• Tuberculosis and Respiratory Diseases
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• v.45 no.1
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• pp.116-127
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• 1998

Background: The natural course of sarcoidosis is variable from spontaneous remission to significant morbidity or death. So the assessment of disease activity is important but no single parameter was generally accepted as a good marker. Recently several studies suggested that adhesion molecules, especially ICAM-1 can be a marker, but there are some controversies. And only few data are available about the relationship of ICAM-1 with clinical follow-up course. Methods: We measured the expression of adhesion molecules on BAL cells by flow cytometry and the level of soluble ICAM-1(sICAM-1) in serum and BALF at the time of diagnosis in 12 patients with active disease and 7 inactive sarcoidosis(5 male, 14 female, mean age: $39.4{\pm}10.7$ years, mean follow-up : $20{\pm}15$ months). Follow-up clinical course were compared with the changes in serum sICAMA-1 level and the adhesion molecule on BAL cells. Results: In the patients with active disease, the ICAM-1 on AM(RMFI: $3.68{\pm}1.71$) and sICAM-1 level in serum($582{\pm}193$ng/ml) and BAL fluid($47.8{\pm}16.5$ng/ml) were all higher than those of 7 inactive disease(RMFI: $1.89{\pm}0.75$, p=0.0298, serum: $294{\pm}117$ ng/ml, p=0.0049, BALF: $20.9{\pm}8.3$ ng/ml). In the active sarcoidosis, ICAM-1 on AM(RMFI : $1.51{\pm}0.84$) and serum sICAM-1 were decreased after the therapy($250{\pm}147$ ng/ml) but no significant change was noted in inactive disease. Also we found the initial ICAM-1 on AM and serum sICAM-1 had a significant correlation with the degree of improvement in PFT after the therapy. During the follow-up, the disease relapsed in 4 patients after the discontinuation of steroid and the serum sICAM-1 level went-up again at the time of relapse. Conclusion: Our data suggest that the serum sICAM-1 level and the ICAM-1 expression on AM can be a good marker of disease activity and also a predictor of outcome in sarcoidosis.

• Journal of Genetic Medicine
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• v.6 no.1
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• pp.74-80
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• 2009

Purpose: To assess the value of first-trimester pregnancy-associated plasma protein-A (PAPP-A), nuchal translucency (NT) and second-trimester alpha-fetoprotein (AFP), human chorionic gonadotropin (hCG), unconjugated estriol (uE3), and inhibin-A in predicting pregnancy complications other than fetal aneuploidy. Materials and Methods: A retrospective study in 3,121 singleton pregnancies with integrated testing was performed at Kangnam CHA hospital between January 2005 and December 2006. Baseline characteristics, pregnancy outcomes, and serum marker levels were obtained by review of the medical records. We analyzed the data to identify associations between the integrated screening markers and adverse pregnancy outcomes. Statistical analyses were performed with the SPSS program. Results: In preterm labor and preeclampsia, high AFP, hCG, and inhibin-A levels and low PAPP-A and NT levels were found to be significantly correlated (P<0.05). Elevated second-trimester inhibin-A levels were associated with preeclampsia (odds ratio 2.843), low birth weight (odds ratio 1.446), and preterm labor (odds ratio 1.287), and while decreased first-trimester PAPP-A levels were associated with preeclampsia (odds ratio 0.51) and preterm labor (odds ratio 0.75). Conclusion: First- and second-trimester maternal serum markers screening can be used for predicting high-risk pregnancies.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.