ABSTRACT
The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.
ABSTRACT
Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening disease occurring several weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Deep immune profiling showed acute MIS-C patients had highly activated neutrophils, classical monocytes and memory CD8+ T-cells; increased frequencies of B-cell plasmablasts and double-negative B-cells. Post treatment samples from the same patients, taken when symptoms were resolving, identified recovery-associated immune features including CD163+ monocytes, emergence of a new population of immature neutrophils and, in some patients, a transient increase in arginase. Plasma profiling identified multiple features shared by MIS-C, Kawasaki Disease and COVID-19 and that therapeutic inhibition of IL6 may be preferable to IL1 or TNF-a. We identified potential new mechanisms of action for IVIG, the most commonly used drug to treat MIS-C. Finally, we showed systemic complement activation with high plasma C5b-9 levels is common in MIS-C, suggesting complement inhibitors could be used to treat the disease.Funding Information: Birmingham Women’s and Children’s Hospital Charity funded the single cell RNA sequencing analysis. No other external funding was received. Declaration of Interests: None declared.Ethics Approval Statement: Reviewed and approved by South of Birmingham Research Ethics Committee (REC: 17/WM/0453, IRAS: 233593).
Subject(s)
Coronavirus Infections , Cryopyrin-Associated Periodic Syndromes , Mucocutaneous Lymph Node Syndrome , Severe Acute Respiratory Syndrome , COVID-19ABSTRACT
Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening disease occurring several weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. MIS-C has overlapping clinical features with Kawasaki Disease (KD), a rare childhood vasculitis, but whether these diseases share underpinning immunological perturbations is unknown. Deep immune profiling showed acute MIS-C patients had highly activated neutrophils, classical monocytes and memory CD8+ T-cells; increased frequencies of B-cell plasmablasts and double-negative B-cells; and increased cytokine levels. Post treatment samples from the same patients, taken when symptoms were resolving, identified recovery-associated immune features including CD163+ monocytes, emergence of a new population of immature neutrophils and, in some patients, a transient increase in arginase. Our data show MIS-C and KD share substantial immunopathology during the acute and recovery stages of disease and identify potential new mechanisms of action for IVIG, a widely used anti-inflammatory drug used to treat MIS-C, KD and other inflammatory diseases.