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Highly Sensitive Spectrofluorimetric and Colorimetric Quantitation of Neomycin Using Nanosilver-Fluorescein Hybrid: A Comprehensive Assessment of Greenness and Blueness.
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Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt.
Two versatile yet simple methods, colorimetric and spectrofluorimetric, were utilized for the quantitation of nonchromophore neomycin using silver nanoparticles modified with fluorescein. Fluorescein was excited at 485 nm (emission at 515 nm); when it is deposited on the surface of silver nanoparticles, its fluorescence intensity at 515 nm is quenched. Neomycin restores the fluorescence level at 515 nm by displacing fluorescein from nanoparticle binding sites.
View Article and Find Full Text PDFPsychometric Properties and Network Analysis of the Arabic Version of Reinforcement Sensitivity Theory of Personality Scale-Short Version in Patients with Anxiety Disorders.
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January 2025
Intelligent Manufacturing Systems (IMS) Centre, Department of Industrial and Manufacturing Systems Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 3P4, Canada.
This study aimed to examine the psychometric properties of the Arabic version of a short version of the Reinforcement Sensitivity Theory of Personality Questionnaire (RST-PQ-S) among a sample of 700 patients with anxiety disorders (53.1% were female). Participants completed the RST-PQ-S, NEO-FFI, Positive Mental Health (PMH), and Kessler Psychological Distress scale.
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Institute of high energy physics, Chinese academy of sciences, Beijing 100049, China.
The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction.
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Department of Management Information Systems, National Chung Hsing University, Taichung, 402, Taiwan.
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School of Artificial Intelligence, Jilin University, 3003 Qianjin Street, 130012 Changchun, China.
Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models.
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