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CoViD-19 Immunopathology & Immunotherapy


Francesco Chiappelli1,*, Allen Khakshooy2, Gillian Greenberg2



1Professor Emeritus, UCLA, Center for the Health Sciences, Los Angeles, CA, USA; 2Pre-M.D. Student, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel;



Francesco Chiappelli, Ph.D., Dr. Endo (h.c), E-mail: Chiappelli.research@gmail.com
*Corresponding author:


Article Type




Received February 25, 2020; Accepted March 10, 2020; Published March 31, 2020



T-cell immuno-pathology in patients with Corona Virus Disease 2019 (CoViD-19) is known [1]. It reports interesting observations on 522 patients with confirmed CoViD-19 symptomatology, compared to 40 control subjects. In brief, remarkable T cytopoenia was recorded by flow cytometry in the CD4+ and the CD8+ populations, which were significantly yet inversely correlated with remarkably increased serum levels of the pro-inflammatory cytokines IL-6, IL-10 and TNF-a. Flow cytometry established a progressive increase in the expression of programmed cell death marker-1 (PD-1) and T cell immunoglobulin & mucin domain 3 (Tim-3) as patients (n=14) deteriorated from prodromal to symptomatic CoViD-19 requiring intensive care. Here, we interpret these observations of Diao et al from our current understanding of T cell immunophysiology and immunopathology following an immune challenge in the form of sustained viral infection, as is the case in CoViD-19, with emphasis on exhausted T cells (Tex). Recent clinical trials to rescue Tex show promising outcomes. The relevance of these interventions for the prevention and treatment of CoViD-19 is discussed. Taken together, the data of Diao et al could proffer the first glimpse of immunopathology and possible immunotherapy for patients with CoViD-19.



Corona Virus Disease 2019 (CoViD-19); T cell exhaustion (Tex) markers, programmed cell death marker 1 (CD279 - PD-1); T cell immunoglobulin & mucin domain-3 (CD366 - Tim-3); cytokine storm; clinical trials



Chiappelli et al. 16(3): 219-222 (2020)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.