• Journal of Gastric Cancer
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• v.20 no.2
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• pp.152-164
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• 2020

Purpose: To compare long-term disease-free survival (DFS) between patients receiving tegafur/gimeracil/oteracil (S-1) or capecitabine plus oxaliplatin (CAPOX) adjuvant chemotherapy (AC) for gastric cancer (GC). Materials and Methods: This retrospective multicenter observational study enrolled 983 patients who underwent curative gastrectomy with consecutive AC with S-1 or CAPOX for stage II or III GC at 27 hospitals in Korea between February 2012 and December 2013. We conducted propensity score matching to reduce selection bias. Long-term oncologic outcomes, including DFS rate over 5 years (over-5yr DFS), were analyzed postoperatively. Results: The median and longest follow-up period were 59.0 and 87.6 months, respectively. DFS rate did not differ between patients who received S-1 and CAPOX for pathologic stage II (P=0.677) and stage III (P=0.899) GC. Moreover, hazard ratio (HR) for recurrence did not differ significantly between S-1 and CAPOX (reference) in stage II (HR, 1.846; 95% confidence interval [CI], 0.693-4.919; P=0.220) and stage III (HR, 0.942; 95% CI, 0.664-1.337; P=0.738) GC. After adjustment for significance in multivariate analysis, pT (4 vs. 1) (HR, 11.667; 95% CI, 1.595-85.351; P=0.016), pN stage (0 vs. 3) (HR, 2.788; 95% CI, 1.502-5.174; P=0.001), and completion of planned chemotherapy (HR, 2.213; 95% CI, 1.618-3.028; P<0.001) were determined as independent prognostic factors for DFS. Conclusions: S-1 and CAPOX AC regimens did not show significant difference in over-5yr DFS after curative gastrectomy in patients with stage II or III GC. The pT, pN stage, and completion of planned chemotherapy were prognostic factors for GC recurrence.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Radiation Oncology Journal
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• v.19 no.2
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• pp.127-135
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• 2001

Purpose : This study is a prospective randomized clinical trial comparing the efficacy and complication of anti-emetic drugs for prevention of nausea and vomiting after radiotherapy which has moderate emetogenic potential. The aim of this study was to investigate whether the anti-emetic efficacy of ondansetron $(Zofran^{\circledR})$ 8 mg bid dose (Group O) is better than the efficacy of metoclopramide 5 mg lid dose (Group M) in patients undergoing fractionated radiotherapy to the abdominal region. Materials and Methods : Study entry was restricted to those patients who met the following eligibility criteria: histologically confirmed malignant disease; no distant metastasis; performance status of not more than ECOG grade 2; no previous chemotherapy and radiotherapy. Between March 1997 and February 1998, 60 patients enrolled in this study. All patients signed a written statement of informed consent prior to enrollment. Blinding was maintained by dosing identical number of tablets including one dose of matching placebo for Group O. The extent of nausea, appetite loss, and the number of emetic episodes were recorded everyday using diary card. The mean score of nausea, appetite loss and the mean number of emetic episodes were obtained in a weekly interval. Results : Prescription error occurred in one patient. And diary cards have not returned in 3 patients due to premature refusal of treatment. Card from one patient was excluded from the analysis because she had a history of treatment for neurosis. As a result, the analysis consisted of 55 patients. Patient characteristics and radiotherapy characteristics were similar except mean age was $52.9{\pm}11.2$ in group M, $46.5{\pm}9.5$ in group O. The difference of age was statistically significant. The mean score of nausea, appetite loss and emetic episodes in a weekly interval was higher in group M than O. In group M, the symptoms were most significant at 5th week. In a panel data analysis using mixed procedure, treatment group was only significant factor detecting the difference of weekly score for all three symptoms. Ondansetron $(Zofran^{\circledR})$ 8 mg bid dose and metoclopramide 5 mg lid dose were well tolerated without significant side effects. There were no clinically important changes In vital signs or clinical laboratory parameters with either drug. Conclusion : Concerning the fact that patients with younger age have higher emetogenic potential, there are possibilities that age difference between two treatment groups lowered the statistical power of analysis. There were significant difference favoring ondansetron group with respect to the severity of nausea, vomiting and loss of appetite. We concluded that ondansetron is more effective anti-emetic agents in the control of radiotherapy-induced nausea, vomiting, loss of appetite without significant toxicity, compared with commonly used drug, i.e., metoclopramide. However, there were patients suffering emesis despite the administration of ondansetron. The possible strategies to improve the prevention and the treatment of radiotherapy-induced emesis must be further studied.

• The Korean Journal of Gastroenterology
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• v.72 no.5
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• pp.271-273
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• 2018

내시경 시술 전 일시적으로 항응고제를 중단하는 것은 위장관 출혈의 위험과 혈전색전증의 위험 사이에 적절한 균형을 잡기 어렵기 때문에 논란의 여지가 많다. 와파린은 새로운 경구용 항응고제(direct oral anticoagulant agent, DOAC)보다 임상의에게 더 친숙하고, 효과를 쉽고 빠르게 전환시킬 수 있다는 장점이 있지만 복잡한 약역동학 특징과 좁은 치료적 범위 때문에 관리가 어렵다. 반면, DOAC는 약물의 모니터링 및 용량 조절 없이 정해진 용량으로 처방이 가능하며, 빠르게 작용하고, 반감기가 짧아 관리가 쉽지만 해독제가 없다는 단점이 있다. 이전 연구들에서 DOAC를 복용한 환자들은 와파린을 복용한 환자들보다 시술과 관련되지 않은 위장관 출혈의 위험이 높았다고 보고한 바 있다. 하지만 시술과 관련된 위장관 출혈 위험에 대하여는 알려진 바가 없는 실정이다. 미국이나 유럽 내시경 가이드라인들에서는 저위험 내시경 시술을 받는 환자들에서는 와파린과 DOAC를 유지하도록 권고하고 있으며, 고위험 시술의 경우에는 와파린를 사용하는 환자들에서 헤파린 교량 요법(heparin bridging)을 시행하도록 권고하고 있다. 임상적으로 DOAC를 사용하는 환자들 또한 혈전색전증을 예방하기 위하여 헤파린 교량 요법을 시행해볼 수 있는데, 와파린 및 DOAC의 헤파린 교량 요법과 관련된 출혈 및 혈전색전증 위험의 차이 또한 명확하지 않다. 따라서 저자들은 1) 와파린과 DOAC 치료를 받는 환자들에서의 출혈, 혈전색전증 및 사망의 위험을 비교하고자 하였으며, 2) 13종류의 고위험 내시경 시술 중에서 시술별 위험을 비교하고, 3) 헤파린 교량 요법이 합병증의 발생을 증가시키지 않는지 확인하고자 본 연구를 진행하였다. 일본 대규모 국가 입원 환자 데이터베이스를 이용하여 2014년 4월부터 2015년 5월까지 시술 전 와파린 또는 DOAC(rivaroxaban, apixaban, dabigatran, edoxaban)를 복용하고, 13종류의 고위험 내시경 시술을 시행받은 20세 이상의 성인 환자 총 16,977명을 확인하였다. 고위험 시술은 용종 절제술, 내시경 점막절제술, 내시경 점막하박리술, 협착 부위의 풍선확장술, 내시경 지혈술, 내시경 정맥결찰술, 내시경 주사 경화요법, 내시경 괄약근절개술, 내시경 초음파 유도하 미세침 흡인 검사, 경피적 위루술을 포함하였다. 일대일 성향 점수 매칭 분석(propensity score matching, 나이, 성별, 체질량 지수, 기저 질환, 병원의 규모, 시술의 종류, 약물의 종류를 매칭)을 시행하여 와파린군과 DOAC군에서 시술 위장관 출혈 및 혈전색전증, 사망의 발생을 비교하였다. 또한 경구항혈전제와 헤파린 교량 치료 시행 유무에 따라, DOAC 단독군, 와파린 단독군, DOAC와 헤파린 교량 요법군, 와파린과 헤파린 교량요법군으로 나누어, 하위군(subgroup) 분석을 시행하였다. 5,046쌍이 성향 점수 매칭 분석에 포함되었으며, 와파린군에서 DOAC군보다 통계적으로 의미 있게 위장관 출혈의 비율이 높았다(12.0% vs. 9.9% p=0.02). 혈전색전증 발생률(5.4% vs. 4.7%)과 입원중 사망률(5.4% vs. 4.7%)은 양 군에서 의미 있는 차이는 없었다. DOAC 종류별로 나누어 하위군 분석을 시행하였을 때, 와파린군은 rivaroxaban군에 비하여 위장관 출혈의 비율이 높았으며, rivaroxaban군, dabigatran군에 비하여 혈전색전증의 비율이 높았고, 입원 중 사망률에서는 의미 있는 차이는 없었다. 내시경 시술의 종류로 보정하였을 때 위장관 출혈 및 혈전색전증, 사망률은 DOAC 단독으로 치료한 환자에서보다 와파린과 헤파린 교량 요법(bridging) 또는 DOAC과 헤파린 교량 요법을 시행한 환자에서 높았다. 시술 종류 중에서는 위루관 삽입술에 비하여 내시경 점막하박리술, 내시경 점막절제술 및 내시경 정맥류결찰술, 내시경 주사경화요법을 시행한 환자에서 위장관 출혈의 위험이 가장 높았으며, 하부 내시경 점막절제술, 하부 용종 절제술, 내시경적 유두괄약근절제술 또는 내시경 초음파 유도하 미세침 흡인 검사는 중등도 위험을 보였다.

• Journal of Nutrition and Health
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• v.54 no.4
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• pp.398-411
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• 2021

Purpose: The objective of this study was to investigate food and nutrition information utilization practices of adults aged between 20 and 30 years to provide the basic data for developing customized content. Methods: Statistical analyses were performed using the SPSS program (ver. 24.0) for the 𝛘²-test, t-test, one-way analysis of variance, and Duncan's multiple range test. Results: Of the 570 subjects surveyed, 45.4% were men, 54.6% were women, 66.3% were in their 20s, 33.7% were in their 30s, 41.4% were single-person households, and 58.6% lived with their families. On average, 14.2% of televisions (TVs), 26.0% of personal computers (PCs), and 63.7% of smartphones were used for more than three hours per day. 30.9% of respondents searched for food and nutrition information more than once a week. 70.0% of the respondents had then applied the information in real life and 54.7% of the respondents said they would share information with others. Information retrieval rate was in the order of 'restaurant (64.8%)', 'diet (57.5%)', and 'food recipes (55.7%)'. Overall satisfaction with food and nutrition information averaged 3.33 on a five-point scale. Satisfaction score was in the order of 'enough description and easy to understand (3.43)', 'matching title and content (3.35)', and 'providing new and novel information (3.22)'. Satisfaction scores were significantly higher in the group that searched for information (p < 0.001), the group that used the retrieved information in real life (p < 0.001), and the group that conveyed this information to others (p < 0.001). Conclusion: To improve information user satisfaction, it is necessary to provide customized information that fits the characteristics of information users. For this purpose, it is necessary to continuously conduct surveys and satisfaction evaluations for each target group.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.