• Tuberculosis and Respiratory Diseases
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• v.45 no.6
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• pp.1236-1251
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• 1998

Background : TNF-$\alpha$ appears to be a central mediator of the host response to sepsis. While TNF-$\alpha$ is mainly considered a proinflammatory cytokine, it can also act as a direct cytotoxic cytokine. However, there are not so many studies about the relationship bet ween TNF-$\alpha$ level and lung injury severity in ALI, particularly regarding the case of ALI caused by direct lung injury such as diffuse pulmonary infection. Recently, a natural defense mechanism, known as the stress response or the heat shock response, has been reported in cellular or tissue injury reaction. There are a number of reports examining the protective role of pre-induced heat stress proteins on subsequent LPS-induced TNF-$\alpha$ release from monocyte or macrophage and also on subsequent LPS-induced ALI in animals. However it is not well established whether the stress protein synthesis such as HSP can be induced from rat alveolar macrophages by in vitro or in vivo LPS stimulation. Methods : We measured the level of TNF-$\alpha$, the percentage of inflammatory cells in bronchoalveolar lavage fluid, protein synthesis in alveolar macrophages isolated from rats at 1, 2, 3, 4, 6, 12, and 24 hours after intratracheal LPS instillation. We performed histologic examination and also obtained histologic lung injury index score in lungs from other rats at 1, 2, 3, 4, 6, 12, 24 h after intratracheal LPS instillation. Isolated non-stimulated macrophages were incubated for 2 h with different concentration of LPS (0, 1, 10, 100 ng/ml, 1, or 10 ${\mu}g/ml$). Other non-stimulated macrophages were exposed at $43^{\circ}C$ for 15 min, then returned to at $37^{\circ}C$ in 5% CO2-95% for 1 hour, and then incubated for 2 h with LPS (0, 1, 10, 100ng/ml, 1, or 10 ${\mu}g/ml$). Results : TNF-$\alpha$ levels began to increase significantly at 1 h, reached a peak at 3 h (P<0.0001), began to decrease at 6 h, and returned to control level at 12 h after LPS instillation. The percentage of inflammatory cells (neutrophils and alveolar macrophages) began to change significantly at 2 h, reached a peak at 6 h, began to recover but still showed significant change at 12 h, and showed insignificant change at 24 h after LPS instillation compared with the normal control. After LPS instillation, the score of histologic lung injury index reached a maximum value at 6 h and remained steady for 24 hours. 35 kDa protein band was newly synthesized in alveolar macrophage from 1 hour on for 24 hours after LPS instillation. Inducible heat stress protein 72 was not found in any alveolar macrophages obtained from rats after LPS instillation. TNF-$\alpha$ levels in supernatants of LPS-stimulated macro phages were significantly higher than those of non-stimulated macrophages(p<0.05). Following LPS stimulation, TNF-$\alpha$ levels in supernatants were significantly lower after heat treatment than in those without heat treatment (p<0.05). The inducible heat stress protein 72 was not found at any concentrations of LPS stimulation. Whereas the 35 kDa protein band was exclusively found at dose of LPS of 10 ${\mu}g/ml$. Conclusion : TNF-$\alpha$ has a direct or indirect close relationship with lung injury severity in acute lung injury or acute respiratory distress syndrome. In vivo and in vitro LPS stimulation dose not induce heat stress protein 72 in alveolar macrophages. It is likely that 35 kDa protein, synthesized by alveolar macrophage after LPS instillation, does not have a defense role in acute lung injury.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.