1. Immune Computation and COVID-19 Mortality: A Rationale for IVIg
- Author
-
Henri Atlan, Sol Efroni, and Irun R. Cohen
- Subjects
Coronavirus disease 2019 (COVID-19) ,Immunology ,Inflammation ,Machine Learning ,Immune system ,medicine ,Immunology and Allergy ,Humans ,COVID-19 Serotherapy ,Aged ,Aged, 80 and over ,Training set ,biology ,business.industry ,SARS-CoV-2 ,Repertoire ,Immunization, Passive ,COVID-19 ,Immunoglobulins, Intravenous ,medicine.disease ,Immunization ,biology.protein ,Antibody ,medicine.symptom ,Cytokine storm ,business ,Cytokine Release Syndrome ,Algorithms - Abstract
COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg).
- Published
- 2021