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Dr. Abeer Elsayed Abdel maksoud :: Publications:

Title:
Serum Levels of Intercellular Adhesion Molecule-1 and TNF- in Patients with COVID-19 and Its Relation to Disease Severity
Authors: Amira E. Ramadan1, Marwa S. Azab1, Abeer E. Abdelmaksoud2, Etemad A. Mohammad3, Nashwa A. Amin3, Amira H. Allam*3, Samah M. Eldsouky4, Amany K. Shahat1
Year: 2022
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: Local
Paper Link: Not Available
Full paper Abeer Elsayed Abdel maksoud_paper2.pdf
Supplementary materials Not Available
Abstract:

Background: Coronavirus disease 2019 (COVID-19) is a major health concern and can be devastating, especially for the elderly. It causes a systemic inflammatory response, involving dysregulation and misexpression of many inflammatory cytokines. Objectives: To assess the cytokine profile in COVID-19 patients and detect its relation with disease severity. Patients and methods: This study included 50 COVID-19 patients confirmed by reverse transcription–polymerase chain reaction (RT-PCR), of whom 20 with severe pneumonia, 20 with moderate and 10 with mild disease, and 50 healthy control. Quantitative ELISA tests were performed for tumour necrosis factor alfa (TNF-α) and intercellular adhesion molecule1 (ICAM-1). Results: Level of ICAM-1 is higher in cases of COVID-19 than healthy controls (median for cases = 420 ng/ml). Also TNF-α level is higher in cases with a median of 165 both levels were higher in CORADS 6 and positively correlated with CT findings. Conclusion: Patients with severe COVID-19 might have a cytokine storm syndrome and increased expression of endothelial cell adhesion molecules as ICAM-1, which is related to COVID-19 and disease severity and may contribute to coagulation dysfunction. Levels of ICAM 1 were associated with more severe CT findings and longer hospital stay, also ICAM 1 and TNF can be used as diagnostic measures as they have a higher sensitivity specificity and predictive values.

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