By quantification of brain metabolites, localized brain proton MRS can non-invasively provide biochemical information from distinct regions of the brain. Quantification of short-TE signals is usually based on a metabolite basis set. The basis set can be obtained by two approaches: (1) by measuring the signals of metabolites in aqueous solution; (2) by quantum-mechanically simulating the theoretical metabolite signals. The purpose of this study was to compare the effect of these two approaches on metabolite concentration estimates. Metabolite concentrations were quantified with the QUEST method, using both approaches. A comparison was performed with the aid of Monte Carlo studies, by using signals simulated from both basis sets. The best results were obtained when the basis set used for the fit was the same as that used to simulate the Monte Carlo signals. This comparison was also performed using in vivo short-TE signals acquired at 7 T from the central region of rat brains. The concentration estimates, with confidence intervals, obtained using both basis sets were in good agreement with values from the literature. The in vivo study showed that, in general, the differences between the estimates obtained with the two basis sets were not statistically significant or scientifically important. Consequently, a simulated basis set can be used in place of a measured basis set.
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J Mol Model
January 2025
Laboratory of Nanostructures and Advanced Materials, Mechanics and Thermofluids, Faculty of Sciences and Technologies, Hassan II University of Casablanca, B.P 146, 20650, Mohammedia, Morocco.
Context: Designing efficient sensitive materials for the detection of volatile organic compounds (VOCs) such as ethanol, acetone, and benzene is stringent owing to the significant environmental and health risks induced by these compounds, in addition to their role as biomarkers for chronic diseases and food quality. This study investigates the adsorption mechanisms of VOC molecules (ethanol, acetone, and benzene) on both non-oxidized and oxidized SnO (110) monolayers and identifies the most suitable surface for gas sensing applications. For this, we examined structural properties, adsorption energies, density of states, gas responses, and recovery times.
View Article and Find Full Text PDFActa Radiol
January 2025
Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
Background: Segmentation of the cochlea in temporal bone computed tomography (CT) is the basis for image-guided otologic surgery. Manual segmentation is time-consuming and laborious.
Purpose: To assess the utility of deep learning analysis in automatic segmentation of the cochleae in temporal bone CT to differentiate abnormal images from normal images.
Front Genet
January 2025
Chongqing Engineering Laboratory of Green Planting and Deep Processing of Famous-Region Drug in the Three Gorges Reservoir Region, College of Biology and Food Engineering, Chongqing Three Gorges University, Chongqing, China.
Introduction: P. Y. Li is a plant used to treat respiratory diseases such as pneumonia, bronchitis, and influenza.
View Article and Find Full Text PDFPhytoKeys
January 2025
Science & Conservation Division, Missouri Botanical Garden, 4344 Shaw Boulevard, St. Louis, MO 63110, USA Missouri Botanical Garden St. Louis United States of America.
Members of the genus L. (Heliconiaceae) have evolved complex interactions with both insect herbivores and hummingbird pollinators in tropical forests and secondary growth where they are abundant and diverse. Many of these same species have also been cultivated as ornamentals around the world for hundreds of years because of their extraordinary colors and forms.
View Article and Find Full Text PDFObjective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
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