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Correcting hypothalamic-pituitary-adrenal axis dysfunction using observer-based explicit nonlinear model predictive

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    This study introduces an automated method using explicit model predictive control (EMPC) to manage the hypothalamic-pituitary-adrenal (HPA) axis. The system effectively estimates stress and hormonal levels, demonstrating successful regulation in simulations.

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    Area of Science:

    • Neuroendocrinology
    • Control Systems Engineering
    • Computational Biology

    Background:

    • The hypothalamic-pituitary-adrenal (HPA) axis regulates the body's stress response by modulating cortisol.
    • Dysregulation of the HPA axis and cortisol levels are implicated in various stress-related disorders.
    • Current treatment methodologies for HPA axis dysregulation often lack automated, adaptive control.

    Purpose of the Study:

    • To propose and evaluate an automated treatment methodology for HPA axis regulation.
    • To develop a closed-loop control strategy using explicit model predictive control (EMPC).
    • To integrate an unknown input observer (UIO) for estimating hormonal levels and stress magnitude.

    Main Methods:

    • An explicit model predictive control (EMPC) strategy was designed for HPA axis regulation.
    • An unknown input observer (UIO) was employed to estimate hormonal concentrations and stress levels.
    • The closed-loop control system was simulated using multiple in silico patient models.

    Main Results:

    • The proposed EMPC controller, informed by the UIO, successfully estimated key hormonal levels within the HPA axis.
    • The controller demonstrated effective regulation of the HPA axis in simulated patient scenarios.
    • The automated control strategy showed promising performance in managing cortisol levels under simulated stress conditions.

    Conclusions:

    • The developed automated treatment methodology using EMPC and UIO offers a novel approach to HPA axis regulation.
    • This control strategy holds potential for managing stress-related disorders by optimizing cortisol levels.
    • In silico validation confirms the effectiveness and feasibility of the proposed closed-loop control system.