Kwangwoon University

About: Kwangwoon University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Thin film & Resonator. The organization has 4020 authors who have published 8217 publications receiving 104365 citations.

Preventing the Solid Cancer Progression via Release of Anticancer-Cytokines in Co-Culture with Cold Plasma-Stimulated Macrophages

Abstract: Non-thermal atmospheric pressure plasma sources operated in ambient environments are known to generate a variety of reactive oxygen and nitrogen species which could be applied for various biomedical applications. Herein, we fabricate a micro-dielectric barrier discharge plasma device by using screen-printing technology and apply it for studying immuno-stimulatory effects. We demonstrate a tumor-suppressive role for plasma-stimulated macrophages in metastatic solid cancers that directly elicit proliferation and are responsible for tumor relapse mediated by mesenchymal shift. Using microarray analysis, we observed that cold plasma stimulates and differentiates monocyte cells into macrophages as demonstrated by expression of several cytokine/chemokine markers. Moreover, plasma treatment stimulates the differentiation of pro-inflammatory (M1) macrophages to a greater extent. These stimulated macrophages favor anti-tumorigenic immune responses against metastasis acquisition and cancer stem cell maintenance in solid cancers in vitro. Differentiation of monocytes into anticancer macrophages could improve the efficacy of plasma treatment, especially in modifying pro-tumor inflammatory microenvironment through effecting highly resistant immunosuppressive tumor cells associated with tumor relapse.

Fabrication and Optimization of a Nanoporous Platinum Electrode and a Non-enzymatic Glucose Micro-sensor on Silicon.

Abstract: In this paper, optimal conditions for fabrication of nanoporous platinum (Pt) were investigated in order to use it as a sensitive sensing electrode for silicon CMOS integrable non-enzymatic glucose micro-sensor applications. Applied charges, voltages, and temperatures were varied during the electroplating of Pt into the formed nonionic surfactant C16EO8 nano-scaled molds in order to fabricate nanoporous Pt electrodes with large surface roughness factor (RF), uniformity, and reproducibility. The fabricated nanoporous Pt electrodes were characterized using atomic force microscopy (AFM) and electrochemical cyclic voltammograms. Optimal electroplating conditions were determined to be an applied charge of 35 mC/mm2, a voltage of -0.12 V, and a temperature of 25 °C, respectively. The optimized nanoporous Pt electrode had an electrochemical RF of 375 and excellent reproducibility. The optimized nanoporous Pt electrode was applied to fabricate non-enzymatic glucose micro-sensor with three electrode systems. The fabricated sensor had a size of 3 mm x 3 mm, air gap of 10 µm, working electrode (WE) area of 4.4 mm2, and sensitivity of 37.5 µA•L/mmol•cm2. In addition, it showed large detection range from 0.05 to 30 mmolL-1 and stable recovery responsive to the step changes in glucose concentration.

Body Fat Assessment Method Using CT Images with Separation Mask Algorithm

Abstract: In recent years, the number of obese population in Korea has been growing up along with the economic development, environmental factors, and the change in life style. Considering the growth of obese population and the adverse effect of obesity on health, it is getting more important to prevent and diagnose the obesity with the quantitative measurement of body fat that has become an important indicator for obesity. In this study, we proposed a procedure for the automated fat assessment from computed tomography (CT) data using image processing technique. The proposed method was applied to a single-CT image as well as CT-volume data, and results were correlated to those of dual-energy X-ray absorptiometry (DEXA) that is known as the reliable method for evaluating body fat. Using single-CT images, correlation coefficients between DEXA and the automated assessment and DEXA and the manual assessment were 0.038 and 0.058, respectively (P > 0.05). Hence, there was no significant correlation between three methods using the proposed method with single-CT images. On the other hand, in case of CT-volume data, the above correlation coefficients were increased to 0.826, 0.812, and 0.805, respectively (P < 0.01). Thus, DEXA and the proposed methods with CT-volume data showed highly significant correlation with each other. The results suggest that the proposed automated assessment using CT-volume data is a reliable method for the evaluation of body fat. It is expected that the clinical application of the proposed procedure will be helpful to reduce the time for the quantitative evaluation of patient’s body fat.

Facial expression recognition using active contour-based face detection, facial movement-based feature extraction, and non-linear feature selection

Abstract: Knowledge about people's emotions can serve as an important context for automatic service delivery in context-aware systems. Hence, human facial expression recognition (FER) has emerged as an important research area over the last two decades. To accurately recognize expressions, FER systems require automatic face detection followed by the extraction of robust features from important facial parts. Furthermore, the process should be less susceptible to the presence of noise, such as different lighting conditions and variations in facial characteristics of subjects. Accordingly, this work implements a robust FER system, capable of providing high recognition accuracy even in the presence of aforementioned variations. The system uses an unsupervised technique based on active contour model for automatic face detection and extraction. In this model, a combination of two energy functions: Chan---Vese energy and Bhattacharyya distance functions are employed to minimize the dissimilarities within a face and maximize the distance between the face and the background. Next, noise reduction is achieved by means of wavelet decomposition, followed by the extraction of facial movement features using optical flow. These features reflect facial muscle movements which signify static, dynamic, geometric, and appearance characteristics of facial expressions. Post-feature extraction, feature selection, is performed using Stepwise Linear Discriminant Analysis, which is more robust in contrast to previously employed feature selection methods for FER. Finally, expressions are recognized using trained HMM(s). To show the robustness of the proposed system, unlike most of the previous works, which were evaluated using a single dataset, performance of the proposed system is assessed in a large-scale experimentation using five publicly available different datasets. The weighted average recognition rate across these datasets indicates the success of employing the proposed system for FER.