• 동의생리병리학회지
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• 제16권6호
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• pp.1201-1206
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• 2002

In oriental medicine, human being is classified into four groups according to their body constitution status (;tae-yang, tae-eum, so-yang, and so-eum persons) considering the differences in function of internal organs and characteristics. Four body constitution, called 'sa-sang' has been recognized as an important factor for diagnosing the patients before madical teratment. Yet, the criteria to divide body constitutions or its scientific principle are not clearly defined. As an initial effort to elucidate biological priciples underlying four body constitution groups, we studied genetic variations among three constitution groups (tae-eum, so-yang, and so-eum persons). Noting distinct responses to ingested food and administered drugs among three groups, SNPs and haplotype experiments were performed in 2D6, 2C9, and 1A2 DNA regions of the cytochrome P450 gene. Significant variability in SNPs types was found in 2D6 region. Moreover, haplotyping in 2D6 region showed relatively high occurrences of haplotype 3 and 5 in so-eum person, haplotype 6 in tae-eum person, and hyplotype 1 in so-yang person. These results indicate that individuals with different body constitutions respond differently to ingested food and drugs, which might reflect constitution-specific genetic background. The genetic approach would therefore be useful to reveal intrinsic differences among four constitution body groups in the responsiveness to various drugs and external stimulations to human body.

• 의철학연구
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• 제15권
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• pp.3-37
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• 2013

조르주 캉길렘의 의철학에서 의학은 기술이라고 주장된다. 이는 실용적 의미가 아니라 인간의 전체성을 반영하는 가치론적 지평에서 제시된다. 이러한 의학의 특징이 캉길렘의 철학적 사유의 동기가 된다. 의학적 지식은 생리학의 단순한 응용이 아니라 환자 개인의 체험에 기초한 임상적 관찰에서 비롯한다. 의학이 과학이고 그 실행이 순수 지식의 응용이라면 환자는 수동적 대상에 머물게 된다. 그러나 환자는 질병에 수동적으로 노출되기보다는 자신의 생애를 통해 이미 습득된 능동적 태도에 의해 질병의 위협에 대처한다. 캉길렘은 이 점을 '규범성'이라고 특징짓는다. 규범성은 개인의 삶의 핵심이지만 실증주의 의학은 이를 설명할 수 없다. 여기서 캉길렘의 의학사상의 생기론적 특징이 나타난다. 캉길렘의 규범성 개념은 일인칭 주관과 관련되기 때문에 기계론적 설명방식을 벗어난다. 캉길렘의 독창성은 개체성과 가치 그리고 규범에서 의학의 본질을 도출하는 데 있다.

• 대한방사선기술학회지:방사선기술과학
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• 제36권3호
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• pp.193-200
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• 2013

2011년 3월 일본 동북해안의 지진과 해일, 그리고 연이은 후쿠시마 원전사고에 따른 국내의 방사선 피폭에 대하여 심각하게 우려되고 있다. 후쿠시마 원전에서 누출된 방사성 물질이 미량이나마 국내에도 검출되고 있어 방사성 물질에 대한 시민들의 불안감이 고조됨과 동시에, 국내 언론에서는 과학적인 접근 보다는 실시간으로 시민의 불안감에 편승하여 대중들을 방사선에 대한 공포로 몰아가는 상황이 자주 연출되고 있다. 이에 현상을 올바르게 직시하고 대안을 제시하기 위해 세계 각국의 보건, 의료기관 및 학술 단체에서는 의료방사선 안전문화 확산을 위해 다양한 정보를 제공하고, 의료방사선이 안전하다는 인식을 일반인에게 확산시키기 위한 캠페인을 실시하고 있다. 이에 국내에서도 지난 2011년 6월 초 식품의약품안전처의 제안으로 '의료용 방사선 관련 유관학회의 공동 심포지엄'의 개최를 위하여 마련된 유관학회 전문가 회의에서 공동 심포지엄의 개최 결정과 더불어, 의료용 방사선 안전을 위한 연합회 구성을 공식화하여 '의료방사선안전문화연합회(KARSM, the Korean Alliance for Radiation Safety and Culture in Medicine)'를 창립하게 되었다. 따라서 본 보고에서는 국외 의료방사선 안전문화 활동들에 대한 소개와 더불어 국내 의료방사선 안전문화 확산을 위한 의료방사선안전문화 연합회의 사업을 중점적으로 소개하고자 한다. 의료방사선 안전문화 확산과 관련한 여러 활동들을 통하여 일반 대중들에게 의료방사선의 막연한 공포감을 해소하고 바람직한 인식 형성에 기여하고자 한다.

• 대한기관윤리심의기구협의회지
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• 제4권1호
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• pp.16-22
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• 2022

Purpose: The traditional ethical study only suggests a blurred insight on the research using medical big data, especially in this rapid-changing and demanding environment which is called "4th Industry Revolution." Current institutional/ethical issues in big data research need to approach with the thoughtful insight of past ethical study reflecting the understanding of present conditions of this study. This study aims to examine the ethical issues that are emerging in recent health care big data research. So, this study aims to survey the public perceptions on of health care big data as part of the process of public discourse and the acceptance of the utility and provision of big data research as a subject of health care information. In addition, the emerging ethical challenges and how to comply with ethical principles in accordance with principles of the Belmont report will be discussed. Methods: Survey was conducted from June 3th August to 6th September 2020. The online survey was conducted through voluntary participation through Internet users. A total of 319 people who completed the survey (±5.49%P [95% confidence level] were analyzed. Results: In the area of the public's perspective, the survey showed that the medical information is useful for new medical development, but it is also necessary to obtain consents from subjects in order to use that medical information for various research purposes. In addition, many people were more concerned about the possibility of re-identifying personal information in medical big data. Therefore, they mentioned the necessity of transparency and privacy protection in the use of medical information. Conclusion: Big data on medical care is a core resource for the development of medicine directly related to human life, and it is necessary to open up medical data in order to realize the public good. But the ethical principles should not be overlooked. The right to self-determination must be guaranteed by means of clear, diverse consent or withdrawal of subjects, and processed in a lawful, fair and transparent manner in the processing of personal information. In addition, scientific and ethical validity of medical big data research is indispensable. Such ethical healthcare data is the only key that will lead to innovation in the future.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• 한국CDE학회논문집
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• 제18권5호
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• pp.309-320
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• 2013

Augmented reality (AR) is considered to be an excellent user interface to a 3D information space embedded within physical reality. For this reason, it has been applied to various applications such as design, medical service, interaction, and collaboration. However, there is no formal way of analyzing the research trend and evolution of augmented reality. This paper identifies the research trend and change in augmented reality (AR) via co-citation analysis. The co-citation analysis provides how the AR research has evolved, who are main contributors, and which papers suggest essential and influencing impact. To systematically analyze the cocitation, we have retrieved 1,145 papers from the Web of Science and applied a scientomertric analysis using CiteSpace. Based on the co-citation analysis of authors and documents, it is possible to analyze the evolution of augmented reality, key authors and papers, and breakthroughs. We have also compared the proposed approach with survey papers written by experts so that the result of the co-citation analysis can compromise the qualitative result done by experts, and thus it can provide a different view and insight for visualizing the research on augmented reality.

• 대한원격탐사학회지
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• 제22권5호
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• Mass Spectrometry Letters
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• 제12권3호
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• pp.93-105
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• 2021

Exosomes have gained the attention of the scientific community because of their role in facilitating intercellular communication, which is critical in disease monitoring and drug delivery research. Exosome research has grown significantly in recent decades, with a focus on the development of various technologies for isolating and characterizing exosomes. Among these efforts is the use of matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS), which offers high-throughput direct analysis while also being cost and time effective. MALDI is used less frequently in exosome research than electrospray ionization due to the diverse population of extracellular vesicles and the impurity of isolated products, both of which necessitate chromatographic separation prior to MS analysis. However, MALDI-MS is a more appropriate instrument for the analytical approach to patient therapy, given it allows for fast and label-free analysis. There is a huge drive to explore MALDI-MS in exosome research because the technology holds great potential, most notably in biomarker discovery. With methods such as fingerprint analysis, OMICs profiling, and statistical analysis, the search for biomarkers could be much more efficient. In this review, we highlight the potential of MALDI-MS as a tool for investigating exosomes and some of the possible strategies that can be implemented based on prior research.

• 한국산업보건학회지
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• 제33권4호
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• pp.412-418
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• 2023

As advances in artificial intelligence (AI) increasingly approach areas once relegated to the realm of science fiction, there is growing public interest in using these technologies for practical everyday tasks in both the home and the workplace. This paper explores the applications of and implications for of using ChatGPT, a conversational AI model based on GPT-3.5 and GPT-4.0, in the field of occupational health and safety. After gaining over one million users within five days of its launch, ChatGPT has shown promise in addressing issues ranging from emergency response to chemical exposure to recommending personal protective equipment. However, despite its potential usefulness, the integration of AI into scientific work and professional settings raises several concerns. These concerns include the ethical dimensions of recognizing AI as a co-author in academic publications, the limitations and biases inherent in the data used to train these models, legal responsibilities in professional contexts, and potential shifts in employment following technological advances. This paper aims to provide a comprehensive overview of these issues and to contribute to the ongoing dialogue on the responsible use of AI in occupational health and safety.

• 정보처리학회논문지D
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• 제18D권5호
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• pp.329-338
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• 2011

문헌에서의 전문용어 인식 연구는 정보검색, 정보추출, 시맨틱 웹, 질의응답 분야 등의 연구를 위한 선행 연구로서, 지금까지 대부분 특정 분야, 특히 생의학 분야에서 집중되어 연구되어 왔다. 그러나 기존 연구들이 특정 도메인 또는 문헌 내부 통계 정보를 활용함으로써 범용적인 전문용어 인식에 한계점을 보여 왔기 때문에, 본 연구에서는 웹 검색 결과와 사전, 후보용어의 문형 특징 등을 활용하는 기계 학습 기반 범용 전문용어 인식 방법을 제안하였다. 제안한 방법을 문헌의 지역 통계 정보를 사용하는 방법(C-value)과 비교 실험하여 80.8%의 F-값으로 6.5%의 성능향상을 보였다. 다양한 응집도 자질들을 접목한 두 번째 실험에서는 Normalized Google Distance 방법과 접목한 방식이 F-값 81.8%의 성능으로 최고의 성능을 나타냈다. 기계 학습 방법으로는 로지스틱 회귀분석, C4.5, SVMs 등을 적용하였는데, 일반적으로 이진 분류에 좋은 성능을 보이는 SVMs과 로지스틱 회귀분석 방법보다 결정 트리 방식의 C4.5가 전반적으로 좋은 성능을 보였다.