Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism

Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Reading Loss in Arabic Language During COVID-19 in the UAE and Proposed Solutions:The Perspectives of Primary-Grade Arabic Language Teachers

The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.

Impact of Remote Learning on Student Performance and Grade: A Virtual World of Education in the COVID-19 Era

The COVID-19 pandemic has had a profound influence on education around the world, with schools and institutions shifting to remote learning to safeguard the safety of students and faculty. Concerns have been expressed about the impact of virtual learning on student performance and grades. The purpose of this study is to investigate the impact of remote learning on student performance and grades, as well as to investigate the obstacles and benefits of this new educational paradigm. The study will examine current literature on the subject, analyze data from surveys and interviews with students and educators, and investigate potential solutions to improve student performance and participation in virtual classrooms. The study’s findings will provide insights into the effectiveness of remote learning and inform ideas to improve student learning and achievement in an educational virtual world. The purpose of this article is to investigate the influence of remote learning on both students and educational institutions. The project will examine existing literature on the subject and collect data from students, instructors, and administrators through questionnaires and interviews. The paper will look at the challenges and opportunities that remote learning presents, such as the effect on student involvement, motivation, and academic achievement, as well as changes in teaching styles and technology. The outcomes of this study will provide insights into the effectiveness of remote learning and will affect future decisions about the usage of virtual learning environments in education. The research will also investigate potential solutions to improve the quality of remote education and handle any issues that occur.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy source.Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants.This study proposes an integrated deep learning-based photovoltaic resource assessment method.Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time.The proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment.The proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and landscape.The experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.

Comparative study on the performance of ConvLSTM and ConvGRU in classification problems-taking early warning of short-duration heavy rainfall as an example

卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元,通过将循环机制与卷积运算相结合,常常用于时空序列的预测.为了明确上述两种模型的收敛速度和分类能力,需要使用相同的模型架构对相同的分类问题进行预测.本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题,使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估.结果表明,ConvGRU的收敛速度比ConvLSTM快约25%.ConvLSTM和ConvGRU的预警性能随地区,时间,降雨强度的变化趋势相似,但大部分ConvLSTM的得分较高,少数情况下ConvGRU的得分较高.

Mechanism of Learning and Memory Impairment in Rats Exposed to Arsenic and/or Fluoride Based on Microbiome and Metabolome

Objective Arsenic(As) and fluoride(F) are two of the most common elements contaminating groundwater resources. A growing number of studies have found that As and F can cause neurotoxicity in infants and children, leading to cognitive, learning, and memory impairments. However, early biomarkers of learning and memory impairment induced by As and/or F remain unclear. In the present study, the mechanisms by which As and/or F cause learning memory impairment are explored at the multi-omics level(microbiome and metabolome).Methods We stablished an SD rats model exposed to arsenic and/or fluoride from intrauterine to adult period.Results Arsenic and/fluoride exposed groups showed reduced neurobehavioral performance and lesions in the hippocampal CA1 region. 16S rRNA gene sequencing revealed that As and/or F exposure significantly altered the composition and diversity of the gut microbiome, featuring the Lachnospiraceae_NK4A136_group, Ruminococcus_1, Prevotellaceae_NK3B31_group, [Eubacterium]_xylanophilum_group. Metabolome analysis showed that As and/or F-induced learning and memory impairment may be related to tryptophan, lipoic acid, glutamate, gamma-aminobutyric acidergic(GABAergic) synapse, and arachidonic acid(AA) metabolism. The gut microbiota, metabolites, and learning memory indicators were significantly correlated.Conclusion Learning memory impairment triggered by As and/or F exposure may be mediated by different gut microbes and their associated metabolites.

BI-D1870 Causes the Rats’ Learning and Memory Acquisition Ability Impairment

Aim: To observe the rats’ learning and memory acquisition ability disturbance induced by BI-D1870. Methods: Male SD rats were randomly divided into control group, solvent control group and BI-D1870 group. The rats in the control group were intraperitoneally injected with saline, while those in the solvent control group were intraperitoneally injected with DMSO + sulfobutyl-β-cyclodextrin solvent, and those in the BI-D1870 group were intraperitoneally injected with BI-D1870. All the rats’ appearance and behavior were daily observed, and body weight was recorded on the day 15, 30, 45, 60, 75 and 82 of BI-D1870 injected. Morris water maze was used to screen the rats’ learning and memory acquisition ability on the day 22 - 25, 52 - 55, and 82 - 85 of training by BI-D1870 treated. The successful rates of the rats’ memory impairment were respectively calculated for three times screening. Results: During the whole experiment, there was no obvious difference in appearance and fur color in all rats. The rats’ agitation began to appear on the day 10th of BI-D1870 given. The agitation rats’ number and rats’ body weight gradually increased along with BI-D1870 treated (P P Conclusion: Intraperitoneal injection of BI-D1870 can induce the rats’ learning and memory acquisition ability disorder.

Characterization of Memory Access in Deep Learning and Its Implications in Memory Management

Due to the recent trend of software intelligence in the Fourth Industrial Revolution,deep learning has become a mainstream workload for modern computer systems.Since the data size of deep learning increasingly grows,managing the limited memory capacity efficiently for deep learning workloads becomes important.In this paper,we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads.First,when comparing instruction and data accesses,data access accounts for 96%–99%of total memory accesses in deep learning workloads,which is quite different from traditional workloads.Second,when comparing read and write accesses,write access dominates,accounting for 64%–80%of total memory accesses.Third,although write access makes up the majority of memory accesses,it shows a low access bias of 0.3 in the Zipf parameter.Fourth,in predicting re-access,recency is important in read access,but frequency provides more accurate information in write access.Based on these observations,we introduce a Non-Volatile Random Access Memory(NVRAM)-accelerated memory architecture for deep learning workloads,and present a new memory management policy for this architecture.By considering the memory access characteristics of deep learning workloads,the proposed policy improves memory performance by 64.3%on average compared to the CLOCK policy.

A Brief Review of the Relationship between Addiction and Memory Systems

This essay will reexamine research on the relationship between human memory and addiction. This paper will review several studies that discussed how memory systems in the human brain are involved in the acquisition of behavior that is learned and is associated with the development of drug addiction and drug relapse. Additional information reveals that when individuals make the transition from recreational drug or impulsive use to compulsive drug abuse, which may result in a neuroanatomical change in areas of the brain from cognitive control guided by the hippocampus/dorsomedial striatum towards conditioned control of behavior managed by the dorsolateral striatum (DLS) [1]. This review also looked at studies that involved experiments with humans and lower animals, which suggested that the hippocampus mediates a cognitive/spatial type of memory, while the dorsal striatum manages stimulus-response (S-R) habit memory, and the amygdala governs the classical conditioning form of learning and stimulus-affective-associative relationships [1]. Overall, these studies utilize the hypothesis of the memory systems view of addiction, and the involvement of learning and memory in the context of drug addiction, which was proposed by them [2]. This theory has been proposed in response to drug addiction research and includes alcohol, amphetamine, and cocaine [1]. The research also explains how stress and anxiety can play a role in how strong emotional excitement can lead to dependent habit memory in rodents and humans [1]. .

Influence of Online Learning Attitude in the Blended Teaching Model on the Final Grades

The combination of online teaching and traditional offline teaching can maximize the advantages of both.Based on the blended teaching of English Reading course,39 students were selected as the research subjects to study the relationship between their online learning attitudes and their grades in the final examination.Judged from the number of times for each student to download teaching resources,the number of assignments submitted online,and the quality of the submitted assignments,each student’s attitude toward online learning was examined comprehensively,and a correlation analysis was conducted through SPSS Statistics 21.0 to explore the influence of online learning attitude on English reading performance.Through data collection and analysis of the online learning attitudes over a 16-week period,a significant positive correlation was found between the online learning attitudes and the English reading grades,indicating that the online learning attitude in the blended learning model plays a crucial role in improving the English reading skill,and students should maintain a positive attitude toward online teaching in blended learning.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Effects of exercise on neurogenesis in the dentate gyrus and ability of learning and memory after hippocampus lesion in adult rats

Objective To explore the effects of exercise on dentate gyrus （DG） neurogenesis and the ability of learning and memory in hippocampus-lesioned adult rats. Methods Hippocampus lesion was produced by intrabippocampal microinjection of kainic acid （KA）. Bromodeoxyuridine （BrdU） was used to label dividing cells. Y maze test was used to evaluate the ability of learning and memory. Exercise was conducted in the form of forced running in a motor-driven running wheel. The speed of wheel revolution was regulated at 3 kinds of intensity： lightly running, moderately running, or heavily running. Results Hippocampus lesion could increase the number of BrdU-labeled DG cells, moderately running after lesion could further enhance the number of BrdU-labeled cells and decrease the error number （EN） in Y maze test, while neither lightly running, nor heavily running had such effects. There was a negative correlation between the number of DG BrdU-labeled cells and the EN in the Y maze test after running. Conclusion Moderate exercise could enhance the DG neurogenesis and ameliorate the ability of learning and memory in hippocampus-lesioned rats.

Role of the hippocampus on learning and memory functioning and pain modulation

The hippocampus, an important part of the limbic system, is considered to be an important region of the brain for learning and memory functioning. Recent studies have demonstrated that synaptic plasticity is thought to be the basis of learning and memory functioning. A series of studies report that similar synaptic plasticity also exists in the spinal cord in the conduction pathway of pain sensation, which may contribute to hyperalgesia, abnormal pain, and analgesia. The synaptic plasticity of learning and memory functioning and that of the pain conduction pathway have similar mechanisms, which are related to the N-methyl-D-aspartic acid receptor. The hippocampus also has a role in pain modulation. As pain signals can reach the hippocampus, the precise correlation between synaptic plasticity of the pain pathway and that of learning and memory functioning deserves further investigation. The role of the hippocampus in processing pain information requires to be identified.

Effect of Panax notoginseng saponins on the expression of beta-amyloid protein in the cortex of the parietal lobe and hippocampus, and spatial learning and memory in a mouse model of senile dementia

BACKGROUND： The pharmacological actions of Panax notoginseng saponins （PNS） lie in removing free radicals, anti-inflammation and anti-oxygenation. It can also improve memory and behavior in rat models of Alzheimer＇s disease. OBJECTIVE： Using the Morris water maze, immunohistochemistry, real-time PCR and RT-PCR, this study aimed to measure improvement in spatial learning, memory, expression of amyloid precursor protein （App） and β -amyloid （A β ）, to investigate the mechanism of action of PNS in the treatment of AD in the senescence accelerated mouse-prone 8 （SAMP8） and compare the effects with huperzine A. DESIGN, TIME AND SETTING： A completely randomized grouping design, controlled animal experiment was performed in the Center for Research ＆ Development of New Drugs, Guangxi Traditional Chinese Medical University from July 2005 to April 2007. MATERIALS： Sixty male SAMP8 mice, aged 3 months, purchased from Tianjin Chinese Traditional Medical University of China, were divided into four groups： PNS high-dosage group, PNS low-dosage group, huperzine A group and control group. PNS was provided by Weihe Pharmaceutical Co., Ltd. （batch No.： Z53021485, Yuxi, Yunan Province, China）. Huperzine A was provided by Zhenyuan Pharmaceutical Co., Ltd. （batch No.： 20040801, Zhejiang, China）. METHODS： The high-dosage group and low-dosage group were treated with 93.50 and 23.38 mg/kg PNS respectively per day and the huperzine A group was treated with 0.038 6 mg/kg huperzine A per day, all by intragastric administration, for 8 consecutive weeks. The same volume of double distilled water was given to the control group. MAIN OUTCOME MEASURES： After drug administration, learning and memory abilities were assessed by place navigation and spatial probe tests. The recording indices consisted of escape latency （time-to-platform）, and the percentage of swimming time spent in each quadrant. The number of A β 1-40, A β 1-42 and App immunopositive neurons in the brains of SAMP8 mice was analyzed by immunohistochemistry. The mRNA content ofApp, tau, acetylcholinesterase, and synaptophysin （Syp） was tested by real time PCR and RT-PCR. RESULTS： The PCR results show that PNS can downregulate the expression of the App gene and upregulate the expression of the Syp gene in the parietal cortex and hippocampus of SAMP8 mice. The therapeutic effects of the PNS high-dosage group were greater than those of the PNS low-dosage group and the huperzine A group （P 〈 0.05）. The results of the Morris water maze and immunohistochemistry indicated that PNS can improve the capacity for spatial learning and memory in SAMP8 mice, and reduce the content of A β 1-40, A β 1-42 and expression of App in the brains of SAMP8 mice. The therapeutic effects of the PNS high-dosage group were greater than that of the PNS low-dosage group and the huperzine A group （P 〈 0.05）. CONCLUSION： These results support the hypothesis that PNS plays a therapeutic and protective role on the pathological lesions and learning dysfunction of Alzheimer＇s disease. The therapeutic effects of PNS for Alzheimer＇s disease are possibly achieved through downregulating the expression of the App gene and upregulating the expression of the Syp gene. The therapeutic effects of PNS are dose-dependent and are greater than the effect of huperzine A.

The Screening of Genes Sensitive to Long-Term, Low-Level Microwave Exposure and Bioinformatic Analysis of Potential Correlations to Learning and Memory

Objective To gain a better understanding of gene expression changes in the brain following microwave exposure in mice. This study hopes to reveal mechanisms contributing to microwave-induced learning and memory dysfunction. Methods Mice were exposed to whole body 2100 MHz microwaves with specific absorption rates (SARs) of 0.45 W/kg, 1.8 W/kg, and 3.6 W/kg for 1 hour daily for 8 weeks. Differentially expressing genes in the brains were screened using high-density oligonucleotide arrays, with genes showing more significant differences further confirmed by RT-PCR. Results The gene chip results demonstrated that 41 genes (0.45 W/kg group), 29 genes (1.8 W/kg group), and 219 genes (3.6 W/kg group) were differentially expressed. GO analysis revealed that these differentially expressed genes were primarily involved in metabolic processes, cellular metabolic processes, regulation of biological processes, macromolecular metabolic processes, biosynthetic processes, cellular protein metabolic processes, transport, developmental processes, cellular component organization, etc. KEGG pathway analysis showed that these genes are mainly involved in pathways related to ribosome, Alzheimer's disease, Parkinson's disease, long-term potentiation, Huntington's disease, and Neurotrophin signaling. Construction of a protein interaction network identified several important regulatory genes including synbindin (sbdn), Crystallin (CryaB), PPP1CA, Ywhaq, Psap, Psmb1, Pcbp2, etc., which play important roles in the processes of learning and memory. Conclusion Long-term, low-level microwave exposure may inhibit learning and memory by affecting protein and energy metabolic processes and signaling pathways relating to neurological functions or diseases.

Enriched environment for offspring improves learning and memory impairments induced by sevoflurane exposure during the second trimester of pregnancy

Studies in animals indicate that sevoflurane exposure in the second trimester of pregnancy has harmful effects on the learning and memory of offspring.Whether an enriched environment can reverse the damage of sevoflurane exposure in the second trimester of pregnancy on the learning and memory of rat offspring remains unclear.In this study,rats at 14 days of pregnancy were exposed to 3.5%sevoflurane for 2 hours and their offspring were treated with an enriched environment for 20 successive days.We found that the enriched environment for offspring increased nestin and Ki67 levels in hippocampal tissue,increased hippocampal neurogenesis,inhibited glycogen synthase kinase 3βactivity,and increased the expression of cell proliferation-relatedβ-catenin and apoptosis-related Bcl-2,indicating that an enriched environment reduces sevoflurane-induced damage by increasing the proliferation of stem cells in the hippocampus.These findings suggest that an enriched environment can reverse the effects of sevoflurane inhaled by rats during the second trimester of pregnancy on learning and memory of offspring.This study was approved by the Animal Ethics Committee of Shengjing Hospital of China Medical University(approval No.2018PS07K)on January 2,2018.

Influence of acupuncture with exercise training on learning and memory functions, as well as microtubule-associated protein-2 and synaptophysin expression in the hippocampal CA3 region, in a rat model of cerebral infarction

The present study was designed to determine microtubule-associated protein-2 and synaptophysin expression in the hippocampal CA3 region in a rat model of middle cerebral artery occlusion. The rats were treated with acupuncture at Baihui （GV 20）, Qubin （GB 7）, and Qianding （GV 21） points, in addition to exercise training. Results were compared with rats undergoing exercise training only. The Y-maze method and immunohistochemistry revealed decreased error frequency of passing through Y-maze, as well as significantly increased microtubule-associated protein-2 and synaptophysin expression, in the acupuncture with exercise training group compared with the model and exercise training groups after 5 weeks. Microtubule-associated protein-2 and synaptophysin expressions negatively correlated with error frequency of passing through the Y-maze. These results suggested that acupuncture combined with exercise training improved learning and memory functions in a rat model of cerebral infarction. The mechanisms of action were hypothesized to be associated with dendritic or synaptic plasticity in the ipsilateral hippocampal CA3 region.