• The Korean Journal of Nuclear Medicine
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• v.36 no.4
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• pp.244-254
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• 2002

Purpose: This study investigated the differences between technetium-99m ethyl cysteinate dimer (Tc-99m ECD) and technetium-99m hexamethylpropylene amine oxime (Tc-99m HMPAO) uptake in the normal brain by means of statistical parametric mapping (SPM) analysis. Materials and Methods: We retrospectively analyzed age and sex matched 53 cases of normal brain SPECT. Thirty-two cases were obtained with Tc-99m ECD and 21 cases with Tc-99m HMPAO. There were no abnormal findings on brain MRIs. All of the SPECT images were spatially transformed to standard space, smoothed and globally normalized. The differences between the Tc-99m ECD and Tc-99m HMPAO SPECT images were statistically analyzed using statistical parametric mapping (SPM'99) software. The differences bgetween the two groups were considered significant ant a threshold of corrected P values less than 0.05. Results: SPM analysis revealed significantly different uptakes of Tc-99m ECD and Tc-99m HMPAO in the normal brains. On the Tc-99m ECD SPECT images, relatively higher uptake was observed in the frontal, parietal and occipital lobes, in the basal ganglia and thalamus, and in the superior region of the cerebellum. On the Tc-99m HMPAO SPECT images, relatively higher uptakes was observed in subcortical areas of the frontal region, temporal lobe, and posterior portion of inferior cerebellum. Conclusion: Uptake of Tc-99m ECD and Tc-99m HMPO in the normallooking brain was significantly different on SPM analysis. The selective use of Tc-99m ECD of Tc-99m HMPAO in brain SPECT imaging appears especially valuable for the interpretation of cerebral perfusion. Further investigation is necessary to determine which tracer is more accurate for diagnosing different clinical conditions.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.1 no.1
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• pp.56-78
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• 1998

Objective: Chronic hepatitis B infection (CHB) occurs in 6% to 10% of population in Korea. In ethinic communities where prevalence of chronic infection is high such as Korea, transmission of hepatitis B infection is either vertical (ie, by perinatal infection) or by close family contact (usually from mothers or siblings) during the first 5 years of life. The development of chronic hepatitis B infection is increasingly more common the earlier a person is exposed to the virus, particularly in fetal and neonatal life. And it progress to cirrhosis and hepatocellular carcinoma, especially in severe liver damage and perinatal infection. Histopathology of CHB is important when evaluating the final outcomes. A numerical scoring system which is a semiquantitatively assessed objective reproducible classification of chronic viral hepatitis, is a valuable tool for statistical analysis when predicting the outcome and evaluating antiviral and other therapies. In this study, a numerical scoring system (Ludwig system) was applied and compared with the conventional histological classification of De Groute. And the comparative analysis of cinical findings, family history, serology, and liver function test by histopathological findings in chronic hepatitis B of children was done. Methods: Ninety nine patients [mean age=9 years (range=17 months to 16 years)] with clinical, biochemical, serological and histological patterns of chronic HBV infection included in this study. Five of these children had hepatocelluar carcinoma. They were 83 male and 16 female children. They all underwent liver biopsies and histologic evaluation was performed by one pathologist. The biopsy specimens were classified, according to the standard criteria of De Groute as follows: normal, chronic lobular hepatitis (CLH), chronic persistent hepatitis (CPH), mild to severe chronic active hepatitis (CAH), or active cirrhosis, inactive cirrhosis, hepatocellular carcinoma (HCC). And the biopsy specimens were also assessed and scored semiquantitatively by the numerical scoring Ludwig system. Serum HBsAg, anti-HBs, HBeAg, anti-HBe, anti-HBc (IgG, IgM), and HDV were measured by radioimunoassays. Results: Male predominated in a proportion of 5.2:1 for all patients. Of 99 patients, 2 cases had normal, 2 cases had CLH, 22 cases had CPH, 40 cases had mild CAH, 19 cases had moderate CAH, 1 case had severe CAH, 7 cases had active cirrhosis, 1 case had inactive cirrhosis, and 5 cases had HCC. The mean age, sex distribution, symptoms, signs, and family history did not differ statistically among the different histologic groups. The numerical scoring system was correlated well with the conventional histological classification. The histological activity evaluated by both the conventional classification and the scoring system was more severe as the levels of serum aminotransferases were higher. In contrast, the levels of serum aminotransferases were not useful for predicting the degree of histologic activity because of its wide range overlapping. When the histological activity was more severe and especially the cirrhosis more progressing, the prothrombin time was more prolonged. The histological severity was inversely related with the duration of seroconversion of HBeAg. Conclusions: The histological activity could not be accurately predicted by clinical and biochemical findings, but by the proper histological classification of the numerical scoring system for the biopsy specimen. The numerical scoring system was correlated well with the conventional histological classification, and it seems to be a valuable tool for the statistical analysis when predicting the outcome and evaluating effects of antiviral and other therapies in chronic hepatitis B in children.

• 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.