• Speech Sciences
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• v.15 no.2
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• pp.111-118
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• 2008

We identified the characteristic impairmants of linguistic semantic memory in patients with prodromal Alzheimer's disease(AD) and mild AD. To elucidate the earliest changes of semantic language function in subjects with AD, performances on confrontation naming test and verbal fluency task were compared among patients with AD patients (n=20), mild AD patients (n=27) and healthy elderly controls (n=20). Tasks in this study included the confrontation naming test of Test of Lexical Processing in Aphasia(TLPA/Japanese) and one-minute verbal fluency task (semantic/ phonetic categories). The results were as follows: 1) Performances of the prodromal AD group showed the comparable to those of the control group on the confrontation naming test, 2) In the semantic/phonetic verbal fluency tasks, the performances of the control group were better than those of the prodromal AD and mild AD groups, but no significant differences were shown between the prodromal AD and the mild AD group.

• BMB Reports
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• v.49 no.7
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• pp.376-381
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• 2016

Several lines of evidence have revealed that phosphorylation of amyloid precursor protein (APP) at Thr668 is involved in the pathogenesis of Alzheimer's disease (AD). Okadaic acid (OA), a protein phosphatase-2A inhibitor, has been used in AD research models to increase tau phosphorylation and induce neuronal death. We previously showed that OA increased levels of APP and induced accumulation of APP in axonal swellings. In this study, we found that in OA-treated neurons, phosphorylation of APP at Thr668 increased and accumulated in axonal swellings by c-jun N-terminal kinase (JNK), and not by Cdk5 or ERK/MAPK. These results suggest that JNK may be one of therapeutic targets for the treatment of AD.

• Korean Journal of Biological Psychiatry
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• v.22 no.2
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• pp.40-46
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• 2015

Neuroglial cells are fundamental for brain homeostasis and defense to intrinsic or extrinsic changes. Loss of their function and over-reactivity to stimuli contribute to the aging of brain. Alzheimer's disease (AD) could be caused by more dramatic response in neuroglia associated with various chemokines and cytokines. Neuroglia of the AD brain shares some phenotypes with aging neuroglia. In addition, neuroglial activation and neuroinflammation are commonly showed in neurodegeneration. Thus neuroglia would be a promising target for therapeutics of AD.

• Journal of Preventive Medicine and Public Health
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• v.39 no.3
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• pp.243-248
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• 2006

Objectives: The Quality of Life-Alzheimer's Disease (QOL-AD) scale is a reliable and valid tool for assessing the quality of life (QOL) in the elderly with dementia. This study aimed to develop the Korean version of Quality of Life-Alzheimer's Disease (KQOL-AD) scale for the demented elderly living in the community. Methods: KQOL-AD was administered to two groups: 24 demented elderly and 72 cognitively impaired elderly with no dementia (CIND) who were living in the community Each elderly person and their caregiver rated the elderly QOL. The Korean version of mini-mental state examination (MMSE-K), the clinical dementia rating (CDR), the activities of daily living (ADL), and the neuropsychiatric inventory (NPI) were also assessed. The reliability and validity of the KQOL-AD were examined. Results: In the dementia group, the internal consistency (Cronbach's $\alpha$), the split half and the test-retest reliabilities of the KQOL-AD were excellent. Scores on the KQOL-AD were significantly correlated with the scores of the NPI, but they were not significantly correlated with scores of the MMSE-K, CDR and ADL. In addition, the CIND group showed similar results to the dementia group. Conclusions: KQOL-AD might be a reliable and valid instrument for assessing QOL in the elderly with dementia It could be used as an important outcome measure for research on the demented elderly.

• Korean Journal of Plant Resources
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• v.20 no.4
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• pp.325-330
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• 2007

This study was conducted to investigate the effect of the mixed extract of P. ginseng C.A. Mey. and C. sinensis K. (Gin-CHF) on the infarction area of hippocampus in the mice with Alzheimer's disease induced by ${\beta}-amyloid({\beta}A)$. The Gin-CHF extract reduced the infarction area of hippocampus, and controlled the injury of brain tissue in the mice with Alzheimer's disease induced by ${\beta}A$. The Gin-CHF extract reduced the Tau protein, GFAP protein, and presenilin1/presenilin2 protein (immunohistochemistry) of hippocampus in the mice with Alzheimer's disease induced by ${\beta}A$. These results suggest that the Gin-CHF extract may be effective for the prevention and treatment of Alzheimer's disease. Investigation into the clinical use of the Gin-CHF extract for Alzheimer's disease is suggested for further research.

• CELLMED
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• v.13 no.14
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• pp.14.1-14.9
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• 2023

Administration of Scopolamine can be considered a psychopharmacological model of Alzheimer's disease (AD). We made an animal model of Alzheimer's disease (AD) by administering Scopolamine to Blab/c mice. In this study, we investigated the effects of Resplex Alpha on memory impairment and cognitive function in mice in a mouse animal model of Scopolamine-induced memory impairment. Through Y-mazed and passive avoidance behavioral assays, we observed that Resplex Alpha recovered Scopolamine-induced short-term memory and cognitive functions. The results of our study imply that Resplex Alpha may be beneficial in the prevention of Alzheimer's disease (AD).

• Sleep Medicine and Psychophysiology
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• v.9 no.1
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• pp.9-13
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• 2002

Alzheimer's disease (AD) is one of the most common and devastating dementing disorders of old age. Most AD patients showed significant alternation of sleep structure as well as cognitive deficit. Typical findings of sleep architecture in AD patients include lower sleep efficiency, higher stage 1 percentage, and greater frequency of arousals. The slowing of EEG activity is also noted. Abnormalities in REM sleep are of particular interest in AD because the cholinergic system is related to both REM sleep and AD. Several parameters representing REM sleep structure such as REM latency, the amount of REM sleep, and REM density are change in patients with AD. Especially, measurements of EEG slowing during tonic REM sleep can be used as an EEG marker for early detection of possible AD. In addition, a structural defect in the suprachiasmatic nucleus is suggested to cause various chronobiological alternations in AD. Most of alternations related to sleep make sleep disturbances common and disruptive symptoms of AD. In this article, the author reviewed the alternation of sleep structure and circadian rhythm in AD patients.

• BMB Reports
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• v.42 no.8
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• pp.467-474
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• 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 Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 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.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.