• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.2
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• pp.175-184
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• 2019

The epidermal growth factor (EGF) has a intrinsic function of inducing growth and proliferation of cells through interacting with cell membrane receptors in human epidermis and dermis layer. These functions of EGF are used as a main ingredient for wound healing medicines and anti-aging cosmetics. As a cosmetic ingredient, the EGF has a problem in exhibiting its natural efficacy due to the lack of the ability to penetrate through the stratum corneum, which is known as the skin barrier. In this study, a recombinant human epidermal growth factor ($MTD_{151}-EGF$) fused with the macromolecule transduction domain $(MTD)_{151}$ with the skin penetration ability was developed to improve the skin penetration efficiency of the EGF. Expression of $MTD_{151}-EGF$ was performed in E. coli transformed with a vector encoding the $MTD_{151}-EGF$ gene and then purified. The purified $MTD_{151}-EGF$ was evaluated using cell proliferation assay, cytotoxicity test and skin penetration test by franz diffusion cell assay and artificial skin. Cell proliferation activity of $MTD_{151}-EGF$ purified to high purity of 99% or above was equivalent to the EGF or better, and cytotoxicity was not observed. In addition, the $MTD_{151}-EGF$ showed an excellent penetration efficiency compared to the EGF in the skin penetration test with EGF and $MTD_{151}-EGF$ labeled by FITC in an artificial skin penetration model. Based on the quantitative analysis of the penetrating substance using franz diffusion cell assay, the amount of penetration was about 16 times more than that of EGF. These results can be regarded as an effective alternative to improve the existing physical transdermal penetration method related to the use of various active ingredients for cosmetics.

• Journal of Life Science
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• v.32 no.1
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• pp.11-22
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• 2022

N-methyl-D-aspartate (NMDA) receptors have received considerable attention regarding their involvement in glutamate-induced neuronal excitotoxicity. Resveratrol has been shown to exhibit neuroprotective effects against this kind of overactivation, but the underlying cellular mechanisms are not yet clearly understood. In this study, HT-22 neuronal cells were treated with NMDA in Mg²⁺-free buffer and subsequently used as an experimental model of glutamate excitotoxicity to elucidate the mechanisms of resveratrol-induced neuroprotection. We found that NMDA treatment causes a drop in MTT reduction ability, disrupts inside-negative transmembrane potential of mitochondria, depletes cellular ATP levels, and stimulates intracellular ROS production. Double fluorescence imaging studies demonstrated an increased formation of mitochondrial permeability transition (MPT) pores accompanied by apoptotic cell death, while cobalt protoporphyrin and bilirubin showed protective effects against NMDA-induced mitochondrial injury. On the other hand, zinc protoporphyrin IX significantly attenuated the protective effects of resveratrol which was itself shown to enhance heme oxygenase-1 (HO-1) mRNA and protein expression levels. In cells transfected with HO-1 small interfering RNA, resveratrol failed to suppress the NMDA-induced effects on MTT reduction ability and MPT pore formation. The present study suggests that resveratrol may prevent mitochondrial injury in NMDA- treated HT-22 cells and that enhanced expression of HO-1 is involved in the underlying cellular mechanism.

• Journal of Nutrition and Health
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• v.48 no.1
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• pp.46-57
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• 2015

Purpose: The aim of this study was to determine an appropriate energy level of breakfast with less risk of chronic disease for Korean adults. Methods: Using data from the 2007~2009 Korean National Health & Nutrition Examination Survey, from a total of 12,238 adults aged 19~64, the final 7,769 subjects were analyzed except subjects who were undergoing treatment for cancer or metabolic disorder. According to the percent of breakfast energy intake versus their estimated energy requirement (EER), the subjects were divided into four groups: < 10% (very low, VL), 10~20% (low, L), 20~30% (moderate, M), ${\geq}30%$ (sufficient, S). All data were analyzed on the metabolic risk and nutritional state after application of weighted value and adjustment of sex, age, residential area, income, education, job or jobless, and energy intake using a general linear model or logistic regression. Results: The subjects of group S were 16.9% of total subjects, group M 39.2%, group L 37.6%, and group VL 6.3%. The VL group included more male subjects, younger-aged (19 to 40 years), urban residents, higher income, higher education, and fewer breakfasts eaters together with family members. Among the 4 groups, the VL group showed the highest waist circumference, while the S group showed the lowest waist circumference, body mass index, and serum total cholesterol. The groups of VL and L with lower intake of breakfast energy showed high percent of energy from protein and fat, and low percent of energy from carbohydrate. With the increase of breakfast energy level, intake of energy, most nutrients and food groups increased, and the percentage of subjects consuming nutrients below EAR decreased. The VL group showed relatively higher intake of snacks, sugar, meat and eggs, oil, and seasonings, and the lowest intake of vegetable. Risk of obesity by waist circumference was highest in the VL group by 1.90 times of the S group and the same trend was shown in obesity by BMI. Risk of dyslipidemia by serum total cholesterol was 1.84 times higher in the VL group compared to the S group. Risk of diabetes by Glu-FBS (fasting blood sugar) was 1.57 times higher in the VL group compared to the S group. Conclusion: The results indicate that higher breakfast energy level is positively related to lower metabolic risk and more desirable nutritional state in Korean adults. Therefore, breakfast energy intake more than 30% of their own EER would be highly recommended for Korean adults.

• Geotechnical Engineering
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• v.14 no.3
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• pp.25-46
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• 1998

This paper shows that there are the results of a series of model tests on the behavior of single pipe pile which is subjected to lateral load in, Nak-dong River sand. The purpose of the present paper is to estimate the effect of Non-homogeneity. constraint condition of pile head, lateral load velocity, relative density, and embedded length of pile on the behavior of single pile. These effects can be quantified only by the results of model tests. Also, these are compared with the results of the numerical methods (p-y method, modified Vlasov method; new ${\gamma}$ parameter, Characteristic Load Method'CLM). In this study, a new ${\gamma}$ parameter equation based on the Vlasov method was developed to calculate the modulus of subgrade reaction (E. : nhz.) proportional to the depth. The p-y method of analysis is characterized by nonlinear behavior. and is an effective method of designing deep foundations subjected to lateral loads. The new method, which is called the characteristic load method (CLM). is simpler than p-y analysis. but its results closely approximates p-y analysis results. The method uses dimensional analysis to characterize the nonlinear behavior of laterally loaded piles with respect to be relationships among dimensionless variables. The modulus of subgrade reaction used in p-y analysis and modified Vlasov method obtained from back analysis using direct shear test (DST) results. The coefficients obtained from DST and the modified ones used for the prediction of lateral behavior of ultimate soil reaction range from 0.014 to 0.05. and from 0.2 to 0.4 respectively. It is shown that the predicted numerical results by the new method (CLM), p-y analysis, and modified Vlasov method (new parameter) agree well with measured results as the relative density increases. Also, the characteristic load method established applicability on the Q-Mnu. relationship below y/D=0.2.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.51-57
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• 2011

Purpose: The aim of this study is to identify clinical usefulness of Wide Beam Reconstruction (WBR) which is called Xpress.cardiac$^{TM}$ to confirm the agreement between segmental perfusion and regional wall motion in myocardium compared to conventional OSEM method. Materials and Methods: Subjects were separated two groups. First group was composed of 20 normal control group. Second group was composed of 10 patients (abnormal group) who had coronary artery disease. Subjects underwent myocardial perfusion SPECT ($^{201}Tl$ rest and $^{99m}Tc$-MIBI stress). Image acquisition and reconstruction were that rest stage was each step per 30, 15 seconds and stress stage was each step per 25, 13 seconds, OSEM and WBR methods were applied. Segmental perfusion and regional wall motion were applied 20-segment model of QPS, QGS algorithm in AutoQuant. Status of perfusion was composed of 5 point scoring system (0=normal, 1=mild, 2=moderate, 3=severe hypokinesia, 4=dyskinesia). Status of regional wall motion was also composed of 5 point scoring (0=normal, 1=mild, 2=moderate, 3=severe hypokinesia, 4=dyskinesia). We evaluated the agreement between conventional OSEM and WBR through automatic quantification value. Results: The agreement of rest segmental perfusion between conventional OSEM and WBR in normal patients was 99% (396/400, k=0.662, p<0.0001) and one of rest regional wall motion was 83.8% (335/400, k=0.283), the agreement of stress segmental perfusion was 95.8%(383/400, k=0.656), one of stress regional wall motion was 87.3% (349/400, k=0.390). The match rate of rest segmental perfusion in abnormal patients was 83% (166/200, k=0.605, p<0.0001) and one of rest regional wall motion was 55.5% (111/200, k=0.385), the agreement of stress segmental perfusion was 79.5% (159/200, k=0.682), one of stress regional wall motion was 63.5% (127/200, k=0.486). Conclusion: Compared to conventional OSEM, WBR method had a good agreement of segmental perfusion in myocardium in normal and abnormal groups. However regional wall motion showed meaningful low agreement. Although WBR offers high resolution and contrast ratio, it is not useful method for gated myocardial perfusion SPECT.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.