SARS-CoV-2 Infection in Children with Cancer: Experience from a Tertiary Care Center in North India

Introduction Children with cancer are immunocompromised due to the disease per se or anticancer therapy. Children are believed to be at a lower risk of severe coronavirus disease 2019 (COVID-19) disease.Objective This study analyzed the outcome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children with cancer.Materials and Methods A retrospective analysis was performed on patients (<= 14 years) with cancer attending the pediatric oncology services of our institute who tested positive for the SARS-CoV-2 infection and those who had COVID-19 disease between August 2020 and May 2021. Real-time reverse transcriptase-polymerase chain reaction performed on the nasopharyngeal swab identified the SARS-CoV-2 infection. The primary endpoints were clinical recovery, interruption of cancer treatment, and associated morbidity and mortality.Results Sixty-six (5.7%) of 1,146 tests were positive for the SARS-CoV-2 infection. Fifty-two (79%) and 14 (21%) patients had hematolymphoid and solid malignancies. Thirty-two (48.5%) patients were asymptomatic. A mild-moderate, severe, or critical disease was observed in 75% (18/24), 12.5% (3/24), and 12.5% (3/24) of the symptomatic patients. The "all-cause" mortality was 7.6% (5/66), with only one (1.5%) death attributable to COVID-19. Two (3%) patients required ventilation. Two (3%) patients had a delay in cancer diagnosis secondary to COVID-19 infection. Thirty-eight (57.6%) had a disruption in anticancer treatment.Conclusion Children with cancer do not appear to be at an increased risk of severe illness due to SARS-CoV-2 infection. Our findings substantiate continuing the delivery of nonintensive anticancer treatment unless sick. However, SARS-CoV-2 infection interrupted anticancer therapy in a considerable proportion of children.

A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.

Genomic Image Representation of Human Coronavirus Sequences for COVID-19 Detection

Coronavirus (CoV) disease 2019 (COVID-19) is a severe pandemic affecting millions worldwide. Due to its rapid evolution, researchers have been working on developing diagnostic approaches to suppress its spread. This study presents an effective automated approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19, among other human CoV diseases with high acceptable accuracy. The GIP technique was applied as follows: first, genomic graphical mapping techniques were used to convert the genome sequences into genomic grayscale images. The frequency chaos game representation (FCGR) and single gray-level representation (SGLR) techniques were used in this investigation. Then, several statistical features were obtained from the images to train and test many classifiers, including the k-nearest neighbors (KNN). This study aimed to determine the efficacy of the FCGR (with different orders) and SGLR images for accurately detecting COVID-19,using a dataset containing both partial and complete genome sequences. The results recommended the fourth-order FCGR image as a proper genomic image for extracting statistical features and achieving accurate classification. Furthermore, the results showed that KNN achieved an overall accuracy of 99.39% in detecting COVID-19, among other human CoV diseases, with 99.48% precision, 99.31% sensitivity, 99.47% specificity, 0.99 F1-score, and 0.99 Matthew's correlation coefficient.