• Journal of Chest Surgery
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• 제50권4호
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• pp.270-274
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• 2017

Background: We evaluated the operative outcomes of an extra-anatomic bypass from the ascending aorta to the abdominal aorta in patients with type II or III Takayasu arteritis (TA) with mid-aortic syndrome. Methods: From 1988 to 2014, 8 patients with type II (n=2) or III (n=6) TA underwent an ascending aorta to abdominal aorta bypass. The mean patient age was $43.5{\pm}12.2years$ and the mean peak pressure gradient between the upper and lower extremities was $54.8{\pm}39.0mm\;Hg$. The median follow-up duration was 54.4 months (range, 17.8 to 177.4 months). Results: There were no cases of operative mortality. The mean peak pressure gradient significantly decreased to $-2.4{\pm}32.3mm\;Hg$ (p=0.017 compared to the preoperative value). Late death occurred in 2 patients. The symptoms of upper extremity hypertension and claudication improved in all patients. The bypass grafts were patent at $47.1{\pm}58.9months$ in 7 patients who underwent follow-up imaging studies. Conclusion: An extra-anatomic ascending aorta to abdominal aorta bypass could be an effective treatment option for severe aortic steno-occlusive disease in patients with type II or III TA, with favorable early and long-term outcomes.

• The Korean Journal of Physiology and Pharmacology
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• 제19권4호
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• pp.319-325
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• 2015

Among solute carrier proteins, the organic anion transporters (OATs) play an important role for the elimination or reabsorption of endogenous and exogenous negatively charged anionic compounds. Among OATs, SLC22A9 (hOAT7) transports estrone sulfate with high affinity. The net decrease of estrogen, especially in post-menopausal women induces rapid bone loss. The present study was performed to search the SNP within exon regions of SLC22A9 in Korean females with osteoporosis. Fifty healthy controls and 50 osteoporosis patients were screened for the genetic polymorphism in the coding region of SLC22A9 using GC-clamped PCR and denaturing gradient gel electrophoresis (DGGE). Six SNPs were found on the SLC22A9 gene from Korean women with/without osteoporosis. The SNPs were located as follows: two SNPs in the osteoporosis group (A645G and T1277C), three SNPs in the control group (G1449T, C1467T and C1487T) and one SNP in both the osteoporosis and control groups (G767A). The G767A, T1277C and C1487T SNPs result in an amino acid substitution, from synonymous vs nonsynonymous substitution arginine to glutamine (R256Q), phenylalanine to serine (F426S) and proline to leucine (P496L), respectively. The Km values and Vmax of the wild type, R256Q, P496L and F426S were 8.84, 8.87, 9.83 and $12.74{\mu}M$, and 1.97, 1.96, 2.06 and 1.55 pmol/oocyte/h, respectively. The present study demonstrates that the SLC22A9 variant F426S is causing inter-individual variation that is leading to the differences in transport of the steroid sulfate conjugate (estrone sulfate) and, therefore this could be used as a marker for certain disease including osteoporosis.

• 디지털융복합연구
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• 제19권7호
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• pp.271-282
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• 2021

본 연구는 미숙아의 재원일수 예측 모형을 머신러닝 기법을 통해 개발하기 위해 수행 되었다. 모형 개발을 위해 질병관리본부에서 수집한 퇴원손상심층조사 자료의 2011년부터 2016년까지 퇴원한 미숙아 6,149건을 이용하였다. 입원 초기 신경망 모형은 설명력(R²)이 0.75로 다른 모형에 비해 우수 하였다. 입원 초기 변수에 임상진단을 CCS(Clinical class ification software)로 변환하여 추가 투입한 모형은 큐비스트(Cubist) 모형의 설명력(R²)이 0.81로 랜덤 포레스트(Random Forests), 그라디언트 부스트(Gradient boost), 신경망(neural network), 벌점화 회귀(Penalty regression) 모형에 비해 성능이 우수 하였다. 본 연구는 전국단위 데이터를 이용한 미숙아의 재원일수 예측 모형을 머신러닝을 통해 제시하고 그 활용 가능성을 확인하였다. 하지만 임상정보, 부모정보 등 데이터의 한계로 향후 성능 향상을 위한 추가 연구가 필요하다.

• Journal of Microbiology and Biotechnology
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• 제22권10호
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• pp.1330-1335
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• 2012

Foot and mouth disease (FMD) is one of the acute infectious diseases in hoofed and even-toed mammals, including pigs, and it occurs via acute infection by Aphthovirus. When FMD is suspected, animals around the location of origin are typically slaughtered and buried. Other methods such as rendering, composting, and incineration have not been verified in practice in Korea. After the FMD incident, the regular monitoring of the microbial community is required, as microorganisms greatly modify the characteristics of the ecosystem in which they live. This is the result of their metabolic activities causing chemical changes to take place in the surrounding environment. In this study, we investigated changes in the microbial community during a 24 week period with DNA extracts from leachate, formed by the decomposition of buried pigs at a laboratory test site, using denaturing gradient gel electrophoresis (DGGE) with a genomic DNA. Our results revealed that Bacteroides coprosuis, which is common in pig excreta, and Sporanaerobacter acetigenes, which is a sulfur-reduced microbe, were continuously observed. During the early stages (0~2 weeks) of tissue decomposition, Clostridium cochlearium, Fusobacterium ulcerans, and Fusobacterium sp., which are involved in skin decomposition, were also observed. In addition, various microbes such as Turicibacter sanguinis, Clostridium haemolyticum, Bacteroides propionicifaciens, and Comamonas sp. were seen during the later stages (16~24 weeks). In particular, the number of existing microbial species gradually increased during the early stages, including the exponential phase, decreased during the middle stages, and then increased again during the later stages. Therefore, these results indicate that the decomposition of pigs continues for a long period of time and leachate is created continuously during this process. It is known that leachate can easily flow into the neighboring environment, so a long-term management plan is needed in burial locations for FMD-infected animals.

• 한국응용곤충학회지
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• 제23권4호
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• pp.197-202
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• 1984

콩 모자이크 바이러스계통 SMV-G7의 감염에 의하여 모자이크 병징이 나타나는 함안 품종과 괴저병징이 나타나는 광교를 공시하여 $320.7m^2$(99평)의 포장 중앙 $2.2m^2$ (1평)에 함안을 파종, 접종한 후 주변 시험구(1평씩 99구)에는 광교를 파종하여 SMV의 발생생태를 조사하였다. 광교에서 SMV 이병주율은 7월 13일에 가장 심하였고 황색수반으로 채집한 진딧물 밀도는 6월 22일에 가장 높게 나타났으며 함안과 인접한 시험구의 SMV 이병주율은 평균 $56\%$, 포장전체의 이병주율은 평균 $20.4\%$로 나타났다. 풍향과 바이러스 이병율을 분석한 결과 SMV의 전파는 바람부는 쪽으로 일정한 gradient를 형성하여 SMV 전파에 중요한 요인으로 나타났다.

• Tuberculosis and Respiratory Diseases
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• 제46권3호
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• pp.420-425
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• 1999

저자들은 심한 청색증을 동반한 만성 간질환에서, 폐 내 동정맥 단락과 동맥혈 저산 소혈증을 보인 간폐증 후군 1예를 경험하였기에 문헌고찰과 함께 보고한다. 단순흉부 X-선 사진에는 망상결절이 하엽 기저부에 주로 분포하였고 고해상 전산화 단층영상에서 확장된 폐혈관이 늑막까지 연장되어 보이고, 특히 비정상적으로 증가된 폐혈관종말지의 확장이 늑막하 폐에 분포하였다. 핵의학 관류검사 및 조영 심초음파 검사를 통해 폐내 단락을 진단하여 보고하는 바이다.

• Genomics & Informatics
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• 제19권1호
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Molecules and Cells
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• 제42권1호
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• pp.45-55
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• 2019

The liver is involved in a wide range of activities in vertebrates and some other animals, including metabolism, protein synthesis, detoxification, and the immune system. Until now, various methods have been devised to study liver diseases; however, each method has its own limitations. In situ liver perfusion machinery, originally developed in rats, has been successfully adapted to mice, enabling the study of liver diseases. Here we describe the protocol, which is a simple but widely applicable method for investigating the liver diseases. The liver is perfused in situ by cannulation of the portal vein and suprahepatic inferior vena cava (IVC), with antegrade closed circuit circulation completed by clamping the infrahepatic IVC. In situ liver perfusion can be utilized to evaluate immune cell migration and function, hemodynamics and related cellular reactions in each type of hepatic cells, and the metabolism of toxic or other compounds by changing the composition of the circulating media. In situ liver perfusion method maintains liver function and cell viability for up to 2 h. This study also describes an optional protocol using density-gradient centrifugation for the separation of different types of hepatic cells, allowing the determination of changes in each cell type. In summary, this method of in situ liver perfusion will be useful for studying liver diseases as a complement to other established methods.

• Journal of Periodontal and Implant Science
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• 제51권4호
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• pp.226-238
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• 2021

Purpose: Although several reports have described the relationship between periodontal disease and cardiovascular disease, information about the association between periodontal disease and the progression of degenerative aortic stenosis (AS) is lacking. Therefore, we performed a retrospective, single-center, pilot study to provide insight into this potential association. Methods: Data from 45 consecutive patients (19 men; median age, 83 years) with mild or moderate degenerative aortic stenosis were analyzed for a mean observation period of 3.3±1.9 years. The total amount of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis and titers of serum immunoglobulin G (IgG) against periodontal bacteria and high-sensitivity C-reactive protein (hs-CRP) were evaluated. Aortic valve area (AVA), maximal velocity (Vmax), mean pressure gradient (mean PG), and the Doppler velocity index (DVI) were evaluated. The change in each parameter per year ([Parameter_LATEST-Parameter_BASELINE]/Follow-up Years) was calculated from the retrospective follow-up echocardiographic data (baseline vs. the most recently collected data [latest]). Results: No correlation was found between the concentration of periodontopathic bacteria in the saliva and AS status/progression. The anti-P. gingivalis antibody titer in the serum showed a significant positive correlation with AVA and DVI. Additionally, there was a negative correlation between the anti-P. gingivalis IgG antibody titer and mean PG. The hs-CRP concentration showed positive correlations with Vmax and mean PG. Meanwhile, a negative correlation was observed between the anti-P. gingivalis IgG antibody titer and ΔAVA/year and Δmean PG/year. The hs-CRP concentration showed positive correlations with Vmax and mean PG, and it was significantly higher in patients with rapid aortic stenosis progression (ΔAVA/year <-0.1) than in their counterparts. Conclusions: Our results suggest that periodontopathic bacteria such as A. actinomycetemcomitans and P. gingivalis are not directly related to the status/progression of degenerative AS. However, inflammation and a lower immune response may be associated with disease progression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.