have insignificant improvement in accuracies, but changing from 2-mers to 3-mers mostly improves accuracies. We also studied individual runs which show that most accuracies improved after the 20th run, indicating that these sequence positions may have more contributions to distinguishing among different COVID-19 variants.

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B.1.1.7 became the dominant variant in Lebanon

Cited by 7 publications

References 6 publications

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

Comparative Diagnostic Accuracy of the STANDARD M10 Assay for the Molecular Diagnosis of SARS-CoV-2 in the Point-of-Care and Critical Care Settings

Classifying COVID-19 variants based on genetic sequences using deep learning models

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