The Binary Model of Chronic Diseases Applied to COVID-19
Copyright © 2021 Elkoshi..
A binary model for the classification of chronic diseases has formerly been proposed. The model classifies chronic diseases as "high Treg" or "low Treg" diseases according to the extent of regulatory T cells (Treg) activity (frequency or function) observed. The present paper applies this model to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The model correctly predicts the efficacy or inefficacy of several immune-modulating drugs in the treatment of severe coronavirus disease 2019 (COVID-19) disease. It also correctly predicts the class of pathogens mostly associated with SARS-CoV-2 infection. The clinical implications are the following: (a) any search for new immune-modulating drugs for the treatment of COVID-19 should exclude candidates that do not induce "high Treg" immune reaction or those that do not spare CD8+ T cells; (b) immune-modulating drugs, which are effective against SARS-CoV-2, may not be effective against any variant of the virus that does not induce "low Treg" reaction; (c) any immune-modulating drug, which is effective in treating COVID-19, will also alleviate most coinfections; and (d) severe COVID-19 patients should avoid contact with carriers of "low Treg" pathogens.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Frontiers in immunology - 12(2021) vom: 26., Seite 716084 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Elkoshi, Zeev [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 29.09.2021 Date Revised 07.12.2022 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.3389/fimmu.2021.716084 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM330815539 |
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| 520 | |a A binary model for the classification of chronic diseases has formerly been proposed. The model classifies chronic diseases as "high Treg" or "low Treg" diseases according to the extent of regulatory T cells (Treg) activity (frequency or function) observed. The present paper applies this model to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The model correctly predicts the efficacy or inefficacy of several immune-modulating drugs in the treatment of severe coronavirus disease 2019 (COVID-19) disease. It also correctly predicts the class of pathogens mostly associated with SARS-CoV-2 infection. The clinical implications are the following: (a) any search for new immune-modulating drugs for the treatment of COVID-19 should exclude candidates that do not induce "high Treg" immune reaction or those that do not spare CD8+ T cells; (b) immune-modulating drugs, which are effective against SARS-CoV-2, may not be effective against any variant of the virus that does not induce "low Treg" reaction; (c) any immune-modulating drug, which is effective in treating COVID-19, will also alleviate most coinfections; and (d) severe COVID-19 patients should avoid contact with carriers of "low Treg" pathogens | ||
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