Accurate Diagnosis Of Coronavirus Disease 2019 Research Articles

Factors affecting the sensitivity of quantitative severe acute respiratory syndrome coronavirus 2 antigen test

IntroductionThe accuracy of nucleic acid amplification tests (NAATs) is affected by various factors; however, studies examining the factors affecting the accuracy of quantitative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen test (QAT) are limited. MethodsA total of 347 nasopharyngeal samples were collected from patients with coronavirus disease 2019 (COVID-19), and the date of onset was obtained from the electronic medical records. The SARS-CoV-2 antigen level was measured using Lumipulse Presto SARS-CoV-2 Ag (Presto), while NAAT was performed using the Ampdirect 2019-nCoV Detection Kit. ResultsPresto had a sensitivity rate of 95.1% (95% confidence interval: 92.8–97.4) in detecting the SARS-CoV-2 antigen in 347 samples. The number of days from symptom onset to sample collection was negatively correlated with the amount of antigen (r = −0.515) and sensitivity of Presto (r = −0.711). The patients’ age was lower in the Presto-negative samples (median age, 39 years) compared with that in the Presto-positive samples (median age, 53 years; p < 0.01). A significant positive correlation was observed between age (excluding teenagers) and Presto sensitivity (r = 0.764). Meanwhile, no association was found between the mutant strain, sex, and Presto results. ConclusionPresto is useful for the accurate diagnosis of COVID-19 owing to its high sensitivity when the number of days from symptom onset to sample collection is within 12 days. Furthermore, age may affect the results of Presto, and this tool has a relatively low sensitivity in younger patients.

A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images

In the Coronavirus disease-2019 (COVID-19) pandemic, for fast and accurate diagnosis of a large number of patients, besides traditional methods, automated diagnostic tools are now extremely required. In this paper, a deep convolutional neural network (CNN) based scheme is proposed for automated accurate diagnosis of COVID-19 from lung computed tomography (CT) scan images. First, for the automated segmentation of lung regions in a chest CT scan, a modified CNN architecture, namely SKICU-Net is proposed by incorporating additional skip interconnections in the U-Net model that overcome the loss of information in dimension scaling. Next, an agglomerative hierarchical clustering is deployed to eliminate the CT slices without significant information. Finally, for effective feature extraction and diagnosis of COVID-19 and pneumonia from the segmented lung slices, a modified DenseNet architecture, namely P-DenseCOVNet is designed where parallel convolutional paths are introduced on top of the conventional DenseNet model for getting better performance through overcoming the loss of positional arguments. Outstanding performances have been achieved with an F1 score of 0.97 in the segmentation task along with an accuracy of 87.5% in diagnosing COVID-19, common pneumonia, and normal cases. Significant experimental results and comparison with other studies show that the proposed scheme provides very satisfactory performances and can serve as an effective diagnostic tool in the current pandemic.

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

SEGMENTATION AND CLASSIFICATION OF COVID-19 MEDICAL IMAGES USING DEEP NEURAL NETWORK

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. In this project, try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections as well as the entire lung from chest CT images, referred to as COVID-SegNet. Besides using local binary pattern for feature extraction and its classification using DAENN. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection Keywords—: Coronavirus disease 2019 pneumonia, COVID-19, deep learning, segmentation, multi-scale feature

Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection.

Since the first report of SARS-CoV-2 virus in Wuhan, China in December 2019, a global outbreak of Corona Virus Disease 2019 (COVID-19) pandemic has been aroused. In the prevention of this disease, accurate diagnosis of COVID-19 is the center of the problem. However, due to the limitation of detection technology, the test results are impossible to be totally free from pseudo-positive or -negative. Improving the precision of the test results asks for the identification of more biomarkers for COVID-19. On the basis of the expression data of COVID-19 positive and negative samples, we first screened the feature genes through ReliefF, minimal-redundancy-maximum-relevancy, and Boruta_MCFS methods. Thereafter, 36 optimal feature genes were selected through incremental feature selection method based on the random forest classifier, and the enriched biological functions and signaling pathways were revealed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Also, protein-protein interaction network analysis was performed on these feature genes, and the enriched biological functions and signaling pathways of main submodules were analyzed. In addition, whether these 36 feature genes could effectively distinguish positive samples from the negative ones was verified by dimensionality reduction analysis. According to the results, we inferred that the 36 feature genes selected via Boruta_MCFS could be deemed as biomarkers in COVID-19.

Evaluation of the QIAstat-Dx Respiratory SARS-CoV-2 panel, a rapid multiplex PCR method for the diagnosis of COVID-19

IntroductionRapid, simple, and accurate methods are required to diagnose coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to evaluate the performance of the QIAstat-Dx Respiratory SARS-CoV-2 Panel (QIAstat-SARS-CoV-2), a rapid multiplex PCR assay for SARS-CoV-2 detection. MethodsNasopharyngeal swabs (NPS) that were obtained from patients with COVID-19 who were diagnosed at the National Center for Global Health and Medicine were used in this study. When the NPS samples were found to be negative for SARS-CoV-2 after treatment, they were used as negative samples. We evaluated the performance of the QIAstat-SARS-CoV-2 comparing SARS-CoV-2 detection with the National Institute of Infectious Diseases in Japan-recommended real-time polymerase chain reaction (RT-PCR) method (NIID-RT-PCR). ResultsIn total, 45 NPS samples were analyzed. The proportion of overall agreement between QIAstat-SARS-CoV-2 and NIID-RT-PCR on 45 samples was 91.0% with a sensitivity of 84.0% (21/25), specificity at 100% (20/20), negative predictive value at 83.3% (20/24), and positive predictive value at 100% (21/21). There were no patients with co-infections with pathogens other than SARS-CoV-2. ConclusionsQIAstat-SARS-CoV-2 showed a high agreement in comparison with the NIID-RT-PCR for the detection of SARS-CoV-2. The QIAstat-SARS-CoV-2 also provided a rapid and accurate diagnosis for COVID-19, even when the concurrent detection of other respiratory pathogens was desired, and therefore, has the potential to direct appropriate therapy and infection control precautions.

Bioconjugated Nanomaterial for Targeted Diagnosis of SARS-CoV-2.

ConspectusInfectious diseases by pathogenic microorganisms are one of the leading causes of mortality worldwide. Healthcare and socio-economic development have been seriously affected for different civilizations because of bacterial and viral infections. According to the Centers for Disease Control and Prevention (CDC), pandemic in 1918 by the Influenza A virus of the H1N1 subtype was responsible for 50 to 100 million deaths worldwide. Similarly, the Asian flu pandemic in 1957, Hong Kong flu in 1968, and H1N1pdm09 flu pandemic in 2009 were responsible for more than 1 million deaths across the globe each time. As per the World Health Organization (WHO), the current pandemic by coronavirus disease 2019 (COVID-19) due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is responsible for more than 4.8 M death worldwide until now. Since the gold standard polymerase chain reaction (PCR) test is more time-consuming, the health care system cannot test all symptomatic and asymptomatic Covid patients every day, which is extremely important to tackle the outbreak. One of the significant challenges during the current pandemic is developing mass testing tools, which is critical to control the virus spread in the community. Therefore, it is highly desirable to develop advanced material-based approaches that can provide a rapid and accurate diagnosis of COVID-19, which will have the capability to save millions of human lives.Aiming for the targeted diagnosis of deadly virus, researchers have developed nanomaterials with various sizes, shapes, and dimensions. These nanomaterials have been used to identify biomolecules via unique optical, electrical, magnetic, structural, and functional properties, which are lacking in other materials. Despite significant progress, nanomaterial-based diagnosis of biomolecules is still facing several obstacles due to low targeting efficiency and nonspecific interactions. To overcome these problems, the bioconjugated nanoparticle has been designed via surface coating with polyethylene glycol (PEG) and then conjugated with antibodies, DNA, RNA, or peptide aptamers. Therefore, the current Account summarizes an overview of the recent advances in the design of bioconjugated nanomaterial-based approached as effective diagnosis of the SARS-CoV-2 virus and the SARS-CoV-2 viral RNA, antigen, or antibody, with a particular focus on our work and other’s work related to this subject. First, we present how to tailor the surface functionalities of nanomaterials to achieve bioconjugated material for targeted diagnosis of the virus. Then we review the very recent advances in the design of antibody/aptamer/peptide conjugated nanostructure, which represent a powerful platform for naked-eye colorimetric detection via plasmonic nanoparticles. We then discuss nanomaterial-based surface-enhanced Raman scattering (SERS) spectroscopy, which has the capability for very low-level fingerprint identification of virus, antigen, and antibody via graphene, plasmonic nanoparticle, and heterostructure material. After that, we summarized about fluorescence and nanoparticle surface energy transfer (NSET)-based on specific identification of SARS-CoV-2 infections via CNT, quantum dots (QDs), and plasmonic nanoparticles. Finally, we highlight the merit and significant challenges of nanostructure-based tools in infectious diseases diagnosis. For the researchers who want to engage in the new development of bioconjugated material for our survival from the current and future pandemics, we hope that this Account will be helpful for generating ideas that are scientifically stimulating and practically challenging.

XViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography.

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

Establishing diagnostic algorithms for SARS-CoV-2 nucleic acid testing in clinical practice.

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic. Our laboratory initially used a two-step molecular assay, first reported by Corman et al, for SARS-CoV-2 identification (the Taiwan Center for Disease Control [T-CDC] method). As rapid and accurate diagnosis of COVID-19 is required to control the spread of this infectious disease, the current study evaluated three commercially available assays, including the TaqPath COVID-19 Combo kit, the cobas SARS-CoV-2 test, and the Rendu 2019-nCoV Assay kit, to establish diagnostic algorithms for clinical laboratories. A total of 790 clinical specimens, including nasopharyngeal swabs, throat swabs, sputum, saliva, stool, endotracheal aspirate, and serum were obtained from patients who were suspected or already confirmed to have COVID-19 at the Taipei Veterans General Hospital from February to May 2020. These specimens were tested for SARS-CoV-2 using the different assays and the performance variance between the assays was analyzed. Of the assays we evaluated, the T-CDC method and the TaqPath COVID-19 Combo kit require lots of hands-on practical laboratory work, while the cobas SARS-CoV-2 test and the Rendu 2019-nCoV Assay kit are fully automated detection systems. The T-CDC method and the TaqPath COVID-19 Combo kit showed similar detection sensitivity; however, the T-CDC method frequently delivered false-positive signals for envelope (E) and/or RNA-dependent RNA polymerase (RdRP) gene detection, thus increasing the risk of reporting false-positive results. A manual test-based testing strategy combining the T-CDC method and the TaqPath COVID-19 Combo kit was developed, which demonstrated excellent concordance rates (>99%) with the cobas and Rendu automatic systems. There were a few cases showing discrepant results, which may be due to the varied detection sensitivities as well as targets among the different platforms. Moreover, the concordance rate between the cobas and Rendu assays was 100%. Based on our evaluation, two SARS-CoV-2 diagnostic algorithms, one focusing on the manual assays and the other on the automatic platforms, were proposed. Our results provide valuable information that allows clinical laboratories to implement optimal diagnostic strategies for SARS-CoV-2 testing based on their clinical needs, such as test volume, turn-around time, and staff/resource limitations.

Interim Guidance for Emergency Medical Services Management of Out-of-Hospital Cardiac Arrest During the COVID-19 Pandemic.

Interim Guidance for Emergency Medical Services Management of Out-of-Hospital Cardiac Arrest During the COVID-19 Pandemic.

Radiology department preventive and control measures and work plan during COVID-19 epidemic-experience from Wuhan.

Since the outbreak of the coronavirus disease 2019 (COVID-19), it had rapidly spread to the whole world and seriously threatened the global health. Imaging examination plays an important role in the clinical diagnosis of this disease, which leads to the high infection risk of the medical staff in the radiology department. In this review, the authors thoroughly summed up the experience in the management and operation of radiology department and shared their experience of the protective and control strategies and work plan during the epidemic, including but not limited to the management framework of the radiology department, the environment and layout in the department, the requirements for protection of different posts and the equipment, as well as the essential diagnosis of COVID-19. It is worth noting that the main goal of the radiology department in every country is to complete the radiology examination safely and make an accurate diagnosis of COVID-19 patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s42058-021-00055-5.

CT features of coronavirus disease 2019 (COVID-19) with an emphasis on the vascular enlargement pattern

PurposeThe vascular enlargement (VE) pattern differs from previously described imaging patterns for pneumonia. This study aimed to investigate the incidence, computed tomography (CT) characteristics, and diagnostic value of the VE pattern in coronavirus disease 2019 (COVID-19). MethodThe CT data of 106 patients with COVID-19 from January 19 to February 29, 2020, and 52 patients with influenza virus pneumonia (IVP) from January 2018 to February 2020 were retrospectively collected. The incidences of the VE pattern between the two groups were compared. The CT manifestations of COVID-19 were analyzed with a particular focus on the VE pattern’s specific CT signs, dynamic changes, and relationships with lesion size and disease severity. ResultsPeripheral and multilobar ground-glass opacities (GGOs) or mixed GGOs with various sizes and morphologies were typical features of COVID-19 on initial CT. The VE pattern was more common in COVID-19 (88/106, 83.02 %) than in IVP (10/52, 19.23 %) on initial CT (P < 0.001). Three special VE-pattern-specific CT signs, including central vascular sign, ginkgo leaf sign, and comb sign, were identified. Four types of dynamic changes in the VE pattern were observed on initial and follow-up CT, which were closely associated with the evolution of lesions and the time interval from the onset of symptoms to initial CT scan. The VE pattern in COVID-19 was more commonly seen in larger lesions and patients with severe–critical type (all P < 0.001). ConclusionsThe VE pattern is a valuable CT sign for differentiating COVID-19 from IVP, which correlates with more extensive or serious disease. A good understanding of the CT characteristics of the VE pattern may contribute to the early and accurate diagnosis of COVID-19 and prediction of the evolution of lesions.

Accurate Diagnosis of COVID-19 by a Novel Immunogenic Secreted SARS-CoV-2 orf8 Protein.

An accurate diagnostic test for early severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the key weapon to control the coronavirus disease 2019 (COVID-19) pandemic. We previously reported that the SARS-CoV-2 genome contains a unique orf8 accessory gene absent from other human-pathogenic coronaviruses. Here, we characterized the SARS-CoV-2 orf8 as a novel immunogenic secreted protein and utilized it for the accurate diagnosis of COVID-19. Extracellular orf8 protein was detected in cell culture supernatant and in sera of COVID-19 patients. In addition, orf8 was found highly immunogenic in COVID-19 patients, who showed early seropositivity for anti-orf8 IgM, IgG, and IgA. We hypothesize that orf8 secretion during SARS-CoV-2 infection facilitates early mounting of B cell response. The serological test detecting anti-orf8 IgG antibody can be used for the early and accurate diagnosis of COVID-19.IMPORTANCE Current commercially available serological tests for COVID-19 patients are detecting antibodies against SARS-CoV-2 nucleoprotein and spike glycoprotein. The antinucleoprotein and antispike antibodies can be accurately detected in patients during the mid or late stage of infection, and therefore, these assays have not been widely used for early diagnosis of COVID-19. In this study, we characterized the secretory property of a SARS-CoV-2 orf8 protein and proposed that orf8 secretion during infection facilitates early mounting of the B cell response. We demonstrated the presence of anti-orf8 antibodies in both symptomatic and asymptomatic patients during the early stage of infection, while the anti-N antibody is not detected. Our serological test detecting anti-orf8 antibodies may facilitate the development of early and accurate diagnosis for COVID-19.

A UK-wide British Society of Thoracic Imaging COVID-19 imaging repository and database: design, rationale and implications for education and research

A UK-wide British Society of Thoracic Imaging COVID-19 imaging repository and database: design, rationale and implications for education and research

Combating the Coronavirus Pandemic: Early Detection, Medical Treatment, and a Concerted Effort by the Global Community.

The World Health Organization (WHO) has declared the outbreak of 2019 novel coronavirus, known as 2019-nCoV, a pandemic, as the coronavirus has now infected over 2.6 million people globally and caused more than 185,000 fatalities as of April 23, 2020. Coronavirus disease 2019 (COVID-19) causes a respiratory illness with symptoms such as dry cough, fever, sudden loss of smell, and, in more severe cases, difficulty breathing. To date, there is no specific vaccine or treatment proven effective against this viral disease. Early and accurate diagnosis of COVID-19 is thus critical to curbing its spread and improving health outcomes. Reverse transcription-polymerase chain reaction (RT-PCR) is commonly used to detect the presence of COVID-19. Other techniques, such as recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), clustered regularly interspaced short palindromic repeats (CRISPR), and microfluidics, have allowed better disease diagnosis. Here, as part of the effort to expand screening capacity, we review advances and challenges in the rapid detection of COVID-19 by targeting nucleic acids, antigens, or antibodies. We also summarize potential treatments and vaccines against COVID-19 and discuss ongoing clinical trials of interventions to reduce viral progression.