• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6327-6330
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• 2013

Haematuria is a common presentation of bladder cancer and requires a full urologic evaluation. This study aimed to develop a scoring system capable of stratifying patients with haematuria into high or low risk groups for having bladder cancer to help clinicians decide which patients need more urgent assessment. This cross-sectional study included all adult patients referred for haematuria and subsequently undergoing full urological evaluation in the years 2001 to 2011. Risk factors with strong association with bladder cancer in the study population were used to design the scoring system. Accuracy was determined by the area under the receiver operating characteristic (ROC) curve. A total of 325 patients with haematuria were included, out of which 70 (21.5%) were diagnosed to have bladder cancer. Significant risk factors associated with bladder cancer were male gender, a history of cigarette smoking and the presence of gross haematuria. A scoring system using 4 clinical parameters as variables was created. The scores ranged between 6 to 14, and a score of 10 and above indicated high risk for having bladder cancer. It was found to have good accuracy with an area under the ROC curve of 80.4%, while the sensitivity and specificity were 90.0% and 55.7%, respectively. The scoring system designed in this study has the potential to help clinicians stratify patients who present with haematuria into high or low r isk for having bladder cancer. This will enable high-risk patients to undergo urologic assessment earlier.

• Nutrition Research and Practice
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• v.8 no.4
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• pp.469-475
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• 2014

BACKGROUND/OBJECTIVE: The goal of the present study was to investigate the effects of moderate caloric restriction on ${\beta}$-cell function and insulin sensitivity in middle-aged obese Korean women. SUBJECTS/METHODS: Fifty-seven obese pre-menopausal Korean women participated in a 12-week calorie restriction program. Data on total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglycerides (TG), and fasting serum levels of glucose, insulin, C-peptide, blood pressure, leptin and anthropometrics were collected. A dietary intake assessment was based on three days of food recording. Additionally, ${\beta}$-cell function [homeostasis model assessment of ${\beta}$-cell (HOMA-${\beta}$), insulinogenic index (ISI), C-peptide:glucose ratio, and area under curve insulin/glucose ($AUC_{ins/glu}$)] and insulin sensitivity [homeostasis model assessment for insulin resistance (HOMA-IR), Quantitative insulin-sensitivity check index (QUICKI) and Matsuda index (MI)] were recorded. RESULTS: When calories were reduced by an average of 422 kcal/day for 12 weeks, BMI (-2.7%), body fat mass (-10.2%), and waist circumference (-5%) all decreased significantly (P < 0.05). After calorie restriction, weight, body fat percentage, hip circumference, BP, TC, HDL-C, LDL-C, plasma glucose at fasting, insulin at fasting and 120 min, $AUC_{glu}$ and the insulin area under the curve all decreased significantly (all P < 0.05), while insulin sensitivity (HOMA-IR, QUICKI and Matsuda index) measured by OGTT improved significantly (P < 0.01). CONCLUSIONS: Moderate weight loss due to caloric restriction with reduction in insulin resistance improves glucose tolerance and insulin sensitivity in middle-aged obese women and thereby may help prevent the development of type 2 diabetes mellitus.

• Biomolecules & Therapeutics
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• v.28 no.4
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• pp.361-369
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• 2020

Tofacitinib, a Janus kinase inhibitor, was developed for the treatment of rheumatoid arthritis. Recently, it has been associated with an increased change in arthritis development in patients with diabetes. Herein, we evaluated the pharmacokinetics of tofacitinib after intravenous (10 mg/kg) and oral (20 mg/kg) administration to rats with streptozotocin-induced diabetes mellitus and control rats. Following intravenous administration of tofacitinib to rats with streptozotocin-induced diabetes mellitus, area under the plasma concentration-time curve from time zero to infinity of tofacitinib was significantly smaller (33.6%) than that of control rats. This might be due to the faster hepatic intrinsic clearance (112%) caused by an increase in the hepatic cytochrome P450 (CYP) 3A1(23) and the faster hepatic blood flow rate in rats with streptozotocin-induced diabetes mellitus than in control rats. Following oral administration, area under the plasma concentration-time curve from time zero to infinity of tofacitinib was also significantly smaller (55.5%) in rats with streptozotocin-induced diabetes mellitus than that in control rats. This might be due to decreased absorption caused by the higher expression of P-glycoprotein and the faster intestinal metabolism caused by the higher expression of intestinal CYP3A1(23), which resulted in the decreased bioavailability of tofacitinib (33.0%) in rats with streptozotocin-induced diabetes mellitus. In summary, our findings indicate that diabetes mellitus affects the absorption and metabolism of tofacitinib, causing faster metabolism and decreased intestinal absorption in rats with streptozotocin-induced diabetes mellitus.

• Korean journal of applied entomology
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• v.22 no.1 s.54
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• pp.7-14
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• 1983

Septoria brown spot closed by Septoria glycines is one of the most serious fungal diseases in soybean. Average yield reduction of 3 varieties for two years was $16.1\%$ by the septoria brown spot inoculation and $9.0\%$ by the natural infection as compared to fungicide-sprayed plots. Number of pods per plant and seed weight were significantly reduced while plant height, number of branches and number of nodes per plant were not affected. Yield reduction was positively correlated to the septoria brown spot severity in all varieties examined. Correlation coefficient $(r=0.38^*)$ between yield reduction and area under the disease progress curve was higher than that (r=0.156) between yield reduction and Van der Plank's apparent infection rate. Potential effect of the septoria brown spot on the soybean yield reduction estimated with the area under the disease progress curve was expressed by the equation of Y=4.38+0.05X $(r=0.0696^*,\;df=25)$.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.289-294
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• 2020

Peptic ulcer disease is a gastrointestinal disorder caused by Helicobacter pylori infection and the use of nonsteroid anti-inflammatory drugs. While many studies have been conducted to find the risk factors of peptic ulcers, there are no studies on the suggestion of peptic ulcer prediction models for Koreans. Therefore, the purpose of this study is to implement peptic ulcer prediction model using machine learning based on demographic information, obesity information, blood information, and nutritional information for middle-aged and elderly people. For model building, wrapper-based variable selection method and naive Bayes algorithm were used. The classification accuracy of the female prediction model was the area under the receiver operating characteristics curve (AUC) of 0.712, and males showed an AUC of 0.674, which is lower than that of females. These results can be used for prediction and prevention of peptic ulcers in the middle and elderly people.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.413-420
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• 2019

The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.21-28
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• 2019

Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients' death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.

• Journal of Trauma and Injury
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• v.35 no.3
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• pp.181-188
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• 2022

Purpose: Inferior vena cava (IVC) collapse is related to hypovolemia. Sonography has been used to measure the IVC diameter, but there is variation depending on the skill of the operator and it is difficult to obtain accurate measurements in patients who have a large amount of intestinal gas or are obese. As a modality to obtain accurate measurements, we measured the diameters of the IVC and aorta on trauma computed tomography scans and investigated the correlation between the IVC to aorta ratio and the shock index in blunt trauma patients. Methods: We retrospectively analyzed the medical records of 588 trauma patients who were transferred to the regional trauma center (level 1) of Wonkang University Hospital from March 2020 to February 2021. We included trauma patients 18 years or older who met the trauma activation criteria and underwent trauma computed tomography scans with intravenous contrast within 40 minutes of admission. The shock index was calculated from vital signs before trauma computed tomography scan, and measurements of the anteroposterior diameter of the IVC (AP), the transverse diameter of the IVC (T), and aorta were made 10 mm above the right renal vein in the venous phase. Results: Overall, 271 patients were included in this study, of whom 150 had a shock index ≤0.7 and 121 had a shock index >0.7. The T to AP ratio and AP to aorta ratio were significantly different between groups. Cutoffs were identified for the T to AP ratio and AP to aorta ratio (2.37 and 0.62, respectively) that produced clinically useful sensitivity and specificity for predicting a shock index >0.7, demonstrating moderate accuracy (T to AP ratio: area under the curve, 0.71; sensitivity, 59%; specificity, 87% and AP to aorta ratio: area under the curve, 0.70; sensitivity, 55%; specificity, 91%). Conclusions: The T to AP ratio and AP to aorta ratio are useful for predicting hemorrhagic shock in trauma patients.