Background: Elevation in brain levels of aluminium can be neurotoxic and can cause learning and memory deficiencies. In Chinese medicine, Morus alba is used as a neuroprotective herb. The current study was intended to discover the recuperative effect of morusin against aluminium trichloride (AlCl3)-induced memory impairment in rats along with biochemical mechanism of its protective action.
Methods: Memory deficiency was produced by AlCl3 (100 mg/kg; p.o.) in experimental animals. Learning and memory activity was measured using Morris water maze (MWM) test model. Central cholinergic activity was evaluated through the measurement of brain acetylcholinesterase (AChE) activity. In addition to the above, oxidative stress was determined through assessment of brain thiobarbituric acid-reactive species (TBARS) and glutathione (GSH) levels.
Results: AlCl3 administration prompted significant deficiency of learning and memory in rats, as specified by a noticeable reduction in MWM presentation. AlCl3 administration also produced a significant deterioration in brain AChE action and brain oxidative stress (increase in TBARS and decrease in GSH) levels. Treatment with morusin (5.0 and 10.0 mg/kg, dose orally) significantly overturned AlCl3- induced learning and memory shortages along with diminution of AlCl3-induced rise in brain AChE activity and brain oxidative stress levels.
Conclusion: It may be concluded that morusin exerts a memory-preservative outcome in mental discrepancies of rats feasibly through its various activities.
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http://dx.doi.org/10.2174/1871524917666161111095335 | DOI Listing |
Adv Sci (Weinh)
January 2025
Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai), Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education), Department of Physics, School of Physics and Electronic Science, East China Normal University, Shanghai, 200241, China.
Controlling polarization states of ferroelectrics can enrich optoelectronic properties and functions, offering a new avenue for designing advanced electronic and optoelectronic devices. Here, ferroelectric semiconductor-based field-effect transistors (FeSFETs) are fabricated, where the channel is a ferroelectric semiconductor (e.g.
View Article and Find Full Text PDFFront Psychol
January 2025
Sorbonne University, CNRS, INSERM, Institute of Biology Paris Seine, Neurosciences Paris Seine, Paris, France.
Transitive inference, the ability to establish hierarchical relationships between stimuli, is typically tested by training with premise pairs (e.g., A + B-, B + C-, C + D-, D + E-), which establishes a stimulus hierarchy (A > B > C > D > E).
View Article and Find Full Text PDFFront Behav Neurosci
January 2025
Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands.
Introduction: Physical exercise has repeatedly been reported to have advantageous effects on brain functions, including learning and memory formation. However, objective tools to measure such effects are often lacking. Eyeblink conditioning is a well-characterized method for studying the neural basis of associative learning.
View Article and Find Full Text PDFSmall Methods
January 2025
School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China.
Optoelectronic synapse devices (OESDs) inspired by human visual systems enable to integration of light sensing, memory, and computing functions, greatly promoting the development of in-sensor computing techniques. Herein, dual-mode integration of bipolar response photodetectors (PDs) and artificial optoelectronic synapses based on ZnO/SnSe heterojunctions are presented. The function of the fabricated device can be converted between the PDs and OESDs by modulating the light intensity.
View Article and Find Full Text PDFZhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Anhui Provincial Center for Disease Control and Prevention, Hefei, Anhui 230601, China.
Objective: To predict the areas of snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of snail spread, so as to provide insights into investigating the trends in areas of snail spread.
Methods: Data pertaining to snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination ().
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