- Intra-Oral Factors Influencing Halitosis in Young Women
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Ho Sun Shon, Kyoung Ok Kim, Jae Kwan Jung, Eun Jong Cha, Su Ok Lee, Kyung Ah Kim
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Osong Public Health Res Perspect. 2018;9(6):340-347. Published online December 31, 2018
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DOI: https://doi.org/10.24171/j.phrp.2018.9.6.08
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 Abstract
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Objectives
The aim of this research was to determine intra-oral factors that affect halitosis in young women.
Methods
This study was performed between March 2014 to May 2014, and included 35 women in their 20s with good oral health. Correlation and logistic regression analyses were performed to investigate the change in halitosis immediately, and 1 hour after scaling.
Results
In both oral gas (OG) and extraoral gas (EG) groups, halitosis was reduced after scaling compared to before scaling. The logistic regression analysis of oral state factors in OG showed that as oral fluid [odds ratio (OR) = 0.792, p = 0.045] and dental plaque (OR = 0.940, p = 0.016) decreased by 1 unit, the OR in the OG group decreased (> 50). In addition, as glucose levels in the oral cavity (OR = 1.245, p = 0.075) and tongue coating index (OR = 2.912, p = 0.064) increased by 1 unit, the OR in the OG group increased (> 50). Furthermore, in the EG group, as oral fluid (OR = 0.66, p = 0.01) and dental plaque (OR = 0.95, p = 0.04) decreased, the OR in the EG group decreased (> 50) significantly.
Conclusion
To control halitosis, it is necessary to increase oral fluid and decrease the amount of tongue plaque. Furthermore, maintaining a healthy oral environment, aided by regular scaling and removal of dental plaque, may significantly control halitosis.
- Biomarker for the Prediction of Major Adverse Cardiac Events in Patients with Non-ST-Segment Elevation Myocardial Infarction
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Ho Sun Shon, Jang-Whan Bae, Kyoung Ok Kim, Eun Jong Cha, Kyung Ah Kim
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Osong Public Health Res Perspect. 2017;8(4):237-246. Published online August 31, 2017
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DOI: https://doi.org/10.24171/j.phrp.2017.8.4.02
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2,347
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N-terminal pro-brain natriuretic peptide (NT-proBNP) is a well-known biomarker for the diagnosis and prognosis of heart failure, and is directly associated with myocardial dysfunction. We evaluated the prognostic value of NT-proBNP for major adverse cardiac events (MACEs) among patients with non-ST-segment elevation myocardial infarction (NSTEMI) from the Korea Acute Myocardial Infarction Registry during their mid-term follow-up period. In this paper, we analyzed NT-proBNP according to various MACE and level of NT-proBNP. We used multivariate logistic regression to determine the risk factors according to MACE type and NT-proBNP levels, and to identify the cutoff value for each MACE by using the receiver operating characteristic (ROC) curve. NT-proBNP was a significant variable among cardiac deaths (p = 0.016), myocardial infarction (p = 0.000), and coronary artery bypass grafting (CABG) (p = 0.000) in patients with MACE compared with those without MACE. Two-vessel coronary artery disease (CAD) (p = 0.037) and the maximum creatinine kinase (max-CK) (p = 0.031) produced significant results in repeat percutaneous coronary intervention. The area under the ROC curve was found to be statistically significant for cardiac death and CABG. NT-proBNP is a useful predictor for 12-month MACEs among patients with NSTEMI and in those with heart failure. We propose that a new index incorporating NT-proBNP, max-CK, and CAD vessel will be useful as a prognostic indicator of MACEs in the future.
- Application of Gap-Constraints Given Sequential Frequent Pattern Mining for Protein Function Prediction
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Hyeon Ah Park, Taewook Kim, Meijing Li, Ho Sun Shon, Jeong Seok Park, Keun Ho Ryu
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Osong Public Health Res Perspect. 2015;6(2):112-120. Published online April 30, 2015
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DOI: https://doi.org/10.1016/j.phrp.2015.01.006
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- Objectives
Predicting protein function from the protein–protein interaction network is challenging due to its complexity and huge scale of protein interaction process along with inconsistent pattern. Previously proposed methods such as neighbor counting, network analysis, and graph pattern mining has predicted functions by calculating the rules and probability of patterns inside network. Although these methods have shown good prediction, difficulty still exists in searching several functions that are exceptional from simple rules and patterns as a result of not considering the inconsistent aspect of the interaction network.
Methods
In this article, we propose a novel approach using the sequential pattern mining method with gap-constraints. To overcome the inconsistency problem, we suggest frequent functional patterns to include every possible functional sequence—including patterns for which search is limited by the structure of connection or level of neighborhood layer. We also constructed a tree-graph with the most crucial interaction information of the target protein, and generated candidate sets to assign by sequential pattern mining allowing gaps.
Results
The parameters of pattern length, maximum gaps, and minimum support were given to find the best setting for the most accurate prediction. The highest accuracy rate was 0.972, which showed better results than the simple neighbor counting approach and link-based approach.
Conclusion
The results comparison with other approaches has confirmed that the proposed approach could reach more function candidates that previous methods could not obtain.
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