Characterizing Immunodeficiencies Using Entropy Phase Transitions as Signatures of the Status of the Immune System

Determining the fitness status of the immune system is one of the central challenges of modern biomedical sciences, given its close association with aging and immunosenescence. Currently, no universally accepted metric allows for determining whether an individual’s immune system is competent, nor the extent to which it is competent across different pathologies. In the present study, a model of complex adaptive networks is developed, in which nodes represent the eleven principal immune cell types, focused on the phase transitions of entropy to distinguish the optimal functioning of the immune network in contrast to what occurs in cases of immunodeficiency states, both primary and secondary. The results of the dynamic simulation indicate distinct profiles between the immunocompetent and immunodeficient states of the immune system. They further provide evidence regarding the condition of the complex immune network and its internal connectivity, suggesting the existence of aging-related patterns in immune response effector networks. This entropy-based approach offers a quantitative, holistic assessment of immune status with potential applications in AI-driven diagnostic systems, disease monitoring, and personalized immunomodulatory therapies.