• 한국인공지능학회지
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• 한국식물병리학회지
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• 제13권1호
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• pp.37-45
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• 1997

Disease incidence (DI), pre-emergence damping-off (PDO), days until the first symptom appeared (DUS), disease progress curve (DPC), and area under disease progress curve (AUDPC) were investigated in vivo after sowing ginseng seeds in each of 37 ginseng-cultivated soils which were sampled from 4 regions in Korea. Non linear fitting parameters, A, B, K and M, were estimated from the Richards' function, one of the disease progress models, by using the DI at each day from the bioassay. Inter- and intra-relationships between disease variables and stand-missing rate (SMR) in fields were investigated by using the simple correlation analysis. Disease variables of the root rot were divided into two groups: variables related to disease incidence, e.g., DI, AUDPC and A parameter, and variables related to disease progress, e.g., B, K and M parameters. DI, AUDPC, and DUS had significant correlations with SMR in ginseng fields, and then it showed that the disease development in vivo corresponded with that in fields. Soil samples could be separated into 3 and 4 groups, respectively, on the basis of the principal component 1 (PC1) and the principal component 2 (PC2), which were derived from the principal component analysis (PCA) of Richards' parameters, A, B, K and M. PC1 accounted for B, K and M parameters, and PC2 accounted for A parameter.

• The Korean Journal of Physiology and Pharmacology
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• 제26권1호
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• pp.1-13
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• 2022

Kidney disease is becoming a global public health issue. Acute kidney injury (AKI) and chronic kidney disease (CKD) have serious adverse health outcomes. However, there is no effective therapy to treat these diseases. Lactoferrin (LF), a multi-functional glycoprotein, is protective against various pathophysiological conditions in various disease models. LF shows protective effects against AKI and CKD. LF reduces markers related to inflammation, oxidative stress, apoptosis, and kidney fibrosis, and induces autophagy and mitochondrial biogenesis in the kidney. Although there are no clinical trials of LF to treat kidney disease, several clinical trials and studies on LF-based drug development are ongoing. In this review, we discussed the possible kidney protective mechanisms of LF, as well as the pharmacological and therapeutic advances. The evidence suggests that LF may become a potent pharmacological agent to treat kidney diseases.

• Journal of Ginseng Research
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• 제48권2호
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• pp.122-128
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• 2024

Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease characterized by hepatic fat accumulation, while nonalcoholic steatohepatitis (NASH) is an advanced form of NAFLD characterized by hepatic inflammation, fibrosis, and liver injury, resulting in liver cirrhosis and hepatocellular carcinoma (HCC). Given the evidence that ginseng and its major bioactive components, ginsenosides, have potent anti-adipogenic, anti-inflammatory, anti-oxidative, and anti-fibrogenic effects, the pharmacological effect of ginseng and ginsenosides on NAFLD and NASH is noteworthy. Furthermore, numerous studies have successfully demonstrated the protective effect of ginseng on these diseases, as well as the underlying mechanisms in animal disease models and cells, such as hepatocytes and macrophages. This review discusses recent studies that explore the pharmacological roles of ginseng and ginsenosides in NAFLD and NASH and highlights their potential as agents to prevent and treat NAFLD, NASH, and liver diseases caused by hepatic steatosis and inflammation.

• Clinical and Experimental Reproductive Medicine
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• 제43권1호
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• pp.1-8
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• 2016

Granulosa cell tumors (GCTs) are rare sex cord-stromal tumors that have been studied for decades. However, their infrequency has delayed efforts to research their etiology. Recently, mutations in human GCTs have been discovered, which has led to further research aimed at determining the molecular mechanisms underlying the disease. Mouse models have been important tools for studying GCTs, and have provided means to develop and improve diagnostics and therapeutics. Thus far, several genetically modified mouse models, along with one spontaneous mouse model, have been reported. This review summarizes the phenotypes of these mouse models and their applicability in elucidating the mechanisms of granulosa cell tumor development.

• Molecules and Cells
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• 제43권10호
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• pp.880-888
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• 2020

Inherited peripheral neuropathy is a heterogeneous group of peripheral neurodegenerative disorders including Charcot-Marie-Tooth disease. Many peripheral neuropathies often accompany impaired axonal construction and function. To study the molecular and cellular basis of axon-defective peripheral neuropathy, we explore the possibility of using Caenorhabditis elegans, a powerful nematode model equipped with a variety of genetics and imaging tools. In search of potential candidates of C. elegans peripheral neuropathy models, we monitored the movement and the body posture patterns of 26 C. elegans strains with disruption of genes associated with various peripheral neuropathies and compiled a database of their phenotypes. Our assay showed that movement features of the worms with mutations in HSPB1, MFN2, DYNC1H1, and KIF1B human homologues are significantly different from the control strain, suggesting they are viable candidates for C. elegans peripheral neuropathy models.

• 대한수학회보
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• 제48권3호
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• pp.555-574
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• 2011

It is well known that the mathematical models provide very important information for the research of human immunodeciency virus type. However, the infection rate of almost all mathematical models is linear. The linearity shows the simple interaction between the T-cells and the viral particles. In this paper, a differential equation model of HIV infection of $CD4^+$ T-cells with Crowley-Martin function response is studied. We prove that if the basic reproduction number $R_0$ < 1, the HIV infection is cleared from the T-cell population and the disease dies out; if $R_0$ > 1, the HIV infection persists in the host. We find that the chronic disease steady state is globally asymptotically stable if $R_0$ > 1. Numerical simulations are presented to illustrate the results.

• 통합자연과학논문집
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• 제4권3호
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• pp.216-221
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• 2011

c-Jun N-terminal kinase-3 (JNK-3) has been shown to mediate neuronal apoptosis and make the promising therapeutic target for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and other CNS disorders. In order to better understand the structural and chemical features of JNK-3, comparative molecular field analysis (CoMFA) was performed on a series of 3,5-disubstituted quinolines derivatives. The best predictions were obtained CoMFA model ($q^2$=0.707, $r^2$=0.972) and the statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The resulting contour map from 3D-QSAR models might be helpful to design novel and more potent JNK3 derivatives.

• 통합자연과학논문집
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• 제5권1호
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• pp.6-12
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• 2012

c-Jun N-terminal kinase-3 (JNK-3) has been identified as a promising target for neuronal apoptosis and has the effective therapeutic for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and other CNS disorders. Herein, we report the essential structural and chemical parameters for JNK-3 inhibitors utilizing comparative molecular field similarity indices analysis (CoMSIA) using the derivatives of 3,5-disubstituted quinolines. The best predictions were obtained CoMSIA model (q2=0.834, r2=0.987) and the statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The resulting contour map from 3D-QSAR models might be helpful to design novel and more potent JNK3 derivatives.

• International Journal of Contents
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• 제17권3호
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.