In-depth understanding of intra- and postdialytic phosphate kinetics is important to adjust treatment regimens in hemodialysis. We aimed to modify and validate a three-compartment phosphate kinetic model to individual patient data and assess the temporal robustness. Intradialytic phosphate samples were collected from the plasma and dialysate of 12 patients during two treatments (HD1 and HD2). 2-h postdialytic plasma samples were collected in four of the patients. First, the model was fitted to HD1 samples from each patient to estimate the mass transfer coefficients. Second, the best fitted model in each patient case was validated on HD2 samples. The best model fits were determined from the coefficient of determination (R ) values. When fitted to intradialytic samples only, the median (interquartile range) R values were 0.985 (0.959-0.997) and 0.992 (0.984-0.994) for HD1 and HD2, respectively. When fitted to both intra- and postdialytic samples, the results were 0.882 (0.838-0.929) and 0.963 (0.951-0.976) for HD1 and HD2, respectively. Eight patients demonstrated a higher R value for HD2 than for HD1. The model seems promising to predict individual plasma phosphate in hemodialysis patients. The results also show good temporal robustness of the model. Further modifications and validation on a larger sample are needed.
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http://dx.doi.org/10.14814/phy2.15899 | DOI Listing |
Adv Sci (Weinh)
January 2025
College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.
View Article and Find Full Text PDFSci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
View Article and Find Full Text PDFGait Posture
December 2024
Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China. Electronic address:
Background: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of the Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing, 100083, China.
The effectiveness of protected areas in mitigating human impacts remains uncertain due to limited in-situ data; however, atmospheric micropollutant deposition in alpine lakes may provide a quantitative approach to evaluate anthropogenic pressures and threats. In this study, the temporal changes of polycyclic aromatic hydrocarbons (PAHs) inside/outside the Siling Co protected area, Tibet were reconstructed. The varying anthropogenic impact history suggested that, unlike the dominance of residential activities (i.
View Article and Find Full Text PDFSci Rep
January 2025
Electrical Computer and Control Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt.
This study presents a novel optimization algorithm known as the Energy Valley Optimizer Approach (EVOA) designed to effectively develop six optimal adaptive fuzzy logic controllers (AFLCs) comprising 30 parameters for a grid-tied doubly fed induction generator (DFIG) utilized in wind power plants (WPP). The primary objective of implementing EVOA-based AFLCs is to maximize power extraction from the DFIG in wind energy applications while simultaneously improving dynamic response and minimizing errors during operation. The performance of the EVOA-based AFLCs is thoroughly investigated and benchmarked against alternative optimization techniques, specifically chaotic billiards optimization (C-BO), genetic algorithms (GA), and marine predator algorithm (MPA)-based optimal proportional-integral (PI) controllers.
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