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https://www.selleckchem.com/products/yo-01027.html Conclusion EEBOV-specific CD8+ and CD4+ T cell responses were significantly higher in Ebola survivors with PES. These findings suggest that pathogenesis may occur as an immune mediated disease via virus-specific T cell immune response or that persistent antigen exposure leads to increased and sustained T cell responses.Study objectives Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea, OSA) but relies on labor-intensive EEG-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and has low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to automatically identify the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter. Methods PPG signals from the diagnostic polysomnographies of patients suspected of OSA (n=894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for 3-stage (wake/NREM/REM), 4-stage (wake/N1+N2/N3/REM), and 5-stage (wake/N1/N2/N3/REM) classification of sleep. Results The 3-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen's kappa (κ) of 0.65. The 4-stage and 5-stage models achieved 68.5% (κ=0.54), and 64.1% (κ=0.51) accuracies, respectively. With the 5-stage model, the total sleep time was underestimated with mean (standard deviation) error of 7.5 (55.2) min. Conclusion The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitorin

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