Sigma-1 receptor (σ1r) activation could attenuate the learning and memory deficits in the AD model, ischemia model and others. In our previous study, the activation of σ1r increased the expression of brain-derived neurotrophic factor (BDNF), possibly through the NR2A-induced pathway, and σ1r agonists might function as neuroprotectant agents in vascular dementia. Here, we used σ1r knockout mice to confirm the role of σ1r. Furthermore, an antagonist of NR2A was first used to investigate whether the NR2A-induced pathway is the necessary link between σ1r and BDNF. The operation of brain ischemia/reperfusion was induced by bilateral common carotid artery occlusion for 20min in C57BL/6 and σ1r knockout mice as the ischemic group. A σ1r agonist, PRE084 (1mg/kg, i.p.), and NR2A antagonist, PEAQX (10mg/kg, i.p.), were administered once daily throughout the experiment. Behavioral tests were performed starting on day 8. On day 22 after brain ischemia/reperfusion, mice were sacrificed and brains were immediately collected and the injured and the hippocampus was isolated and stored at -80°C for western blot analysis. After ischemic operation, contrast with the σ1r knockout mice, PRE084 significantly ameliorated learning and memory impairments in the behavioral evaluation, and prevented the protein decline of BDNF, NR2A, CaMKIV and TORC1 expression in wild-type mice. However, the effects of PRE084 on CaMKIV-TORC1-CREB and BDNF, even for learning and memory impairment, were antagonized by the co-administration of PEAQX, an antagonist of NR2A. The activation of σ1r improves the impairment of learning and memory in the ischemia/reperfusion model, and the expression of BDNF, which may have been achieved through the NR2A-CaMKIV-TORC1 pathway.
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http://dx.doi.org/10.1016/j.jns.2017.03.027 | DOI Listing |
Psychol Rev
January 2025
Department of Experimental Psychology, University of Groningen.
Time is a central dimension against which perception, action, and cognition play out. From anticipating when future events will happen to recalling how long ago previous events occurred, humans and animals are exquisitely sensitive to temporal structure. Empirical evidence seems to suggest that estimating time prospectively (i.
View Article and Find Full Text PDFFront Neuroinform
December 2024
Institute of Theoretical Physics, Jagiellonian University, Kraków, Poland.
Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Industrial Engineering, King Khalid University, Abha, 61421, Saudi Arabia.
The integration of renewable energy sources has resulted in an increasing intricacy in the functioning and organization of power systems. Accurate load forecasting, particularly taking into account dynamic factors like as climatic and socioeconomic impacts, is essential for effective management. Conventional statistical analysis and machine learning methods struggle with accurately capturing the intricate temporal relationships present in load data.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Computer Science & Engineering, K L E F Deemed To Be University, Green Fields, Vaddeswaram, Guntur (dt), Andhra Pradesh, 521230, India.
Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.
View Article and Find Full Text PDFFront Plant Sci
December 2024
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.
Introduction: In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation.
Methods: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring.
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