• 대한치매학회지
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• 제17권4호
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• pp.156-162
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• 2018

Background and Purpose: Previous studies have reported conflicting results about the prevalence of seizures in Alzheimer's disease (AD). There are few epidemiological studies on this topic in Asia. Thus, the objective of this study was to examine demographic and clinical characteristics as well as incidence for seizures in AD patients compared to non-AD patients in a prospective, longitudinal, community-based cohort with a long follow-up. Methods: Data were collected from National Health Insurance Service-National Elderly Cohort (NHIS-elderly) Database to define patients with AD from 2004-2006 using Korean Classification Diseases codes G30 and F00. We performed a 1:5 case-control propensity score matching based on age, sex, and household income. We conducted Cox proportional hazards regression analysis to estimate the risk of epilepsy in AD patients. Results: In the cohort study, patients with AD had higher risk for epilepsy than those without AD, with hazard ratio of 2.773 (95% confidence interval [CI], 2.515-3.057). This study also showed that male gender and comorbidities such as hypertension, hyperlipidemia, diabetes, and chronic kidney disease increased the risk of developing epilepsy. Patients with AD had 1.527 (95% CI, 1.375-1.695) times higher mortality rate than those in the control group. Conclusions: AD patients have significantly higher risk of developing epilepsy than non-AD patients.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.940-952
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• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.

• Journal of Ginseng Research
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• 제40권1호
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• pp.9-17
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• 2016

Background: Alzheimer's disease (AD) is a progressive brain disease, for which there is no effective drug therapy at present. Ginsenoside Rg1 (G-Rg1) and G-Rg2 have been reported to alleviate memory deterioration. However, the mechanism of their anti-AD effect has not yet been clearly elucidated. Methods: Ultra performance liquid chromatography tandem MS (UPLC/MS)-based metabolomics was used to identify metabolites that are differentially expressed in the brains of AD mice with or without ginsenoside treatment. The cognitive function of mice and pathological changes in the brain were also assessed using the Morris water maze (MWM) and immunohistochemistry, respectively. Results: The impaired cognitive function and increased hippocampal $A{\beta}$ deposition in AD mice were ameliorated by G-Rg1 and G-Rg2. In addition, a total of 11 potential biomarkers that are associated with the metabolism of lysophosphatidylcholines (LPCs), hypoxanthine, and sphingolipids were identified in the brains of AD mice and their levels were partly restored after treatment with G-Rg1 and G-Rg2. G-Rg1 and G-Rg2 treatment influenced the levels of hypoxanthine, dihydrosphingosine, hexadecasphinganine, LPC C 16:0, and LPC C 18:0 in AD mice. Additionally, G-Rg1 treatment also influenced the levels of phytosphingosine, LPC C 13:0, LPC C 15:0, LPC C 18:1, and LPC C 18:3 in AD mice. Conclusion: These results indicate that the improvements in cognitive function and morphological changes produced by G-Rg1 and G-Rg2 treatment are caused by regulation of related brain metabolic pathways. This will extend our understanding of the mechanisms involved in the effects of G-Rg1 and G-Rg2 on AD.

• 대한의생명과학회지
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• 제26권1호
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• pp.1-7
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• 2020

Alzheimer's disease (AD) is the most common neurodegenerative disorder and is expected to become more and more widespread as life expectancy increases. New therapeutic target, as well as the identification of mechanisms responsible for pathology, is urgently needed. Recently, microglial actin cytoskeleton has been proposed as a beneficial role in axon regeneration of brain injury. This review highlights in understanding of the characteristics of microglial actin cytoskeleton and discuss the role of specific actin-interacting proteins and receptors in AD. The precise mechanisms and functional aspects of motility by microglia require further study, and the regulation of microglial actin cytoskeleton might be a potential therapeutic strategy for neurological diseases.

• 센서학회지
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• 제22권4호
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• pp.281-285
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• 2013

In this study a biosensor was developed by using Drosophila cells expressing a gustatory receptor Gr5a and an ion sensitive field effect transistors (ISFETs) sensor device, which demonstrated significant compatibility with the Drosophila cells expressing Gr5a and their response to sugar. These results suggested that the newly developed cell based biosensor has a potential as a simple and easy screening device for Alzheimer's disease in the future.

• 한국임상약학회지
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• 제24권4호
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• pp.255-264
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• 2014

Objective: The aims of this study are to investigate the total volume of prescribed medicines against Alzheimer's disease (AD) and the trends of usage by analyzing the claims-data from the Korea National Health Insurance Service. Method: The demographic and claims-data were included the major AD treating medicines such as donepezil, galantamine, rivastigmine and memantine, and analyzed during the period of 2010~2012. The assessing criteria were gender, age, habitation, types of medical institution, code of ingredients, outcomes of treatment, volume and amount of claims, and the numbers of patients with dementias. After trimming the data, it were analyzed by the market size, demographic traits, characteristics of medical service, characteristics of each anti-AD medicine, etc. Results: Among the chosen 4 medicines, donepezil had the top prescription volumes. Most prevalent prescribing preparations of donepezil were conventional types. However, among the non-conventional types, oro-dispersible formulation is the fast increasing one in both volume and growth rate. This specialized preparations to improve both toleration and adherence, tend to being prescribed generally at the tertiary medical institutions. While the younger patients with mild-to-moderate AD mostly treated by expensive medicines in resident at the tertiary hospitals, the rest older patients with severe AD have been treated non-expensive one at long-term care facilities. Conclusion: AD is a chronic illness therefore, long-term use of therapeutic medications are highly important. If an anti-AD treatment was applied steadily in the earlier stages, it would be achieved not only improving the quality of life of patient but also reducing the expenses in the medical and nursing cares. As the socioeconomical impacts of AD is expanding, healthcare professionals need to aware the importance of pharmacotherapy and to improve sociopolitical fundamentals.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.205-215
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• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

• 생물정신의학
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• 제16권4호
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• pp.238-245
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• 2009

Objectives : Mild Alzheimer's disease(AD) is uncertain to be related to visuospatial working memory subsystem dysfunction. We used the self ordered pointing test(SOPT) to find the characteristics of visuospatial working memory in mild AD. Methods : We compared the visuospatial working memory abilities of 20 patients with mild AD and 20 normal elderly controls(NC) using SOPT, of which stimuli consisted of two stimuli types(A : abstract, C : concrete) and two stimuli numbers(8 and 12). Therefore, working memory was tested using C8, C12, A8, and A12 stimuli conditions in SOPT. Mixed-model ANOVA was conducted with the AD and NC groups as between-subjects factor, with stimuli types and stimuli numbers as the within-subjects factors and with SOPT error rates as the dependent variable. Results : The AD group showed higher error rates in SOPT than the NC group. The NC group showed low error rates in concrete stimuli than in abstract stimuli and in small stimuli numbers than in large stimuli numbers. And the AD group showed no differences between stimuli types or stimuli numbers. Conclusion : AD patients showed a poor performance in visuospatial working memory using concrete stimuli. The result suggests that there is a non-transformation from visual input to phonological working memory in AD. Patients with AD showed a poor performance although in small stimuli number condition of SOPT. It suggests that in AD, visuospatial working memory is not working well although in low central executive loads.

• 대한치매학회지
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• 제17권4호
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• pp.137-147
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• 2018

Background and Purpose: Previous studies have suggested a decreased cancer risk among patients with Alzheimer's disease (AD). There remains a lack of data on the specific types of cancer and risk factors for developing cancer in AD. We evaluated the association between AD and cancer risk, and we examined specific types of cancer. Methods: A population-based longitudinal study was conducted using the National Health Insurance Service-Senior cohort for 2002-2013. A total of 4,408 AD patients were included in the study, as were 19,150 matched controls. Potential associations between the risk of cancer and AD were analyzed using Cox proportional hazard regressions. Results: Cancer developed in 12.3% of the AD group patients and in 18.5% of control group subjects. AD was associated with a reduced risk of cancer (hazard ratio [HR], 0.70; 95% confidence intervals, 0.64-0.78). The risk of head and neck cancers was significantly reduced (HR, 0.49), as were risks for cancers of the digestive tract, including stomach cancer (HR, 0.42), colorectal cancer (HR, 0.61), liver and biliary tract cancers (HR, 0.68), and pancreatic cancer (HR, 0.55). Lung and prostate cancer risks were also significantly lower for the AD group (HR, 0.52 and HR, 0.72, respectively). Conclusions: Our results showed an inverse association between AD and cancer. Further research involving a large number of patients in a hospital based-study is needed to address the biological associations between cancer development and dementia, including AD.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.