Permafrost viremia and immune tweening
The immune system, an exquisitely regulated physiological system, utilizes a wide spectrum of soluble factors and multiple cell populations and subpopulations at diverse states of maturation to monitor and protect the organism against foreign organisms. Immune surveillance is ensured by distinguishi...
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ftpubmed:oai:pubmedcentral.nih.gov:10598357 2023-11-12T04:24:31+01:00 Permafrost viremia and immune tweening Penhaskashi, Jaden Sekimoto, Olivia Chiappelli, Francesco 2023-06-30 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598357/ https://doi.org/10.6026/97320630019685 en eng Biomedical Informatics http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598357/ http://dx.doi.org/10.6026/97320630019685 © 2023 Biomedical Informatics https://creativecommons.org/licenses/by/3.0/This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. Bioinformation Research Article Text 2023 ftpubmed https://doi.org/10.6026/97320630019685 2023-10-29T01:00:34Z The immune system, an exquisitely regulated physiological system, utilizes a wide spectrum of soluble factors and multiple cell populations and subpopulations at diverse states of maturation to monitor and protect the organism against foreign organisms. Immune surveillance is ensured by distinguishing self-antigens from self-associated with non-self (e.g., viral) peptides presented by major histocompatibility complexes (MHC). Pathology is often identified as unregulated inflammatory responses (e.g., cytokine storm), or recognizing self as a non-self entity (i.e., auto-immunity). Artificial intelligence (AI), and in particular specific machine learning (ML) paradigms (e.g., Deep Learning [DL]) proffer powerful algorithms to better understand and more accurately predict immune responses, immune regulation and homeostasis, and immune reactivity to challenges (i.e., immune allostasis) by their intrinsic ability to interpret immune parameters, pathways and events by analyzing large amounts of complex data and drawing predictive inferences (i.e., immune tweening). We propose here that DL models play an increasingly significant role in better defining and characterizing immunological surveillance to ancient and novel virus species released by thawing permafrost. Text permafrost PubMed Central (PMC) Bioinformation 19 6 685 691 |
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Research Article Penhaskashi, Jaden Sekimoto, Olivia Chiappelli, Francesco Permafrost viremia and immune tweening |
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Research Article |
description |
The immune system, an exquisitely regulated physiological system, utilizes a wide spectrum of soluble factors and multiple cell populations and subpopulations at diverse states of maturation to monitor and protect the organism against foreign organisms. Immune surveillance is ensured by distinguishing self-antigens from self-associated with non-self (e.g., viral) peptides presented by major histocompatibility complexes (MHC). Pathology is often identified as unregulated inflammatory responses (e.g., cytokine storm), or recognizing self as a non-self entity (i.e., auto-immunity). Artificial intelligence (AI), and in particular specific machine learning (ML) paradigms (e.g., Deep Learning [DL]) proffer powerful algorithms to better understand and more accurately predict immune responses, immune regulation and homeostasis, and immune reactivity to challenges (i.e., immune allostasis) by their intrinsic ability to interpret immune parameters, pathways and events by analyzing large amounts of complex data and drawing predictive inferences (i.e., immune tweening). We propose here that DL models play an increasingly significant role in better defining and characterizing immunological surveillance to ancient and novel virus species released by thawing permafrost. |
format |
Text |
author |
Penhaskashi, Jaden Sekimoto, Olivia Chiappelli, Francesco |
author_facet |
Penhaskashi, Jaden Sekimoto, Olivia Chiappelli, Francesco |
author_sort |
Penhaskashi, Jaden |
title |
Permafrost viremia and immune tweening |
title_short |
Permafrost viremia and immune tweening |
title_full |
Permafrost viremia and immune tweening |
title_fullStr |
Permafrost viremia and immune tweening |
title_full_unstemmed |
Permafrost viremia and immune tweening |
title_sort |
permafrost viremia and immune tweening |
publisher |
Biomedical Informatics |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598357/ https://doi.org/10.6026/97320630019685 |
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permafrost |
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permafrost |
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Bioinformation |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598357/ http://dx.doi.org/10.6026/97320630019685 |
op_rights |
© 2023 Biomedical Informatics https://creativecommons.org/licenses/by/3.0/This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |
op_doi |
https://doi.org/10.6026/97320630019685 |
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