Correlating COVID-19 Infection Severity and Prognosis with Cytokines Level

Abstract

Background: It is noted that severe acute respiratory syndrome caused by coronavirus (SARS-CoV2) is considered one of the most important issue under research nowadays. Most patients reveal mild flu like symptoms while some are asymptomatic, there is a considerable percentage that requires special hospital care that may extend to intensive care. Upon studying immune system response to COVID, both innate and adaptive immunity had role in defense against infection, involving leucopenia with all types of T cell low count associated with abnormal rise of the cytokine level the what so called cytokine storm which lead to severe lung damage and severe respiratory symptoms. Objectives: Our study aims to classify and identify prognosis of disease by examining selected cytokines and correlating them with severity of symptoms that could help to predict course of disease in patients and detect clinically their improvement. Also by analyzing different immune parameters, novel therapeutic ways could be detected. Methodology: COVID-19 Confirmation was based on RT-PCR along with blood analysis involving differential measurement of leucocytic count and ELISA measurement of selected cytokines was done. Results: Data obtained in our study from SARS-CoV-2 infected patients have shown that most cases had been characterized by a cytokine storm with elevated levels of IL-1, IL-6 and TNF-α respectively that decline upon improvement of clinical conditions. Conclusion: The novel coronavirus mainly acts on lymphocytes, especially T cells. T cell types analysis especially easinophils and lymphocytes can help in early diagnosis of cases. Cytokines level monitoring is of great benefit in early screening, diagnosis, and classification of patients before clinical symptoms and complications arouse where prognostic features will be available for future patients outcomes whether complete cure or ICU admission will be required based on his cytokine profiling system.