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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
  • 1,806 View
  • 13 Download
  • 1 Citations
AbstractAbstract PDF
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

Citations to this article as recorded by  
  • 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
  • 1,920 View
  • 14 Download
  • 11 Citations
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • Population stratification in type 2 diabetes mellitus: A systematic review
    Sam Hodgson, Sukhmani Cheema, Zareena Rani, Doyinsola Olaniyan, Ellen O’Leary, Hermione Price, Hajira Dambha‐Miller
    Diabetic Medicine.2022;[Epub]     CrossRef
  • The Prediction of Diabetes
    Lalit Kumar, Prashant Johri
    International Journal of Reliable and Quality E-He.2022; 11(1): 1.     CrossRef
  • Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine
    Mohammad Farhan Khan, Gazal Kalyan, Sohom Chakrabarty, M. Mursaleen
    Nutrients.2022; 14(14): 2794.     CrossRef
  • Supervised and unsupervised algorithms for bioinformatics and data science
    Ayesha Sohail, Fatima Arif
    Progress in Biophysics and Molecular Biology.2020; 151: 14.     CrossRef
  • Medical Internet of things using machine learning algorithms for lung cancer detection
    Kanchan Pradhan, Priyanka Chawla
    Journal of Management Analytics.2020; 7(4): 591.     CrossRef
  • 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
  • 1,925 View
  • 15 Download
  • 4 Citations
AbstractAbstract 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.

Citations

Citations to this article as recorded by  
  • 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
    International Journal of Environmental Research an.2021; 18(9): 4439.     CrossRef
  • 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

PHRP : Osong Public Health and Research Perspectives