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The field of immunometabolism investigates and describes the effects of metabolic rewiring in immune cells throughout activation and the fates of these cells. Recently, it has been appreciated that immunometabolism plays an essential role in the progression of viral infections, cancer, and autoimmune diseases. Regarding COVID‐19, the aberrant immune response underlying the progression of diseases establishes two major respiratory pathologies, including acute respiratory distress syndrome (ARDS) or pneumonia‐induced acute lung injury (ALI). Both innate and adaptive immunity (T cell‐based) were impaired in the course of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection. Current findings have deciphered that macrophages (innate immune cells) are involved in the inflammatory response seen in COVID‐19. It has been demonstrated that immune system cells can change metabolic reprogramming in some conditions, including autoimmune diseases, cancer, and infectious disease, including COVID‐19. The growing findings on metabolic reprogramming in COVID‐19 allow an exploration of metabolites with immunomodulatory properties as future therapies to combat this hyperinflammatory response. The elucidation of the exact role and mechanism underlying this metabolic reprograming in immune cells could help apply more precise approaches to initial diagnosis, prognosis, and in‐hospital therapy. This report discusses the latest findings from COVID‐19 on host metabolic reprogramming and immunometabolic responses.

Purpose This purpose of this study is to perfrom the analysis of COVID-19 with the help of blood samples. The blood samples used in the study consist of more than 100 features. So to process high dimensional data, feature reduction has been performed by using the genetic algorithm. Design/methodology/approach In this study, the authors will implement the genetic algorithm for the prediction of COVID-19 from the blood test sample. The sample contains records of around 5,644 patients with 111 attributes. The genetic algorithm such as relief with ant colony optimization algorithm will be used for dimensionality reduction approach. Findings The implementation of this study is done through python programming language and the performance evaluation of the model is done through various parameters such as accuracy, sensitivity, specificity and area under curve (AUC). Originality/value The implemented model has achieved an accuracy of 98.7%, sensitivity of 96.76%, specificity of 98.80% and AUC of 92%. The results have shown that the implemented algorithm has performed better than other states of the art algorithms.

One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 . Many researchers have faced various types of challenges for finding the accurate model which can automatically detect the COVID-19 using Computed pulmonary Tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based on ResNet 50, named CORNet for the detection of COVID-19, and also performed the retrospective and multicenter analysis for the extraction of visual characteristics from volumetric chest CT scans during COVID-19 detection. Between August 2016 and May 2020, the datasets were obtained from six hospitals. Results are evaluated on the image dataset consisting of a total of 10,052 CT scan images generated from 7,850 patients, and the average age of the patients was 50 years. The implemented model has achieved the sensitivity and specificity of 90% and 96%, per scanned image with an AUC of 0. 95.