Background: Extended periods of hypoperfusion in an advanced heart failure (HF) places patients at high risk for neurobehavioral compromise, which has not been studied systematically. It is also not clear how intravenous inotropic therapy and mechanical cardiac assist devices (MCAD) affect cognitive function.
Methods: This prospective cross-sectional cognitive preliminary study evaluated 252 potential heart transplant candidates assessing functions in memory, motor, and processing speed. Patients were divided into three HF groups based on severity of disease: group 1 outpatients (n = 113), group 2 in-patients requiring inotropic infusion (n = 83), and group 3 inpatients likely requiring MCAD support (n = 56). Aggregate z-scores for memory, motor, and processing speed and independent samples t tests assessed intergroup differences on 13 cognitive measures.
Results: A broad pattern of cognitive impairment was observed within the advanced HF group; fewer deficits were found in group 1 outpatients and more severe deficits in group 3 MCAD subjects. A difference in motor functions was observed as the earliest abnormality, with group 3 showing significant changes compared with group 1. The most dramatic changes were seen in domain mental processing speed along with specific verbal and visual memory functions, which were slower in group 3 compared with groups 1 and 2.
Conclusions: Cognitive deficits are common in advanced HF and worsen with increasing severity of HF. Appropriately designed and randomized studies will be needed to demonstrate if earlier MCAD implantation is warranted to arrest cognitive dysfunction and better postimplantation adaptation.
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http://dx.doi.org/10.1016/j.athoracsur.2005.12.010 | DOI Listing |
Sensors (Basel)
December 2024
Department of Automation, Xiamen University, Xiamen 361102, China.
Recent advancements in the field of object tracking have been notably influenced by Siamese-based trackers, which have demonstrated considerable progress in their performance and application. Researchers frequently emphasize the precision of trackers, yet they tend to neglect the associated complexity. This oversight can restrict real-time performance, rendering these trackers inadequate for specific applications.
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December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
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December 2024
Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.
Recently, AI systems such as autonomous driving and smart homes have become integral to daily life. Intelligent multi-sensors, once limited to single data types, now process complex text and image data, demanding faster and more accurate processing. While integrating NPUs and sensors has improved processing speed and accuracy, challenges like low resource utilization and long memory latency remain.
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December 2024
College of Engineering, Huaqiao University, Quanzhou 362021, China.
Grasping objects of irregular shapes and various sizes remains a key challenge in the field of robotic grasping. This paper proposes a novel RGB-D data-based grasping pose prediction network, termed Cascaded Feature Fusion Grasping Network (CFFGN), designed for high-efficiency, lightweight, and rapid grasping pose estimation. The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model's ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels.
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December 2024
Institute of Applied Physics, Jiangxi Academy of Sciences, Nanchang 330000, China.
Although approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these problems, this study combines traditional visual detection methods and deep neural network technology to propose a transfer learning multi-channel fusion decision network without significantly increasing the number of network layers or the structural complexity. Each channel of the network is designed according to the characteristics of different types of defects.
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