Osong Public Health and Research Perspectives

Mi Sun Park

3 Articles

Prevalence of Farm and Slaughterhouse Workers Carrying Shiga Toxin-Producing Escherichia coli in Korea: Sahyun Hong, Seung Eun Song, Kyung Hwan Oh, Seung Hak Kim, Seok ju Yoo, Hyun Sul Lim, Mi Sun Park; Osong Public Health Res Perspect. 2011;2(3):198-201. Published online December 31, 2011; DOI: https://doi.org/10.1016/j.phrp.2011.11.045

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Objectives
The aim of this study was to investigate the distribution of Shiga toxin (Stx) gene-positive stool samples from dairy farmer and slaughterhouse workers in Gyeonggi-Do province.
Methods
A total of 621 samples from healthy farmers and 198 samples from slaughterhouse workers were screened by polymerase chain reaction (PCR) for Shiga toxigenic Escherichia coli (STEC) infection on stool samples.
Results
The PCR product of Stx-encoding genes was detected in 21 (3.4%) of 621 farmers and 15 (7.6%) of 198 slaughterhouse workers’ stool samples. Distribution of the Stx PCR positive workers by age increment revealed an increase in STEC infection with age increment in both workers. Distribution of the Stx PCR positive workers by working years revealed an increase in STEC infection with working years in farmers.
Conclusion
These results of the study show that slaughterhouse workers are at higher risk of STEC infection than farmers. In addition, slaughterhouse workers have a more potential source of food contamination of STEC and transmission.

Citations

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Occupation and Industry Data Quality Among Select Notifiable Conditions in Washington State
Sara Wuellner, Cheri Levenson
Journal of Public Health Management and Practice.2024; 30(1): 36. CrossRef
Prevalence and characteristics of Shiga toxin-producingEscherichia coli(STEC) from cattle in Korea between 2010 and 2011
Eun Kang, Sun Young Hwang, Ka Hee Kwon, Ki Yeon Kim, Jae Hong Kim, Yong Ho Park
Journal of Veterinary Science.2014; 15(3): 369. CrossRef

Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data: Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang-Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu-Bum Kwack, Chaeshin Chu, Hyung-Lae Kim; Osong Public Health Res Perspect. 2011;2(2):75-82. Published online June 30, 2011; DOI: https://doi.org/10.1016/j.phrp.2011.07.005

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Abstract PDF Supplementary Material

Objectives
Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.
Methods
We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.
Results
We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.
Conclusions
The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.

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Population stratification in type 2 diabetes mellitus: A systematic review
Sam Hodgson, Sukhmani Cheema, Zareena Rana, Doyinsola Olaniyan, Ellen O’Leary, Hermione Price, Hajira Dambha‐Miller
Diabetic Medicine.2022;[Epub] CrossRef
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Perspective: Advancing Understanding of Population Nutrient–Health Relations via Metabolomics and Precision Phenotypes
Stephanie Andraos, Melissa Wake, Richard Saffery, David Burgner, Martin Kussmann, Justin O'Sullivan
Advances in Nutrition.2019; 10(6): 944. CrossRef
Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes
Dennis H. Murphree, Elaheh Arabmakki, Che Ngufor, Curtis B. Storlie, Rozalina G. McCoy
Computers in Biology and Medicine.2018; 103: 109. CrossRef
Machine Learning and Data Mining Methods in Diabetes Research
Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
Computational and Structural Biotechnology Journal.2017; 15: 104. CrossRef
Survey on clinical prediction models for diabetes prediction
N. Jayanthi, B. Vijaya Babu, N. Sambasiva Rao
Journal of Big Data.2017;[Epub] CrossRef
Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes
Longfei Han, Senlin Luo, Jianmin Yu, Limin Pan, Songjing Chen
IEEE Journal of Biomedical and Health Informatics.2015; 19(2): 728. CrossRef
Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim
Osong Public Health and Research Perspectives.2015; 6(4): 224. CrossRef

Genotypic Characterization of Vibrio vulnificus Clinical Isolates in Korea: Hye Sook Jeong, Jun Young Kim, Se Mi Jeon, Mi Sun Park, Seong Han Kim; Osong Public Health Res Perspect. 2011;2(1):8-14. Published online June 30, 2011; DOI: https://doi.org/10.1016/j.phrp.2011.04.008

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Abstract PDF

Objectives
Vibrio vunificus is known to cause septicemia and severe wound infections in patients with chronic liver diseases or an immuno-compromised condition. We carried out the molecular characterization of V. vulnificus isolates from human Vibrio septicemia cases based on pulsed-field gel electrophoresis (PFGE) using NotI and SfiI.
Methods
and Results PFGE was used to characterize a total of 78 strains from clinical cases after NotI or SfiI digestion. The geographical distribution of PFGE patterns for the strains from the southern part of Korea, a high-risk region for Vibrio septicemia, indicated that the isolates from southeastern Korea showed a comparatively higher degree of homology than those from southwestern Korea.
Conclusions
We report the genetic distribution of V. vulnficus isolated from Vibrio septicemia cases during 2000–2004 in Korea. This method has potential use as a subspecies-typing tool for V. vulnificus strains isolated from distant geographic regions.

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Effect of Seawater Temperature Increase on the Occurrence of Coastal Vibrio vulnificus Cases: Korean National Surveillance Data from 2003 to 2016
Jungsook Kim, Byung Chul Chun
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PCR-based evidence showing the presence of Vibrio vulnificus in wound infection cases in Mangaluru, India
Caroline D’Souza, Ballamoole Krishna Kumar, Sachidananda Kapinakadu, Ranjith Shetty, Indrani Karunasagar, Iddya Karunasagar
International Journal of Infectious Diseases.2018; 68: 74. CrossRef
Genotypic Diversity and Population Structure of Vibrio vulnificus Strains Isolated in Taiwan and Korea as Determined by Multilocus Sequence Typing
Hye-Jin Kim, Jae-Chang Cho, Paul J Planet
PLOS ONE.2015; 10(11): e0142657. CrossRef
The Road Less Traveled
Chaeshin Chu
Osong Public Health and Research Perspectives.2011; 2(1): 1. CrossRef

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