Hanyang University · Medicine
Professor Jong Min Lee's research lab specializes in translational biomedical research, focusing on the molecular mechanisms of inflammatory diseases and neurodegenerative disorders. The lab investigates key regulatory proteins such as transglutaminase 2 in neuroinflammation and explores biomarkers like lymphopenia for clinical prognosis in infectious diseases such as COVID-19. Additionally, the lab develops advanced medical image analysis techniques, particularly deep learning-based methods like U-Net with multi-scale highlighting, for precise segmentation of brain white matter hyperintensities to support early diagnosis of cognitive decline and dementia.
Figures are computed from collected data and may differ slightly.
Transglutaminase 2 (TGase 2) expression is increased in inflammatory diseases. We demonstrated previously that inhibitors of TGase 2 reduce nitric oxide (NO) generation in a lipopolysaccharide (LPS)-treated microglial cell line. However, the precise mechanism by which TGase 2 promotes inflammation remains unclear. We found that TGase 2 activates the transcriptional activator nuclear factor (NF)-kappaB and thereby enhances LPS-induced expression of inducible nitric-oxide synthase. TGase 2 activat
We aimed to identify whether lymphopenia is a reliable prognostic marker for COVID-19. Using data derived from a Korean nationwide longitudinal cohort of 5628 COVID-19 patients, we identified propensity-matched cohorts (<i>n</i> = 770) with group I of severe lymphopenia (absolute lymphocyte counts [ALC]: <500/mm<sup>3</sup>, <i>n</i> = 110), group II of mild-to-moderate lymphopenia (ALC: ≥500-<1000/mm<sup>3</sup>, <i>n</i> = 330), and group III, no lymphopenia (ALC: ≥1000/mm<sup>3</sup>, <i>n</i
White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challeng
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