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Permafrost viremia and immune tweening



Jaden Penhaskashi1, Olivia Sekimoto2 &  Francesco Chiappelli3, 4*



1Division of West Valley Dental Implant Center, Encino, CA 91316, USA; 2Cajulis Pediatric Dentistry, Chula Vista, CA 91914, USA; 3Dental Group of Sherman Oaks, CA 91403 , USA; 4Center for the Health Sciences, UCLA, Los Angeles, CA, USA, *Corresponding author





Article Type

Research Article



Received June 1, 2023; Revised June 30, 2023; Accepted June 30, 2023, Published June 30, 2023



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.



Permafrost pathogens, Viremia, Inflammation, Machine Learning, Deep Learning, Deep Ensemble Learning, Molecular Dynamics Simulation, Neuronal Networks, Generative Adversarial Networks, Tracking Responders EXpanding, Immune tweening, TcR[αβ] (T cell receptor endowed with the α and β chains), T cell immunity, B cell immunity, CD45R0 (marker of immune cell memory differentiation: ultimate restriction fragment [0] of the common leukocyte antigen, cluster of differentiation [CD]45), CD45RA (marker of naive CD4 & CD8 T cells, first restriction fragment [A] of CD45), TRegs (CD4/8+CD45RA+/R0+FoxP3+), CD279 (programmed cell death marker-1), CD62L (l-selectin, marker of T cells migration), CD25 (α-chain of the interleukin[IL]2 receptor, marker of T cell activation), Tim-3 (T cell immunoglobulin & mucin domain 3), GlycA (glycoprotein acetylation, systemic biomarker of systemic inflammation and autoimmunity), Autologous Immune Enhancement Therapy.



Penhaskashi et al. Bioinformation 19(6): 685-691 (2023)


Edited by

P Kangueane






Biomedical Informatics



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.