• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.2037-2041
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• 2015

Background: Relationships between poor prognosis of ovarian malignancies and changes in complete blood count parameters have been proposed previously. In this work, we aimed to evaluate clinicopathologic features in adolescents with adnexal masses and sought to establish any predictive value of the platelet to lymphocyte ratio (PLR) in diagnosis. Materials and Methods: This retrospective study was conducted on 196 adolescent females with adnexal masses. Three groups were constituted with respect to clinical or histopathology results: group 1, non-neoplastic patients (n:65); group 2, neoplastic patients (n:68); and group 3 expectantly managed patients (n:63). The main parameters recorded from the hospital database and patient files were age, body mass index (BMI), chief symptoms, diameter of the mass (DOM), tumor marker levels, complete blood count values including absolute neutrophil, lymphocyte, and platelet counts, mean platelet volume, platelet distribution width, and platecrit, surgical features, and postoperative histopathology results. Results: The expectantly managed patients were younger than the other groups (p=0.007). The mean body mass index (BMI) was higher in the neoplastic group (p=0.016). Preoperative DOM, CA125, mean platelet volume and PLR were statistically significantly different between the groups (p<0.05). ROC curve analysis demonstrated that increased PLR (AUC, 0.609; p=0.011) and BMI (AUC, 0.611; p=0.011) may be discriminative factors in predicting ovarian neoplasms in adolescents preoperatively. When the cut-off point for the PLR level was set to 140, the sensitivity and specificity levels were found to be 65.7% and 57.6%, respectively. Conclusions: We suggest that beside a careful preoperative evaluation including clinical characteristics, ultrasonographic features and tumor markers, PLR may predict ovarian neoplasms in adolescents.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• Journal of Korean Biological Nursing Science
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• v.22 no.2
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• pp.119-126
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• 2020

Purpose:Weight gain after kidney transplantation is a critical factor that can lead to poor outcomes with cardiovascular complications. Many studies have been conducted to identify predictive markers of future weight changes at the time of transplant. Recently, circulating exosomes and its contents including miRNAs and proteins have attracted attention as potential biomarkers. In this pilot study, we investigated exosomal proteins and weight change after kidney transplant. Methods: Recipients (n = 10) were classified into two groups; weight gainers (n = 5, 9.7 ± 4.4kg) and weight losers (n = 5, -6.4 ± 1.8kg) based on their weight changes at 12-months posttransplant. Based on the exosomal protein profiles obtained by the LC-MS/MS, differentially expressed proteins were identified between the groups. Results: Concentration and the mean size of exosomes significantly increased at 12-months compared to the baseline (p= .009) in the total group. Eleven exosomal proteins were found at the baseline as differentially expressed between the two groups. In the weight gain group, complement proteins including HV169, C3, C4B, and C4A, were significantly upregulated. Conclusion: Our pilot study suggests that exosomal complementary proteins are associated with weight gain after kidney transplantation. Further studies are needed to clarify the role of these exosomal proteins in the underlying mechanisms of weight changes in kidney transplant recipients.

• Journal of Korean Public Health Nursing
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• v.4 no.2
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• pp.25-34
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• 1990

The Purpose of this study were 1) to find out the characteristics of health promoting Ii festyles of the study samples, 2) to determine the relationships of physical health and mental health, 3) to determine the relationships of health promoting lifestyles and health status. The health promotion model was tested with a volunteer sample of 141 female students in a university in Seoul. The health promoting lifestyle was measured by the scales developed by Walker and Pender(1987). Health status was measured by Cornell medical Index. Pearson's product moment correlations and stepwise multiple regression technique were used to analyze the data. The results are summarized as follows; 1. The items with the highest frequency of the subscales of health promoting lifestyle were 'look forward to the future' $(133,\;95.0\%)$ in self actualization, 'Enjoy being touched and touching people close to me' $(122,\; 87.14\%)$ in relationships with others. The strongest correlation was between general competence In self care and nutritional practice(r=5388, P<. 0001). 2. Fatigability, frequency of illness, miscellaneous diseases, habit, mood and feeling patterns were predictive of mental health. 3. Total health promoting lifestyles explained $14\%$ of the variance for health status. Relationships with others explained $20.9\%$ of the variance for health status. In conclusion, because the most variance explained was $420.9\%$, there must be other variables not accounted for by the model. that influence health promoting behaviors. Psychological factors accounted for more variance than other factors. Intervention studies focused on psychological factors as a means of altering behaviors have great potential for the design of interventions to increase health promoting behaviors. Further testing of the model with this population should be done.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.643-646
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• 2016

Background: Depression is the most common psychological reactions in women with breast cancer. This study aimed at investigating the amount of depression and its role in predicting the quality of life of women suffering from breast cancer. Materials and Methods: The present descriptive study in volved a correlation method with 98 women living in Kermanshah-Iran with breast cancer. According to the access to the patients and the condition of conducting the research, they were chosen by available sampling. Life quality inventory (World Health Organization, 1989) and depression inventory (Beck et al., 2000) were used to gather the data. Moreover, to analyze the relationships among the variables correlation analysis with Pearson method, as well as multiple regression with the enter method and frequency analysis were applied. Results: The findings revealed that not only is depression high, but also there is a negative significant relationship between depression and the quality of life, with predictive potential. Conclusions: The finding of a relationship between depression and the quality of life points to the need for addressing psychological problems of the affected individuals more appropriately. It is suggested that we consider psychological and educational services for patients in treatment planning to make people aware of different psychological aspects of their disease and ways of struggling and overcoming the problems.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.189-196
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• 2008

To build autonomous agents who can make a decision on behalf of humans in time-critical complex environments, the formulation of operational knowledge base could be essential. This paper proposes the methodology of how to formulate the knowledge base and evaluates it in a practical application domain. We analyze threat data received from the multiple sensors of Aircraft Survivability Equipment(ASE) for Korean helicopters, and integrate the threat data into the inductive model through compilation technique which extracts features of the threat data and relations among them. The compiled protocols of state-action rules can be implemented as the brain of the ASE. They can reduce the amounts of reasoning, and endow the autonomous agents with reactivity and flexibility. We report experimental results that demonstrate the distinctive and predictive patterns of threats in simulated battlefield settings, and show the potential of compilation methods for the successful detection of threat systems.

• YAKHAK HOEJI
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• v.59 no.3
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• pp.92-97
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• 2015

KIOM-MA128 is a novel Korean herbal medicine with anti-atopic, anti-inflammatory and anti-asthmatic effects. This article presents the first pharmacokinetic study on KIOM-MA128. The purpose of this study was to characterize a pharmacokinetic characteristic of matrine, a potential marker of KIOM-MA128, in rats using population pharmacokinetic model. 1, 2 and 8 g/kg of KIOM-MA128 were administered to rats orally and plasma concentrations of matrine was determined by HPLC-MS/MS. Non-compartmental analysis (NCA) was performed using Phoenix$^{(R)}$ and pharmacokinetic model was built using NONMEM$^{(R)}$. This model was validated with internal validation which is visual predictive check (VPC) and bootstrap. The NCA result of matrine showed that $C_{max}$ was 294.24, 552.22 and 868.65 ng/ml, $AUC_{inf}$ was 1273.05, 2724.76 and $9743.25ng{\cdot}hr/ml$ and $T_{max}$ was 1, 1.3 and 2.3 hr for the doses of 1, 2, and 8 g/kg, respectively. The rat plasma concentrations were described very well with one-compartment model. Pharmacokinetic model for matrine was successfully developed and evaluated. Finally, our model is helpful to understand pharmacokinetic characteristic of KIOM-MA128.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.693-697
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• 2015

Our aims were to evaluate the clinical performance of human telomerase RNA gene component (hTERC gene) amplification assay with high-risk human papillomavirus (HR-HPV) DNA test of Hybrid Capture 2 DNA test (HC2), for the detection of high grade cervical precancerous lesions and cancer (CIN 2+). In addition, the association shown between hTERC gene amplification and HPV DNA test positive in women with and without cervical neoplasia was assessed. There were 92 women who underwent cytology, HR-HPV DNA test, hTERC gene amplification test, colposcopy and biopsy. We compared the clinical performance of hTERC gene test along with HR-HPV DNA test of women with colposcopy and routine screening. The samples were histology-confirmed high-grade cervical intraepithelial neoplasia (CIN 2) or worse (CIN2+) as the positive criterion. The test of hTERC gene showed the hTERC gene amplification positivity increased with the severity of histological abnormality and cytological abnormality. The test of hTERC gene showed higher specificity than HR-HPV DNA test for high-grade lesions (84.4% versus 50%) and also higher positive predictive value (90.4% versus 76.5%). Our results predicted that hTERC gene amplification demonstrated more specific performance for predicting the risk of progression and offer a strong potential as a tool for triage in cervical cancer screening, with the limited sensitive as HR-HPV DNA test.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.3929-3937
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• 2016

Gastric cancer (GC) is a major health problem worldwide and is one of the ten most commonly diagnosed cancers in Jordan. GC is usually diagnosed at late aggressive stages in which treatment options are limited. Recently, heat shock proteins (HSPs) were found to be overexpressed in a wide range of malignancies have been considered as promising candidate biomarkers for GC. The aim of this study was to investigate pathogenic roles of a panel of cytosolic HSPs including HSP90, HSP70, HSP60 and HSP27 in GC. Immunohistochemistry was used to assess the level of expression of these proteins in archived tumor samples (N=87) representing various pathological characteristics of GC. HSP90, HSP60 and HSP27 were expressed abundantly in gastric tumors. On the other hand, HSP70 was reduced significantly and also found to be associated with Helicobacter pylori infection in tissues collected from GC patients. Furthermore, HSP27 was found to be associated with the level of differentiation. Our findings indicate a role of HSP70 as a potential prognostic biomarker, patients harboring positive HSP70 expression displaying worse disease free survival than those with negative HSP70 expression. Differential expression of HSPs may play crucial roles in the initiation and progression of GC, and could be exploited as future therapeutic targets.