• The Journal of Korean Society for Radiation Therapy
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• v.29 no.1
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• pp.37-48
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• 2017

Purpose: The most basic conditions of radiation therapy is to prevent unnecessary exposure of normal tissue. The risk factors that are important o evaluate the dose emitted to the lung and heart from radiation therapy for breast cancer. Therefore, comparing the dose factors of a normal tissue according to the radion treatment position and Seeking an effective radiation treatment for breast cancer through the analysis of the correlation relationship. Materials and Methods: Computed tomography was conducted among 30 patients with left breast cancer in supine and prone position. Eclipse Treatment Planning System (Ver.11) was established by computerized treatment planning. Using the DVH compared the incident dose to normal tissue by position. Based on the result, Using the SPSS (ver.18) analyzed the dose in each normal tissue factors and Through the correlation analysis between variables, independent sample test examined the association. Finally The HI, CI value were compared Using the MIRADA RTx (ver. ad 1.6) in the supine, prone position Results: The results of computerized treatment planning of breast cancer in the supine position were V20, $16.5{\pm}2.6%$ and V30, $13.8{\pm}2.2%$ and Mean dose, $779.1{\pm}135.9cGy$ (absolute value). In the prone position it showed in the order $3.1{\pm}2.2%$, $1.8{\pm}1.7%$, $241.4{\pm}138.3cGy$. The prone position showed overall a lower dose. The average radiation dose 537.7 cGy less was exposured. In the case of heart, it showed that V30, $8.1{\pm}2.6%$ and $5.1{\pm}2.5%$, Mean dose, $594.9{\pm}225.3$ and $408{\pm}183.6cGy$ in the order supine, prone position. Results of statistical analysis, Cronbach's Alpha value of reliability analysis index is 0.563. The results of the correlation analysis between variables, position and dose factors of lung is about 0.89 or more, Which means a high correlation. For the heart, on the other hand it is less correlated to V30 (0.488), mean dose (0.418). Finally The results of independent samples t-test, position and dose factors of lung and heart were significantly higher in both the confidence level of 99 %. Conclusion: Radiation therapy is currently being developed state-of-the-art linear accelerator and a variety of treatment plan technology. The basic premise of the development think normal tissue protection around PTV. Of course, if you treat a breast cancer patient is in the prone position it take a lot of time and reproducibility of set-up problems. Nevertheless, As shown in the experiment results it is possible to reduce the dose to enter the lungs and the heart from the prone position. In conclusion, if a sufficient treatment time in the prone position and place correct confirmation will be more effective when the radiation treatment to patient.

• Journal of Chest Surgery
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• v.39 no.4 s.261
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• pp.289-297
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• 2006

Background: Surgery for Stanford type A aortic dissection shows a high operative mortality rate and frequent postoperative brain injury. This study was designed to find out the risk factors leading to operative mortality and brain injury after surgical repair in patients with type A aortic dissection. Material and Method: One hundred and eleven patients with type A aortic dissection who underwent surgical repair between February, 1995 and January 2005 were reviewed retrospectively. There were 99 acute dissections and 12 chronic dissections. Univariate and multivariate analysis were performed to identify risk factors of operative mortality and brain injury. Resuit: Hospital mortality occurred in 6 patients (5.4%). Permanent neurologic deficit occurred in 8 patients (7.2%) and transient neurologic deficit in 4 (3.6%). Overall 1, 5, 7 year survival rate was 94.4, 86.3, and 81.5%, respectively. Univariate analysis revealed 4 risk factors to be statistically significant as predictors of mortality: previous chronic type III dissection, emergency operation, intimal tear in aortic arch, and deep hypothemic circulatory arrest (DHCA) for more than 45 minutes. Multivariate analysis revealed previous chronic type III aortic dissection (odds ratio (OR) 52.2), and DHCA for more than 45 minutes (OR 12.0) as risk factors of operative mortality. Pathological obesity (OR 12.9) and total arch replacement (OR 8.5) were statistically significant risk factors of brain injury in multivariate analysis. Conclusion: The result of surgical repair for Stanford type A aortic dissection was good when we took into account the mortality rate, the incidence of neurologic injury, and the long-term survival rate. Surgery of type A aortic dissection in patients with a history of chronic type III dissection may increase the risk of operative mortality. Special care should be taken and efforts to reduce the hypothermic circulatory arrest time should alway: be kept in mind. Surgeons who are planning to operate on patients with pathological obesity, or total arch replacement should be seriously consider for there is a higher risk of brain injury.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.22-29
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• 2019

Validamycin A is an aminoglycoside fungicide produced by Streptomyces hygroscopicus that inhibits trehalase. The purpose of this study was to develop a method for detecting validamycin A in agricultural samples to establish MRL values for use in Korea. The validamycin A residues in samples were extracted using methanol/water (50/50, v/v) and purified with a hydrophilic-lipophilic balance (HLB) cartridges. The analyte was quantified and confirmed by liquid chromatograph-tandem mass spectrometer (LC-MS/MS) in positive ion mode using multiple reaction monitoring (MRM). Matrix-matched calibration curves were linear over the calibration ranges (0.005~0.5 ng) into a blank extract with $R^2$ > 0.99. The limits of detection and quantification were 0.005 and 0.01 mg/kg, respectively. For validation validamycin A, recovery studies were carried out three different concentration levels (LOQ, $LOQ{\times}10$, $LOQ{\times}50$, n = 5) with five replicates at each level. The average recovery range was from 72.5~118.3%, with relative standard deviation (RSD) less than 10.3%. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the NIFDS (National Institute of Food and Drug Safety) guideline (2016). Therefore, the proposed analytical method is accurate, effective and sensitive for validamycin A determination in agricultural commodities.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.25-32
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• 2019

Purpose: The size, shape, and volume of prosthetic appliance depend on the metal artifacts resulting from dental implant during head and neck treatment with radiation. This reduced the accuracy of contouring targets and surrounding normal tissues in radiation treatment plan. Therefore, the purpose of this study is to obtain the images of metal representing the size of tooth through MVCT, SMART-MAR CT and KVCT, evaluate the volumes, apply them into the proton therapy plan, and analyze the difference of dose distribution. Materials and Methods : Metal A ($0.5{\times}0.5{\times}0.5cm$), Metal B ($1{\times}1{\times}1cm$), and Metal C ($1{\times}2{\times}1cm$) similar in size to inlay, crown, and bridge taking the treatments used at the dentist's into account were made with Cerrobend ($9.64g/cm^3$). Metal was placed into the In House Head & Neck Phantom and by using CT Simulator (Discovery CT 590RT, GE, USA) the images of KVCT and SMART-MAR were obtained with slice thickness 1.25 mm. The images of MVCT were obtained in the same way with $RADIXACT^{(R)}$ Series (Accuracy $Precision^{(R)}$, USA). The images of metal obtained through MVCT, SMART-MAR CT, and KVCT were compared in both size of axis X, Y, and Z and volume based on the Autocontour Thresholds Raw Values from the computerized treatment planning equipment Pinnacle (Ver 9.10, Philips, Palo Alto, USA). The proton treatment plan (Ray station 5.1, RaySearch, USA) was set by fusing the contour of metal B ($1{\times}1{\times}1cm$) obtained from the above experiment by each CT into KVCT in order to compare the difference of dose distribution. Result: Referencing the actual sizes, it was appeared: Metal A (MVCT: 1.0 times, SMART-MAR CT: 1.84 times, and KVCT: 1.92 times), Metal B (MVCT: 1.02 times, SMART-MAR CT: 1.47 times, and KVCT: 1.82 times), and Metal C (MVCT: 1.0 times, SMART-MAR CT: 1.46 times, and KVCT: 1.66 times). MVCT was measured most similarly to the actual metal volume. As a result of measurement by applying the volume of metal B into proton treatment plan, the dose of $D_{99%}$ volume was measured as: MVCT: 3094 CcGE, SMART-MAR CT: 2902 CcGE, and KVCT: 2880 CcGE, against the reference 3082 CcGE Conclusion: Overall volume and axes X and Z were most identical to the actual sizes in MVCT and axis Y, which is in the superior-Inferior direction, was regular in length without differences in CT. The best dose distribution was shown in MVCT having similar size, shape, and volume of metal when treating head and neck protons. Thus it is thought that it would be very useful if the contour of prosthetic appliance using MVCT is applied into KVCT for proton treatment plan.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.