Abstract
Novel developments in bioinformation, bioinformatics and biostatistics, including artificial intelligence (AI), play a timely and critical role in translational care. Case in point, the extent to which viral immune surveillance is regulated by immune cells and soluble factors, and by non-immune factors informs the administration of health care. The events by which health is regained following viral infection is an allostatic process, which can be modeled using Hilbert's and Volterra's mathematical biology criteria, and biostatistical methodologies such as linear multiple regression. Health regained following viral infection can be given as Y being the sum-total of the positive factors and events (∏) that inherently push allostasis forward (i.e., the orderly process of immune activation and maturation) and the negative (N) factors and events that, allostatically speaking, interfere with regaining health. Any gaps in knowledge are filled by AI-aided immune tweening. Proof of concept can be tested with the fast-gaining infection using tick-borne Bunyavirus that cause severe fever with thrombocytopenia syndrome (SFTS).
Highlights
Recent progress in the administration of health care has identified two principal facets: translational research, the generation of fundamental research evidence about the underlying pathobiology for implementation in patient care, and translational effectiveness, the consensus of the best available evidence for clinical intervention
major histocompatibility complex (MHC) Class I only trigger the activation of cluster differentiation # 3 (CD3)/T cell receptor (TcR) cells that express the CD8 moiety, and MHC Class II trigger CD3/TcR cells that express CD4 [3,4,5,6]
The question becomes, knowing what we know today about the constituents of ΣΠ and of ΣN, can we not design, by means of bioinformatics, artificial Π’s and N’s, that may push the organism’s response more securely through all the allostatic phases to Y, the homeostatic state of health regained following a viral infection? Physiology has been able to a related feat by producing bioinformatics particles, which when injected in patients help improve both diagnostics and treatment
Summary
Recent progress in the administration of health care has identified two principal facets: translational research, the generation of fundamental research evidence about the underlying pathobiology for implementation in patient care, and translational effectiveness, the consensus of the best available evidence for clinical intervention. The presentation of antigenic non-self via MHC to either CD8+ or CD4+ T cells initiates the process of activation, proliferation and terminal maturation Together, these events ensure a complete, effective and efficient immune surveillance against viral infection, and can be monitored by functional and phenotypic studies. It is simple to consider the existence of factors and or events that fall neither in the positive nor negative variables As this computerized system develops, the elucidation of more specific factors impacting regained homeostasis will lead to the implementation of a seminal biostatistical technique referred to as error fractionation. The model allows us to predict quantifiably the extent to which the outcome variable of health regained (that is of recovered homeostasis) following a viral infection is effected when either of the variables is changed, while holding the other constant. The residual random error can be further fractionated by hierarchically expanding the model, as our understanding of immunophysiology fills the remaining gaps in knowledge, which will be temporarily filled by the process of AI-aided immune tweening
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