Publication Name	World Journal of Clinical Infectious Diseases
Manuscript ID	65056
Country	China

Category	Infectious Diseases
Manuscript Type	Meta-Analysis
Article Title	Lymphocyte count predicts the severity of COVID-19: Evidence from a meta-analysis
Manuscript Source	Unsolicited Manuscript
All Author List	Yi-Si Zhao and Ying-Xi Yu
Funding Agency and Grant Number
Corresponding Author	Yi-Si Zhao, MM, Doctor, Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi road, Yuzhong District, Chongqing 400016, China. 476663973@qq.com
Key Words	COVID-19; Lymphocyte count; Coronavirus; Severe of disease; Meta-analysis
Core Tip	Lymphocyte count reflects immune function and inflammatory state of body with infectious disease. Severe acute respiratory syndrome coronavirus 2 spreads and invades through respiratory mucosa, triggers a series of immune responses and induces cytokine storm in vivo, resulting in changes in immune components such as lymphocytes. Previous studies have shown that the decrease in lymphocyte count can be used as an indicator of severity for both severe acute respiratory syndrome and Middle East respiratory syndrome, both of which are coronavirus infections. Therefore, this paper systematically reviews and meta-analysis the literature evaluating the diagnostic and prognostic utility of the lymphocyte count in patients with viral pneumonia by coronavirus infections.
Citation	Zhao YS, Yu YX. Lymphocyte count predicts the severity of COVID-19: Evidence from a meta-analysis. World J Clin Infect Dis 2021; 11(3): 49-59

Received	2021-02-27 12:05
Peer-Review Started	2021-02-27 12:20
To Make the First Decision
Return for Revision	2021-03-31 05:32
Revised	2021-04-03 14:30
Second Decision	2021-09-09 03:22
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-09-10 09:32
Articles in Press	2021-09-10 09:32
Publication Fee Transferred
Edit the Manuscript by Language Editor	2021-10-26 18:04
Typeset the Manuscript	2021-11-02 00:47

ISSN	2220-3176 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Copyright	© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com