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.
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.
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.
Biological markers play a pivotal role in the early and accurate diagnosis of Alzheimer’s disease, enabling precise identification and monitoring of therapeutic interventions. The detection of central β-amyloid and Tau proteins has become an indispensable tool in clinical trials. Recent years have witnessed substantial progress in the development of readily accessible and cost-effective blood biomarkers. This comprehensive article provides a comprehensive overview of the clinical applications of blood biomarkers, encompassing β-amyloid, phosphorylated Tau protein, neurofilament light chain protein, and glial fibrillary acidic protein, all of which have demonstrated clinical relevance in Alzheimer’s disease diagnosis. Notably, phosphorylated Tau protein exhibits superior diagnostic efficacy. The incorporation of blood biomarkers facilitates early screening, accurate diagnosis, and efficacious treatment of Alzheimer’s disease.
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG 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 systematically review the effect of vitamin D (VitD) supplementation on cognitive function in people with cognitive impairment and non-cognitive disorders. MethodsThe PubMed, Web of Science, Cochrane Library, EMbase, CBM, CNKI, WanFang Data and VIP databases were searched to collect randomized controlled trials (RCTs) about the effect of VitD supplementation on cognitive function of patients with cognitive impairment or non-cognitive disorders from inception to March, 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using RevMan 5.4 software. Results A total of 19 articles including 8 684 cases were included. The results of meta-analysis showed that mini-mental state examination (MMSE) score (MD=1.70, 95%CI 1.20 to 2.21, P<0.01), Montreal cognitive assessment (MoCA) score (MD=1.51, 95%CI 1.00 to 2.02, P<0.01), Wechsler Adult Intelligence Scale-Revised (WAIS-RC) score (MD=9.12, 95%CI 7.77 to 10.47, P<0.01) and working memory (SMD=1.87, 95%CI 1.07 to 2.67, P<0.01) in the VitD group of patients with cognitive impairment were all better than those in the control group. However, the overall cognitive function and working memory of the non-cognitive impairment population were not significantly different compared with the control group. In terms of language fluency and language memory, there was no significant difference between the VitD group and the control group. In terms of the executive functions, at the intervention time of> 6 months, the VitD and control groups were statistically significant (SMD=0.15, 95%CI 0.01 to 0.28, P=0.03). Conclusion Current evidence suggests that VitD supplementation can effectively improve the overall cognitive function and working memory of patients with cognitive impairment, and has a positive effect on executive function at an intervention time of >6 months. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.
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.
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.