Background: and Purpose The National Brain Biobank of Korea (NBBK) is a brain bank consortium supported by the Korea Disease Control and Prevention Agency and the Korea National Institute of Health, and was launched in 2015 to support research into neurodegenerative disease dementia (NDD). This study aimed to introduce the NBBK and describes clinicopathological correlations based on analyses of data collected from the NBBK. Methods: Four hospital-based brain banks have been established in South Korea: Samsung Medical Center Brain Bank (SMCBB), Seoul National University Hospital Brain Bank (SNUHBB), Pusan National University Hospital Brain Bank (PNUHBB), and Myongji Hospital Brain Bank (MJHBB). Clinical and pathological data were collected from these brain banks using standardized protocols. The prevalence rates of clinical and pathological diagnoses were analyzed in order to characterize the clinicopathological correlations. Results: Between August 2016 and December 2023, 185 brain specimens were collected and pathologically evaluated (SNUHBB: 117; PNUHBB: 27; SMCBB: 34; MJHBB: 7). The age at consent was 70.8±12.6 years, and the age at autopsy was 71.7±12.4 years. The four-most-common clinical diagnoses were Alzheimer’s disease (AD) dementia (20.0%), idiopathic Parkinson’s disease (15.1%), unspecified dementia (11.9%), and cognitively unimpaired (CU) (11.4%).Most cases of unspecified dementia had a pathological diagnosis of central nervous system (CNS) vasculopathy (31.8%) or AD (31.8%). Remarkably, only 14.2% of CU cases had normal pathological findings. The three-most-common pathological diagnoses were AD (26.5%), CNS vasculopathy (14.1%), and Lewy body disease (13.5%). Conclusions These clinical and neuropathological findings provide a deeper understanding of the mechanisms underlying NDD in South Korea.

Background: and Purpose The National Brain Biobank of Korea (NBBK) is a brain bank consortium supported by the Korea Disease Control and Prevention Agency and the Korea National Institute of Health, and was launched in 2015 to support research into neurodegenerative disease dementia (NDD). This study aimed to introduce the NBBK and describes clinicopathological correlations based on analyses of data collected from the NBBK. Methods: Four hospital-based brain banks have been established in South Korea: Samsung Medical Center Brain Bank (SMCBB), Seoul National University Hospital Brain Bank (SNUHBB), Pusan National University Hospital Brain Bank (PNUHBB), and Myongji Hospital Brain Bank (MJHBB). Clinical and pathological data were collected from these brain banks using standardized protocols. The prevalence rates of clinical and pathological diagnoses were analyzed in order to characterize the clinicopathological correlations. Results: Between August 2016 and December 2023, 185 brain specimens were collected and pathologically evaluated (SNUHBB: 117; PNUHBB: 27; SMCBB: 34; MJHBB: 7). The age at consent was 70.8±12.6 years, and the age at autopsy was 71.7±12.4 years. The four-most-common clinical diagnoses were Alzheimer’s disease (AD) dementia (20.0%), idiopathic Parkinson’s disease (15.1%), unspecified dementia (11.9%), and cognitively unimpaired (CU) (11.4%).Most cases of unspecified dementia had a pathological diagnosis of central nervous system (CNS) vasculopathy (31.8%) or AD (31.8%). Remarkably, only 14.2% of CU cases had normal pathological findings. The three-most-common pathological diagnoses were AD (26.5%), CNS vasculopathy (14.1%), and Lewy body disease (13.5%). Conclusions These clinical and neuropathological findings provide a deeper understanding of the mechanisms underlying NDD in South Korea.

Background: and Purpose The National Brain Biobank of Korea (NBBK) is a brain bank consortium supported by the Korea Disease Control and Prevention Agency and the Korea National Institute of Health, and was launched in 2015 to support research into neurodegenerative disease dementia (NDD). This study aimed to introduce the NBBK and describes clinicopathological correlations based on analyses of data collected from the NBBK. Methods: Four hospital-based brain banks have been established in South Korea: Samsung Medical Center Brain Bank (SMCBB), Seoul National University Hospital Brain Bank (SNUHBB), Pusan National University Hospital Brain Bank (PNUHBB), and Myongji Hospital Brain Bank (MJHBB). Clinical and pathological data were collected from these brain banks using standardized protocols. The prevalence rates of clinical and pathological diagnoses were analyzed in order to characterize the clinicopathological correlations. Results: Between August 2016 and December 2023, 185 brain specimens were collected and pathologically evaluated (SNUHBB: 117; PNUHBB: 27; SMCBB: 34; MJHBB: 7). The age at consent was 70.8±12.6 years, and the age at autopsy was 71.7±12.4 years. The four-most-common clinical diagnoses were Alzheimer’s disease (AD) dementia (20.0%), idiopathic Parkinson’s disease (15.1%), unspecified dementia (11.9%), and cognitively unimpaired (CU) (11.4%).Most cases of unspecified dementia had a pathological diagnosis of central nervous system (CNS) vasculopathy (31.8%) or AD (31.8%). Remarkably, only 14.2% of CU cases had normal pathological findings. The three-most-common pathological diagnoses were AD (26.5%), CNS vasculopathy (14.1%), and Lewy body disease (13.5%). Conclusions These clinical and neuropathological findings provide a deeper understanding of the mechanisms underlying NDD in South Korea.

Background: Alcoholic liver disease (ALD) includes a wide clinical spectrum from acute alcoholic hepatitis to severe cirrhosis and/or hepatocellular carcinoma. Until now, there has been no report revealing the bleeding tendency of ALD compared to other diseases in liver transplantation (LT). Thus, we analyzed blood loss and transfusion amounts during operation according to the etiologies of liver disease and model for end-stage liver disease (MELD) score. Methods: Out of 874 recipients who underwent LT, a total of 146 patients were excluded by our exclusion criteria. We compared 728 recipients’ baseline characteristics, operation time, blood loss, and transfusion amounts between ALD and nonALD according to MELD score. Results: The number of patients in the ALD group was 130 (17.9%), and 598 (82.1%) in the non-ALD group. The ALD group showed younger age, higher MELD score, and a higher proportion of deceased donor LT than the non-ALD group. Intraoperative blood loss and transfusions of red blood cells (RBCs), fresh frozen plasma, and platelets were significantly higher in the ALD group. When stratified by MELD score (cut-off: 20), ALD patients in both high and low MELD subgroups demonstrated greater blood loss and RBC transfusion requirements, even when international normalized ratio and platelet counts were similar. In multivariate logistic regression analysis, ALD was a significant risk factor for massive transfusion (odds ratio 1.813, 95% confidence interval 1.158–2.840, p=0.009). Conclusion The ALD group showed increased bleeding tendency than the non-ALD group during LT, irrespective of MELD score. This suggests that transplant surgeons should anticipate greater blood loss and ensure adequate transfusion resources during LT for ALD patients.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.