• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.1
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• pp.100-108
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• 2021

Purpose: Percutaneous endoscopic gastrostomy (PEG) tube placements are commonly performed pediatric endoscopic procedures. Because of underlying disease, these patients are at increased risk for airway-related complications. This study compares patient characteristics and complications following initial PEG insertion with general endotracheal anesthesia (GETA) vs. anesthesia-directed deep sedation with a natural airway (ADDS). Methods: All patients 6 months to 18 years undergoing initial PEG insertion within the endoscopy suite were considered for inclusion in this retrospective cohort study. Selection of GETA vs. ADDS was made by the anesthesia attending after discussion with the gastroenterologist. Results: This study included 168 patients (GETA n=38, ADDS n=130). Cohorts had similar characteristics with respect to sex, race, and weight. Compared to ADDS, GETA patients were younger (1.5 years vs. 2.9 years, p=0.04), had higher rates of severe American Society of Anesthesiologists (ASA) disease severity scores (ASA 4-5) (21% vs. 3%, p<0.001), and higher rates of cardiac comorbidities (39.5% vs. 18.5%, p=0.02). Significant associations were not observed between GETA/ADDS status and airway support, 30-day readmission, fever, or pain medication in unadjusted or adjusted models. GETA patients had significantly increased length of stay (eβ=1.55, 95% confidence interval [CI]=1.11-2.18) after adjusting for ASA class, room time, anesthesia time, fever, and cardiac diagnosis. GETA patients also had increased room time (eβ=1.20, 95% CI=1.08-1.33) and anesthesia time (eβ=1.50, 95% CI=1.30-1.74) in adjusted models. Conclusion: Study results indicate that younger and higher risk patients are more likely to undergo GETA. Children selected for GETA experienced longer room times, anesthesia times, and hospital length of stay.

• The Korea Journal of Herbology
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• v.33 no.5
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• pp.1-8
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• 2018

Objectives : The aim of this study is to investigate the preventive effect of Puerariae Flos ethanol extract (PE) on methionin and choline deficient (MCD)-diet-induced Nonalcoholic fatty liver disease (NAFLD) in C57BL/6J mice. Methods : In the in vivo experiments, C57BL/6J mice were divided into 4 groups; Normal group, Control group, MCD+PE 100 group, and MCD+PE 300 group. After 4 weeks, body weight, liver weight, biochemical parameters for liver function test, histological changes, reverse transcription polymerase chain reaction (RT-PCR), and Western blot were assessed. Results : Mice lost body weight with the MCD-diet and the MCD+PE 100 group and MCD+PE 300 groups lost less than the control group, though showed no statistical significance. Liver weights were decreased by the MCD diet, but MCD+PE 300 groups were increased significantly. In the liver function test, all the values were decreased with the MCD-diet, MCD+PE 100 group and MCD+PE 300 groups were increased significance. In histological findings of the livers, MCD-diet induced severe fatty accumulation in the livers, but this fatty change was reduced in the MCD+PE 100 group and MCD+PE 300 groups was inhibited respectively. In lipid accumulation factors (such as SREBP-1c, $C/EBP{\alpha}$, PPAR-${\gamma}$), MCD+PE 100 group and MCD+PE 300 groups showed inhibitory effect on liver lipogenesis by reducing associated gene expressions caused by MCD diet. Conclusions : We were able to know that Puerariae Flos ethanol extract (PE) shown hepatoprotective effects via a decrease on the hepatic lipogenesis factors in the experimental NAFLD Models.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.701-708
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• 2024

The advent of transformer architectures in deep learning has been a major breakthrough in natural language processing research. Object name recognition is a branch of natural language processing and is an important research area for tasks such as information retrieval. It is also important in the biomedical field, but the lack of Korean biomedical corpora for training has limited the development of Korean clinical research using AI. In this study, we built a new biomedical corpus for Korean biomedical entity name recognition and selected language models pre-trained on a large Korean corpus for transfer learning. We compared the name recognition performance of the selected language models by F1-score and the recognition rate by tag, and analyzed the errors. In terms of recognition performance, KlueRoBERTa showed relatively good performance. The error analysis of the tagging process shows that the recognition performance of Disease is excellent, but Body and Treatment are relatively low. This is due to over-segmentation and under-segmentation that fails to properly categorize entity names based on context, and it will be necessary to build a more precise morphological analyzer and a rich lexicon to compensate for the incorrect tagging.

• Nutrition Research and Practice
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• v.13 no.1
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• pp.64-69
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• 2019

BACKGROUND/OBJECTIVES: Alzheimer's disease is a neurodegenerative disease that induces symptoms such as a decrease in motor function and cognitive impairment. Increases in the aggregation and deposition of amyloid beta protein ($A{\beta}$) in the brain may be closely correlated with the development of Alzheimer's disease. In this study, the effects of an adzuki bean extract on the aggregation of $A{\beta}$ were examined; moreover, the anti-Alzheimer's activity of the adzuki extract was examined. MATERIALS/METHODS: First, we undertook thioflavin T (ThT) fluorescence analysis and transmission electron microscopy (TEM) to evaluate the effect of an adzuki bean extract on $A{\beta}_{42}$ aggregation. To evaluate the effects of the adzuki extract on the symptoms of Alzheimer's disease in vivo, $A{\beta}_{42}$-overexpressing Drosophila were used. In these flies, overexpression of $A{\beta}_{42}$ induced the formation of $A{\beta}_{42}$ aggregates in the brain, decreased motor function, and resulted in cognitive impairment. RESULTS: Based on the results obtained by ThT fluorescence assays and TEM, the adzuki bean extract inhibited the formation of $A{\beta}_{42}$ aggregates in a concentration-dependent manner. When $A{\beta}_{42}$-overexpressing flies were fed regular medium containing adzuki extract, the $A{\beta}_{42}$ level in the brain was significantly lower than that in the group fed regular medium only. Furthermore, suppression of the decrease in motor function, suppression of cognitive impairment, and improvement in lifespan were observed in $A{\beta}_{42}$-overexpressing flies fed regular medium with adzuki extract. CONCLUSIONS: The results reveal the delaying effects of an adzuki bean extract on the progression of Alzheimer's disease and provide useful information for identifying novel prevention treatments for Alzheimer's disease.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.139-148
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• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• BMB Reports
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• v.43 no.4
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• pp.225-232
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• 2010

Parkinson's Disease (PD) is a common neurodegenerative disease characterized by the progressive degeneration of nigrostriatal dopaminergic (DA) neurons. Although the causative factors of PD remain elusive, many studies on PD animal models or humans suggest that glial activation along with neuroinflammatory processes contribute to the initiation or progression of PD. Additionally, several groups have proposed that dysfunction of the blood-brain barrier (BBB) combined with infiltration of peripheral immune cells play important roles in the degeneration of DA neurons. However, these neuroinflammatory events have only been investigated separately, and the issue of whether these phenomena are neuroprotective or neurotoxic remains controversial. We here review the current knowledge regarding the functions of these neuroinflammatory processes in the brain. Finally, we describe therapeutic strategies for the regulation of neuroinflammation with the goal of improving the symptoms of PD.

• Herbal Formula Science
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• v.25 no.4
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• pp.537-551
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• 2017

Objectives : Jinnoe-san (JNS) is a novel herbal formula consisting of five oriental medicinal herbs including Polygalae Radix, Prunellae Spica, Perillae Herba, Betulae Cortex, and Lonicerae Flos. In this study, we investigated the effects and molecular mechanism of JNS on Parkinson's disease in vitro model. Methods : The effects of JNS on 1-methyl-4-phenylpyridinium ($MPP^+$)-induced cell death in SH-SY5Y cells were evaluated with a cell viability assay, flow cytometry, and western blots analysis. The effects of JNS on lipopolysaccharide (LPS)-stimulated BV2 microglia were determined with a nitric oxide (NO) assay, enzyme linked immunosorbent assays, and western blots analysis. Result : $MPP^+$-induced cell death in SH-SY5Y cells was significantly reduced by JNS pre-treatment in a dose-dependent manner. JNS inhibited the production of reactive oxygen species, mitochondria dysfunction, and apoptosis induced by $MPP^+$ in SH-SY5Y cells. Furthermore, JNS significantly activated Akt and ERK in SH-SY5Y cells and the ability of JNS to prevent mitochondria dysfunction by $MPP^+$ was antagonized by pre-treatment of LY294002 and PD98059, an Akt and ERK inhibitor, respectively. In addition, JNS inhibited LPS-induced NO and $PGE_2$ production as well as iNOS expression and secretion of TNF-${\alpha}$, pro-inflammatory cytokines without affecting the cell viability. JNS also suppressed LPS-induced ERK activation. Conclusions : These results demonstrate that JNS has a protective effect on the dopaminergic neurons against $MPP^+$-induced neurotoxicity and anti-inflammatory effect on the LPS-stimulated microglia. These findings provide evidences for JNS to be considered as a new prescription for treating Parkinson's disease.

• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Journal of Environmental Science International
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• v.26 no.3
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• pp.345-362
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• 2017

The primary purpose of this study was to determine the risk of various disease outcomes due to exposure to cyanobacteria toxin (microcystin-LR) through drinking water in a Korean watershed. In order to determine the risk in a more quantitative way, the risk assessment framework developed by the National Research Council (NRC) of the United States (US) - hazard identification, dose-response relationship, exposure assessment, and risk characterization - was used in this study. For dose-response relationships, a computer software (BenchMark Dose Software (BMDS)) developed by the US Environmental Protection Agency (EPA) was used to fit the data from previous studies showing the relationship between the concentration of microcystin-LR and various disease outcomes into various dose-response models. For exposure assessment, the concentrations of microcystin-LR in the source water and finished water in a Korean watershed obtained from a recent study conducted by the Ministry of Environment of Korea were used. Finally, the risk of various disease outcomes due to exposure to cyanobacteria toxin (microcystin-LR) through drinking water was characterized by Monte-Carlo simulation using Crystall Ball program (Oracle Inc.) for adults and children. The results of this study suggest that the risk of disease due to microcystin-LR toxin through drinking water is very low and it appears that current water treatment practice should be able to protect the public from the harmful effects of cyanobacteria toxin (microcystin-LR) through drinking water.

• Journal of Preventive Medicine and Public Health
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• v.51 no.5
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• pp.248-256
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• 2018

Objectives: Several studies have investigated the effects of serum uric acid (SUA) levels on chronic kidney disease (CKD), with discrepant results. The effect of SUA levels on CKD development was studied in the Korean rural population. Methods: A total of 9695 participants aged ${\geq}40years$ were recruited from 3 rural communities in Korea between 2005 and 2009. Of those participants, 5577 who participated in the follow-up and did not have cerebrovascular disease, myocardial infarction, cancer, or CKD at baseline were studied. The participants, of whom 2133 were men and 3444 were women, were grouped into 5 categories according to their quintile of SUA levels. An estimated glomerular filtration rate of < $60mL/min/1.73m^2$ at the time of follow-up was considered to indicate newly developed CKD. The effects of SUA levels on CKD development after adjusting for potential confounders were assessed using Cox proportional hazard models. Results: Among the 5577 participants, 9.4 and 11.0% of men and women developed CKD. The hazard ratio (HR) of CKD was higher in the highest quintile of SUA levels than in the third quintile in men (adjusted HR, 1.60; 95% confidence interval [CI], 1.02 to 2.51) and women (adjusted HR, 1.56; 95% CI, 1.14 to 2.15). Furthermore, CKD development was also more common in the lowest quintile of SUA levels than in the third quintile in men (adjusted HR, 1.83; 95% CI, 1.15 to 2.90). The effect of SUA was consistent in younger, obese, and hypertensive men. Conclusions: Both high and low SUA levels were risk factors for CKD development in rural Korean men, while only high levels were a risk factor in their women counterparts.