Persistent Systemic Stress-Signaling State (PSSS): A Systems-Level Model of Constrained Recovery Dynamics in Post-Acute Infection Syndromes

This preprint introduces the Persistent Systemic Stress-Signaling State (PSSS) as a formally defined systems-level model of constrained recovery dynamics in Post-Acute Infection Syndromes (PAIS), including Long COVID, ME/CFS, Q-fever fatigue syndrome, and post-treatment Lyme disease. Rather than locating pathology in a singular lesion or subsystem abnormality, PSSS characterizes these conditions as altered regulatory regimes within an integrated physiological network. The central disturbance is defined as a sustained or readily reactivated elevation of system-level stress-signaling (S), combined with reduced or unstable recovery capacity (RC), leading to prolonged or nonlinear recovery kinetics (RK) following perturbation. This version advances beyond descriptive framing by: Establishing explicit axioms and formal definitions Introducing a minimal dynamical core model (L–EM–S–RC interaction structure) Defining Endogenous Maintenance (EM) as a distinct internal activation driver Formalizing the structural possibility of stable high-activation attractor states Providing a structured Recovery Kinetics Validation Protocol (RK–VP) Including a detailed falsification matrix Clarifying scope boundaries and exclusions The framework treats Recovery Capacity (RC) as a latent system property inferable from longitudinal perturbation–recovery trajectories rather than static biomarkers. It argues that cross-sectional abnormalities are secondary to the primary dynamical feature: impaired or unstable return-to-baseline behavior across domains. PSSS is positioned as a complementary meta-framework capable of integrating immune, autonomic, metabolic, vascular, neurocognitive, and cellular hypotheses without reducing PAIS to any single mechanistic driver. Mechanistic models become dynamically relevant insofar as they alter damping efficiency, endogenous maintenance, or attractor stability within the system. The model is explicitly falsifiable. It would be weakened or rejected if normal recovery kinetics, preserved damping behavior, absence of threshold sensitivity, or sufficient dominant structural pathology fully explain observed symptoms. By centering recovery dynamics and regulatory stability, this framework aims to provide a coherent backbone for longitudinal measurement strategies, comparative mechanistic evaluation, and state-based intervention research. This work represents independent conceptual development intended to resolve explanatory fragmentation in PAIS research through formal systems modeling.