ABSTRACT
COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach's effectiveness. © World Scientific Publishing Company.
ABSTRACT
Objectives: Cytokine release syndrome (CRS) is believed to be responsible for death in COVID-19. Tocilizumab is an interleukin (IL)-6 receptor antagonist, IL-6 being identified as a major component of the CRS cascade. The objective of the study was to determine if tocilizumab can prevent mortality and morbidity in moderate-to-severe COVID-19 pneumonia. Method(s): Patients admitted to the ICU between the time period of June 2020-August 2020 were included in this retrospective and cohort study conducted at GCS medical college, hospital and research center. Patients had to be more than 18 years of age and were required to have a positive reverse transcription polymerase chain reaction report for COVID-19. After applying the inclusion/exclusion criteria, 119 patients were considered for final analysis. Tocilizumab was administered as a single dose of 8 mg/kg in 22 patients. Rest of the patients received standard of care regime. The primary outcome was either discharge or death of the patients and the requirement of invasive mechanical ventilation during their hospital stay. The secondary outcome was the length of hospital stay. Appropriate demographic, clinical, and laboratory data were documented. Statistical analysis was done with appropriate clinical tests with significance set at p<0.05. Result(s): Tocilizumab significantly reduced deaths in patients as well as the need for mechanical ventilation with NNT=3 and 5, respectively. The same held true even when the data were adjusted for age, gender, and number of comorbidities. Number of comorbidities had a negative association with mortality and need for mechanical ventilation irrespective of administration of tocilizumab as evidenced by multivariable logistic regression. There was no effect of tocilizumab in shortening the hospital stay in patients. Conclusion(s): Tocilizumab seems to be a promising agent for the treatment of moderate to severe COVID-19 pneumonia and similar agents hold promise for any similar future emerging infections. Copyright © 2022 The Authors.