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The Adaptation Model of Immunity

Authors: Masoud H, Manjili;

The Adaptation Model of Immunity

Abstract

Although 'self-nonself' and 'danger' theories have improved our understanding of the immune system, successful immunotherapy of cancer and many autoimmune diseases still remain far from reach. This indicates that our knowledge of how the immune system decides to respond effectively or ineffectively is limited. Emerging evidence suggest that decision-making during the immune response is not solely determined by 'nonself' entity of the antigen or damage-associated 'danger' signals. This article provides an overview of the 'self-nonself' and 'danger' models, and suggests that 'adaptation' signals are needed to guarantee immunological tolerance that has been observed during the immune response toward 'self', 'nonself' or even 'danger'. This should be facilitated by dynamic expression of adapting receptors (ARs) and adapting ligands on cells of the immune system and other somatic cells. Any alterations in the expression of ARs on certain tissues would result in tissue-specific autoimmune diseases or spontaneous regression of cancer. Identification of such ARs and their nominal adapting ligands could lead to the discovery of currently unknown receptors and their implications in the treatment of cancer, solid organ transplantation and autoimmune diseases.

Keywords

Immunity, Models, Immunological, Adaptation, Physiological, Autoantigens, Autoimmune Diseases, Gene Expression Regulation, Neoplasms, Immune Tolerance, Animals, Humans

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    popularity
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    Top 10%
    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Average
Average
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