• Biomedical Science Letters
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• v.19 no.3
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• pp.233-238
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• 2013

Aspirin is still the mainstay of antiplatelet therapy in the cardiovascular and cerebrovascular disease. However, some patients are not responsive to the antithrombotic action of aspirin. The aim of this study was to assess the prevalence and clinical characteristics of aspirin resistance in patients with cerebral infarction. We tested platelet function in 557 patients who had been treated with aspirin in J general hospital. Platelet function was tested using the multiple electrode platelet aggregometry (MEA). Platelet reactivity was expressed as area under the aggregation curve (AUC, U) and >30 AUC was defined as aspirin resistance. Aspirin resistance was detected in 16.2% patients. There was not any significant differences in age, gender between aspirin resistance and aspirin sensitive patients. WBC was significantly higher in patients with aspirin resistance (P < .05). HDL-cholesterol was significantly higher in patients with aspirin sensitive (P < .05). Aspirin resistance was positive correlation with platelet count (r =.314, P =.003). The prevalence of aspirin resistance in cerebral infarction was 16.2%, and platelet count were related with aspirin resistance.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.221-225
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• 2023

In this paper, we proposed an efficient method for rapid diagnosis of COVID-19 by voice. A novel Strided Convolution Separable Transformer (SC-SepTr) is proposed by modifying the conventional Separable Transformer (SepTr) for audio signal recognition. The proposed method reduces the memory and computational requirements to enable rapid diagnosis of COVID-19. As a result of experiments on Coswara, it was shown that the proposed method perform rapid diagnosis with guaranteeing Area Under the Curve (AUC) performance even for a relatively small amount of learning data.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Journal of The Geomorphological Association of Korea
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• v.24 no.4
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• pp.75-89
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• 2017

Sinkholes are key karst landforms that primarily evolve through the dissolution of limestone, and it posing a significant threat to roads, buildings, and other man-made structures. This study aims to analyze the area susceptible to sinkhole development using GIS and to identify potential danger area from sinkholes. Eight sinkhole related factors (slope angle, distance to caves, distance to faults, bedrock lithology, soil depth, drainage class, distance to mines, and distance to traffic routes) were constructed as spatial databases with sinkhole inventory. Based on the spatial database, sinkhole susceptibility maps were produced using nearest neighbor distance and frequency ratio models. The maps were verified with prediction rate curve and area under curve. The result indicates that the nearest neighbor distance and frequency ratio models predicted 95.3% and 94.4% of possible sinkhole locations respectively. Furthermore, to identify potential sinkhole danger area, the susceptibility map was compared with population distribution and land use map. It has been found that very highly susceptible areas are along Osipcheon and southeast southwest part of Hajang-myeon and south part of Gagok-myeon of Samcheok-si. Among those areas, it has been identified that potential sinkhole danger areas are Gyo-dong, Seongnae-dong, Jeongna-dong, Namyang-dong and Dogye-eup. These results can be useful in the aspects of land use planning and hazard prevention and management.

• Journal of the Korean Society of Radiology
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• v.81 no.5
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• pp.1164-1174
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• 2020

Purpose To evaluate a deep learning model to predict recurrence of thyroid tumor using preoperative ultrasonography (US). Materials and Methods We included representative images from 229 US-based patients (male:female = 42:187; mean age, 49.6 years) who had been diagnosed with thyroid cancer on preoperative US and subsequently underwent thyroid surgery. After selecting each representative transverse or longitudinal US image, we created a data set from the resulting database of 898 images after augmentation. The Python 2.7.6 and Keras 2.1.5 framework for neural networks were used for deep learning with a convolutional neural network. We compared the clinical and histological features between patients with and without recurrence. The predictive performance of the deep learning model between groups was evaluated using receiver operating characteristic (ROC) analysis, and the area under the ROC curve served as a summary of the prognostic performance of the deep learning model to predict recurrent thyroid cancer. Results Tumor recurrence was noted in 49 (21.4%) among the 229 patients. Tumor size and multifocality varied significantly between the groups with and without recurrence (p < 0.05). The overall mean area under the curve (AUC) value of the deep learning model for prediction of recurrent thyroid cancer was 0.9 ± 0.06. The mean AUC value was 0.87 ± 0.03 in macrocarcinoma and 0.79 ± 0.16 in microcarcinoma. Conclusion A deep learning model for analysis of US images of thyroid cancer showed the possibility of predicting recurrence of thyroid cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3451-3455
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• 2015

Objective: To explore the application of joint detection of serum AFP, CA19-9, CA125 and CEA in identification and diagnosis of cholangiocarcinoma (CC). Materials and Methods: The levels of serum AFP, CA19-9, CA125 and CEA of both 30 patients with CC and 30 patients with hepatocellular carcinoma (HCC) were assessed. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic effects of single and joint detection of those 4 kinds of tumor markers for CC. Results: The levels of serum CA19-9, CA125 and CEA in CC patients were higher than that in HCC patients,whereas that of serum AFP was significantly lower s. The area under ROC curve of single detection of serum AFP, CA19-9, CA125 and CEA were 0.05, 0.86, 0.84 and 0.83, with the optimal cutoff values of 15.4 ng/ml, 125.1 U/ml, 95.7 U/ml and 25.9 ng/ml, correspondingly, and the percentage correct single diagnosis was <79%. With joint detection, the diagnostic effect of combined AFP, CA19-9, CA125 and CEA was the highest, with an area under the ROC curve of 0.94 (95%CI 0.88~0.99). Conclusions: Single detection of serum CA19-9, CA125 and EA is not meaningful. The sensitivity, specificity, the rate of correct diagnosis and the area under ROC curve of joint detection of AFP, CA19-9, CA125 and CEA are highest, indicating that the joint detection of these 4 tumor markers is of great importance in the diagnosis of CC.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.293-297
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• 2013

Background: : Chemotherapy-induced thrombocytopenia (CIT) is an important cause of morbitity in patients with cancer. Aim: To investigate the effect of the baseline plasma D-dimer level, an important marker for thrombotic activity, on chemotherapy-induced thrombocytopenia in patients with stage III colon cancer. Materials and Methods: A total of 43 (28 men) eligible patients were divided into two groups according to whether they exhibited chemotherapy-induced thrombocytopenia: Group 1 (n=21) and Group 2 (n=22). Comparison was made using demographic, histopathologic, and laboratory variables. Additionally, baseline plasma D-dimer levels underwent receiver operation characteristics curve analysis, and areas under the curve were calculated. Sensitivity, specificity, and positive and negative likelihood rates were then determined. Results: The incidence of chemotherapy-induced thrombocytopenia had a significant correlation with baseline platelet count (r=0.568, P=0.031) and baseline plasma D-dimer levels (r=0.617, P=0.036). When the cut-off point for the latter was set as 498 ng/mL, the area under the curve was 0.89 (95%CI: 0.74-0.93), the sensitivity was 91.4%, the specificity was 89.7%, the positive likelihood rate was 3.64 and the negative likelihood rate was 0.24 for chemotherapy-induced thrombocytopenia diagnosis. Conclusions: The baseline level of plasma D-dimer could help to differentiate high-risk patients for chemotherapy-induced thrombocytopenia.

• Journal of Environmental Science International
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• v.23 no.3
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• pp.511-520
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• 2014

The MFFn(Mass first flush), EMCs(Event mean concentrations) and runoff loads were analyzed for various rainy events(monitoring data from 2011 to 2012) in transportation area(rail road in station). The pollutant EMCs by volume of stormwater runoff showed the BOD5 9.6 mg/L, COD 29.9 mg/L, SS 16.7 mg/L, T-N 3.271 mg/L, T-P 0.269 mg/L in the transportation areas(Railroad in station). The average pollutant loading by unit area of stormwater runoff showed the BOD5 $27.26kg/km^2$, COD $92.55kg/km^2$, SS $50.35kg/km^2$, T-N $10.13kg/km^2$ and T-P $10.13kg/km^2$ in the transportation areas. Estimated NCL-curve(Normalized cumulated-curve) was evaluated by comparison with observed MFFn. MFFn was estimated by varying n-value from 10% to 90% on the rainy events. The n-value increases, MFFn is closed to '1'. As time passed, the rainfall runoff was getting similar to ratio of pollutants accumulation. The result of a measure of the strength of the linear relationship between observed data and expected data under model was good.

• Geomechanics and Engineering
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• v.32 no.4
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• pp.411-426
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• 2023

Soft clay is widely distributed in the southeast coastal areas of China. Many large underground structures, such as subway stations and underground pipe corridors, are shallow buried in the soft clay foundation, so the dynamic characteristics of the soft clay must be considered to the seismic design of underground structures. In this paper, the dynamic characteristics of saturated soft clay in Shanghai under the bidirectional excitation for earthquake loading are studied by dynamic triaxial tests, comparing the backbone curve and hysteretic curve of the saturated soft clay under different confining pressures with those under different vibration frequencies. Considering the coupling effects of the confining pressure and the vibration frequency, a fitting model of the maximum dynamic shear modulus was proposed by the multiple linear regression method. The M-D model was used to fit the variations of the dynamic shear modulus ratio with the shear strain. Based on the Chen model and the Park model, the effects of the consolidation confining pressure and the vibration frequency on the damping ratio were studied. The results can provide a reference to the earthquake prevention and disaster reduction in soft clay area.

• Imaging Science in Dentistry
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• v.34 no.1
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• pp.1-5
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• 2004

Purpose: To evaluate the diagnostic accuracy of occlusal and proximal caries detection using CCD, CMOS, PSP and film system. Materials and Methods : 32 occlusal and 30 proximal tooth surfaces were radiographed under standardized conditions using 3 digital systems; CCD (CDX-2000HQ, Biomedysis Co., Seoul, Korea), CMOS (Schick, Schick Inc., Long Island, USA), PSP (Digora/sup (R)/FMX, Orion Co./Soredex, Helsinki, Finland) and I film system (Kodak Insight, Eastman Kodak, Rochester, USA). 5 observers examined the radiographs for occlusal and proximal caries using a 5-point confidence scale. The presence of caries was validated histologically and radiographically. Diagnostic accuracy was evaluated using ROC curve areas (Az). Results: Analysis using ROC curves revealed the area under each curve which indicated a diagnostic accuracy. For occlusal caries, Kodak Insight film had an Az of 0.765, CCD one of 0.730, CMOS one of 0.742 and PSP one of 0.735. For proximal caries, Kodak Insight film had an Az of 0.833, CCD one of 0.832, CMOS one of 0.828 and PSP one of 0.868. No statistically significant difference was noted between any of the imaging modalities. Conclusion: CCD, CMOS, PSP and film performed equally well in the detection of occlusal and proximal dental caries. CCD, CMOS and PSP-based digital images provided a level of diagnostic performance comparable to Kodak Insight film.