• Journal of Oriental Neuropsychiatry
• /
• v.28 no.4
• /
• pp.359-371
• /
• 2017

Objectives: The objective of this study was to review the effect of oral administration of herbal medicines on the improvement of memory in Alzheimer's disease animal model reported in Korean domestic journals. Methods: The Korean databases (Koreantk, KISS) were searched with memory as a popular search term. During the searches, only animal study reports were reviewed. Data of animal models, intervention, observation methods of measuring indicators were extracted from the databases. Results: Typically, 36 articles were reviewed. Twenty-two studies used scopolamine to induce Alzheimer's disease, 24 studies used complex herbal medicines, and 12 studies used simple herbal medicines. Polygalae Radix and Acori Rhizoma were the most frequently used herbal medicines to improve memory in Alzheimer model. To evaluate the effect of herbal medicines, 36 studies used macroscopy, 16 studies used molecular biological analysis, 21 studies used biochemical analysis, 15 studies used histological analysis, and 11 studies used hematological analysis. Each study showed significant improvement with respect to memory indicators. Conclusions: Overall, the results suggest that treatment employing herbal medicines is an effective option to treat memory impairment in Alzheimer's disease.

• Journal of Korean Biological Nursing Science
• /
• v.22 no.3
• /
• pp.172-175
• /
• 2020

Purpose: The purpose of this paper is to provide nurses with a concise review on neurodegenrative dementias. This review includes pathophysiology, clinical course, and tips on management of dementias from Alzheimer's disease (AD), Parkinson disease (PD) and lewy body dementia (LBD). Considering increasing numbers of dementia cases among older adults, nurses who are cognizant about dementia care are instrumental in maximizing daily activities and quality of life of patients with cognitive impairment and dementia.

• Korean Journal of Biological Psychiatry
• /
• v.21 no.4
• /
• pp.115-117
• /
• 2014

Variety of biomarkers that are related to the Alzheimer's disease and its diagnosis and progress have been found. However, research lacks in predicting the reaction of the treatment. In addition, there is no definite treatment reaction to the disease but rather it is varied. The purpose of this review article is to study the research of the biomarkers that are able to predict the treatment reaction. There was a research that illustrated a relationship between plasma amyloid ${\beta}$ peptide, cerebrospinal fluid tau, neuroanatomical biomarkers and acetylcholinesterase inhibitors. Polymorphisms in genes of the cholinergic markers AChE, BuChE, ChAT and PON-1 were found to be associated with better clinical response to acetylcholinesterase inhibitors. Many pharmacogenetic studies have been conducted to evaluate the impact of the lipoprotein apolipoprotein E (APOE) genotype on treatment response to acetylcholinesterase inhibitor. However, there is no significant influence of the APOE genotypes on treatment response. Further research is needed to find other predictors of treatment with acetylcholinesterase inhibitors in patients with Alzheimer's disease.

• Journal of Information Processing Systems
• /
• v.17 no.1
• /
• pp.178-190
• /
• 2021

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2010.06a
• /
• pp.280-280
• /
• 2010

Alzheimer disease is a progressive neurodegenerative disease of the aged, characterized by memory loss and dementia. For diagnosis of Alzheimer disease we have simply modified macrophage with amyloid beta bonded with different molecules. Modified Macrophage was observed with microscope for co-localization of amyloid beta molecule. For this experiment we used fluoroscene labeling substances. The macrophage was modified also with cell staining method. For cell staining method was used avidin-biotin reaction principles. All experiments were carried out on poly-L-lysine coated and sterilized glass substrates. In the presentation we will show the further investigations and applications with modified macrophage.

• BMB Reports
• /
• v.42 no.8
• /
• pp.467-474
• /
• 2009

The modification of proteins by reversible phosphorylation is a key mechanism in the regulation of various physiological functions. Abnormal protein kinase or phosphatase activity can cause disease by altering the phosphorylation of critical proteins in normal cellular and disease processes. Alzheimer' disease (AD), typically occurring in the elderly, is an irreversible, progressive brain disorder characterized by memory loss and cognitive decline. Accumulating evidence suggests that protein kinase and phosphatase activity are altered in the brain tissue of AD patients. Tau is a highly recognized phosphoprotein that undergoes hyperphosphorylation to form neurofibrillary tangles, a neuropathlogical hallmark with amyloid plaques in AD brains. This study is a brief overview of the altered protein phosphorylation pathways found in AD. Understanding the molecular mechanisms by which the activities of protein kinases and phosphatases are altered as well as the phosphorylation events in AD can potentially reveal novel insights into the role aberrant phosphorylation plays in the pathogenesis of AD, providing support for protein phosphorylation as a potential treatment strategy for AD.

• Journal of Haehwa Medicine
• /
• v.8 no.1
• /
• pp.793-825
• /
• 1999

For the development of drugs for alzheimer,s disease, the study was done to review the oriental pathology, clinical data, recent trends for research and strategy for future study. The results were as follows: 1. The medical term Chi-dsi implying alzheimer,s disease was referred for the first time in a medical book, Hwatasheneubijeon written by Hwa-Ta and its differentiation and treatment were studied more in Ming or Ching dynasties. Chi-dai can be differentated as weak(虛) syndrome and Shi(實) syndrome. This can be caused by deficiencies of renal Yin, renal Yang, cardiac Yin and hepatic blood, while that by deficiencies of pathological fluid(痰飮) and clotted blood(瘀血). 2. Dementia can be roughly classified as alzheimer's disease and multi-infarct disease. Its causes were known to be cholinergic transmitter, C-peptide, amyloid-${\beta}$, apolipoprotein, APP(amyloid precursor protein), TGF, MMP-9 and free radical. 3. In Korea experimental studies were chiefly done for the elimataion of C-peptide, amyloid-${\beta}$, apolipoprotein, APP for alzheimer's disease, for the development of drug inhibiting degerative change following CVA and loss of memory and also administrative measure was done by support of government. 4. Drugs of dimentia developed so far were Chi-Dai dan, extracts from aloe, mushroom, green tea, Ganoderma and also folic acid, vitamin C, DHEA and silk amino acid were reported to be effective in dimenta. 5. Future strategic research had better be done on dementia-inducing factors such as acetylcholine, C-peptide, amyloid-${\beta}$, apolipoprotein, APP, TGF, MMP-9 and free radical, development of animal model for dimentia, clinical study, epidemiology, nursing and administrative studies and also consortium for dimentia research should be formed so that repeated investment be avoided.

• International Journal of Computer Science & Network Security
• /
• v.24 no.5
• /
• pp.53-63
• /
• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.233-242
• /
• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• The Journal of the Korean life insurance medical association
• /
• v.18
• /
• pp.39-50
• /
• 1999