Sang-Heon Yoon

BACKGROUND: Systemic lupus erythematosus (SLE) is a rare autoimmune disease for which a population-based survey on the prevalence of the disease in South Korea has not yet been conducted. Our goal was to estimate the nationwide prevalence of SLE. METHODS: The International Classification of Diseases, Tenth Revision (ICD-10) code for SLE diagnosis-M32-was tentatively given when patients were suspected to have SLE before 2009. As such, the positive predictive value (PPV) of the M32 code shown in medical bills reflecting true SLE was uncertain. We attempted to estimate the prevalence of SLE in South Korea using national administrative database data from 2004-2006. We approximated the actual number of SLE patients by analyzing a list of SLE-coded patients provided by the National Health Insurance (NHI) and Health Insurance Review and Assessment Service. Prevalence was estimated by multiplying the PPV of the M32 diagnostic code by the number of patients receiving the code. The PPV was determined by three methods: direct investigation of the medical records of patients randomly selected from the SLE-coded patients list; assessment of all SLE patients treated at 56 selected hospitals in South Korea; and extrapolation from sub-groups at a single institute to the sub-groups of the national NHI data. RESULTS: The estimated number of national SLE cases was between 9000 and 11,000, depending on the method of ascertainment, corresponding to a prevalence of 18.8-21.7 per 100,000 people. CONCLUSIONS: This is the first report of a nationwide prevalence survey of SLE in South Korea. National databases may serve as a resource for epidemiologic studies of rare autoimmune diseases like SLE.

This paper describes an approach that provides Internet-based support for a genome center to map human chromosome 12, as a collaboration between laboratories at the Albert Einstein College of Medicine in Bronx, New York, and the Yale University School of Medicine in New Haven, Connecticut. Informatics is well established as an important enabling technology within the genome mapping community. The goal of this paper is to use the chromosome 12 project as a case study to introduce a medical informatics audience to certain issues involved in genome informatics and in the Internet-based support of collaborative bioscience research. Central to the approach described is a shared database (DB/12) with Macintosh clients in the participating laboratories running the 4th Dimension database program as a user-friendly front end, and a Sun SPARCstation-2 server running Sybase. The central component of the database stores information about yeast artificial chromosomes (YACs), each containing a segment of human DNA from chromosome 12 to which genome markers have been mapped, such that an overlapping set of YACs (called a "contig") can be identified, along with an ordering of the markers. The approach also includes 1) a map assembly tool developed to help biologists interpret their data, proposing a ranked set of candidate maps, 2) the integration of DB/12 with external databases and tools, and 3) the dissemination of the results. This paper discusses several of the lessons learned that apply to many other areas of bioscience, and the potential role for the field of medical informatics in helping to provide such support.

The higher incidence of liver disease in the Asian population raises a great concern to clinicians. To understand the gene functions involved in different stages of the disease, microarray expression data of histological progressive grades, starting from the dysplastic nodule in cirrhotic liver to hepatocellular carcinoma Edmonson grade III are analyzed. The statistical procedures are divided into two parts: First, microarray data are suitably normalized, including a method of analysis of variance (ANOVA). There are great differences of opinion regarding the currently used normalization methods. In order to proceed to the second part of statistical analyses of gene-pair associations, these normalization methods need first to be compared. Based on the assumption that a union set of significant genes from these normalization methods includes sufficiently general and well-defined, differentially expressed genes, one must carry out the second part of statistical analyses of searching for evidence of altered gene-gene relationships with progression of the disease. Significantly altered gene-pair associations are identified with the ratio of gene-pair correlations. The methods are illustrated with replicated microarray expression data.