• Information Systems Review
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• v.26 no.2
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• pp.137-153
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• 2024

This study aimed to propose early diagnosis and management of dementia, which is increasing in aging societies, and suggest commercial utilization strategies by leveraging digital healthcare technologies, particularly lifelog data collected from wearable devices. By introducing new approaches to dementia prevention and management, this study sought to contribute to the field of dementia prediction and prevention. The research utilized 12,184 pieces of lifelog information (sleep and activity data) and dementia diagnosis data collected from 174 individuals aged between 60 and 80, based on medical pathological diagnoses. During the research process, a multidimensional dataset including sleep and activity data was standardized, and various machine learning algorithms were analyzed, with the random forest model showing the highest ROC-AUC score, indicating superior performance. Furthermore, an ablation test was conducted to evaluate the impact of excluding variables related to sleep and activity on the model's predictive power, confirming that regular sleep and activity have a significant influence on dementia prevention. Lastly, by exploring the potential for commercial utilization strategies of the developed model, the study proposed new directions for the commercial spread of dementia prevention systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1729-1738
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• 2021

Machine learning has a close relationship with cognitive psychology and brain science and is developing together. This paper analyzes the OASIS-3 dataset using machine learning techniques and proposes a model for predicting dementia. Dimensional reduction through PCA (Principal Component Analysis) is performed on the data quantifying the volume of each area among OASIS-3 data, and only important elements (features) are extracted and then various machine learning including gradient boosting and stacking Apply the models and compare the performance of each. Unlike previous studies, the proposed technique has a great differentiation because it uses not only the brain biometric data, but also basic information data such as the participant's gender and medical information data of the participant. In addition, it was shown that the proposed technique through various performance evaluations is a model that can better predict dementia by finding features that are more related to dementia among various numerical data.

• Journal of Korean Academy of Nursing
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• v.48 no.2
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• pp.143-153
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• 2018

Purpose: The purpose of this study was to identify factors predicting behavioral and psychological symptoms of dementia (BPSD) in persons with dementia. Factors including the patient, caregiver, and environment based on the multi-dimensional behavioral model were tested. Methods: The subjects of the study were 139 pairs of persons with dementia and their caregivers selected from four geriatric long-term care facilities located in S city, G province, Korea. Data analysis included descriptive statistics, inverse normal transformations, Pearson correlation coefficients, Spearman's correlation coefficients and hierarchical multiple regression with the SPSS Statistics 22.0 for Windows program. Results: Mean score for BPSD was 40.16. Depression (${\beta}=.42$, p<.001), exposure to noise in the evening noise (${\beta}=-.20$, p=.014), and gender (${\beta}=.17$, p=.042) were factors predicting BPSD in long-term care facilities, which explained 25.2% of the variance in the model. Conclusion: To decrease BPSD in persons with dementia, integrated nursing interventions should consider factors of the patient, caregiver, and environment.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.46-53
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• 2017

알츠하이머병은 치매를 유발하는 가장 주된 원인 질환으로 환자들은 인지장애를 겪게 된다. 현재 치료약으로 사용되는 약으로는 acetylcholinesterase 저해재가 있지만 이 약들의 효과는 미비하다. 그래서 인지기능에 영향을 미친다고 알려진 신경전달물질인 GABA, Glutamate, acetylcholine의 수치를 조절 할 수 있는 $5-HT_6$ receptor antagonist가 현재 개발되고 있다. 현재 여러 antagonist들이 임상실험 되었고, 인지 능력향상에 효과를 보이고 있다. 그러나, $5-HT_6$ receptor의 구조가 밝혀지지 않아 아직 원자적 수준의 결합 분석이 이루어지지 않았으므로 이 부분에 대한 연구가 필요하다. 따라서 본 연구에서는 Homology modeling을 통해 receptor의 구조를 예측하고, 현재 임상실험 중인 antagonist들 중 7개를 docking을 통해 단백질과 리간드의 결합을 예측하였다. Edison에서 Galaxy TBM과 Galaxy Refine을 사용하여 Homology modeling 한 결과 GPCR의 전형적인 모델에 특징적으로 긴 cterminal을 가졌다는 것을 확인 할 수 있었다. 생성된 구조를 가지고 Edison의 Dock 프로그램으로 7개의 antagonist가 어떠한 결합을 하는지 분석하였다. 그 결과, binding pose에 공통적으로 Trp102, Asp106, Val107, Pro177, Phe188, Val189, Ala192, Phe284, Phe285, Asn288, Thr306, Tyr310이 관여하는 것을 docking을 통해 알 수 있었다. 특히, Phe285는 7개의 antagonist 중에 4개와의 interaction을 하고 있는 것을 관찰하였다. 이 연구를 통하여 $5-HT_6$에 효과적으로 결합하여 치료효과를 낼 수 있는 신약을 개발할 수 있다.

• Korean Journal of Food Science and Technology
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• v.51 no.2
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• pp.160-168
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• 2019

This study aimed to determine whether double-processed ginseng berry extract (PGBC) could improve learning and memory in an $A\hat{a}42$-induced Alzheimer's mouse model. Passive avoidance test (PAT) and Morris water-maze test (MWMT) were performed after mice were treated with PGBC, followed by acetylcholine (ACh) measurement and glial fibrillary acidic protein (GFAP) detection for brain damage. Furthermore, acetylcholinesterase (AChE) activity and choline acetyltransferase (ChAT) expression were analyzed using Ellman's and qPCR assays, respectively. Results demonstrated that PGBC contained a high amount of ginsenosides (Re, Rd, and Rg3), which are responsible for the clearance of $A{\hat{a}} 42$. They also helped to significantly improve PAT and MWMT performance in the $A{\hat{a}} 42-induced$ Alzheimer's mouse model when compared to the normal group. Interestingly, ACh and ChAT were remarkably upregulated and AChE activities were significantly inhibited, suggesting PGBC to be a palliative adjuvant for treating Alzheimer's disease. Altogether, PGBC was found to play a positive role in improving cognitive abilities. Thus, it could be a new alternative solution for alleviating Alzheimer's disease symptoms.