• Title/Summary/Keyword: a real-time analysis

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The Clinical Characteristics of Lung Cancer in Patients with Idiopathic Pulmonary Fibrosis (특발성 폐섬유화증에 동반된 폐암 환자의 임상적 특정)

  • Park, Joo-Hun;Lee, Jin-Seong;Song, Koun-Sik;Shim, Tae-Sun;Lim, Chae-Man;Koh, Youn-Suck;Lee, Sang-Do;Kim, Woo-Sung;Kim, Won-Dong;Kim, Dong-Soon
    • Tuberculosis and Respiratory Diseases
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    • v.46 no.5
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    • pp.674-684
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    • 1999
  • Background : It has been generally known that the incidence of lung cancer is higher in the patients with idopathic pumonary fibrosis (IPF) than those in general population. The reported incidence was variable from 4.8 to 43.2%. There were controversies on the most frequent cell type (squamous cell carcinoma vs. adenocarcinoma) and no study was done about the real concordance of cancer and the fibrotic lesion. And the pulmonary fibrosis may influence not only the development of cancer but also the treatment and prognosis of the cancer, but there was no report on that point. Method : Total 63 patients ($66.8{\pm}7.8$ year, M : F=61 : 2) were diagnosed as IPF combined with lung cancer (IFF-CA) at Asan Medical Center. A retrospective analysis was done about the risk factors of the lung cancer, pulmonary function test, the site of cancer(especially the relationship of the cancer with the fibrotic lesion), the histologic types, and the stage of cancer. The histologic types were compared with those of 2,660 patients with lung cancer who were diagnosed at the same institute for the same period. The effect of IPF on the treatment of the cancer was evaluated with the survival time after the detection of lung cancer. Results : The lung cancer was found in 63(22.9%) out of 281 patients with IPF. But in most of them(45 patients), lung cancer was detected at the same time with IPF and only in 18 patients, the cancer was diagnosed during the follow-up($25.2{\pm}17.7$ months) of IPF. So in our study, 6.7% of patients with IPF developed lung cancer during the course of the disease. The age ($66.8{\pm}7.84$ vs. $63.4{\pm}11.1$ years), percentage of smoker (88.9 vs. 67.2%), and the male gender (96.8 vs. 67.6%) were significantly higher in IPF-CA compared with lone IPF (p<0.05). The odds ratio of smoking was 4.7 compared with non smoking IPF controls. The lung cancer was located more frequently in the upper lobe and 55.5% was in the periphery of lung. The cancer was developed in the fibrotic lesion in 23 patients (35.9%), and in the majority of the patients, the cancer was separated from the fibrosis. The cell type of the lung cancer in IPF-CA was squamous cell carcinoma 34.9%, adenocarcinoma 30.2%, small cell carcinoma 19.0%, large cell undifferenciated carcinoma 6.3%, and others 9.5%. No significant difference in the distribution of histologic type of the lung cancer was found between IPF-CA and lone lung cancer. There was no significant difference in demographic features, cell types, location and the stage of the cancer between the group with concurrent IPF-CA and the group with cancer diagnosed during the follow up of IPF. There was a tendency (but statistically not significant : p=0.081) of higher incidence of adenocarcinoma among the cancers developed in the fibrotic area(43.5%) (F-CA) than in the cancers in non-fibrotic area (22.5%) (NF-CA). The prognosis of the patients with F-CA was poor (median survival : 4 months) compared with the patients with NF-CA (7 months, p=0.013), partly because the prevalence of severe IPF (the extent of fibrosis in HRCT 50%) was higher in F-CA group. Conclusion : These data suggest that the lung cancer in the patients with IPF has similar features to the ordinary lung cancer.

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Implementation of integrated monitoring system for trace and path prediction of infectious disease (전염병의 경로 추적 및 예측을 위한 통합 정보 시스템 구현)

  • Kim, Eungyeong;Lee, Seok;Byun, Young Tae;Lee, Hyuk-Jae;Lee, Taikjin
    • Journal of Internet Computing and Services
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    • v.14 no.5
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    • pp.69-76
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    • 2013
  • The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.