Kyung Hee University · 医学
Professor Kwang-Woo Kim's research lab focuses on the genetic and molecular mechanisms underlying autoimmune diseases, particularly rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), with a strong emphasis on gene-environment interactions and the role of human leukocyte antigen (HLA) variants in disease susceptibility. The lab also investigates the pathogenesis of spondyloarthritis (SpA), especially through MHC class I associations in the Korean population. In addition to biomedical research, the lab explores energy-efficient building technologies, such as predictive control systems for radiant floor heating using artificial neural networks, and evaluates the performance of warm-mix asphalt under extreme temperature conditions.
Figures are computed from collected data and may differ slightly.
Our findings provide useful insights regarding RA genetic aetiology and variant-driven RA pathogenesis.
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of complex etiology that primarily affects women of childbearing age. The development of SLE is attributed to the breach of immunological tolerance and the interaction between SLE-susceptibility genes and various environmental factors, resulting in the production of pathogenic autoantibodies. Working in concert with the innate and adaptive arms of the immune system, lupus-related autoantibodies mediate immune-complex deposition i
Our findings of significant gene-environment interaction effects indicate that a physical interaction between citrullinated autoantigens produced by smoking and HLA-DR molecules is characterized by the HLA-DRβ1 4-amino acid haplotype, primarily by positions 11 and 13.
The objective of this study is to improve the control performance of the radiant floor heating system in apart-ment buildings. For this, predictive control, which is simple and also compatible with the existing system, is suggested, and its performance is evaluated. The control system of radiant floor heating should be easily adapted to thermal mass characteristics and building load variations without complication in real application. In this study, predictive control using the ANN (Artificial N
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