Abstract
COVID-19 infection tends to be more lethal in older persons than in the young; death results from an over-active 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).
Original language | English |
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Pages (from-to) | 195-203 |
Number of pages | 9 |
Journal | Critical Reviews in Immunology |
Volume | 40 |
Issue number | 3 |
DOIs | |
State | Published - 2020 |
Bibliographical note
Publisher Copyright:© 2020 by Begell House, Inc.
Keywords
- Inflammation
- Machine learning
- Public repertoires
- Test data
- Training data