False-negative RT-PCR in SARS-CoV-2 disease: experience from an Italian COVID-19 unit

Paolo, Marcello Di; Iacovelli, Alessandra; Olmati, Federica; Menichini, Ilaria; Oliva, Alessandra; Carnevalini, Martina; Graziani, Elda; Mastroianni, Claudio Maria; Palange, Paolo

doi:10.1183/23120541.00324-2020

Cited by 35 publications

(36 citation statements)

References 12 publications

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“…Among nucleic acid tests, the RT-PCR test is a gold standard for COVID-19 diagnosis and has reported sensitivity up to 75% [ 19 ]. Although this is a decent performance, this could create a problem on a large scale [ 8 , 17 , 26 , 27 ] reports that there is a high False Negative rate for RT-PCR test in COVID-19 diagnosis, and it could have severe repercussions considering the high infection rate of COVID-19. The other problem is that the RT-PCR test is time-consuming by design.…”

Section: Methodsologymentioning

confidence: 99%

AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans

Kuchana

Srivastava

Das

et al. 2020

Multimed Tools Appl

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Coronavirus (COVID-19) has spread throughout the world, causing mayhem from January 2020 to this day. Owing to its rapidly spreading existence and high death count, the WHO has classified it as a pandemic. Biomedical engineers, virologists, epidemiologists, and people from other medical fields are working to help contain this epidemic as soon as possible. The virus incubates for five days in the human body and then begins displaying symptoms, in some cases, as late as 27 days. In some instances, CT scan based diagnosis has been found to have better sensitivity than RT-PCR, which is currently the gold standard for COVID-19 diagnosis. Lung conditions relevant to COVID-19 in CT scans are ground-glass opacity (GGO), consolidation, and pleural effusion. In this paper, two segmentation tasks are performed to predict lung spaces (segregated from ribcage and flesh in Chest CT) and COVID-19 anomalies from chest CT scans. A 2D deep learning architecture with U-Net as its backbone is proposed to solve both the segmentation tasks. It is observed that change in hyperparameters such as number of filters in down and up sampling layers, addition of attention gates, addition of spatial pyramid pooling as basic block and maintaining the homogeneity of 32 filters after each down-sampling block resulted in a good performance. The proposed approach is assessed using publically available datasets from GitHub and Kaggle. Model performance is evaluated in terms of F1-Score, Mean intersection over union (Mean IoU). It is noted that the proposed approach results in 97.31% of F1-Score and 84.6% of Mean IoU. The experimental results illustrate that the proposed approach using U-Net architecture as backbone with the changes in hyperparameters shows better results in comparison to existing U-Net architecture and attention U-net architecture. The study also recommends how this methodology can be integrated into the workflow of healthcare systems to help control the spread of COVID-19.

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Section: Methodsologymentioning

confidence: 99%

AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans

Kuchana

Srivastava

Das

et al. 2020

Multimed Tools Appl

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“…COVID-19-RdRp/Hel was able to detect 15.4% more samples as positive that tested negative with RdRp-P2 and no crossreactivity was observed with other respiratory coronaviruses (Chan et al, 2020). However, RT-PCR-based detection is also associated with false-negative results, which might be due to the low viral load in patients' throats, improper handling of RNA samples, or lack of adequate internal controls (Di Paolo et al, 2020;Kelly et al, 2020). To improve this limitation of RT-PCR, droplet digital PCR was optimized for the detection of SARS-CoV-2 with a lower LOD than conventional RT-PCR (Suo et al, 2020).…”

Section: Real-time Polymerase Chain Reactionmentioning

confidence: 97%

Nanotechnology-Based Approaches for the Detection of SARS-CoV-2

Gupta

Sagar

Priyadarshi

et al. 2020

Front. Nanotechnol.

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The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a pandemic has been validated as an extreme clinical calamity and has affected several socio-economic activities globally. Proven transmission of this virus occurs through airborne droplets from an infected person. The recent upsurge in the number of infected individuals has already exceeded the number of intensive care beds available to patients. These extraordinary circumstances have elicited the need for the development of diagnostic tools for the detection of the virus and, hence, prevent the spread of the disease. Early diagnosis and effective immediate treatment can reduce and prevent an increase in the number of cases. Conventional methods of detection such as quantitative real-time polymerase chain reaction and chest computed tomography scans have been used extensively for diagnostic purposes. However, these present several challenges, including prolonged assay requirements, labor-intensive testing, low sensitivity, and unavailability of these resources in remote locations. Such challenges urgently require fast, sensitive, and accurate diagnostic techniques for the timely detection and treatment of coronavirus disease 2019 (COVID-19) infections. Point-of-care biosensors that include paper- and chip-based diagnostic systems are rapid, cost-effective, and user friendly. In this article nanotechnology-based potential biosensors for SARS-CoV-2 diagnosis are discussed with particular emphasis on a lateral flow assay, a surface-enhanced Raman scattering-based biosensor, a localized surface plasmon resonance-based biosensor, Förster resonance energy transfer, an electrochemical biosensor, and artificial intelligence-based biosensors. Several biomolecules, such as nucleic acids, antibodies/enzymes, or aptamers, can serve as potential detection molecules on an appropriate platform, such as graphene oxide, nanoparticles, or quantum dots. An effective biosensor can be developed by using appropriate combinations of nanomaterials and technologies.

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“…During the treatment course in the hospital, only 3 of these 16 (18.7%) patients were found to be RT-qPCR positive upon re-testing. 6 However, when the RT-qPCR assays were used to test the nasopharyngeal swabs of the patients with mild nonspecific symptoms or asymptomatic persons with suspected infection, the problems of the false-positive test results began to surface. For one notable example, 77 positive SARS-CoV-2 test results on a group of football players in the United States all turned out to be false positives on repeat testing.…”

Section: Introductionmentioning

confidence: 99%

Partial N Gene Sequencing for SARS-CoV-2 Verification and Pathway Tracing

Lee¹,

McGrath²,

Connolly³

et al. 2021

IMCRJ

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When SARS-CoV-2 prevalence is low, many RT-qPCR-positive test results are false positives. Sequencing of a 398-bp cDNA PCR amplicon derived from a highly conserved segment with single nucleotide polymorphisms of the nucleocapsid (N) gene in presumptive positive samples can verify true positives and differentiate at least 27 phylogenetically distinct strains of SARS-CoV-2 for helping track virus strain movement between individuals and across geographical areas. We report using this partial N gene sequencing method to confirm a case of mild COVID-19 disease. The patient was first seen on March 15, 2020, in the emergency department of the university hospital in Dublin, Ireland. RT-qPCR test on a nasopharyngeal swab sample was positive for SARS-CoV-2. Partial sequencing of the N gene in the residue of the tested RNA extract showed a characteristic set of 3-consecutive GGG-to-AAC mutations at positions 28881, 28882, 28883, which is known to first appear in samples collected in Continental Europe in February 2020. Using this sequencing-based method to re-test 9 reference nasopharyngeal swab samples supplied by the Connecticut State Department of Public Health Microbiology Laboratory revealed that 2 of the 9 positive samples had a single nucleotide mutation in the 398-base segment of the SARS-CoV-2 N gene. One of the 2 mutant samples showed a mutation at position 28821, which was first reported in a sample recently collected in the neighboring New York state. The other sample showed a novel frameshift nucleotide "A" insertion between position 29051 and position 29057, which co-existed with its wildtype parental virus in one sample. Routine sequencing of RT-qPCR-positive samples can minimize or eliminate false-positive SARS-CoV-2 test results that may cause unnecessary anxiety among the population and prevent false-positive tests from shutting down schools and workplaces unnecessarily as businesses try to resume normal operations in the community.

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False-negative RT-PCR in SARS-CoV-2 disease: experience from an Italian COVID-19 unit

Cited by 35 publications

References 12 publications

AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans

AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans

Nanotechnology-Based Approaches for the Detection of SARS-CoV-2

Partial N Gene Sequencing for SARS-CoV-2 Verification and Pathway Tracing

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