Intracellular aggregates of α-synuclein are the pathological hallmark of Parkinson's disease (PD) and dementia with Lewy bodies (DLB), being linked to neurotoxicity. Multiple triggers of α-synuclein aggregation have been implicated, including raised copper. The potential protective role of the endogenous copper-/zinc-binding proteins, metallothioneins (MT), has been explored in relation to copper-induced α-synuclein aggregation. Up-regulated endogenous expression of MT was induced in SHSY-5Y cells by the synthetic glucocorticoid analogue, dexamethasone. After treatment to induce endogenous MT expression, immunofluorescence confocal microscopy was used to quantify protein aggregates in cells with/without copper treatment. MT induction resulted in significant (p < 0.01), dose-dependent up-regulation of MT expression and significant reduction in Cu-dependent α-synuclein intracellular aggregates (p < 0.01) that could be suppressed by MT-specific siRNA. Ubiquitous (MT-2) and brain-specific (MT-3) isoforms were investigated by transient transfection of the GFP-fusion proteins, observing equivalent α-synuclein aggregate suppression by each. These studies indicate MT induction could have potential in PD/DLB neuroprotective therapy by suppressing α-synuclein aggregation.
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http://dx.doi.org/10.1007/s12640-017-9825-7 | DOI Listing |
Sensors (Basel)
December 2024
Automation Department, North China Electric Power University, Baoding 071003, China.
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December 2024
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean-geodesic network (EGNet), which uses point cloud-voxel-mesh data to characterize detail, contour, and geodesic features, respectively.
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December 2024
School of Information and Communication, Guilin University of Electronic Technology, 1 Xiamen Road, Guilin 541004, China.
This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy-Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.
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December 2024
School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China.
RGB-T salient object detection (SOD) has received considerable attention in the field of computer vision. Although existing methods have achieved notable detection performance in certain scenarios, challenges remain. Many methods fail to fully utilize high-frequency and low-frequency features during information interaction among different scale features, limiting detection performance.
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December 2024
Department of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies.
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