Permafrost viremia and immune tweening

The immune system, an exquisitely regulated physiological utilizes a wide spectrum of soluble factors and multiple cell populations subpopulations at diverse states maturation to monitor 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 major histocompatibility complexes (MHC). Pathology often identified as unregulated inflammatory responses cytokine storm), or recognizing self entity (i.e., auto-immunity). Artificial intelligence (AI), in particular specific machine learning (ML) paradigms Deep Learning [DL]) proffer powerful algorithms better understand more accurately predict responses, regulation homeostasis, reactivity challenges allostasis) their intrinsic ability interpret parameters, pathways events analyzing large amounts complex data drawing predictive inferences tweening). We propose here that DL models play increasingly significant role defining characterizing immunological ancient novel virus species released thawing permafrost.