Electroconvulsive therapy (ECT) is the treatment of choice in severe geriatric depression. High remission rates may be influenced by specific brain morphology characteristic of geriatric depression. Our objective was to identify the relationship between brain structure, symptom profile, and ECT response. In a naturalistic cohort of 55 patients with a major depressive disorder, structural magnetic resonance imaging (MRI) was performed before ECT. Voxel-based morphometry was applied to determine regional differences in gray matter (GM) volume between patients and 23 matched healthy controls. Depressed patients with psychotic symptoms showed significantly higher remission rates and smaller regional GM volume of the left inferior frontal gyrus (IFG). Patients with late onset depression showed smaller regional GM volume of the bilateral lateral temporal cortex. Larger size of response in the whole patient group was related to smaller pretreatment regional GM volume of the right lateral temporal cortex, whereas faster speed of response was related to smaller pretreatment regional GM volume of the right IFG. ECT is most effective in depressed patients with psychotic symptoms. In this study the presence of psychotic symptoms was related to pretreatment smaller GM volume of the left IFG and bilateral temporal cortex. Smaller volume of the IFG pretreatment was related to faster treatment response, and smaller volume of the right lateral temporal cortex pretreatment was related to larger response to ECT. These results are possibly explained by the connectivity between these brain regions and an interconnected network that is particularly activated by the ECT-induced seizures.
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http://dx.doi.org/10.1016/j.pscychresns.2014.03.002 | DOI Listing |
Sensors (Basel)
December 2024
Institute of Mechanical Intelligence, Scuola Superiore Sant'Anna, Via G. Moruzzi 1, 56124 Pisa, Italy.
CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources.
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December 2024
Faculty of Computer Science, Polish-Japanese Academy of Information Technology, 86 Koszykowa Street, 02-008 Warsaw, Poland.
Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans ( = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated.
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December 2024
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI).
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Agricultural, Alimentary, Environmental and Forestry Sciences, Biosystem Engineering Division-DAGRI, University of Florence, Piazzale delle Cascine 15, 50144 Florence, Italy.
The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees' canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response.
View Article and Find Full Text PDFPlants (Basel)
December 2024
National Soil Quality Aksu Observation Experimental Station, Aksu 843000, China.
The contradiction between increased irrigation demand and water scarcity in arid regions has become more acute for crops as a result of global climate change. This highlights the urgent need to improve crop water use efficiency. In this study, four irrigation volumes were established for drip-irrigated maize under plastic mulch: 2145 m ha (W1), 2685 m ha (W2), 3360 m ha (W3), and 4200 m ha (W4).
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