• Tuberculosis and Respiratory Diseases
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• v.49 no.6
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• pp.724-732
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• 2000

Background : In patients with severe chronic lung diseases even a small pneumothorax can result in life-threatening respiratory distress. It is important to treat the attack by chest tube drainage until the lung expands. Pneumothorax with a persistent air leak that does not resolve under prolonged tube thoracostomy suction is usually treated by open operation to excise or oversew a bulla or cluster of blebs to stop the air leak. Pleurodesis by the instillation of chemical agents is used for the patient who has persistent air leak and is not good candidate for surgical treatment. When the primary trial of pleurodesis with common agent fails, it is uncertain which agent should be used f or stopping the air leak by pleurodesis. It is well known that inappropriate drainage of hemothorax results in severe pleural adhesion and thickening. Based on this idea, some reports described a successful treatment with autologous blood instillation for pneumothorax patients with or without residual pleural space. We tried pleurodesis with autologous bood for pneumothorax with persistent air leak and then we evaluated the efficacy and safety. Methods : Fifteen patients who had persistent air leak in the pneumothorax complicated from the severe chronic lung disease were enrolled. They were not good candidates for surgical treatment and doxycycline pleurodesis failed to stop up their air leaks. We used a mixture of autologous blood and 50% dextrose for pleurodesis. Effect and complications were assessed by clinical out∞me, chest radiography and pulmonary function tests. Results : The mean duration of air leak was 18.4${\pm}$6.16 days before ABP (autologous blood and dextrose pleurodesis) and $5.2{\pm}1.68$ days after ABP. The mean severity of pain was $2.3{\pm}0.70$ for DP(doxycycline pleurodesis) and $1.7{\pm}0.59$ for ABDP (p<0.05). There was no other complication except mild fever. Pleural adhesion grade was a mean of $0.6{\pm}0.63$. The mean dyspnea scale was $1.7{\pm}0.46$ before pneumothrax and $2.0{\pm}0.59$ after ABDP (p>0.05). The mean $FEV_1$ was $1.47{\pm}1.01$ before pneumothorax and $1.44{\pm}1.00$ after ABDP (p>0.05). Except in 1 patient, 14 patients had no recurrent pneumothorax. Conclusion : Autologous blood pleurodesis (ABP) was successful for treatment of persistent air leak in the pneumothorax. It was easy and inexpensive and involved less pain than doxycycline pleurodesis. It did not cause complications and severe pleural adhesion. We report that ABP can be considered as a useful treatment for persistent air leak in the pneumothorax complicated from the severe chronic lung disease.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• Korean Journal of Poultry Science
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• v.16 no.2
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• pp.105-127
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• 1989

Although poultry industry in Japan has been much developed in recent years, it still needs to be developed , compared with developed countries. Since the poultry market in Korea is expected to be opened in the near future it is necessary to maximize the Productivity to reduce the production costs and to develop the scientific, technologies and management organization systems for the improvement of the quality in poultry production. Followings ale the summary of poultry industry in Japan. 1. Poultry industry in Japan is almost specized and commercialized and its management system is ： integrated, cooperative and developed to industrialized intensive style. Therefore, they have competitive power in the international poultry markets. 2. Average egg weight is 48-50g per day (Max. 54g) and feed requirement is 2. 1-2. 3. 3. The management organization system is specialized and farmers in small scale form complex and farmers in large scale are integrated.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.