Psychoneuroimmunology-informed strategies for early detection of biological threat exposure: Insights from experimental rodent models.

The growing frequency of emerging infectious diseases, antimicrobial resistance, and accidental or deliberate biological threat releases underscores the urgent need for early detection of biological threat exposure. Current diagnostic approaches primarily rely on identification of specific pathogens or toxins, often after clinical symptoms have emerged. An alternative approach could exploit the body's conserved psychoneuroimmunological (PNI) response to biological threat. This approach could provide a host-centred framework capable of detecting real-time deviations from baseline PNI status, even in response to previously unencountered threats. Using lipopolysaccharide (LPS) as a well-characterised model of immune challenge, we examine experimental work in rodents to evaluate how early-phase sickness behaviour, and the neuroimmune interactions that drive this response, can inform the development of wearable early-warning systems. Drawing on anatomical, molecular, electroencephalography (EEG), and behavioural studies, we identify early PNI alterations with potential utility for real-time threat detection and discuss analytical frameworks through which these signals may be leveraged. Converging evidence indicates that peripheral immune challenges rapidly engage central immunosensory neural circuits, followed by recruitment of autonomic, stress-related systems, and higher-order integrative centres. This response is strongly modulated by interindividual and contextual factors, producing marked heterogeneity in response type, timing, and magnitude, underscoring the need for continuous, personalised monitoring. We discuss how advances in wearable biosensing, combined with machine-learning-based approaches, could leverage autonomic, EEG, and behavioural signals to detect subtle deviations from an individual's PNI baseline, and outline knowledge gaps to translate mechanistic insights into real-time surveillance systems.