• Journal of Physiology & Pathology in Korean Medicine
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• v.16 no.6
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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.

• Philosophy of Medicine
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• v.15
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• pp.3-37
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• 2013

George Canguilhem(1904-1995) is one of the rare French philosophers of the 20th century to develop an approach that was shaped by a medical education. For him, medicine is considered as "a technique or an art at the junction of many different sciences, rather than a proper science." The thesis that medicine is a technique is presented not at a practical level, but on an axiological horizon which reflects the totality of humanity. This character of medicine became a motive that concretized Canguilhem's philosophical thinking. Medical knowledge is not an application of physiology, but is derived from clinical observations which are based on the personal experiences of each patient. If medicine were based on scientific knowledge and its practice the very application of this pure knowledge, the patient might be a passive object. However, the patient doesn't remain passive, but reacts to the menace of disease according to attitude that the patient developed over the course of his or her life. Canguilhem characterizes this point as 'normativity', the core of individual life, which eludes positivist medicine. Here appear the essential contents of his vitalism. Although they emphasized the activity of individual living being, other modern French vitalists didn't consider this dimension of norms. Since the normativity in Canguilhem concerns the subjectivity of the first person, it avoids a mechanical form of explanation. Thus Canguilhem's originality is found in his derivation of the essence of medicine from individuality, values and norms.

• Journal of radiological science and technology
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• v.36 no.3
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• pp.193-200
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• 2013

There are many concerns about radiation exposure in Korea after Fukushima Nuclear Plant Accident on 2011 in Japan. As some isotope materials are detected in Korea, people get worried about the radioactive material. In addition, the mass media create an air of anxiety that jump on the people's fear instead of scientific approach. Therefore, for curbing this flow, health, medical institute from the world provide a variety of information about medical radiation safety and hold the campaign which can give people the image that medical radiation is safe. At this, the Korean Food and Drug Administration(KFDA) suggested that make the alliance of medical radiation safety and culture on August, 2011. Seven societies and institutions related medical radiation started to research and advertise the culture of medical radiation safety in Korea. In this report, mainly introduce the activities of the Korean Alliance for Radiation Safety and Culture in Medicine(KARSM) for spreading culture of medical radiation safety from 2011 to 2012.

• The Journal of KAIRB
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• v.4 no.1
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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.

• Proceedings of the KSRS Conference
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• v.2
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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.

• Korean Journal of Computational Design and Engineering
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• v.18 no.5
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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.

• Korean Journal of Remote Sensing
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• v.22 no.5
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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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• v.12 no.3
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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.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.4
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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.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.329-338
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• 2011

Terminology recognition system which is a preceding research for text mining, information extraction, information retrieval, semantic web, and question-answering has been intensively studied in limited range of domains, especially in bio-medical domain. We propose a domain independent terminology recognition system based on machine learning method using dictionary, syntactic features, and Web search results, since the previous works revealed limitation on applying their approaches to general domain because their resources were domain specific. We achieved F-score 80.8 and 6.5% improvement after comparing the proposed approach with the related approach, C-value, which has been widely used and is based on local domain frequencies. In the second experiment with various combinations of unithood features, the method combined with NGD(Normalized Google Distance) showed the best performance of 81.8 on F-score. We applied three machine learning methods such as Logistic regression, C4.5, and SVMs, and got the best score from the decision tree method, C4.5.