The Binary Model of Chronic Diseases Applied to COVID-19
Zeev Elkoshi · 2021 · Frontiers in Immunology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
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 associ
Abstract by Zeev Elkoshi, Frontiers in Immunology (2021) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3389/fimmu.2021.716084