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Related Concept Videos

Antigens Involved in Adaptive Immunity01:26

Antigens Involved in Adaptive Immunity

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An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
Complete Antigens
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Special Features of Adaptive Immunity01:20

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The adaptive immune system, a crucial component of the overall immune response, offers a highly specialized defense against pathogens. It involves specific cell types and features, enabling it to combat infections effectively and efficiently.
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The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
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Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
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The T and B lymphocytes of the adaptive immune system develop from common lymphoid progenitor cells in the bone marrow. These progenitors give rise to precursors that eventually develop into both T and B lymphocytes. As these precursors mature, they gain the ability to detect and respond to foreign antigens in the body, a process known as immunocompetence. Additionally, these precursors acquire self-tolerance, a process that ensures they do not react to self-antigens. This intricate system...
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Related Experiment Video

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Generation of Human Alloantigen-specific T Cells from Peripheral Blood
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Adaptive discrimination between harmful and harmless antigens in the immune system by predictive coding.

Kana Yoshido1, Honda Naoki1,2,3,4

  • 1Laboratory of Theoretical Biology, Graduate School of Biostudies, Kyoto University, Yoshida-Konoecho, Sakyo, Kyoto 606-8315, Japan.

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Summary

The immune system learns antigen risks using a predictive coding framework, similar to machine learning. This model explains how T cells adaptively discriminate between harmful and harmless substances based on past experiences.

Keywords:
Immunologycomputing methodologymachine learningmathematical biosciences

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Area of Science:

  • Immunology
  • Computational Biology
  • Machine Learning

Background:

  • The immune system distinguishes between harmful and harmless antigens through experience, but the mechanism remains unclear.
  • Predictive coding, a machine learning concept where systems update predictions based on errors, offers a potential framework.

Purpose of the Study:

  • To model the population dynamics of T cells using predictive coding principles.
  • To understand how the immune system learns to discriminate antigen risks.

Main Methods:

  • Developed a computational model simulating T cell population dynamics.
  • Applied the concept of predictive coding to model conventional and regulatory T cell behavior.
  • Utilized numerical simulations to analyze antigen concentration and input rapidness effects.

Main Results:

  • The model demonstrates that the immune system identifies antigen risks based on concentration and input speed.
  • Prediction error signals in T cells may drive differentiation into memory T cells.
  • The model successfully reproduced history-dependent immune responses, such as in allergies.

Conclusions:

  • This study presents a novel predictive coding framework for understanding immune system learning.
  • The findings offer insights into adaptive immune responses and antigen risk assessment.
  • The model provides a basis for further research into immune system memory and discrimination.