• The Korean journal of internal medicine
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• v.39 no.2
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• pp.228-237
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• 2024

Background/Aims: Long-term use of acid suppressants such as proton pump inhibitors (PPIs) and histamine 2 receptor antagonist (H2RA) has been associated with the risk of osteoporotic fracture. Acid suppressants and muco-protective agents (MPAs) are often used together as anti-ulcer agents. We evaluated the association between the risk of osteoporotic fracture and the combined use of these anti-peptic agents. Methods: A population-based case-control study was conducted by analyzing the Korean National Health Insurance Data from 2014 to 2020. Patients who had been prescribed anti-peptic agents, such as PPI, H2RA, or MPA, were included. Considering the incidence of osteoporotic fractures, the case group (n = 14,704) and control group (n = 58,816) were classified by 1:4 matching based on age and sex. Results: The use of all types of anti-peptic agents was associated with an increased risk of osteoporotic fractures (PPI: hazard ratio [HR], 1.31; H2RA: HR, 1.44; and MPA: HR, 1.33; all p < 0.001). Compared to PPI alone, the combined use of "PPI and H2RA" (HR, 1.58; p = 0.010) as well as "PPI, H2RA, and MPA" (HR, 1.71; p = 0.001) was associated with an increased risk of osteoporotic fracture. However, compared with PPI alone, "MPA and PPI or H2RA" was not associated with an increased risk of osteoporotic fracture. Conclusions: This study found that the combined use of "PPI and H2RA" was associated with a higher risk of osteoporotic fractures. In cases where deemed necessary, the physicians may initially consider prescribing the combination use of MPA.

• Journal of the Korean Society for information Management
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• v.34 no.2
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• pp.81-95
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• 2017

This study examined two characteristics of library and information science (LIS) journals in Korea through analysis of journal coupling with common authors. This study also illustrated the trend of Korean LIS research in the recent decade. The notable change is that record management and archival studies settle in LIS domain as a major research area. We introduced two indicators, Publishing Preference Index (PPI) and Researcher Attraction Index (RAI), based on the degree of common authors among journals. Both indicators revealed notable changes in author coupling, including reversal of PPIs in some journals, which can be interpreted as proof of changes in their author groups. The RAI analysis, which measured the degree of journals' attractiveness to Korean LIS researchers and author sharing between two journals, illustrated the journals' states in a domain; this result can help find both an isolated journal and strongly bonded journals in the specific domain. Journal coupling with common authors introduced in this study proved to be an effective investigative method for illustrating journals' states in a specified domain as well as a multidisciplinary area.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.251-275
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• 2011

In this paper, we propose a novel kernel called a semantic parse tree kernel that extends the parse tree kernel previously studied to extract protein-protein interactions(PPIs) and shown prominent results. Among the drawbacks of the existing parse tree kernel is that it could degenerate the overall performance of PPI extraction because the kernel function may produce lower kernel values of two sentences than the actual analogy between them due to the simple comparison mechanisms handling only the superficial aspects of the constituting words. The new kernel can compute the lexical semantic similarity as well as the syntactic analogy between two parse trees of target sentences. In order to calculate the lexical semantic similarity, it incorporates context-based word sense disambiguation producing synsets in WordNet as its outputs, which, in turn, can be transformed into more general ones. In experiments, we introduced two new parameters: tree kernel decay factors, and degrees of abstracting lexical concepts which can accelerate the optimization of PPI extraction performance in addition to the conventional SVM's regularization factor. Through these multi-strategic experiments, we confirmed the pivotal role of the newly applied parameters. Additionally, the experimental results showed that semantic parse tree kernel is superior to the conventional kernels especially in the PPI classification tasks.