• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.1
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• pp.77-91
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• 1996

In order to elucidate the biological etiology and the effects of comorbidity on biological variables in tic disorders, plasma serotonin (5-hydroxlfryptamine, 5-HT) and 5-hydroxy- indoleacetic acid (5-HIAA) we.e measured in 87 tic disorders and 30 control subjects. The 87 tic disorder were composed of 45 Tourette's disorder(TS), 22 chronic motor tic disorders (CMT) and 20 transient tic disorders (TTD). Among these patients,43 patients were pure tic disorder (PT), 28 subject also had attention deficit hyperactivity disorder (T+ADHD) and 16 subjects had obsessive compulsive disorders (T+ OCD) as comorbid disorders. The results are summarized as follows : 1) Plasma 5-HT levels showed significant positive correlations with plasma 5-HIAA levels (Pennon r=0.77, p<0.05). 2) Plasma 5-HT and 5-HIAA levels showed no significant correlation with age in tic disorders. 3) Plasma 5-HIAA and 5-HT levels showed no significant correlations with age in control subjects. 4) There was significant difference in plasma 5-HT levels among TS, CMT, TTD and control groups (ANOVA F=34.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and ITD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TTD groups. 5) There was significant difference in plasma 5-HIAA levels among TS, CMT, TTD and control groups (ANOVA F=26.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and TTD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TID groups.f) There was significant difference in plasma 5-HT and 5-HIAA levels among PT, T+ADHD, T+OCD and contol groups (ANOVA 5-HT, F=37.59, df=3, 113, p<0.01, 5-HIAA, F=27.37, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed signiscant differences between control and PT, control and T+ADHD and control and T+OCB. But, post-hoc test showed no significant differences between PT and T+ADHD, PT and T+ OCD and T+ADHD and T+ OCD. These results show that decreased 5-HT and 5-HIAA levels may play a role in the genesis of tic disorders, but these findings have no significant correlations with the severity of tic disorders. And the comorbid disorders of tics may have minimal effects on the biochemical abnormalities. Future studies must be focused on the effects of serotonin agonists and antagonists on tic disorders and molecular biological methodology may enhance to elucidate the mechanisms of these abnormal findings.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.77-82
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• 2010

Purpose: As PET/CT come into wide use, it caused increasing of expose in clinical use. Therefore, Korea Food and Drug Administration issued Patient DRL (Diagnostic Reference Level) in CT scan. In this study, to build the basis of patient dose reduction, we analyzed effective dose in transmission scan with CT scan. Materials and Methods: From February, 2010 to March 180 patients (age: $55{\pm}16$, weight: $61.0{\pm}10.4$ kg) who examined $^{18}F$-FDG PET/CT in Asan Medical Center. Biograph Truepoint 40 (SIEMENS, GERMANY), Biograph Sensation 16 (SIEMENS, GERMANY) and Discovery STe8 (GE healthcare, USA) were used in this study. Per each male and female average of 30 patients doses were analyzed by one. Automatic exposure control system for controlling the dose can affect the largest by a patient's body weight less than 50 kg, 50-60 kg less, 60 kg more than the average of the three groups were divided doses. We compared that measured value of CT-expo v1.7 and ImPACT v1.0. The relationship between body weight and the effective dose were analyzed. Results: When using CT-Expo V1.7, effective dose with BIO40, BIO16 and DSTe8 respectably were $6.46{\pm}1.18$ mSv, $9.36{\pm}1.96 $mSv and $9.36{\pm}1.96$ mSv for 30 male patients respectably $6.29{\pm}0.97$ mSv, $10.02{\pm}2.42$ mSv and $9.05{\pm}2.27$ mSv for 30 female patients respectably. When using ImPACT v1.0, effective dose with BIO40, BIO16 and DSTe8 respectably were $6.54{\pm}1.21$ mSv, $8.36{\pm}1.69$ mSv and $9.74{\pm}2.55$Sv for 30 male patients respectably $5.87{\pm}1.09$ mSv, $8.43{\pm}1.89$ mSv and $9.19{\pm}2.29$ mSv for female patients respectably. When divided three groups which were under 50 kg, 50~60 kg and over 60 kg respectably were 6.27 mSv, 7.67 mSv and 9.33 mSv respectably using CT-Expo V1.7, 5.62 mSv, 7.22 mSv and 8.91 mSv respectably using ImPACT v1.0. Weight and the effective dose coefficient analysis showed a very strong positive correlation(r=743, r=0.693). Conclusion: Using such a dose evaluation programs, easier to predict and evaluate the effective dose possible without performing phantom study and such dose evaluation programs could be used to collect basic data for CT dose management.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.