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.
ObjectiveTo systematically review the diagnostic value of FDG-PET, Aβ-PET and tau-PET for Alzheimer ’s disease (AD).MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect diagnostic tests of FDG-PET, Aβ-PET and tau-PET for AD from January 2000 to February 2020. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by Meta-Disc 1.4 and Stata 14.0 software.ResultsA total of 31 studies involving 3 718 subjects were included. The results of meta-analysis showed that, using normal population as control, the sensitivity/specificity of FDG-PET and Aβ-PET in diagnosing AD were 0.853/0.734 and 0.824/0.771, respectively. Only 2 studies were included for tau-PET and meta-analysis was not performed.ConclusionsFDG-PET and Aβ-PET can provide good diagnostic accuracy for AD, and their diagnostic efficacy is similar. Due to limited quality and quantity of the included studies, more high quality studies are required to verify the above conclusions.
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 (AD) is an irreversible neurodegenerative disorder that damages patients’ memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.
Objective To investigate the effect of continuous positive airway pressure (CPAP) on sleep disorder and neuropsychological characteristics in patients with early Alzheimer’s disease (AD) combined with obstructive sleep apnea hypopnea syndrome (OSAHS). Methods A total of forty-two early AD patients with OSAHS were randomly divided into a CPAP combined treatment group (20 cases) and a simple medicine treatment group (22 cases). The changes of neurocognitive function were assessed by Montreal Cognitive Assessment (MoCA), Mini-mental State Examination (MMSE) and Hopkins Verbal Learning Test-revised (HVLT). Patient Health Questionnaire-9 (PHQ9) was used to evaluate the depression mood changes. The sleep characteristics and respiratory parameters were evaluated by polysomnography. The changes of the patients’ sleep status were assessed by Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI). The changes of sleep status, cognitive function and mood in the CPAP combined treatment group were compared before and three months after CPAP treatment, and with the simple medicine treatment group. Results After three months of CPAP treatment, the ESS, PSQI and PHQ9 scores in the CPAP combined treatment group were significantly decreased compared with those before treatment, whereas MoCA, MMSE and HVLT (total scores and recall ) in the CPAP combined treatment group were increased compared with those before treatment (P<0.05). After CPAP treatment, the respiratory parameters apnea hypopnea index in the CPAP combined treatment group was significantly lower than that before treatment (P<0.05), and the minimum blood oxygen saturation was significantly higher than that before treatment (P<0.05). However, the sleep characteristics and parameters did not show statistically significant changes compared with those before treatment (P>0.05). The ESS, PSQI and PHQ9 scores were significantly reduced in the CPAP combined treatment group compared with the simple medicine treatment group (P<0.05), while there was no statistically significant changes of cognitive scores between the two groups (P>0.05). Conclusions The degree of low ventilation and hypoxia is alleviated, and the daytime sleepiness and depression is improved in early AD patients with OSAHS after three-month continuous CPAP treatment. Cognitive function is significantly improved, whereas there is no significant change in sleep structure disorder.
Krüppel-like factor 4 (KLF4) is a member of the sample Kruppel transcription factor protein family, is an evolutionary conservative contain zinc finger transcription factors, involved in regulating many cellular processes, such as cell growth, proliferation, differentiation and invasion, KLF4 expression in a variety of tissues and cells in the body, has widely in many physiological and pathological conditions. Many studies have shown that KLF4 is involved in neurobiological processes such as neuroinflammation, oxidative stress, apoptosis and axon regeneration, and is closely related to a variety of nervous system diseases such as epilepsy, stroke, and Alzheimer’s disease. Now KLF4 in its role in the development of nervous system diseases were reviewed, help to understand the pathogenesis of the disease and clinical treatment for diseases of the nervous system to provide potential targets.
Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary 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.
Alzheimer’s disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision. The performance of the model was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Experimental results showed that the proposed framework improved ACC and AUC by 3% and 4% on average compared with other mainstream frameworks in the two tasks of AD classification and mild cognitive impairment conversion classification, and could find the key voxel blocks that trigger the disease, providing an effective basis for AD auxiliary diagnosis.