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1.
J Clin Lab Anal ; : e24211, 2021 Dec 29.
Article in English | MEDLINE | ID: covidwho-1589067

ABSTRACT

BACKGROUND: Presently, the global spread of COVID-19 is still going on, with more than 0.6 million new cases confirmed per day (as of November 20, 2021). However, since China entered a post-epidemic phase in mid-March 2020, the daily number of new domestic infections in the Chinese mainland has been maintained at almost zero or single digits, which was attributed to a series of effective measures for COVID-19 prevention and control adopted by the Chinese government. Among these measures, SARS-CoV-2 nucleic acid testing holds key role for the timely confirmation and isolation of the infections to prevent further transmission. METHODS: Referring to the national policy requirements, since April 30, 2020, The Affiliated Hospital of Qingdao University has conducted SARS-CoV-2 nucleic acid testing in its PCR laboratory for patients and social workers, as well as for environmental monitoring and employee screening. As of mid-November 2020, the daily amount of single-tube samples for nucleic acid testing rose above 4,000. RESULTS: In this article, a rapid and highly effective approach for SARS-CoV-2 nucleic acid daily testing is presented, allowing five technicians to complete nucleic acid testing in 6,500 single-tube samples in one day with a high level of quality. Using this approach, since the samples entered the PCR laboratory, all testing results were reported in 2.5-3 h with satisfactory quality control and precise reporting criterion as prerequisites. CONCLUSION: This testing approach provides a referable workflow for other testing institutions and is expected to play an important role in COVID-19 prevention and control.

2.
Preprint in English | EuropePMC | ID: ppcovidwho-296428

ABSTRACT

Background: The pathological features of severe cardiac injury induced by COVID-19 and relevant clinical features is unknown.<br><br>Methods: This autopsy cohort study, including hearts from 26 deceased patients hospitalized in intensive care unit due to COVID-19, was conducted at four sites in Wuhan, China. Cases were divided into neutrophil-infiltration group and no-neutrophil group according to histopathological identification of neutrophilic infiltrates or not.<br><br>Findings: Among 26 cases, four cases had active myocarditis with histopathological examination. All cases with myocarditis accompanied with extensive neutrophil infiltration, while cases without myocarditis did not. Detection rates of interleukin-6 (100% vs 4.6%) and tumor necrosis factor-α (100% vs 31.8%) in neutrophil-infiltration group were significantly higher compared to no-neutrophil group (p<0.05 for both). At admission, patients with neutrophil infiltration in myocardium had significantly higher baseline values of aspartate aminotransferase, D dimer and high-sensitivity C reactive protein compared to other 22 patients (p<0.05 for all). During hospitalization, patients with neutrophil infiltration had a significantly higher maximum of creatine kinase (CK)-MB (median 280.0 vs 38.7IU/L, p=0.04), and a quantitatively higher top Troponin I (median 1.112 vs 0.220ng/ml, p=0.56) than patients without neutrophil infiltration.<br><br>Interpretation: In hearts from deceased patients with severe COVID-19 , active myocarditis was commonly infiltrated with neutrophils. Cases with neutrophil-infiltrated myocarditis had a series of severe abnormal laboratory tests at admission, and a high maximum of CK-MB during hospitalization. Role of neutrophil on severe heart injury and even systemic condition in COVID-19 should be emphasized.<br><br>Funding Information: : Emergency Key Program of Guangzhou Laboratory, Grant No. EKPG21-32. <br><br>Declaration of Interests: None exist.<br><br>Ethics Approval Statement: Full autopsy was performed after patient death with the approval of the ethics committees and written consent of patient relatives in accordance with regulations issued by the National Health Commission of China and the Helsinki Declaration.

3.
Preprint in English | EuropePMC | ID: ppcovidwho-293163

ABSTRACT

The coronavirus disease 2019 (COVID-19) has been ravaging throughout the world for almost two years and has severely impaired both human health and the economy. The causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) employs the viral RNA-dependent RNA polymerase (RdRp) complex for genome replication and transcription, making RdRp an appealing target for antiviral drug development. Although the structure of the RdRp complex has been determined, the function of RdRp has not been fully characterized. Here we reveal that in addition to RNA dependent RNA polymerase activity, RdRp also shows exoribonuclease activity and consequently proofreading activity. We observed that RdRp and nsp14-ExoN, when combined, exhibit higher proofreading activity compared to RdRp alone. Moreover, RdRp can recognize and utilize nucleoside diphosphate (NDP) as substrate to synthesize RNA and can also incorporate β-d-N4-hydroxycytidine (NHC) into RNA while using diphosphate form molnupiravir as substrate.

4.
Front Cell Infect Microbiol ; 11: 741147, 2021.
Article in English | MEDLINE | ID: covidwho-1512020

ABSTRACT

The coronavirus disease 2019 (COVID-19) has caused and is still causing tremendous damage to the global economy and human health. Qualitative reverse transcription-PCR (RT-qPCR) is the golden standard for COVID-19 test. However, the SARS-CoV-2 variants may not only make vaccine less effective but also evade RT-qPCR test. Here we suggest an innovative primer design strategy for the RT-qPCR test of SARS-CoV-2. The principle is that the primers should be designed based on both the nucleic acid sequence and the structure of the protein encoded. The three nucleotides closest to the 3' end of the primer should be the codon which encodes the tryptophan in the structure core. Based on this principle, we designed a pair of primers targeting the nucleocapsid (N) gene. Since tryptophan is encoded by only one codon, any mutation that occurs at this position would change the amino acid residue, resulting in an unstable N protein. This means that this kind of SARS-CoV-2 variant could not survive. In addition, both our data and previous reports all indicate that the mutations occurring at other places in the primers do not significantly affect the RT-qPCR result. Consequently, no SARS-CoV-2 variant can escape detection by the RT-qPCR kit containing the primers designed based on our strategy.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Mutation , RNA, Viral/genetics , Real-Time Polymerase Chain Reaction , Sensitivity and Specificity
5.
Sustain Cities Soc ; 76: 103485, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1492614

ABSTRACT

The lack of detailed COVID-19 cases at a fine spatial resolution restricts the investigation of spatial disparities of its attack rate. Here, we collected nearly one thousand self-reported cases from a social media platform during the early stage of COVID-19 epidemic in Wuhan, China. We used kernel density estimation (KDE) to explore spatial disparities of epidemic intensity and adopted geographically weighted regression (GWR) model to quantify influences of population dynamics, transportation, and social interactions on COVID-19 epidemic. Results show that self-reported COVID-19 cases concentrated in commercial centers and populous residential areas. Blocks with higher population density, higher aging rate, more metro stations, more main roads, and more commercial point-of-interests (POIs) have a higher density of COVID-19 cases. These five explanatory variables explain 76% variance of self-reported cases using an OLS model. Commercial POIs have the strongest influence, which increase COVID-19 cases by 28% with one standard deviation increase. The GWR model performs better than OLS model with the adjusted R 2 of 0.96. Spatial heterogeneities of coefficients in the GWR model show that influencing factors play different roles in diverse communities. We further discussed potential implications for the healthy city and urban planning for the sustainable development of cities.

7.
Multimed Tools Appl ; : 1-14, 2021 Jul 07.
Article in English | MEDLINE | ID: covidwho-1366157

ABSTRACT

The multimedia service company, Netflix, increased the number of new subscribers during the Coronavirus pandemic age. Intrusion detection systems for multimedia platforms can prevent the platform from network attacks. An intelligent intrusion detection system is proposed for the security IP Multimedia Subsystem (IMS) based on machine learning technology. For increasing the accuracy of the classifiers, it is vital to select the critical features to construct the intrusion detection system. Two-class classifiers, including the Decision Tree, Support Vector Machine, and Naive Bayesian, are selected to evaluate intrusion detection accuracy. According to the three classifiers' accuracy values, the most critical features are selected based on the features' ranking orders. Six critical features are selected:Service, dst_host_same_srv_rate, Flag, Protocol Type, Dst_host_rerror_rate, and Count. Numerical comparison with state_of_the_art shows that critical features improve intrusion detection accuracy, which can be better than the deep learning method.

8.
Endocr Res ; : 1-7, 2021 Aug 19.
Article in English | MEDLINE | ID: covidwho-1364665

ABSTRACT

PURPOSE: The purpose of this study is to review observational studies on the effect of insulin use and mortality in patients with COVID-19 and diabetes. METHODS: A systematic literature search was performed using the PubMed, Medline, EMBASE, and Cochrane Library databases. The meta-analysis was performed using a random effects model, and I2 was applied to evaluate heterogeneity. Sensitivity and subgroup analyses were also performed. RESULTS: Overall, 1,338 patients over six studies were ultimately included. Insulin use was related to a higher risk of death in diabetic patients with COVID-19 compared to those who did not use insulin (odds ratio: 2.59, 95% confidence interval: 1.66-4.05; P < .0001; I2: 57%). CONCLUSIONS: This meta-analysis revealed a correlation between insulin usage and increased mortality in diabetic patients with COVID-19. These results showed that insulin requirement in patients with COVID-19 and diabetes might indicate a poor prognosis.

9.
Med Image Anal ; 74: 102205, 2021 12.
Article in English | MEDLINE | ID: covidwho-1347757

ABSTRACT

With the global outbreak of COVID-19 in early 2020, rapid diagnosis of COVID-19 has become the urgent need to control the spread of the epidemic. In clinical settings, lung infection segmentation from computed tomography (CT) images can provide vital information for the quantification and diagnosis of COVID-19. However, accurate infection segmentation is a challenging task due to (i) the low boundary contrast between infections and the surroundings, (ii) large variations of infection regions, and, most importantly, (iii) the shortage of large-scale annotated data. To address these issues, we propose a novel two-stage cross-domain transfer learning framework for the accurate segmentation of COVID-19 lung infections from CT images. Our framework consists of two major technical innovations, including an effective infection segmentation deep learning model, called nCoVSegNet, and a novel two-stage transfer learning strategy. Specifically, our nCoVSegNet conducts effective infection segmentation by taking advantage of attention-aware feature fusion and large receptive fields, aiming to resolve the issues related to low boundary contrast and large infection variations. To alleviate the shortage of the data, the nCoVSegNet is pre-trained using a two-stage cross-domain transfer learning strategy, which makes full use of the knowledge from natural images (i.e., ImageNet) and medical images (i.e., LIDC-IDRI) to boost the final training on CT images with COVID-19 infections. Extensive experiments demonstrate that our framework achieves superior segmentation accuracy and outperforms the cutting-edge models, both quantitatively and qualitatively.


Subject(s)
COVID-19 , Humans , Lung/diagnostic imaging , Machine Learning , SARS-CoV-2 , Tomography, X-Ray Computed
10.
Nat Cell Biol ; 23(7): 718-732, 2021 07.
Article in English | MEDLINE | ID: covidwho-1303773

ABSTRACT

Patients with Coronavirus disease 2019 exhibit low expression of interferon-stimulated genes, contributing to a limited antiviral response. Uncovering the underlying mechanism of innate immune suppression and rescuing the innate antiviral response remain urgent issues in the current pandemic. Here we identified that the dimerization domain of the SARS-CoV-2 nucleocapsid protein (SARS2-NP) is required for SARS2-NP to undergo liquid-liquid phase separation with RNA, which inhibits Lys63-linked poly-ubiquitination and aggregation of MAVS and thereby suppresses the innate antiviral immune response. Mice infected with an RNA virus carrying SARS2-NP exhibited reduced innate immunity, an increased viral load and high morbidity. Notably, we identified SARS2-NP acetylation at Lys375 by host acetyltransferase and reported frequently occurring acetylation-mimicking mutations of Lys375, all of which impaired SARS2-NP liquid-liquid phase separation with RNA. Importantly, a peptide targeting the dimerization domain was screened out to disrupt the SARS2-NP liquid-liquid phase separation and demonstrated to inhibit SARS-CoV-2 replication and rescue innate antiviral immunity both in vitro and in vivo.


Subject(s)
Nucleocapsid Proteins/immunology , Nucleocapsid Proteins/metabolism , SARS-CoV-2/genetics , Animals , Immunity, Innate/immunology , Immunity, Innate/physiology , Mice , Nucleocapsid Proteins/genetics , RNA Viruses/genetics , SARS-CoV-2/physiology
11.
Med Phys ; 48(5): 2337-2353, 2021 May.
Article in English | MEDLINE | ID: covidwho-1155243

ABSTRACT

PURPOSE: The worldwide spread of the SARS-CoV-2 virus poses unprecedented challenges to medical resources and infection prevention and control measures around the world. In this case, a rapid and effective detection method for COVID-19 can not only relieve the pressure of the medical system but find and isolate patients in time, to a certain extent, slow down the development of the epidemic. In this paper, we propose a method that can quickly and accurately diagnose whether pneumonia is viral pneumonia, and classify viral pneumonia in a fine-grained way to diagnose COVID-19. METHODS: We proposed a Cascade Squeeze-Excitation and Moment Exchange (Cascade-SEME) framework that can effectively detect COVID-19 cases by evaluating the chest x-ray images, where SE is the structure we designed in the network which has attention mechanism, and ME is a method for image enhancement from feature dimension. The framework integrates a model for a coarse level detection of virus cases among other forms of lung infection, and a model for fine-grained categorisation of pneumonia types identifying COVID-19 cases. In addition, a Regional Learning approach is proposed to mitigate the impact of non-lesion features on network training. The network output is also visualised, highlighting the likely areas of lesion, to assist experts' assessment and diagnosis of COVID-19. RESULTS: Three datasets were used: a set of Chest x-ray Images for Classification with bacterial pneumonia, viral pneumonia and normal chest x-rays, a COVID chest x-ray dataset with COVID-19, and a Lung Segmentation dataset containing 1000 chest x-rays with masks in the lung region. We evaluated all the models on the test set. The results shows the proposed SEME structure significantly improves the performance of the models: in the task of pneumonia infection type diagnosis, the sensitivity, specificity, accuracy and F1 score of ResNet50 with SEME structure are significantly improved in each category, and the accuracy and AUC of the whole test set are also enhanced; in the detection task of COVID-19, the evaluation results shows that when SEME structure was added to the task, the sensitivities, specificities, accuracy and F1 scores of ResNet50 and DenseNet169 are improved. Although the sensitivities and specificities are not significantly promoted, SEME well balanced these two significant indicators. Regional learning also plays an important role. Experiments show that Regional Learning can effectively correct the impact of non-lesion features on the network, which can be seen in the Grad-CAM method. CONCLUSIONS: Experiments show that after the application of SEME structure in the network, the performance of SEME-ResNet50 and SEME-DenseNet169 in both two datasets show a clear enhancement. And the proposed regional learning method effectively directs the network's attention to focus on relevant pathological regions in the lung radiograph, ensuring the performance of the proposed framework even when a small training set is used. The visual interpretation step using Grad-CAM finds that the region of attention on radiographs of different types of pneumonia are located in different regions of the lungs.


Subject(s)
COVID-19 , Deep Learning , Algorithms , Humans , Neural Networks, Computer , SARS-CoV-2 , X-Rays
12.
Eur Radiol ; 31(8): 6096-6104, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1100961

ABSTRACT

OBJECTIVE: The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. METHODS: We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. RESULTS: The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. CONCLUSION: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. KEY POINTS: • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.


Subject(s)
COVID-19 , Deep Learning , Algorithms , Artificial Intelligence , COVID-19 Testing , Humans , SARS-CoV-2 , Tomography, X-Ray Computed
13.
Revista Romana de Medicina de Laborator ; 29(1):85-91, 2021.
Article in English | GIM | ID: covidwho-1082180

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) has spread rapidly in China and globally. In order to control the spread of the epidemic, it is important to find an efficient diagnostic method. Objectives: The aim of this study was to assess the responses of antibodies during SARS-CoV-2 infection in relation to disease severity and to evaluate the association between the positive rate of antibody detection and nucleic acid test.

14.
IEEE J Biomed Health Inform ; 25(5): 1336-1346, 2021 05.
Article in English | MEDLINE | ID: covidwho-1075741

ABSTRACT

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography, Thoracic/methods , Humans , Lung/diagnostic imaging , SARS-CoV-2
15.
Cell Res ; 31(2): 126-140, 2021 02.
Article in English | MEDLINE | ID: covidwho-1015005

ABSTRACT

The current coronavirus disease 2019 (COVID-19) pandemic presents a global public health challenge. The viral pathogen responsible, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), binds to the host receptor ACE2 through its spike (S) glycoprotein, which mediates membrane fusion and viral entry. Although the role of ACE2 as a receptor for SARS-CoV-2 is clear, studies have shown that ACE2 expression is extremely low in various human tissues, especially in the respiratory tract. Thus, other host receptors and/or co-receptors that promote the entry of SARS-CoV-2 into cells of the respiratory system may exist. In this study, we found that the tyrosine-protein kinase receptor UFO (AXL) specifically interacts with the N-terminal domain of SARS-CoV-2 S. Using both a SARS-CoV-2 virus pseudotype and authentic SARS-CoV-2, we found that overexpression of AXL in HEK293T cells promotes SARS-CoV-2 entry as efficiently as overexpression of ACE2, while knocking out AXL significantly reduces SARS-CoV-2 infection in H1299 pulmonary cells and in human primary lung epithelial cells. Soluble human recombinant AXL blocks SARS-CoV-2 infection in cells expressing high levels of AXL. The AXL expression level is well correlated with SARS-CoV-2 S level in bronchoalveolar lavage fluid cells from COVID-19 patients. Taken together, our findings suggest that AXL is a novel candidate receptor for SARS-CoV-2 which may play an important role in promoting viral infection of the human respiratory system and indicate that it is a potential target for future clinical intervention strategies.


Subject(s)
COVID-19/metabolism , Proto-Oncogene Proteins/metabolism , Receptor Protein-Tyrosine Kinases/metabolism , Respiratory Mucosa/cytology , SARS-CoV-2/physiology , Spike Glycoprotein, Coronavirus/metabolism , Bronchi/cytology , Bronchi/metabolism , Cell Line , Humans , Lung/cytology , Lung/metabolism , Models, Molecular , Protein Interaction Domains and Motifs , Proto-Oncogene Proteins/analysis , Receptor Protein-Tyrosine Kinases/analysis , Respiratory Mucosa/metabolism , SARS-CoV-2/chemistry , Spike Glycoprotein, Coronavirus/analysis , Virus Internalization
16.
Environ Int ; 147: 106361, 2021 02.
Article in English | MEDLINE | ID: covidwho-987643

ABSTRACT

Corona virus disease 2019 has spread worldwide, and appropriate drug design and screening activities are required to overcome the associated pandemic. Using computational simulation, blockade mechanism of SARS-CoV-2 spike receptor binding domain (S RBD) and human angiotensin converting enzyme 2 (hACE2) was clarified based on interactions between RBD and hesperidin. Interactions between anti-SARS-CoV-2 drugs and therapy were investigated based on the binding energy and druggability of the compounds, and they exhibited negative correlations; the compounds were classified into eight common types of structures with highest activity. An anti-SARS-CoV-2 drug screening strategy based on blocking S RBD/hACE2 binding was established according to the first key change (interactions between hesperidin and S RBD/hACE2) vs the second key change (interactions between anti-SARS-CoV-2 drugs and RBD/hACE2) trends. Our findings provide valuable information on the mechanism of RBD/hACE2 binding and on the associated screening strategies for anti-SARS-CoV-2 drugs based on blocking mechanisms of pockets.


Subject(s)
COVID-19 , Pharmaceutical Preparations , Humans , Peptidyl-Dipeptidase A , SARS-CoV-2 , Spike Glycoprotein, Coronavirus
18.
Adv Mater ; 32(43): e2004901, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-756243

ABSTRACT

The COVID-19 pandemic has taken a significant toll on people worldwide, and there are currently no specific antivirus drugs or vaccines. Herein it is a therapeutic based on catalase, an antioxidant enzyme that can effectively breakdown hydrogen peroxide and minimize the downstream reactive oxygen species, which are excessively produced resulting from the infection and inflammatory process, is reported. Catalase assists to regulate production of cytokines, protect oxidative injury, and repress replication of SARS-CoV-2, as demonstrated in human leukocytes and alveolar epithelial cells, and rhesus macaques, without noticeable toxicity. Such a therapeutic can be readily manufactured at low cost as a potential treatment for COVID-19.


Subject(s)
Anti-Inflammatory Agents/therapeutic use , Antioxidants/therapeutic use , Betacoronavirus/drug effects , Catalase/therapeutic use , Coronavirus Infections/drug therapy , Pneumonia, Viral/drug therapy , Animals , Anti-Inflammatory Agents/pharmacokinetics , Antioxidants/pharmacokinetics , Betacoronavirus/physiology , COVID-19 , Catalase/pharmacokinetics , Cell Line , Coronavirus Infections/metabolism , Coronavirus Infections/virology , Humans , Leukocytes/drug effects , Leukocytes/metabolism , Leukocytes/virology , Macaca mulatta , Mice , Mice, Inbred BALB C , Oxidative Stress/drug effects , Pandemics , Pneumonia, Viral/metabolism , Pneumonia, Viral/virology , Pulmonary Alveoli/drug effects , Pulmonary Alveoli/metabolism , Pulmonary Alveoli/virology , SARS-CoV-2 , Virus Replication/drug effects
19.
Complementary Therapies in Clinical Practice ; : 101131, 2020.
Article | WHO COVID | ID: covidwho-4388
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