• Korean Journal of Veterinary Service
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• v.45 no.2
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• pp.71-77
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• 2022

African swine fever (ASF) is a fatal viral disease in pigs, with a short incubation period and causing immediate death. Few studies exist on the Asian epidemic ASF virus (ASFV) challenge in older pigs, including growing and fattening pigs and sows. We aimed to investigate clinical outcomes, pathomorphological lesions, and viral distribution in organs of 3-month-old growing pigs that were inoculated with the ASFV isolated in Vietnam. The clinical outcomes were recorded daily, and the dead or euthanized pigs immediately underwent necropsy. Viral loads were determined in 10 major organs using quantitative polymerase chain reaction. The average incubation period in growing pigs was more delayed (5.2±0.9 dpi) than that in weaned pigs, and the clinical signs were milder in growing pigs than in weaned pigs. The digestive and respiratory clinical signs in growing pigs showed at the end period of life, but these were observed at an early stage of infection in weaned pigs. The pathomorphological features were severe and nonspecific with hemorrhagic lesions in various organs. The viral loads in organs from growing pigs were higher than those from piglets, and the number of viral copies was related to gross lesions in the tonsil and intestine. In the absence of vaccines against ASF, early clinical detection is important for preventing the spread of the virus. Our findings elucidated that the clinical signs and gross lesions in growing pigs differed from those in weaned pigs, which provide valuable information for diagnosis of pigs with suspected ASF infection.

• Journal of Nutrition and Health
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• v.56 no.4
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• pp.377-390
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• 2023

Purpose: Diabetes mellitus (DM) causes body fluid imbalance because of hyperglycemia, but there is a lack of research on the relationship between DM and body fluid imbalance in the Korean population. This study compared the differences in body fluid composition and dietary intake between individuals with type 2 DM (T2DM) and a normal control (NC) group without the disease. Methods: In this study, 36 subjects with T2DM and 21 without diabetes were divided into the T2DM and NC groups. The subjects were divided into four subgroups to assess differences in body fluid volume according to sex: men T2DM group (n = 24), men NC group (n = 9), women T2DM group (n = 12), and women NC group (n = 12). The body fluid composition was measured using bioelectrical impedance analysis, including intracellular water (ICW), extracellular water (ECW), total body water (TBW), ECW/ICW, and ECW/TBW. Nutrient intake was evaluated using their dietary records. Results: The results showed that the ECW/ICW and the ECW/TBW were significantly higher in the T2DM group compared to the NC group. Both men and women in the T2DM group showed significantly higher ECW/ICW and ECW/TBW than the respective NC group. The T2DM group had a higher carbohydrate, dietary fiber, vitamin A, vitamin C, sodium, and potassium intake per 1,000 kcal and lower total daily energy, fat, and cholesterol intake per 1,000 kcal than the NC group. Conclusion: These results suggest a positive association between T2DM and body fluid imbalance. This study can be used widely as basic data for the evaluation and diagnosis of diabetic complications in the future.

• Research in Plant Disease
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• v.29 no.1
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• pp.88-93
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• 2023

To investigate the incidence status of viruses in major cultivated areas of watermelon and melon in Chungbuk Province, samples were collected from 2020 to 2021 in vinyl greenhouse of Jincheon and Eumseong and examined for virus infection using reverse transcription polymerase chain reaction. Of the six viruses on watermelon that was analyzed in this study, watermelons were infected with cucumber mosaic virus (CMV), watermelon mosaic virus (WMV), cucumber green mottle mosaic virus (CGMMV), and cucurbit aphid-borne yellows virus (CABYV). The incidence rate of CMV was 20.9-35.0%, WMV 0.4-15.8%, CGMMV 1.6-38.5%, and CABYV was 3.5-3.7% from 2020 to 2021. But strangely, there were no incidence of zucchini yellow mosaic virus and cucurbit chlorotic yellows virus (CCYV) during investigation. From this result, we knew the major virus was CGMMV on watermelon in Chungbuk Province. Molecular diagnosis assays of the two melon viruses, showed that melons were infected with CABYV and CCYV from 2020 to 2021. The incidence rate of CABYV was 53.9-92.2% and CCYV was 2.7-20.8%. The incidence of CABYV was high in melon cultivation of Jincheon and Eumseong, Chungbuk. Afterwards, it is necessary to establish a control management strategy for reduce the incidence of CABYV. Furthermore, we must pay attention that of CCYV even if the incidence was low.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Tuberculosis and Respiratory Diseases
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• v.86 no.2
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• pp.142-149
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• 2023

Background: Coronavirus disease 2019 (COVID-19) is an ongoing global public health threat and different variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified. This study aimed to analyse the factors associated with negative conversion of polymerase chain reaction (PCR) and prognosis in critically ill patients according to the SARS-CoV-2 variant. Methods: This study retrospectively analysed 259 critically ill patients with COVID-19 who were admitted to the intensive care unit of a tertiary medical center between January 2020 and May 2022. The Charlson comorbidity index (CCI) was used to evaluate comorbidity, and a negative PCR test result within 2 weeks was used to define negative PCR conversion. The cases were divided into the following three variant groups, according to the documented variant of SARS-CoV-2 at the time of diagnosis: non-Delta (January 20, 2020-July 6, 2021), Delta (July 7, 2021- January 1, 2022), and Omicron (January 30, 2022-April 24, 2022). Results: The mean age of the 259 patients was 67.1 years and 93 (35.9%) patients were female. Fifty (19.3%) patients were smokers, and 50 (19.3%) patients were vaccinated. The CCI (hazard ratio [HR], 1.555; p<0.001), vaccination (HR, 0.492; p=0.033), and Delta variant (HR, 2.469; p=0.002) were significant factors for in-hospital mortality. The Delta variant (odds ratio, 0.288; p=0.003) was associated with fewer negative PCR conversion; however, vaccination (p=0.163) and remdesivir (p=0.124) treatments did not. Conclusion: The Delta variant of SARS-CoV-2 is associated with lower survival and negative PCR conversion. Contrary to expectations, vaccination and remdesivir may not affect negative PCR conversion in critically ill patients with COVID-19.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• BMB Reports
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• v.56 no.10
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• pp.569-574
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• 2023

Aberrant DNA methylation plays a pivotal role in the onset and progression of colorectal cancer (CRC), a disease with high incidence and mortality rates in Korea. Several CRC-associated diagnostic and prognostic methylation markers have been identified; however, due to a lack of comprehensive clinical and methylome data, these markers have not been validated in the Korean population. Therefore, in this study, we aimed to obtain the CRC methylation profile using 172 tumors and 128 adjacent normal colon tissues of Korean patients with CRC. Based on the comparative methylome analysis, we found that hypermethylated positions in the tumor were predominantly concentrated in CpG islands and promoter regions, whereas hypomethylated positions were largely found in the open-sea region, notably distant from the CpG islands. In addition, we stratified patients by applying the CpG island methylator phenotype (CIMP) to the tumor methylome data. This stratification validated previous clinicopathological implications, as tumors with high CIMP signatures were significantly correlated with the proximal colon, higher prevalence of microsatellite instability status, and MLH1 promoter methylation. In conclusion, our extensive methylome analysis and the accompanying dataset offers valuable insights into the utilization of CRC-associated methylation markers in Korean patients, potentially improving CRC diagnosis and prognosis. Furthermore, this study serves as a solid foundation for further investigations into personalized and ethnicity-specific CRC treatments.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.618-631
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• 2023

The brain houses vital hormonal regulatory structures such as the hypothalamus and pituitary gland, which may confer unique susceptibilities to critical illness-related corticosteroid insufficiency (CIRCI) in patients with neurological disorders. In addition, the frequent use of steroids for therapeutic purposes in various neurological conditions may lead to the development of steroid insufficiency. This abstract aims to highlight the significance of understanding these relationships in the context of patient care and management for physicians. Neurological disorders may predispose patients to CIRCI due to the role of the brain in hormonal regulation. Early recognition of CIRCI in the context of neurological diseases is essential to ensure prompt and appropriate intervention. Moreover, the frequent use of steroids for treating neurological conditions can contribute to the development of steroid insufficiency, further complicating the clinical picture. Physicians must be aware of these unique interactions and be prepared to evaluate and manage patients with CIRCI and steroid insufficiency in the context of neurological disorders. This includes timely diagnosis, appropriate steroid administration, and careful monitoring for potential adverse effects. A comprehensive understanding of the interplay between neurological disease, CIRCI, and steroid insufficiency is critical for optimizing patient care and outcomes in this complex patient population.