Objective To generate eukaryotic expression vector of pcDNA3.1-β-site amyloid precursor protein cleaving enzyme (BACE) and obtain its transient expression in COS-7 cells. Methods A 1.5 kb cDNA fragment was amplified from the total RNA of the human neuroblastoma cells by the RT-PCR method and was cloned into the plasmid pcDNA3.1. The vector was identified by the double digestion with restriction enzymes BamHI and XhoI and was sequenced by the Sanger-dideoxy-mediated chain termination. The expression of the BACE gene was detected by immunocytochemistry. Results The results showed that the cDNA fragment included 1.5 kb total coding region. The recombinant eukaryotic cell expression vector of pcDNA3.1-BACE was constructed successfully, and the sequence of insert was identical to the published sequence. The COS-7 cells transfected with the pcDNA3.1BACE plasmid expressed a high level of the BACE protein in the cytoplasm. Conclusion The recombinant plasmid pcDNA3.1-BACE can provide a very useful tool for the research on the cause of Alzheimer’s disease and lay an important foundation for preventing Alzheimer’s disease.
Objective To evaluate the efficacy and safety of memantine in the treatment of Alzheimer’s disease (AD). Methods The randomized controlled trials (RCTs) about memantine vs. donepezil for patients with AD from January 1989 to July 2011 were searched in CBM, CNKI, WanFang Data, MEDLINE, OVID, EMbase and The Cochrane Library. Two reviewers independently screened the literatures, extracted the data, and evaluated the methodological quality. Then meta-analyses were conducted by using RevMan 5.0 software. Results The total 12 RCTs were included. Among the 2 716 patients involved, 1 459 were in the memantine group, while the other 1 302 were in the donepezil group. The results of meta-analyses showed that the efficacy of the memantine group was superior to that of the donepezil group in MMSE (MD=0.53, 95%CI 0.21 to 0.85, P=0.001), CIBIC-Plus (MD= –0.19, 95%CI –0.31 to –0.07, P=0.002), NPI (MD= –2.9, 95%CI –4.57 to –1.22, P=0.000 7) and SIB (MD=3.12, 95%CI 0.57 to 5.67, P=0.02), with significant differences; but the efficacy of the two groups was similar in ADCS-ADL19 (MD=0.29, 95%CI –0.03 to 0.60, P=0.07). There was no significant difference between the two groups in incidence of side effects (RR=1.14, 95%CI 0.94 to 1.38, P=0.17), but the tolerability of the memantine group was much better (RR=0.78, 95%CI 0.63 to 0.97, P=0.03). Conclusion Based on the current studies, memantine is superior to donepezil in treating Alzheimer’s disease (AD) at present. Although the side effects are similar to donepezil, memantine has much better intolerability and is considered to be safe and effective. For the quality restrictions and possible publication bias of the included studies, more double blind RCTs with high quality are required to further assess the effects.
Objective To evaluate the relationship between genetic polymorphism of ApoE and Alzheimer’s disease in Chinese population. Methods Such databases as PubMed, EBSCO, CNKI, CBM, and WangFang Data were searched from their establishment to December 2010 to collect the literature about the relationship between genetic polymorphism of ApoE and Alzheimer’s disease in Chinese population. RevMan 5.0 was adopted to conduct consistency check and data merging, and to evaluate publication bias. Results ApoEε4 was the risky allele (Plt;0.05) in Chinese population, and its pooled odds ratios and 95%CI was 3.53 (2.49 to 5.00). ApoEε3 was the protective alleles (Plt;0.05) in Chinese population, and its pooled odds ratios and 95%CI was 0.52 (0.40 to 0.68). ApoEε4/ε4, ApoEε4/ε3, and ApoEε4/ε2 were the risky genotypes (all Plt;0.05) in Chinese population, and their pooled odds ratios and 95%CI were 10.17 (4.25 to 24.19), 2.57 (2.04 to 3.25), and 1.94 (1.13 to 3.34), respectively. ApoEε3/ε3 was the protective genotype (Plt;0.05) in Chinese population, and its pooled odds ratios and 95%CI was 0.67 (0.57 to 0.77). Conclusion In Chinese population, some ApoE alleles and genotypes are associated with Alzheimer’s disease.
Amyloid β-protein (Aβ) deposition is an important prevention and treatment target for Alzheimer’s disease (AD), and early detection of Aβ deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aβ deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aβ plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aβ plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aβ plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.
Normal brain aging and a serious of neurodegenerative diseases may lead to decline in memory, attention and executive ability and poorer quality of life. The mechanism of the decline is not clear now and is still a hot issue in the fields of neuroscience and medicine. A large number of researches showed that resting state functional brain networks based functional magnetic resonance imaging (fMRI) are sensitive and susceptive to the change of cognitive function. In this paper, the researches of brain functional connectivity based on resting fMRI in recent years were compared, and the results of subjects with different levels of cognitive decline including normal brain aging, mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were reviewed. And the changes of brain functional networks under three different levels of cognitive decline are introduced in this paper, which will provide the basis for the detection of normal brain aging and clinical diseases.
In order to solve the problem of early classification of Alzheimer’s disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD (n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer’s disease.
Alzheimer’ s disease is the most common kind of dementia without effective treatment. Via early diagnosis, early intervention after diagnosis is the most effective way to handle this disease. However, the early diagnosis method remains to be studied. Neuroimaging data can provide a convenient measurement for the brain function and structure. Brain structure network is a good reflection of the fiber structural connectivity patterns between different brain cortical regions, which is the basis of brain’s normal psychology function. In the paper, a brain structure network based on pattern recognition analysis was provided to realize an automatic diagnosis research of Alzheimer’s disease and gray matter based on structure information. With the feature selection in pattern recognition, this method can provide the abnormal regions of brain structural network. The research in this paper analyzed the patterns of abnormal structural network in Alzheimer’s disease from the aspects of connectivity and node, which was expected to provide updated information for the research about the pathological mechanism of Alzheimer’s disease.
In this paper, a new method for the classification of Alzheimer’s disease (AD) using multi-feature combination of structural magnetic resonance imaging is proposed. Firstly, hippocampal segmentation and cortical thickness and volume measurement were performed using FreeSurfer software. Then, histogram, gradient, length of gray level co-occurrence matrix and run-length matrix were used to extract the three-dimensional (3D) texture features of the hippocampus, and the parameters with significant differences between AD, MCI and NC groups were selected for correlation study with MMSE score. Finally, AD, MCI and NC are classified and identified by the extreme learning machine. The results show that texture features can provide better classification results than volume features on both left and right sides. The feature parameters with complementary texture, volume and cortical thickness had higher classification recognition rate, and the classification accuracy of the right side (100%) was higher than that of the left side (91.667%). The results showed that 3D texture analysis could reflect the pathological changes of hippocampal structures of AD and MCI patients, and combined with multi-feature analysis, it could better reflect the essential differences between AD and MCI cognitive impairment, which was more conducive to clinical differential diagnosis.
Caveolin-1 (Cav-1) protein plays a very important role in the central nervous system, and is closely related to Alzheimer’s disease (AD). Through literature review, this article summarizes the present research status of Cav-1 protein in the field of AD from three aspects: the relationship between Cav-1 gene and AD; the relationship of Cav-1 protein with learning and memory; the relationship of Cav-1 protein with amyloid β-protein and Tau protein. And the aim of this paper is to provide a new thought and evidence for exploring the mechanism of AD via Cav-1 protein.
ObjectivesTo systematically review the efficacy and safety of butylphthalide soft capsule with routine treatment for Alzheimer’s disease (AD).MethodsDatabases including CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, and The Cochrane Library were electronically searched from September 2002 to July 2018 to collect randomized controlled trials of butylphthalide soft capsule with routine treatment for Alzheimer’s disease. The trial was screened based on inclusion and exclusion criteria, and the methodological quality of the included trial was assessed. Meta-analysis was then performed by Revman 5.3 software.ResultsA total of 8 studies involving 576 patients were included. The butylphthalide soft capsule group included 283 patients and the control group included 293 patients. The result of meta-analysis showed that butylphthalide soft capsule with routine treatment (Donepezil hydrochloride or Memantine or EGb761) significantly improved the score of mini-mental state examination (MMSE) (MD=3.19, 95% CI 2.69 to 3.69, P<0.001) and clinical efficacy (RR=1.36, 95%CI 1.21 to 1.53, P<0.001). There was no significant difference in number of adverse events between the butylphthalide group and the control group (RR=1.13, 95%CI 0.77 to 1.67, P=0.52).ConclusionsBased on the routine treatment, combining with butylphthalide soft capsule can further facilitate cognitive function of AD and improve clinical efficacy. At the same time, no increase in adverse reactions has been found. However, due to the low quality of the included studies, more high quality randomized controlled trials are required to verify the results.