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.
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.
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.
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.
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.
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.
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.
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.
Objective To analyze the characteristic and temporal trend in mortality and disease burden of Alzheimer’s disease (AD) and other forms of dementia in Guangzhou from 2008 to 2019, and estimate the disease burden attributable to smoking to provide evidence for promoting local health policy of prevention and intervention of dementia. Methods Based on the data of Guangzhou surveillance point of the National Mortality Surveillance System (NMSS), the crude mortality, standardized mortality, years of life lost (YLL) of AD and other dementia were calculated. The indirect method was used to estimate years lived with disability (YLD) and disability-adjusted life years (DALY).The distribution and changing trends of the index rates were compared from 2008 to 2019 using Joinpoint Regression Program. Based on the data of Guangzhou Chronic Disease and Risk Factors Monitoring System in 2013, the indexes of disease burden of AD and other forms of dementia attributable to smoking in 2018 was calculated. Results The standardized mortality rate, YLL rate, YLD rate and DALY rate of AD and other forms of dementia in Guangzhou increased from 0.45/100 000, 0.05‰, 0.02‰ and 0.07 ‰ in 2008 to 1.28/100 000, 0.15‰, 0.07‰ and 0.22‰ in 2019, respectively. The average annual changing trend was statistically significant (AAPC=11.30%, 13.09%, 13.09%, 13.09%, P<0.001）. In most years, the mortality and disease burden of women were higher than those of men, but men had higher growing trend than women in standardized mortality rate, YLL rate, YLD rate and DALY rate from 2008 to 2019, with a slower growing speed after the year 2012.The disease burden of dementia attributable to smoking in men was significantly higher than that in women. Conclusion The mortality and disease burden of AD and other forms of dementia in Guangzhou have dramatically increased over the past twelve years. Intervention against modifiable factors such as smoking, and prevention and screening for dementia in key populations should be strengthened. Support policies for dementia care management should be adopted to reduce the disease burden caused by premature death and disability.
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.