ObjectiveTo systematically review the relationship between Cadmium (Cd) level and Alzheimer's disease (AD). MethodWe searched PubMed, EMbase, CNKI, WanFang Data and CBM databases from inception to December 2014 to collect case-control studies about the relationship between Cd level and AD. Two reviewers screened literature, extracted data and evaluated the risk of bias of included studies, and then meta-analysis was performed by using RevMan 5.3 software. ResultsA total of 11 studies were included, among them 8 studies were included into final meta-analysis. Three studies including 154 patients and 141 controls reported the relationship of serum Cd concentrations and AD, and the result of meta-analysis showed that the higher serum Cd level was found in the AD group than the control group (SMD=0.36, 95%CI 0.12 to 0.59, P=0.003). Six studies including 358 patients and 423 controls reported the relationship of blood Cd concentrations and AD, and the result of meta-analysis showed that there was no significant difference of blood Cd levels between both groups (SMD=0.35, 95%CI -0.14 to 0.84, P=0.16). ConclusionSerum Cd concentrations may be associated with AD, but blood Cd concentrations not. Due to the limitation of quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.
Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disease is irreversible, and there has been no cure for the disease yet. Hence, early prognosis of AD is important for the development of new drugs and measures to slow the progression of the disease. Mild cognitive impairment (MCI) is a state between AD and healthy controls (HC). Studies have shown that patients with MCI are more likely to develop AD than those without MCI. Therefore, accurate screening of MCI patients has become one of the research hotspots of early prognosis of AD. With the rapid development of neuroimaging techniques and deep learning, more and more researchers employ deep learning methods to analyze brain neuroimaging images, such as magnetic resonance imaging (MRI), for early prognosis of AD. Hence, in this paper, a three-dimensional multi-slice classifiers ensemble based on convolutional neural network (CNN) and ensemble learning for early prognosis of AD has been proposed. Compared with the CNN classification model based on a single slice, the proposed classifiers ensemble based on multiple two-dimensional slices from three dimensions could use more effective information contained in MRI to improve classification accuracy and stability in a parallel computing mode.
This study aims to explore the diagnosis in patients with Alzheimer's disease (AD) based on magnetic resonance (MR) images, and to compare the differences of bilateral hippocampus in classification and recognition. MR images were obtained from 25 AD patients and 25 normal controls (NC) respectively. Three-dimensional texture features were extracted from bilateral hippocampus of each subject. The texture features that existed significant differences between AD and NC were used as the features in a classification procedure. Back propagation (BP) neural network model was built to classify AD patients from healthy controls. The classification accuracy of three methods, which were principal components analysis, linear discriminant analysis and non-linear discriminant analysis, was obtained and compared. The correlations between bilateral hippocampal texture parameters and Mini-Mental State Examination (MMSE) scores were calculated. The classification accuracy of nonlinear discriminant analysis with a neural network model was the highest, and the classification accuracy of right hippocampus was higher than that of the left. The bilateral hippocampal texture features were correlated to MMSE scores, and the relative of right hippocampus was higher than that of the left. The neural network model with three-dimensional texture features could recognize AD patients and NC, and right hippocampus might be more helpful to AD diagnosis.
Objective To observe the changes of retinal nerve fiber layer (RNFL) thickness in patients with Alzheimer's disease (AD). Methods Twenty eyes of 40 patients with mild and (or) moderate AD confirmed by clinical examination (AD group) were included in the study. There were 11 males and 9 females with an average age of (72.75±8.25) years. Age and gender-matched normal 20 objectives were in the normal control group. Among them, there were 11 males and 9 females with a mean age of (71.05±7.08) years. There was no significant difference in gender composition, age and intraocular pressure between the two groups (P＞0.05). There were significant differences in visual acuity, cup disc ratio and mini-mental state examination score (P＜0.05). All eyes underwent high-resolution optical coherence tomography (OCT) examination. With a diameter of 3.4 mm and a center on the center of the optic disc, circular fast scans on optic disc were performed to obtain an average disc RNFL thickness, signal threshold ＞6. Computer image analysis system was used to measure the RNFL thickness from superior, inferior, temporal and nasal quadrants, and the average RNFL thickness. The changes of RNFL thickness between the two groups and between different eyes of the same group were compared. Results Compared with the normal control group, the average (t=5.591), superior (t=8.169, 8.053) and inferior (t=12.596, 11.377) thickness of RNFL in both eyes in AD group were thinner, the differences were significant (P＜0.05); the temporal (t=1.966, 0.838）and nasal (t=2.071, 0.916) thickness of RNFL in both eyes of AD group were thinner, but the difference was not statistically significant (P＞0.05). There was no significant difference of the mean and different quadrant RNFL thickness between different eyes in AD group and normal control group (AD group: t=0.097, 0.821, 0.059, 0.020, 0.116; normal control group: t=0.791, 1.938, 1.806, 2.058, 1.005; P＞0.05). Conclusion The RNFL thickness around the optic disc in AD patients is thinner; This occurs first in superior and inferior quadrants of the optic disc.
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.
ObjectiveTo systematically review the correlation between apolipoprotein E (ApoE) polymorphism and sporadic Alzheimer's disease (SAD) in Chinese population. MethodsThe case-control studies about the relationship between ApoE polymorphism and SAD in Chinese population were electronically retrieved in PubMed, EMbase, CBM, The Cochrane Library (Issue 8, 2013), CNKI, VIP, and WanFang Data from the date of their establishment to August 2013. Literature screening according to the inclusion and exclusion criteria, data extraction and methodological quality assessment of the included stuides were completed by two reviewers independently. Meta-analysis was then conducted using Stata 12.0 software. ResultsA total of 50 case-control studies invovling 3 396 cases and 4 917 controls were finally included. The results of meta-analysis showed that, in Chinese, the risk of SAD was 2.89 times higher in population with allele ε4 than in population with allele ε3 (OR=2.89, 95%CI 2.61 to 3.19, P < 0.001); 7.24 times higher in those with ε4/ε4 genotype than in those with ε3/ε3 genotype (OR=7.24, 95%CI 5.11 to 10.24, P < 0.001); 2.90 times higher in ε3/ε4 genotype than in ε3/ε3 genotype (OR=2.90, 95%CI 2.56 to 3.29, P < 0.001); 2.11 times higher in ε2/ε4 genotype than in ε3/ε3 genotype (OR=2.11, 95%CI 1.64 to 2.72, P < 0.001); and no statistic significance was found in the risk of SAD compared ε2/ε3, ε2/ε2 genotypes and ε2 allele with ε3/ε3 genotype and ε3 allele. ConclusionFor Chinese population, ApoE allele ε4 is significantly associated with the onset of SAD, and genotype ε4/ε4 is a high risk factor of SAD. While allele ε2 is not associated with the onset of SAD. Since a great deal of current studies failed to conduct stratified analysis, it is suggested to further conduct relevant relevant studies according to clinical classification of SAD and patients' characteristics.
With the exacerbation of aging population in China, the number of patients with Alzheimer's disease (AD) is increasing rapidly. AD is a chronic but irreversible neurodegenerative disease, which cannot be cured radically at present. In recent years, in order to intervene in the course of AD in advance, many researchers have explored how to detect AD as early as possible, which may be helpful for effective treatment of AD. Imaging genomics is a kind of diagnosis method developed in recent years, which combines the medical imaging and high-throughput genetic omics together. It studies changes in cognitive function in patients with AD by extracting effective information from high-throughput medical imaging data and genomic data, providing effective guidance for early detection and treatment of AD patients. In this paper, the association analysis of magnetic resonance image (MRI) with genetic variation are summarized, as well as the research progress on AD with this method. According to complexity, the objects in the association analysis are classified as candidate brain phenotype, candidate genetic variation, genome-wide genetic variation and whole brain voxel. Then we briefly describe the specific methods corresponding to phenotypic of the brain and genetic variation respectively. Finally, some unsolved problems such as phenotype selection and limited polymorphism of candidate genes are put forward.
ObjectiveTo obverse the changes of macular choroidal thickness (CT) in patients with mild to moderate Alzheimer’s disease (AD).MethodsThis was a case-control study. Twenty-one patients with mild to moderate AD confirmed by Neurology Department of Jinhua Central Hospital from November 2016 to June 2018 and 21 age-matched control subjects were concluded in the study. There was no significant difference in age (t=0.128), intraocular pressure (t=0.440) and axial length (t=1.202) between the two groups (P＞0.05). There was significant difference in mini-mental state examination score (t=8.608, P＜0.05). CT was measured by OCT with enhanced depth imaging technique in the subfoveal choroid, at 0.5 mm and 1.0 mm from the center of the fovea nasal (NCT0.5, 1.0 mm), temporal (TCT0.5, 1.0 mm), superior (SCT0.5, 1 .0 mm), and inferior (ICT0.5, 1.0 mm). Independent-samples t test was used to compare the results obtained from these two groups.ResultsSFCT (t=2.431), NCT0.5, 1.0 mm (t=3.341, 2.640), TCT0.5, 1.0 mm (t=3.340, 2.899), SCT0.5, 1.0 mm (t=3.576, 3.751) and ICT0.5, 1.0 mm (t=2.897, 2.903) were significantly thinner in AD eyes than those in control eyes.ConclusionCompared with healthy subjects, patients with mild to moderate AD showed a significant reduction in CT.
Alzheimer's disease is a common neuro-degenerative disease. The clinical diagnosis mainly depends on the patient's complaint, the score of mini-mental state examination and Montreal cognitive assessment scale, and the comprehensive judgment of MRI and other imaging examinations. Retina is homologous to brain tissue, and their vascular systems have similar physiological characteristics to small blood vessels in the brain. Numerous studies found that the thickness of retinal nerve fiber layer, visual function, retinal blood vessels and retinal oxygen saturation were changed in AD patients to different degrees. To explore the formation mechanism and significance of ocular fundus changes in AD patients will be helpful to select specific, sensitive and simple methods for early observation and evaluation of AD.