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

This paper introduces the Persistent Systemic Threat-Signaling State (PSTS) framework — the updated designation of the previously published Persistent Systemic Stress-Signaling State (PSSS) model — as a minimal nonlinear dynamical systems description of post-acute infection syndromes (PAIS), including Long COVID, ME/CFS, Q-fever fatigue syndrome, and post-treatment Lyme disease. PSTS defines these conditions as disorders of recovery dynamics, rather than as consequences of a single causal mechanism. The central hypothesis is that persistent illness arises from a self-sustaining interaction between threat-signaling intensity (T) and recovery capacity (RC). Sustained activation depletes recovery capacity, while reduced recovery capacity impairs the system’s ability to dampen activation. This feedback loop stabilizes the system in a high-activation attractor state that persists independently of the original trigger. A core contribution of the framework is a formal dynamical account of post-exertional malaise (PEM). PEM — characterized by delayed onset, disproportionate severity, asymmetric recovery, and state-dependent thresholds — is structurally incompatible with linear causal models. These defining features emerge necessarily from a nonlinear system operating near a stability boundary with memory, and are explained as dynamical consequences of the T–RC interaction structure rather than as unexplained clinical observations. Version 2.1 extends the framework with a mechanistic hypothesis of the post-exertional crash as a coordinated multidomain response. Progressive metabolic stress is proposed to generate damage-associated molecular patterns (DAMPs), which, via activation of the Cell Danger Response (CDR) and cytokine-mediated brain signaling, trigger the conserved sickness behavior program. This links the abstract dynamical state transition (RC → RCcrit) to established biological pathways, providing a multi-level explanation of the crash without reducing the model to a single mechanism. The model is deliberately minimal and is formalized through two coupled differential equations describing the evolution of T and RC. An extended formulation incorporates state-dependent recovery efficiency, nonlinear depletion dynamics, a critical threshold (RCcrit), and metabolic latency (τ) to account for delayed deterioration and threshold behavior. The framework predicts the clinical heterogeneity observed across and within PAIS conditions as a structural property of the underlying dynamical system. Different patients occupy distinct positions within a shared stability landscape, generating qualitatively different regulatory regimes (e.g., stable-shifted, unstable-brittle, oscillatory). This provides the basis for Regulatory State Profiling (RSP) as a strategy for stratified measurement and intervention. Operationalization is addressed through a Recovery Kinetics Validation Protocol (RK-VP), which defines a longitudinal perturbation–recovery paradigm using wearable-based physiological monitoring. The framework specifies explicit falsification criteria based on recovery dynamics, threshold behavior, and cross-domain coupling, making it empirically testable despite its abstract formulation. PSTS is positioned as a structural meta-framework that integrates diverse biological findings — immune, autonomic, metabolic, neuroendocrine, and vascular — as state-maintaining operators within a shared dynamic configuration, rather than as competing primary causes.