Non-immune Modulators of Cellular Immune Surveillance to HIV-1 and Other Retroviruses: Future Artificial Intelligence-Driven Goals and Directions
Immune surveillance to viruses and other foreign pathogens involves a specific process, which is well-characterized in terms of the immune and non-immune cells and factors involved, and their specific timeline. For example, first exposure to a viral antigen involves major histocompatibility complex type I molecules to present the ‘self’ and ‘nonself’ antigen to CD3 + CD8 + CD45RA+ T cells. This presentation initiates the event of T cell activation, which results in the production of T cell growth factor (interleukin 2, IL-2), interferon-gamma (IFN-g) and other immune soluble products, which together act to promote the clonal proliferative expansion of the responding cell population. Soluble immune factors produced during this initial phase of the immune response also initiate the maturation of the responding T cells into CD3 + CD8 + CD45R0+ memory cells against that specific viral antigen, and of related cell populations that act to control and dampen cellular immune reactivity. Contemporaneously, these soluble immune factors trigger non-immune cells to release of non-immune soluble factors, including pituitary and adrenocortical hormones (e.g., glucocorticoids), which finely modulate immune cell responses. Together, immune and non-immune cells and soluble factors act in concert to engender and sustain a finely tuned immune surveillance process, whose ultimate end (Y) is to regain homeostatic balance of the organism – a healthy state, that is eradication of the immune-pathological signs and symptoms that derived from the viral infection, referring here to homeostatic balance Y.
In brief, immune and non-immune cells and soluble factors are concerted predicting factors for regaining Y, to the same extent as the initial viral challenge, and other factors related to the state of heterostasis of the organism. These states, which include unhealthy eating habits, sleep deprivation, stress, and the like, act in concert to delay, hamper and counter Y.
Therefore, from a biostatistical viewpoint, the problem becomes a relatively basic multiple regression, in which the outcome Y, the homeostatic state of health regained following a viral infection, is simply the sum of positive and negative factors and/or events. Positive factors and events (Π) inherently push allostasis forward (i.e., the orderly process of immune activation and maturation), but the negative (Ν) factors and events, allostatically speaking, interfere with attaining Y. Simplistically, Y is the product of the fine, coordinated and time-regulated interaction between all the interacting Π’s and Ν’s during the immune surveillance process (Y = ΣΠ + ΣΝ).
The question then becomes, knowing what we know today about the constituents of ΣΠ and of ΣΝ, can we not design, by means of bioinformatics, artificial Π’s and Ν’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 regulate cholesterol levels. Future artificial intelligence (AI) advances will produce the artificial Π’s and Ν’s, which will aid regaining Y. In the meanwhile, as science continues to complete our knowledge of all the Π’s and of all the Ν’s involved, “tweening”, the computerized process by which, knowing the end-product of a sequence, the steps in-between can be programmed, will be applied to our conceptualization of immune surveillance events.
In conclusion, the novel science of immune-tweening will helps us understand and complete a set of immune and non-immune events that lead to Y, the homeostatic state of health regained following a viral infection. AI, on the other hand, holds strong promise to help us generate and produce bioinformatics or ‘micro-adjuvants’, as it were, of immune surveillance. We envisage that these giant steps in the future of viral immunity will first be achieved in the context of infection with the human immunodeficiency virus (HIV) because it has become the model for our understanding of anti-viral immune surveillance.
KeywordsViral immune surveillance Neuroendocrine modulators of cellular immunity Allostasis Bayesian prediction model Artifial intelligence Immune-tweening
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