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Sun-Hee Park 3 Articles
Corrigendum to “Investigation of Biofilm Formation and its Association with the Molecular and Clinical Characteristics of Methicillin-resistant Staphylococcus aureus” [Volume 4, Issue 5, October 2013, Pages 225–232]
Jeong-Ok Cha, Jae Il Yoo, Jung Sik Yoo, Hae-Sun Chung, Sun-Hee Park, Hwa Su Kim, Yeong Seon Lee, Gyung Tae Chung
Osong Public Health Res Perspect. 2014;5(2):116-116.   Published online April 30, 2014
DOI: https://doi.org/10.1016/j.phrp.2014.04.002
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Investigation of Biofilm Formation and its Association with the Molecular and Clinical Characteristics of Methicillin-resistant Staphylococcus aureus
Jeong-Ok Cha, Jae Il Yoo, Jung Sik Yoo, Hae-Sun Chung, Sun-Hee Park, Hwa Su Kim, Yeong Seon Lee, Gyung Tae Chung
Osong Public Health Res Perspect. 2013;4(5):225-232.   Published online October 31, 2013
DOI: https://doi.org/10.1016/j.phrp.2013.09.001
  • 2,199 View
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  • 46 Citations
AbstractAbstract PDF
Objectives
To investigate the biofilm-forming related factors against MRSA bloodstream isolates and evaluates their clinical features and treatment outcomes by biofilm production.
Methods
We collected 126 consecutive methicillin-resistant Staphylococcus aureus (MRSA) causing blood stream infections (BSIs) at 10 tertiary hospitals from 2007 to 2009. We investigated biofilm-forming ability using a microtiter plate assay, and molecular characteristics including multilocus sequence typing, staphylococcal cassette chromosome mec and accessory gene regulator types. We compared the clinical characteristics and outcomes of patients infected with biofilm-forming and non-biofilm-forming MRSA isolates.
Results
Of the 126 samples, 86 (68.3%), including 5 strong level (OD570 ≥ 1.0) and 81 weak level (0.2 ≤ OD570 < 1.0), had biofilm-forming capacity. Detection of fibronectinbinding protein in biofilm-forming strains was significantly higher than biofilm non-forming ones (p = 0.001) and three enterotoxin genes (sec-seg-sei) islands had a high frequency regardless of biofilm production. However, biofilm-forming strains were more likely to be multidrug resistant (three or more non-β-lactam antibiotics) than biofilm non-forming ones [79.2% vs. 59.2%, p = 0.015, odds ratio (OR) 2.629, 95% confidence interval (CI) 1.92–5.81]. Clinical features of patients with BSIs caused by biofilm-forming MRSA strains were more likely to be hospital onset [77.9% vs. 60.0%, p = 0.024, OR 2.434, 95% CI 1.11–5.33) and more frequently occurred in patients with use of invasive devices [85.7% vs. 61.2%, p = 0.002, OR 3.879, 95% CI 1.61–8.97]. The other clinical features were compared with the clinical outcomes of the two groups and were not significant (p > 0.05).
Conclusion
Biofilm-forming MRSA strains showed higher frequency of fnbB gene than biofilm non-forming ones and more incidence rates on particular genotypes. And, their patient's features were not significantly different between two groups in this study, except for several clinical factors.

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Modeling for Estimating Influenza Patients from ILI Surveillance Data in Korea
Joo-Sun Lee, Sun-Hee Park, Jin-Woong Moon, Jacob Lee, Yong Gyu Park, Yong Kyun Roh
Osong Public Health Res Perspect. 2011;2(2):89-93.   Published online June 30, 2011
DOI: https://doi.org/10.1016/j.phrp.2011.08.001
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  • 7 Citations
AbstractAbstract PDF
Objective Prediction of influenza incidence among outpatients from an influenza surveillance system is important for public influenza strategy.
Methods
We developed two influenza prediction models through influenza surveillance data of the Korea Centers for Disease Control and Prevention (each year, each province and metropolitan city; total reported patients with influenza-like illness stratified by age) for 6 years from 2005 to 2010 and disease-specific data (influenza code J09-J11, monthly number of influenza patients, total number of outpatients and hospital visits) from the Health Insurance Review and Assessment service.
Results
Incidence of influenza in each area, year, and month was estimated from our prediction models, which were validated by simulation processes. For example, in November 2009, Seoul and Joenbuk, the final number of influenza patients calculated by prediction models A and B underestimated actual reported cases by 64 and 833 patients, respectively, in Seoul and 6 and 9 patients, respectively, in Joenbuk. R-square demonstrated that prediction model A was more suitable than model B for estimating the number of influenza patients.
Conclusion
Our prediction models from the influenza surveillance system could estimate the nationwide incidence of influenza. This prediction will provide important basic data for national quarantine activities and distributing medical resources in future pandemics.

Citations

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