Jin-hong Kim
Seoul National University · 医学
研究室紹介
Professor Jin-hong Kim's research lab focuses on the molecular and cellular mechanisms underlying osteoarthritis (OA) pathogenesis, with a particular emphasis on extracellular matrix remodeling, chondrocyte senescence, and redox homeostasis. The lab investigates key regulatory molecules such as microRNAs (e.g., miR-204) and selenoproteins (e.g., SEPHS1) that modulate cartilage matrix metabolism and oxidative stress in aging and mechanically stressed joints. Using integrative approaches combining molecular biology, biochemistry, and mechanotransduction studies, the lab aims to uncover novel therapeutic targets for OA. Additionally, the lab explores biomaterials for controlled drug delivery, particularly in the context of protein permeation through polymer membranes.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15A progressive loss of cartilage matrix leads to the development of osteoarthritis (OA). Matrix homeostasis is disturbed in OA cartilage as the result of reduced production of cartilage-specific matrix and increased secretion of catabolic mediators by chondrocytes. Chondrocyte senescence is a crucial cellular event contributing to such imbalance in matrix metabolism during OA development. Here, we identify miR-204 as a markedly up-regulated microRNA in OA cartilage. miR-204 is induced by transcri
Aging and mechanical overload are prominent risk factors for osteoarthritis (OA), which lead to an imbalance in redox homeostasis. The resulting state of oxidative stress drives the pathological transition of chondrocytes during OA development. However, the specific molecular pathways involved in disrupting chondrocyte redox homeostasis remain unclear. Here, we show that selenophosphate synthetase 1 (SEPHS1) expression is downregulated in human and mouse OA cartilage. SEPHS1 downregulation impai
Anchorage to a compliant extracellular matrix (ECM) and contact with neighboring cells impose important constraints on the proliferation of epithelial cells. How anchorage and contact dependence are inter-related and how cells weigh these adhesive cues alongside soluble growth factors to make a net cell cycle decision remain unclear. Here, we show that a moderate 4.5-fold stiffening of the matrix reduces the threshold amount of epidermal growth factor (EGF) needed to over-ride contact inhibition
Osteoarthritis (OA) is characterized by impairment of the load-bearing function of articular cartilage. OA cartilage matrix undergoes extensive biophysical remodeling characterized by decreased compliance. In this study, we elucidate the mechanistic origin of matrix remodeling and the downstream mechanotransduction pathway and further demonstrate an active role of this mechanism in OA pathogenesis. Aging and mechanical stress, the two major risk factors of OA, promote cartilage matrix stiffening
We evaluated changes in incidence, relative survival (RS), and conditional survival (CS) of head and neck squamous cell carcinoma (HNSCC), focusing on oral tongue squamous cell carcinoma (OTSCC). Data of 74 680 HNSCC patients from 1976 to 2015 were obtained from the Surveillance, Epidemiology, and End Results database. Five anatomical sites and their subsites were analyzed. Annual percent change (APC) of incidence was calculated. RS and CS were compared across the four decades. Adjusted hazard r
Abstract The permeation of riboflavin and insulin through poly(vinyl alcohol) (PVA) and chitosan blend membrane was conducted. The permeability coefficients of both solutes through the crosslinked PVA and chitosan blend membrane were in the order of 10 −6 −10 −7 cm 3 cm/cm 2 s and showed a pH dependence. The pH‐dependent permeation behavior was discussed in terms of water content and water structure inside of the swollen membrane. Riboflavin and insulin were presumed to permeate through the free
Contact-inhibition of proliferation constrains epithelial tissue growth, and the loss of contact-inhibition is a hallmark of cancer cells. In most physiological scenarios, cell-cell contact inhibits proliferation in the presence of other growth-promoting cues, such as soluble growth factors (GFs). How cells quantitatively reconcile the opposing effects of cell-cell contact and GFs, such as epidermal growth factor (EGF), remains unclear. Here, using quantitative analysis of single cells within mu
PURPOSE: Malignant phyllodes tumor of the breast (MPTB) accounts for less than 1% of whole breast neoplasm. Surgery is regarded as the primary treatment of choice in patients with MPTB, but the necessity of postoperative radiation therapy (RT) has been a subject of debate. Our aim was to evaluate effects of postoperative RT for MPTB using a large population database. METHODS: Using the Surveillance, Epidemiology, and End Results Program (SEER) database (1983-2013), clinico-pathologic prognostic
OBJECTIVE: To investigate the dose-related effects of extracorporeal shock wave therapy (ESWT) for knee osteoarthritis. METHODS: Seventy-five subjects were recruited, 60 of which met the inclusion criteria. The patients were randomly classified into two groups: group L, which was a low-energy group (n=30; 1,000 shocks/session; energy flux density [EFD], 0.040 mJ/mm(2)) and group M, which was a medium-energy group (n=30; 1,000 shocks/session; EFD, 0.093 mJ/mm(2)). For each group, 1,000 shock wave
Osteoarthritis (OA) is a prevalent degenerative disease, which involves progressive and irreversible destruction of cartilage matrix. Despite efforts to reconstruct cartilage matrix in osteoarthritic joints, it has been a difficult task as adult cartilage exhibits marginal repair capacity. Here we report the identification of tankyrase as a regulator of the cartilage anabolism axis based on systems-level factor analysis of mouse reference populations. Tankyrase inhibition drives the expression o
Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were