Osong Public Health and Research Perspectives

Kyung Ah Kim

2 Articles

Intra-Oral Factors Influencing Halitosis in Young Women: Ho Sun Shon, Kyoung Ok Kim, Jae Kwan Jung, Eun Jong Cha, Su Ok Lee, Kyung Ah Kim; Osong Public Health Res Perspect. 2018;9(6):340-347. Published online December 31, 2018; DOI: https://doi.org/10.24171/j.phrp.2018.9.6.08

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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.

Citations

Citations to this article as recorded by

Prevalence and associated factors of self‐reported halitosis among institutionalized adolescents: Cross‐sectional study
Francisco Wilker Mustafa Gomes Muniz, Laura Barreto Moreno, Taciane Menezes da Silviera, Cassiano Kuchenbecker Rösing, Paulo Roberto Grafitti Colussi
International Journal of Dental Hygiene.2023; 21(2): 409. CrossRef
Validation of the Romanian Version of the Halitosis Associated Life-Quality Test (HALT) in a Cross-Sectional Study among Young Adults
Raluca Briceag, Aureliana Caraiane, Gheorghe Raftu, Melania Lavinia Bratu, Roxana Buzatu, Liana Dehelean, Mariana Bondrescu, Felix Bratosin, Bogdan Andrei Bumbu
Healthcare.2023; 11(19): 2660. CrossRef
Role of Probiotics in Halitosis of Oral Origin: A Systematic Review and Meta-Analysis of Randomized Clinical Studies
Nansi López-Valverde, Antonio López-Valverde, Bruno Macedo de Sousa, Cinthia Rodríguez, Ana Suárez, Juan Manuel Aragoneses
Frontiers in Nutrition.2022;[Epub] CrossRef
Microbiota in intra-oral halitosis – characteristics, effects of antibacterial mouth rinse treatment
D. S. Vikina, I. N. Antonova, V. V. Tec, T. E. Lazareva
Parodontologiya.2020; 25(1): 4. CrossRef

Biomarker for the Prediction of Major Adverse Cardiac Events in Patients with Non-ST-Segment Elevation Myocardial Infarction: Ho Sun Shon, Jang-Whan Bae, Kyoung Ok Kim, Eun Jong Cha, Kyung Ah Kim; Osong Public Health Res Perspect. 2017;8(4):237-246. Published online August 31, 2017; DOI: https://doi.org/10.24171/j.phrp.2017.8.4.02

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30 Download
5 Citations

Abstract PDF

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.

Citations

Citations to this article as recorded by

Serum IL-38 Level Was Associated with Incidence of MACE in the STEMI Patients
Chengbo Lu, Fanghui Zhou, Huimin Xian, Siyuan Sun, Jingkun Yue, Ying Zhang, Qi Zhao, Xing Luo, Yang Li
International Journal of General Medicine.2023; Volume 16: 2987. CrossRef
Nomogram M Prognostic Value for Major Adverse Cardiac and Cerebral Events after Elective Cardiac Surgery with Cardiopulmonary Bypass
L. B. Berikashvili, A. N. Kuzovlev, M. Yа. Yadgarov, K. K. Kadantseva, E. A. Ozhiganova, V. V. Likhvantsev
Messenger of ANESTHESIOLOGY AND RESUSCITATION.2022; 19(2): 6. CrossRef
Association of N-terminal pro-brain natriuretic peptide level with adverse outcomes in patients with acute myocardial infarction: A meta-analysis
Shenghui Shen, Jianhua Ye, Xiangzhong Wu, Xiaoling Li
Heart & Lung.2021; 50(6): 863. CrossRef
Effect of phenylacetamide isolated from lepidium apetalum on myocardial injury in spontaneously hypertensive rats and its possible mechanism
Qi Zhang, Peipei Yuan, Meng Li, Yang Fu, Ying Hou, Yaping Sun, Liyuan Gao, Yaxin Wei, Weisheng Feng, Xiaoke Zheng
Pharmaceutical Biology.2020; 58(1): 597. CrossRef
Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework
Samaneh Layeghian Javan, Mohammad Mehdi Sepehri, Hassan Aghajani
Journal of Biomedical Informatics.2018; 88: 70. CrossRef

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