KAIST

<(From left)Dr. Keungmo Yang, Professor Won-Il Jeong, Ph.D candidate Kyurae Kim> Excessive alcohol consumption causes alcoholic liver disease, and about 20% of these cases progress to alcohol-associated steatohepatitis (ASH), which can lead to liver cirrhosis and liver failure. Early diagnosis and treatment are therefore extremely important. A KAIST research team has identified a new molecular mechanism in which alcohol-damaged liver cells increase reactive oxygen species (ROS), leading

<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi> Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study dra

<(From left)Prof. Greg S.B Suh, Dr. Jieun Kim, Dr. Shinhye Kim, Researcher Wongyo Jeong) “How does our brain distinguish glucose from the many nutrients absorbed in the gut?” Starting with this question, a KAIST research team has demonstrated that the brain can selectively recognize specific nutrients—particularly glucose—beyond simply detecting total calorie content. This study is expected to offer a new paradigm for appetite control and the treatment of metabolic

<(From left) Ph. D Candidate Ju Yeon Chung, Prof.Hyun Jung Chung, Ph.D candidate Seungju Yang, Ph.D candidate Ayoung Park, Dr. Yoon-Kyoung Hong from Asan Medical Center, Prof. Yong Pil Chong, Dr. Eunhee Jeon> Candida, a type of fungus, which can spread throughout the body via the bloodstream, leading to organ damage and sepsis. Recently, the incidence of candidiasis has surged due to the increase in immunosuppressive therapies, medical implants, and transplantation. Korean researc

<(From the left) Prof. Hyun Uk Kim, Ph.D candiate Hae Deok Jung, Ph.D candidate Jina Lim, Prof.Yoosik Kim from the Department of Chemical and Biomolecular Engineering> One of the biggest obstacles in cancer treatment is drug resistance in cancer cells. Conventional efforts have focused on identifying new drug targets to eliminate these resistant cells, but such approaches can often lead to even stronger resistance. Now, researchers at KAIST have developed a computational framework to pr