• Journal of Korean Neurosurgical Society
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• v.57 no.3
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• pp.152-158
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• 2015

Objective : The purpose of this study to develop new deep-brain stimulation system for long-term use in animals, in order to develop a variety of neural prostheses. Methods : Our system has two distinguished features, which are the fully implanted system having wearable wireless power transfer and ability to change the parameter of stimulus parameter. It is useful for obtaining a variety of data from a long-term experiment. Results : To validate our system, we performed pre-clinical test in Parkinson's disease-rat models for 4 weeks. Through the in vivo test, we observed the possibility of not only long-term implantation and stability, but also free movement of animals. We confirmed that the electrical stimulation neither caused any side effect nor damaged the electrodes. Conclusion : We proved possibility of our system to conduct the long-term pre-clinical test in variety of parameter, which is available for development of neural prostheses.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1075-1081
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• 2016

The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.241-250
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• 2015

An important scientific objective of longitudinal studies involves tracking the probability of a subject having certain health condition over the course of the study. Proper definitions and estimates of disease risk tracking have important implications in the design and analysis of long-term biomedical studies and in developing guidelines for disease prevention and intervention. We study in this paper a class of rank-tracking probabilities to describe a subject's conditional probabilities of having certain health outcomes at two different time points. Linear mixed effects models are considered to estimate the tracking probabilities and their ratios of interest. We apply our methods to an epidemiological study of childhood cardiovascular risk factors.

• Biomolecules & Therapeutics
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• v.28 no.1
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• pp.1-17
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• 2020

Myeloid-derived suppressor cells (MDSCs) are immature myeloid cells that exert suppressive function on the immune response. MDSCs expand in tumor-bearing hosts or in the tumor microenvironment and suppress T cell responses via various mechanisms, whereas a reduction in their activities has been observed in autoimmune diseases or infections. It has been reported that the symptoms of various diseases, including malignant tumors, can be alleviated by targeting MDSCs. Moreover, MDSCs can contribute to patient resistance to therapy using immune checkpoint inhibitors. In line with these therapeutic approaches, diverse oligonucleotide-based molecules and small molecules have been evaluated for their therapeutic efficacy in several disease models via the modulation of MDSC activity. In the current review, MDSC-targeting oligonucleotides and small molecules are briefly summarized, and we highlight the immunomodulatory effects on MDSCs in a variety of disease models and the application of MDSC-targeting molecules for immuno-oncologic therapy.

• Journal of Preventive Medicine and Public Health
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• v.42 no.6
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• pp.371-376
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• 2009

In this article, we reviewed the literature on risk assessment (RA) models with and without molecular genomic markers and the current utility of the markers in the pharmacogenetic field. Epidemiological risk assessment is applied using statistical models and equations established from current scientific knowledge of risk and disease. Several papers have reported that traditional RA tools have significant limitations in decision-making in management strategies for individuals as predictions of diseases and disease progression are inaccurate. Recently, the model added information on the genetic susceptibility factors that are expected to be most responsible for differences in individual risk. On the continuum of health care, from diagnosis to treatment, pharmacogenetics has been developed based on the accumulated knowledge of human genomic variation involving drug distribution and metabolism and the target of action, which has the potential to facilitate personalized medicine that can avoid therapeutic failure and serious side effects. There are many challenges for the applicability of genomic information in a clinical setting. Current uses of genetic markers for managing drug therapy and issues in the development of a valid biomarker in pharmacogenetics are discussed.

• Journal of Lipid and Atherosclerosis
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• v.7 no.2
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• pp.88-97
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• 2018

The prevalence of atherothrombotic disease continues to rise, presenting an increasing number of challenges to modern society and creating interest in functional foods. Platelet activation, adhesion, and aggregation at vascular endothelial disruption sites are key events in atherothrombotic disease. Physiological challenges such as hyperlipidemia, obesity, and cigarette smoking are associated with vascular changes underlying platelet aggregation and inflammatory processes. However, it is difficult to determine the beneficial response of functional foods in healthy subjects. To address this problem, challenge models and high-risk models related to smokers, obesity, and dyslipidemia are proposed as sensitive measures to evaluate the effects of functional foods in healthy subjects. In this review, we construct a model to evaluate the effects of functional food such as natural products on blood flow based on a human intervention study.

• Molecules and Cells
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• v.44 no.12
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• pp.861-878
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• 2021

The human genome contains many retroviral elements called human endogenous retroviruses (HERVs), resulting from the integration of retroviruses throughout evolution. HERVs once were considered inactive junk because they are not replication-competent, primarily localized in the heterochromatin, and silenced by methylation. But HERVs are now clearly shown to actively regulate gene expression in various physiological and pathological conditions such as developmental processes, immune regulation, cancers, autoimmune diseases, and neurological disorders. Recent studies report that HERVs are activated in patients suffering from coronavirus disease 2019 (COVID-19), the current pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. In this review, we describe internal and external factors that influence HERV activities. We also present evidence showing the gene regulatory activity of HERV LTRs (long terminal repeats) in model organisms such as mice, rats, zebrafish, and invertebrate models of worms and flies. Finally, we discuss several molecular and cellular pathways involving various transcription factors and receptors, through which HERVs affect downstream cellular and physiological events such as epigenetic modifications, calcium influx, protein phosphorylation, and cytokine release. Understanding how HERVs participate in various physiological and pathological processes will help develop a strategy to generate effective therapeutic approaches targeting HERVs.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.353-360
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• 2022

Laryngeal disease harms quality of life, and laryngoscopy is critical in identifying causative lesions. This study extracts and analyzes using radiomics quantitative features from the lesion in laryngoscopy images and will fit and validate a classifier for finding meaningful features. Searching the region of interest for lesions not classified by the YOLOv5 model, features are extracted with radionics. Selected the extracted features are through a combination of three feature selectors, and three estimator models. Through the selected features, trained and verified two classification models, Random Forest and Gradient Boosting, and found meaningful features. The combination of SFS, LASSO, and RF shows the highest performance with an accuracy of 0.90 and AUROC 0.96. Model using features to select by SFM, or RIDGE was low lower performance than other things. Classification of larynx lesions through radiomics looks effective. But it should use various feature selection methods and minimize data loss as losing color data.

• Biomolecules & Therapeutics
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• v.31 no.3
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• pp.276-284
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• 2023

Sinapic acid (SA) is a phenolic acid that is widely distributed in fruits and vegetables, which has various bioactivities, such as antidiabetic, anticancer and anti-inflammatory functions. Over-activated microglial is involved in the development progress of neurodegenerative diseases, such as Parkinson's disease and Alzheimer's disease. The objective of this study was to investigate the effect of SA in microglia neuroinflammation models. Our results demonstrated that SA inhibited secretion of the nitric oxide (NO) and interleukin (IL)-6, reduced the expression of inducible nitric oxide synthase (iNOS) and enhanced the release of IL-10 in a dose-dependent manner. Besides, our further investigation revealed that SA attenuated the phosphorylation of AKT and MAPK cascades in LPS-induced microglia. Consistently, oral administration of SA in mouse regulated the production of inflammation-related cytokines and also suppressed the phosphorylation of MAPK cascades and AKT in the mouse cerebral cortex. These results suggested that SA may be a possible therapy candidate for anti-inflammatory activity by targeting the AKT/MAPK signaling pathway.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.