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Sepsis is responsible for over 50% of hospital deaths. Changes in mitochondrial redox state can be indicative of cellular and organ function. Therefore, a method to evaluate mitochondrial utilization of oxygen continuously is critical. Cytochrome-c oxidase (CCO) is a mitochondrial enzyme that participates in oxidative phosphorylation and interacts with near-infrared light, potentially yielding an optical indicator of cellular metabolism. In this work, we use a computational framework to study the feasibility of utilizing photoplethysmography (PPG) signals for detecting CCO when HbO2 and Hb are present. We use a 3D Monte Carlo model of light absorption and transport in tissue to generate optical readouts in the form of temporal PPG signals corresponding to different physiological states. Furthermore, a machine learning model is trained to predict the CCO redox state from PPG signals containing different CCO and hemoglobin species concentrations.
Anastasia Goulopoulos,Michael Alvarez,Heidy Sierra, andWalfre Franco
"A computational framework to analyze time and location-dependent NIRS readouts for detecting cytochrome c oxidase", Proc. SPIE PC12822, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2024, PC128220A (19 March 2024); https://doi.org/10.1117/12.3003150
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Anastasia Goulopoulos, Michael Alvarez, Heidy Sierra, Walfre Franco, "A computational framework to analyze time and location-dependent NIRS readouts for detecting cytochrome c oxidase," Proc. SPIE PC12822, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2024, PC128220A (19 March 2024); https://doi.org/10.1117/12.3003150