Post-Acute Infection Syndromes as Modulated Low-Scale System States: A Systems-Level Hypothesis Framework for Microcirculatory Exchange, Functional Scaling Loss, and Diagnostic Blind Spots in PAIS

This upload provides Version V1R1 of an independent, hypothesis-generating systems-level preprint on post-acute infection syndromes (PAIS). The manuscript does not report primary clinical data, does not establish causality, and does not propose diagnostic or therapeutic recommendations. Its main structured contribution is a reference and inference map in Appendix E, intended to make the framework inspectable, criticizable, and reusable for future research design. Post-acute infection syndromes (PAIS), including Long COVID, post-viral fatigue states, and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), remain difficult to classify, diagnose, and explain. Affected individuals may experience substantial functional impairment, while standard diagnostic tests often remain non-specific or within conventional reference ranges. This discrepancy should not be interpreted too quickly as absence of biological dysfunction. Rather, it may indicate that some currently used diagnostic and conceptual frameworks insufficiently capture dynamic, distributed, or systems-level aspects of the condition. This paper proposes a hypothesis-generating systems framework in which PAIS is conceptualized not primarily as the direct consequence of a single trigger, but as a modulated low-scale system state. Different stressors may initiate the transition into this state, while endothelial, microcirculatory, rheological, immunological, autonomic, metabolic, iron-related, and mitochondrial modulators may influence whether the organism returns to baseline or stabilizes at reduced functional capacity. The framework is offered as an independent systems-level perspective intended to complement, not replace, existing biomedical research. Its aim is to make diagnostic blind spots more explicit, support interdisciplinary discussion, and transform current uncertainty into more precise, testable research questions.