• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7793-7797
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• 2014

Objective: The aim of this study was to evaluate predictive role of risk of malignancy index in discriminating between benign and malignant adnexal masses preoperatively. Methods: This retrospective study was conducted with a total of 569 patients with adnexal masses/ovarian cysts managed surgically at our clinic between January 2006 and January 2012. Obtained data from patient files were age, gravidity, parity, menopause status, ultrasound findings and CA125 levels. For all patients ultrasound scans were performed. For the assessment of risk of malignancy index (RMI) Jacobs' model was used. Histopathologic results of all patients were recorded postoperatively. Malignancy status of the surgically removed adnexal mass was the gold standard. Results: Of the total masses, 245 (43.1%) were malignant, 316 (55.5%) were benign and 8 (1.4%) were borderline. The mean age of benign cases was lower than malign cases ($35.2{\pm}10.9$ versus $50.8{\pm}13.4$, p<0.001). Four hundred and five of them (71.2%) were in premenopausal period. Malignant tumors were more frequent in postmenopausal women (81% versus 29%, p<0.001). All ultrasound parameters of RMI were statistically significantly favorable for malignant masses. In our study ROC curve analysis for RMI provided maximum Youden index at level of 163.85. When we based on cutoff level for RMI as 163.85 sensitivity, specificity, PPV, NPV was calculated 74.7%, 96.2%, 94% and 82.6%, respectively. Conclusions: RMI was found to be a significant marker in preoperative evaluation and management of patients with an adnexal mass, and was useful for referring patients to tertiary care centers. Although utilization of RMI provides increased diagnostic accuracy in preoperative evaluation of patient with an adnexal mass, new diagnostic tools with higher sensitivity and specificity are needed to discriminate ovarian cancer from benign masses.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.6
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• pp.788-794
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• 2013

Mastitis set apart as clinical and sub clinical is a disease complex of dairy cattle, with sub clinical being the most important economically. Of late, laboratories showed interest in developing biochemical markers to diagnose sub clinical mastitis (SCM) in herds. Many workers reported noteworthy alternation of acute phase proteins (APPs) and nitric oxide, (measured as nitrate+nitrite = NOx) in milk due to intra-mammary inflammation. But, the literature on validation of these parameters as indicators of SCM, particularly in riverine milch buffalo (Bubalus bubalis) milk is inadequate. Hence, the present study focused on comparing several APPs viz. ${\alpha}_1$-anti trypsin, ${\alpha}_1$-acid glycoprotein, fibrinogen and NOx as indicators of SCM in buffalo milk. These components in milk were estimated using standardized analytical protocols. Somatic cell count (SCC) was done microscopically. Microbial culture was done on 5% ovine blood agar. Of the 776 buffaloes (3,096 quarters) sampled, only 347 buffaloes comprising 496 quarters were found positive for SCM i.e. milk culture showed growth in blood agar with $SCC{\geq}2{\times}10^5$ cells/ml of milk. The cultural examination revealed Gram positive bacteria as the most prevalent etiological agent. It was observed that ${\alpha}_1$-anti trypsin and NOx had a highly significant (p<0.01) increase in SCM milk, whereas, the increase of ${\alpha}_1$-acid glycoprotein in infected milk was significant (p<0.05). Fibrinogen was below detection level in both healthy and SCM milk. The percent sensitivity, specificity and accuracy, predictive values and likelihood ratios were calculated taking bacterial culture examination and $SCC{\geq}2{\times}10^5$ cells/ml of milk as the benchmark. Udder profile correlation coefficient was also used. Allowing for statistical and epidemiological analysis, it was concluded that ${\alpha}_1$-anti trypsin indicates SCM irrespective of etiology, whereas ${\alpha}_1$-acid glycoprotein better diagnosed SCM caused by gram positive bacteria. NOx did not prove to be a good indicator of SCM. It is recommended measuring both ${\alpha}_1$-anti trypsin and ${\alpha}_1$-acid glycoprotein in milk to diagnose SCM in buffalo irrespective of etiology.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3407-3412
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• 2015

Background: The aim of this study was to evaluate and compare the accuracy of diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) value, and time-intensity curve (TIC) type analysis derived from dynamic contrast-enhanced MR imaging (DCE-MRI) in differentiating benign from malignant adnexal masses. Materials and Methods: 47 patients with 56 adnexal masses (27 malignant and 29 benign) underwent DWI and DCE-MRI examinations, prior to surgery. DWI signal intensity, mean ADC value, and TIC type were determined for all the masses. Results: High signal intensity on DWI and type 3 TIC were helpful in differentiating benign from malignant adnexal masses (p<0.001). The mean ADC value was significantly lower in malignant adnexal masses (p<0.001). An ADC value< $1.20{\times}10^{-3}mm^2/s$ may be the optimal cutoff for differentiating between benign and malignant tumors. The negative predictive value for low signal intensity on DWI, and type 1 TIC were 100%. The pairwise comparison among the receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of TIC was significantly larger than the AUCs of DWI and ADC (p<0.001 for comparison of TIC and DWI, p<0.02 for comparison of TIC and ADC value). Conclusions: DWI, ADC value and TIC type derived from DCE-MRI are all sensitive and relatively specific methods for differentiating benign from malignant adnexal masses. By comparing these functional MR techniques, TIC was found to be more accurate than DWI and ADC.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.657-662
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• 2014

Background: The epidermal growth factor receptor (EGFR) mutation status of lung cancer is important because it means that EGFR-tyrosine kinase inhibitor treatment is indicated. The purpose of this prospective study is to determine whether EGFR mutation status could be identified with reference to preoperative factors. Materials and Methods: One hundred-forty eight patients with lung cancer (111 adenocarcinomas, 25 squamous cell carcinomas and 12 other cell types) were enrolled in this study. The EGFR mutation status of each lung cancer was analyzed postoperatively. Results: There were 58 patients with mutant EGFR lung cancers (mutant LC) and 90 patients with wild-type EGFR lung cancers (wild-type LC). There were significant differences in gender, smoking status, maximum tumor diameter in chest CT, type of tumor shadow, clinical stage between mutant LC and wild-type LC. EGFR mutations were detected only in adenocarcinomas. Maximum standardized uptake value (SUVmax:$3.66{\pm}4.53$) in positron emission tomography-computed tomography of mutant LC was significantly lower than that ($8.26{\pm}6.11$) of wild-type LC (p<0.0001). Concerning type of tumor shadow, the percentage of mutant LC was 85.7% (6/7) in lung cancers with pure ground glass opacity (GGO), 65.3%(32/49) in lung cancers with mixed GGO and 21.7%(20/92) in lung cancers with solid shadow (p<0.0001). For the results of discriminant analysis, type of tumor shadow (p=0.00036) was most significantly associated with mutant EGFR. Tumor histology (p=0.0028), smoking status (p=0.0051) and maximum diameter of tumor shadow in chest CT (p=0.047) were also significantly associated with mutant EGFR. The accuracy for evaluating EGFR mutation status by discriminant analysis was 77.0% (114/148). Conclusions: Mutant EGFR is significantly associated with lung cancer with pure or mixed GGO, adenocarcinoma, never-smoker, smaller tumor diameter in chest CT. Preoperatively, EGFR mutation status can be identified correctly in about 77 % of lung cancers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.581-588
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• 2017

This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata.

• Journal of Microbiology and Biotechnology
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• v.17 no.9
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• pp.1437-1444
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• 2007

A response surface model was developed for predicting the growth rates of Staphylococcus aureus in tryptic soy broth (TSB) medium as a function of combined effects of temperature, pH, and NaCl. The TSB containing six different concentrations of NaCl (0, 2, 4, 6, 8, and 10%) was adjusted to an initial of six different pH levels (pH 4, 5, 6, 7, 8, 9, and 10) and incubated at 10, 20, 30, and $40^{\circ}C$. In all experimental variables, the primary growth curves were well ($r^2=0.9000$ to 0.9975) fitted to a Gompertz equation to obtain growth rates. The secondary response surface model for natural logarithm transformations of growth rates as a function of combined effects of temperature, pH, and NaCl was obtained by SAS's general linear analysis. The predicted growth rates of the S. aureus were generally decreased by basic (pH 9-10) or acidic (pH 5-6) conditions and higher NaCl concentrations. The response surface model was identified as an appropriate secondary model for growth rates on the basis of correlation coefficient (r=0.9703), determination coefficient ($r^2=0.9415$), mean square error (MSE=0.0185), bias factor ($B_f=1.0216$), and accuracy factor ($A_f=1.2583$). Therefore, the developed secondary model proved reliable for predictions of the combined effect of temperature, NaCl, and pH on growth rates for S. aureus in TSB medium.

• Food Science of Animal Resources
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• v.33 no.2
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• pp.155-161
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• 2013

This study developed predictive models to evaluate the kinetic behaviors of Bacillus cereus and Staphylococcus aureus in milk during storage at various temperatures. B. cereus and S. aureus (3 Log CFU/mL) were inoculated into milk and stored at $10^{\circ}C$, $15^{\circ}C$, $20^{\circ}C$, and $30^{\circ}C$, as well as $5^{\circ}C$, $15^{\circ}C$, $25^{\circ}C$, and $35^{\circ}C$, respectively, while bacterial populations were enumerated. The growth data were fitted to the modified Gompertz model to estimate kinetic parameters, including the maximum specific growth rate (${\mu}_{max}$; Log CFU/[$mL{\cdot}h$]), lag phase duration (LPD; h), lower asymptote ($N_0$; Log CFU/mL), and upper asymptote ($N_{max}$; Log CFU/mL). To describe the kinetic behavior of B. cereus and S. aureus, the parameters were fitted to the square root model as a function of storage temperature. Finally, the developed models were validated with the observed data, and Bias (B) and Accuracy (A) factors were calculated. Cell counts of both bacteria increased with storage time. Primary modeling yielded the following parameters; ${\mu}_{max}$: 0.14-0.75 and 0.06-0.51 Log CFU/mL/h; LPD: 1.78-14.03 and 0.00-1.44 h, $N_0$: 3.10-3.37 and 2.09-3.07 Log CFU/mL, and $N_{max}$: 7.59-8.87 and 8.60-9.32 Log CFU/mL for B. cereus and S. aureus, respectively. Secondary modeling yielded a determination of coefficient ($R^2$) of 0.926.0.996. B factors were 1.20 and 0.94, and A factors were 1.16 and 1.08 for B. cereus and S. aureus, respectively. Thus, the mathematical models developed here should be useful in describing the kinetic behaviors of B. cereus and S. aureus in milk during storage.

• Journal of Conservation Science
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• v.32 no.4
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• pp.491-500
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• 2016

Fog is atmospheric in which tiny drops of water vapor are suspended in the air near the ground. Its form, occurrence, etc., change according to the temperature, relative humidity, wind and geographical features of the space around it. In particular, fog tends to occur near a source of water because of temperature and relative humidity difference. These days, climate change is increasingly affecting the occurrence of fog. Therefore the purpose of this study was to investigate how fog affects materials that are part of our cultural properties through outdoor exposure tests and artificial degradation. The degradation evaluation of materials as a function of fog occurrence frequency, showed that the color of metals changed noticeably, whereas dyed silk and Dancheong showed degradation on the surface and color differences but no particular tendencies. Therefore, damage prediction by color differences as a function of fog occurrence frequency was based on metal samples, which showed constant color differences. Through a comparison of the predictive value and color difference by outdoor exposure, the accuracy and applicability of the damage prediction formula was confirmed. If a more complex damage prediction formula is created, it is expected that prediction of the degree of material damage in the field would be possible.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.375-385
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• 2015

The study is to develop specific strategies to prevent adolescents' depression, early detection and intervention services. This study was a descriptive research study with the purpose of predictors of protective factors for depression in adolescent using decision making tree analysis. The subjects for the study were 485 student in G city. This study collected data between September 23, 2013 and September 26, 2013 and analyzed them with frequency analysis, percentage, the mean and standard deviation, ${\chi}^2$-test, t-test, and a decision-making tree by using SPSS 20.0 program. From the data analysis, the predictive model for protective factors related to depression in adolescent with 4 pathways, 12 nodes. The common predicting variables of depression in adolescent were characteristics, family cohesion, parent adolescent communication, peer communication. The specialty of training data and test data was 76.0% and 65.4%. The sensitivity of training data was 78.2% and 63.7%. As for the classification accuracy, training data and test data explained 70.1% and 69.7%. Parent adolescent communication and peer communication to decrease depression of Korean middle and high school students are necessary. This study should contribute as baseline data for intervention strategies and planning ability of depression prevention in adolescents.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.121-133
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• 2017

The propose of study is to examine the characteristics of companies with high possibility to form an internal control weakness using forecasting model. This study use the actual listed/unlisted companies' data from K_financial institution. The first conclusion is that discriminant model is more valid than logit model to predict internal control weak companies. A discriminant model for predicting the vulnerability of internal control has high classification accuracy and has low the Type II error that is incorrectly classifying vulnerable companies to normal companies. The second conclusion is that the characteristic of weak internal control companies have a low credit rating, low asset soundness assessment, high delinquency rates, lower operating cash flow, high debt ratios, and minus operating profit to the net sales ratio. As not only a case of listed companies but unlisted companies which did not occur in previous studies are extended in this study, research results including the forecasting model can be used as a predictive tool of financial institutions predicting companies with high potential internal control weakness to prevent asset losses.