• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.2
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• pp.15-19
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• 2022

Purpose [⁶⁸Ga]PSMA-11 is needed the high reproducibility, excellent radiochemical yield and purity. In term of radiation safety, the radiation exposure of operator for its production also should be considered. In this work, we performed a comparative study for the fully automated synthesis of [⁶⁸Ga]PSMA-11 between non-cassette type and cassette type. Materials and Methods Two different type of modules (TRACERlab FX N pro for non-cassette type and BIKBox for cassette type) were used for the automated production of [⁶⁸Ga]PSMA-11. According to the previously identified elution profile, Only 2.5 ml with high radioactivity was used for the reaction. After adjusting the pH of the reaction solution with HEPES buffer solution, the precursor was added and reacted with at 95 ℃ for 15 minutes. The reaction mixture was separated and purified using a C18 light cartridge. The product was eluted with 50% EtOH/saline solution and diluted with saline. It was completed by sterilizing filter. In the non-cassette type, the aforementioned process must be prepared directly. However, in the cassette method, synthesis was possible simply by installing a kit that was already completed. Results Both total [⁶⁸Ga]PSMA-11 production time were 25±3(non-cassette type) and 23±3 minutes(cassette type). The radiochemical yield of the non-cassette type(65.5±5.7%) was higher than that of the cassette type(61.6±4.8%) after sterilization filter. The non-cassette type took about 120 minutes of preparation time before synthesis due to washing of synthesizer and reagent preparation. However, since the cassette type does not require washing and reagent preparation, it took about 20 minutes to prepare before synthesis. Both type of synthesizer had a radiochemical high purity(>99%). Conclusion The non-cassette type production of [⁶⁸Ga]PSMA-11 showed higher radiochemical yield and lower cost than the cassette type. However, The cassette type has an advantage in terms of preparation time, convenience, and equipment maintenance.

• Korea Science and Art Forum
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• v.20
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• pp.255-265
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• 2015

When it comes to how to define history·culture streets, people think of the streets as street environments that would create local identity in association with this local community's particular historical and cultural resources as well as urban streets. In order to build such streets, any relevant fields first need to apply some original design based on understanding on historical and cultural resources. With Street of Gaya in Gimhae selected as a research subject, this study aims to look into components and color characteristics of the history·culture street and finds ways to create other streets of that kind. As a frame to understand the history·culture streets, what this study would come up with is considered significant in that it helps the value to be re-recognized and promoted. In order to achieve the research goal, the study (1) extracted components of streetscapes referring to relevant previous researches and then, (2) analyzed a current status of these components of Street of Gaya via field investigation. (3) The study examined color characteristics of each of the components. Findings of the research are summarized as follows. (1) From a comprehensive point of view, the study categorized and subdivided the components of the history·culture street into nonphysical and physical elements. (2) After analyzing the current status of the components, the study learned that Street of Gaya basically consists of historical and cultural remains and sculptures as well as street facilities. (3) Results of the color investigation reported that the plan on designing of Street of Gaya had been processed with a focus laid on harmony of historical remains and cultural remains which are told to be natural components. However, the study also figured out that as long as relevant fields want to create different identity in each section and to efficiently deliver information, they should first prepare this smart design system to integrate each pieces of a streetscape as a whole.

• Health Policy and Management
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• v.34 no.1
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• pp.78-86
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• 2024

Background: The purpose of this study was to determine the effectiveness of the specialty hospital system by comparing the medical use of inpatients who had artificial joint replacement surgery in specialty hospitals and non-specialty hospitals. Methods: This study utilized 2021-2022 healthcare benefit claims data provided by the Health Insurance Review and Assessment Service. The dependent variable is inpatient medical use which is measured in terms of charges per case and length of stay. The independent variable was whether the hospital was designated as a specialty hospital, and the control variables were patient-level variables (age, gender, insurer type, surgery type, and Charlson comorbidity index) and medical institution-level variables (establishment type, classification, location, number of orthopedic surgeons, and number of nurses). Results: The results of the multiple regression analysis between charges per case and whether a hospital is designated as a specialty hospital showed a statistically significant negative relationship between charges per case and whether a hospital is designated as a specialty hospital. This suggests a significant low in charges per case when a hospital is designated as a specialty hospital compared to a non-specialty hospital, indicating that there is a difference in medical use outcomes between specialty hospitals and non-specialty hospitals inpatients. Conclusion: The practical implications of this study are as follows. First, the criteria for designating specialty hospitals should be alleviated. In our study, the results show that specialty hospitals have significantly lower per-case costs than non-specialty hospitals. Despite the cost-effectiveness of specialty hospitals, the high barriers to be designated for specialty hospitals have gathered the specialty hospitals in metropolitan and major cities. To address the regional imbalance of specialty hospitals, it is believed that ease the criteria for designating specialty hospitals in non-metropolitan areas, such as introducing "semi-specialty hospitals (tentative name)," will lead to a reduction in health disparities between regions and reduce medical costs. Second, it is necessary to determine the appropriateness of the size of hospitals' medical staff. The study found that the number of orthopedic surgeons and nurses varied in charges per case. Therefore, it is believed that appropriately allocating hospital medical staff can maximize the cost-effectiveness of medical services and ultimately reduce medical costs.

• Pediatric Infection and Vaccine
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• v.31 no.1
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• pp.113-121
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• 2024

Purpose: In response to the surge in coronavirus disease 2019 (COVID-19) omicron variant cases, we have implemented preemptive testing for preschool and school. The purpose is to quickly detect COVID-19 cases using a rapid antigen test (RAT) kit so that normal school activities can continue. Methods: The results entered in The Healthcare Self-Test App were merged with the information on the status of confirmed cases in the COVID-19 Information Management System by Korea Disease Control and Prevention Agency (KDCA) for preschool and school of students and staffs March 2 to May 1, 2022 to analyze the RAT positive rate and positive predictive value of RAT. Results: In preschool and school 19,458,575 people were tested, weekly RAT positive rate ranged from 1.10% to 5.90%, positive predictive value of RAT ranged from 86.42% to 93.18%. By status, RAT positive rate ranged from 1.13% to 6.16% for students, 0.99% to 3.93% for staffs, positive predictive value of RAT ranged from 87.19% to 94.03% for students, 77.55% to 83.10% for staffs. RAT positive rate by symptoms ranged from 76.32% to 88.02% for those with symptoms and 0.34% to 1.11% for those without symptoms. As a result of preschool and school RAT, 943,342 confirmed cases were preemptively detected, before infection spread in preschool and school. Conclusions: RAT was well utilized to detect confirmed cases at an early stage, reducing the risk of transmission to minimize the educational gap in preschool and school. To compensate for the limitations of RAT, further research should continue to reevaluate the performance of RAT as new strains of viruses continue to emerge. We will have to come up with various ways to utilize it, such as performing periodic and repeated RAT and parallel polymerase chain reaction.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.77-82
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• 2010

Purpose: As PET/CT come into wide use, it caused increasing of expose in clinical use. Therefore, Korea Food and Drug Administration issued Patient DRL (Diagnostic Reference Level) in CT scan. In this study, to build the basis of patient dose reduction, we analyzed effective dose in transmission scan with CT scan. Materials and Methods: From February, 2010 to March 180 patients (age: $55{\pm}16$, weight: $61.0{\pm}10.4$ kg) who examined $^{18}F$-FDG PET/CT in Asan Medical Center. Biograph Truepoint 40 (SIEMENS, GERMANY), Biograph Sensation 16 (SIEMENS, GERMANY) and Discovery STe8 (GE healthcare, USA) were used in this study. Per each male and female average of 30 patients doses were analyzed by one. Automatic exposure control system for controlling the dose can affect the largest by a patient's body weight less than 50 kg, 50-60 kg less, 60 kg more than the average of the three groups were divided doses. We compared that measured value of CT-expo v1.7 and ImPACT v1.0. The relationship between body weight and the effective dose were analyzed. Results: When using CT-Expo V1.7, effective dose with BIO40, BIO16 and DSTe8 respectably were $6.46{\pm}1.18$ mSv, $9.36{\pm}1.96 $mSv and $9.36{\pm}1.96$ mSv for 30 male patients respectably $6.29{\pm}0.97$ mSv, $10.02{\pm}2.42$ mSv and $9.05{\pm}2.27$ mSv for 30 female patients respectably. When using ImPACT v1.0, effective dose with BIO40, BIO16 and DSTe8 respectably were $6.54{\pm}1.21$ mSv, $8.36{\pm}1.69$ mSv and $9.74{\pm}2.55$Sv for 30 male patients respectably $5.87{\pm}1.09$ mSv, $8.43{\pm}1.89$ mSv and $9.19{\pm}2.29$ mSv for female patients respectably. When divided three groups which were under 50 kg, 50~60 kg and over 60 kg respectably were 6.27 mSv, 7.67 mSv and 9.33 mSv respectably using CT-Expo V1.7, 5.62 mSv, 7.22 mSv and 8.91 mSv respectably using ImPACT v1.0. Weight and the effective dose coefficient analysis showed a very strong positive correlation(r=743, r=0.693). Conclusion: Using such a dose evaluation programs, easier to predict and evaluate the effective dose possible without performing phantom study and such dose evaluation programs could be used to collect basic data for CT dose management.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.