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.

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.

Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

DNA hypomethylation promotes learning and memory recovery in a rat model of cerebral ischemia/reperfusion injury

Cerebral ischemia/reperfusion injury impairs learning and memory in patients.Studies have shown that synaptic function is involved in the formation and development of memory,and that DNA methylation plays a key role in the regulation of learning and memory.To investigate the role of DNA hypomethylation in cerebral ischemia/reperfusion injury,in this study,we established a rat model of cerebral ischemia/reperfusion injury by occlusion of the middle cerebral artery and then treated the rats with intraperitoneal 5-aza-2′-deoxycytidine,an inhibitor of DNA methylation.Our results showed that 5-aza-2′-deoxycytidine markedly improved the neurological function,and cognitive,social and spatial memory abilities,and dose-dependently increased the synaptic density and the expression of SYP and SHANK2 proteins in the hippocampus in a dose-dependent manner in rats with cerebral ischemia/reperfusion injury.The effects of 5-aza-2′-deoxycytidine were closely related to its reduction of genomic DNA methylation and DNA methylation at specific sites of the Syp and Shank2 genes in rats with cerebral ischemia/reperfusion injury.These findings suggest that inhibition of DNA methylation by 5-aza-2′-deoxycytidine promotes the recovery of learning and memory impairment in a rat model of cerebral ischemia/reperfusion injury.These results provide theoretical evidence for stroke treatment using epigenetic methods.

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 Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification.These limitations can result in the misjudgment of models,leading to a degradation in overall detection performance.This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above limitations.The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations.The memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration mechanisms.These two modules together effectively alleviate the problem of generalization.Furthermore,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature spaces.Such a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly identification.The proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly detection.To validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and MSL.The results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,respectively.These findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.

Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

Optimization and Deployment of Memory-Intensive Operations in Deep Learning Model on Edge

As a large amount of data is increasingly generated from edge devices,such as smart homes,mobile phones,and wearable devices,it becomes crucial for many applications to deploy machine learning modes across edge devices.The execution speed of the deployed model is a key element to ensure service quality.Considering a highly heterogeneous edge deployment scenario,deep learning compiling is a novel approach that aims to solve this problem.It defines models using certain DSLs and generates efficient code implementations on different hardware devices.However,there are still two aspects that are not yet thoroughly investigated yet.The first is the optimization of memory-intensive operations,and the second problem is the heterogeneity of the deployment target.To that end,in this work,we propose a system solution that optimizes memory-intensive operation,optimizes the subgraph distribution,and enables the compiling and deployment of DNN models on multiple targets.The evaluation results show the performance of our proposed system.

Basic roles of key molecules connected with NMDAR signaling pathway on regulating learning and memory and synaptic plasticity

With key roles in essential brain functions ranging from the long-term potentiation(LTP) to synaptic plasticity,the N-methyl-D-aspartic acid receptor(NMDAR) can be considered as one of the fundamental glutamate receptors in the central nervous system.The role of NMDA R was first identified in synaptic plasticity and has been extensively studied.Some molecules,such as Ca^(2+),postsynaptic density 95(PSD-95),calcium/calmodulin-dependent protein kinase II(Ca MK II),protein kinase A(PKA),mitogen-activated protein kinase(MAPK) and cyclic adenosine monophosphate(c AMP) responsive element binding protein(CREB),are of special importance in learning and memory.This review mainly focused on the new research of key molecules connected with learning and memory,which played important roles in the NMDAR signaling pathway.

Microglia regulation of synaptic plasticity and learning and memory

Microglia are the resident macrophages of the central nervous system.Microglia possess varied morphologies and functions.Under normal physiological conditions,microglia mainly exist in a resting state and constantly monitor their microenvironment and survey neuronal and synaptic activity.Through the C1 q,C3 and CR3"Eat Me"and CD47 and SIRPα"Don't Eat Me"complement pathways,as well as other pathways such as CX3 CR1 signaling,resting microglia regulate synaptic pruning,a process crucial for the promotion of synapse formation and the regulation of neuronal activity and synaptic plasticity.By mediating synaptic pruning,resting microglia play an important role in the regulation of experience-dependent plasticity in the barrel cortex and visual cortex after whisker removal or monocular deprivation,and also in the regulation of learning and memory,including the modulation of memory strength,forgetfulness,and memory quality.As a response to brain injury,infection or neuroinflammation,microglia become activated and increase in number.Activated microglia change to an amoeboid shape,migrate to sites of inflammation and secrete proteins such as cytokines,chemokines and reactive oxygen species.These molecules released by microglia can lead to synaptic plasticity and learning and memory deficits associated with aging,Alzheimer's disease,traumatic brain injury,HIV-associated neurocognitive disorder,and other neurological or mental disorders such as autism,depression and post-traumatic stress disorder.With a focus mainly on recently published literature,here we reviewed the studies investigating the role of resting microglia in synaptic plasticity and learning and memory,as well as how activated microglia modulate disease-related plasticity and learning and memory deficits.By summarizing the function of microglia in these processes,we aim to provide an overview of microglia regulation of synaptic plasticity and learning and memory,and to discuss the possibility of microglia manipulation as a therapeutic to ameliorate cognitive deficits associated with aging,Alzheimer's disease,traumatic brain injury,HIV-associated neurocognitive disorder,and mental disorders.

Eleutheroside B or E enhances learning and memory in experimentally aged rats

Eleutheroside B or E, the main component of Acanthopanax, can relieve fatigue, enhance memory, and improve human cognition. Numerous studies have confirmed that high doses of acetylcholine significantly attenuate clinical symptoms and delay the progression of Alzheimer＇s disease. The present study replicated a rat model of aging induced by injecting quinolinic acid into the hippocampal CA1 region. These rats were intraperitoneally injected with low, medium and high doses of eleutheroside B or E （50, 100, 200 mg/kg）, and rats injected with Huperzine A or PBS were used as controls. At 4 weeks after administration, behavioral tests showed that the escape latencies and errors in searching for the platform in a Morris water maze were dose-dependently reduced in rats treated with medium and high-dose eleutheroside B or E. Hematoxylin-eosin staining showed that the number of surviving hippocampal neurons was greater and pathological injury was milder in three eleutheroside B or E groups compared with model group. Hippocampal homogenates showed enhanced cholinesterase activity, and dose-dependent increases in acetylcholine content and decreases in choline content following eleutheroside B or E treatment, similar to those seen in the Huperzine A group. These findings indicate that eleutheroside B or E improves learning and memory in aged rats. These effects of eleutheroside B or E may be mediated by activation of cholinesterase or enhanced reuse of choline to accelerate the synthesis of acetylcholine in hippocampal neurons.

Impacts of Passive Smoking on Learning and Memory Ability of Mouse Offsprings and Intervention by Antioxidants

Objective To determine the impact of passive smoking and the protective effect of antioxidants such as vitamin E and quercetin on learning and memory ability of mouse offsprings. Methods A passive smoking model of pregnant mice was established. Learning and memory ability was evaluated by the water maze test and long term potentiation （LTP）. Nitric oxide （NO）, content, nitric oxide synthase （NOS）, acetylcholinesteras （Ache） activity in brain, vitamin E concentration, and reactive oxygen species （ROS） in serum were determined. The latency period （the time during which the mice swim from the starting position to the ending position） and errors （the number of mice entering the blind end） in control and antioxidant intervention groups were compared with those in the smoke exposure group after 6 days. Results The latency period as well as errors in the air, control diet, tobacco smoke （TS）, and vitamin E diet groups were decreased significantly as compared with the TS and control diet groups （P〈O.05）. LTP was restrained in the TS and control diet groups. LTP in all the antioxidant diet groups was significantly increased compared with the TS and control diet groups. In addition, NOS and acetylcholinesteras （Ache） activitiy was significantly higher in the TS and control diet groups than in the air and control diet group. NO content was not significantly different among the different groups, and significantly lower in the TS and vitamin E diet groups than in the TS group, control diet group, quercetin diet group, and mixture diet group （P〈0.05）. Vitamin E concentration and ROS activity in serum were correlated with the outcome of water maze and LTP. Conclusion Passive smoking reduces LTP formation by disturbing the hippocampus function of mice, by decreasing NOS （especially vitamin E） partially improve the learning and memory smoke during pregnancy. and Ache activity and increasing NO content. Antioxidants ability of offsprings whose mothers are exposed to tobacco

Kongsheng Zhenzhong pill's effect on the learning and memory ability and its neuroprotective effects in vascular dementia rats

Clinical reports have demonstrated that the Kongsheng Zhenzhong pill （KSZZP）, a classical prescription deriving from Valuable Prescription for Emergencies, has good therapeutic effects on vascular dementia. However, the mechanisms that mediate its effects remain unclear. In this study, the expression of N-methyI-D-aspartate receptor 1 mRNA, the content of nitric oxide, and the concentration of calcium in neurons was determined with in situ hybridization, spectrophotometry and flow cytometry, respectively. In addition, the expressions of N-methyI-D-aspartate receptor 1, nerve growth factor protein, and glial cell line-derived neurotrophic factor protein were detected with immunohistochemistry. We found that KSZZP could significantly decrease the expression of N-methyI-D-aspartate receptor 1 mRNA and protein, the content of nitric oxide, and the concentration of calcium in neurons. KSZZP also increased the expression of nerve growth factor and glial cell line-derived neurotrophic factor protein in the hippocampus CA1 region and in the cerebral cortex. Morris water maze and passive avoidance tests verified that KSZZP ameliorated the cognitive impairments of vascular dementia rats. Moreover, the KSZZP-induced improvements in the cognitive functions of vascular dementia rats were correlated with both inhibition of N-methyl-D-aspartate-induced excitable neurotoxicity and elevation of neurotrophic factor expression.

Protective Effect of Ginkgo Biloba Leaf Extract on Learning and Memory Deficit Induced by Aluminum in Model Rats

Objective： To examine the protective effect of Ginkgo biloba leaf extract （GbE） on learning and memory deficit induced by aluminum chloride （AlCl3）, and explore its mechanisms. Methods： The rat models with learning and memory deficit were induced by administering via gastrogavage and drinking of AlCl3 solution. And the model rats were treated with GbE at the dose of 50, 100, 200 mg/kg every day for 2 months accompanied with drinking of AlCl3 solution, respectively. Their abilities of spatial learning and memory were tested by Morris water maze, and the acetylcholinesterase （ACHE） activity in serum was assayed with chemical method, the AChE expression in hippocampus was observed by immunohistochemistry assay, and then quantitative analysis was done by BI 2000 image analysis system. Results： Learning and memory deficit of rats could be induced by AlCl3 solution （P〈0.01）, and AChE expressions in rats hippocampus were increased （P〈0.01）; GbE ameliorated learning and memory deficit and reduced AChE expression in rats hippocampus in a dose-dependent manner, while GbE significantly increased serum AChE activity at the dose of 200 mg/kg each day （P〈0.05）. Conclusion： GbE can ameliorate learning and memory deficit induced by AlCl3, which may be due to its inhibition of the AChE expression in hippocampus.

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.

Episodic Learning and Memory in Prodromal Huntington’s Disease: The Role of Multimodal Encoding and Selective Reminding

This study investigated episodic memory in prodromal HD. Three groups were compared (N = 70): mutation carriers with less than 12.5 years to disease onset (n = 16), mutation carriers with 12.5 or more years to disease onset (n = 16), and noncarriers (n = 38). Episodic memory was assessed using the Fuld Object Memory Evaluation, which included multimodal presentation and selective reminding, and the Claeson-Dahl Learning Test which included verbal repeated presentation and recall trials. Both carrier groups demonstrated deficient episodic memory compared to noncarriers. The results suggest deficient episodic memory in prodromal HD, and that inconsistent retrieval contributes to these deficits. Multimodal presentation attenuates the deficits.

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.