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.
Alzheimer’s disease (AD) is a chronic central neurodegenerative disease. The pathological features of AD are the extracellular deposition of senile plaques formed by amyloid-β oligomers (AβOs) and the intracellular accumulation of neurofibrillary tangles formed by hyperphosphorylated tau protein. In this paper, an in vitro pathological model of AD based on neuronal network chip and its real-time dynamic analysis were presented. The hippocampal neuronal network was cultured on the microelectrode array (MEA) chip and induced by AβOs as an AD model in vitro to simultaneously record two firing patterns from the interneurons and pyramidal neurons. The spatial firing patterns mapping and cross-correlation between channels were performed to validate the degeneration of neuronal network connectivity. This biosensor enabled the detection of the AβOs toxicity responses, and the identification of connectivity and interactions between neuronal networks, which can be a novel technique in the research of AD pathological model in vitro.
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.
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.
Alzheimer’s disease (AD) is the most common degenerative disease of the nervous system. Studies have found that the 40 Hz pulsed magnetic field has the effect of improving cognitive ability in AD, but the mechanism of action is not clear. In this study, APP/PS1 double transgenic AD model mice were used as the research object, the water maze was used to group dementia, and 40 Hz/10 mT pulsed magnetic field stimulation was applied to AD model mice with different degrees of dementia. The behavioral indicators, mitochondrial samples of hippocampal CA1 region and electrocardiogram signals were collected from each group, and the effects of 40 Hz pulsed magnetic field on mouse behavior, mitochondrial kinetic indexes and heart rate variability (HRV) parameters were analyzed. The results showed that compared with the AD group, the loss of mitochondrial crest structure was alleviated and the mitochondrial dynamics related indexes were significantly improved in the AD + stimulated group (P < 0.001), sympathetic nerve excitation and parasympathetic nerve inhibition were improved, and the spatial cognitive memory ability of mice was significantly improved (P < 0.05). The preliminary results of this study show that 40 Hz pulsed magnetic field stimulation can improve the mitochondrial structure and mitochondrial kinetic homeostasis imbalance of AD mice, and significantly improve the autonomic neuromodulation ability and spatial cognition ability of AD mice, which lays a foundation for further exploring the mechanism of ultra-low frequency magnetic field in delaying the course of AD disease and realizing personalized neurofeedback therapy for AD.
ObjectiveTo systematically review the diagnostic value of miRNAs for Alzheimer’s disease (AD).MethodsPubMed, Web of Science, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect diagnostic tests of miRNAs for AD from inception to October 31, 2020. Two researchers independently screened literature, extracted data, and assessed the risk of bias of the included studies. RevMan 5.3 and Stata 14.0 software were used for meta-analysis. ResultsA total of 22 studies involving 4 006 subjects were included. The meta-analysis results showed that the pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and the areas under the working characteristic curve of miRNA in AD diagnosis were 0.83 (95%CI 0.79 to 0.87), 0.80 (95%CI 0.76 to 0.83), 4.07 (95%CI 3.37 to 4.92), 0.21 (95%CI 0.17 to 0.27), 19.20 (95%CI 12.96 to 28.48) and 0.88 (95%CI 0.85 to 0.90), respectively. ConclusionThe current evidence shows that miRNAs have a high diagnostic value for AD. However, because of the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusion.
Alzheimer's disease (AD) is a neurodegenerative disorder with insidious onset and poor prognosis, and it is the primary cause of senile dementia. Its early diagnosis is challenging, and existing methods mostly rely on high-cost and invasive examinations. As an integral part of the central nervous system, the retina provides a non-invasive and efficient observation window for AD diagnosis. In recent years, with the development of ophthalmic imaging technologies such as optical coherence tomography, optical coherence tomography angiography, and hyperspectral imaging, a growing number of studies have revealed that AD patients exhibit retinal structural changes, structural and functional abnormalities of retinal blood vessels, and that amyloid-beta and Tau deposits can be detected via specific imaging methods—suggesting that these changes may occur prior to brain lesions. Meanwhile, the integrated analysis of multimodal imaging shows promising prospects in identifying retinal biomarkers and predicting AD risk, demonstrates the significant potential of retinal imaging technology in the early screening, diagnosis, and disease progression monitoring of AD, and provides a new source of biomarkers and potential clinical applications for AD research.
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.
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.
