Peripheral nerve injury and oxidative stress can severely impair Schwann cell function by disrupting the expression of key myelin proteins, promoting intracellular lipid accumulation, and damaging mitochondrial integrity. These pathological changes are central to various neurodegenerative disorders and chemotherapy-induced peripheral neuropathy, yet effective therapeutic approaches remain limited. Clemastine, an FDA-approved antihistamine with known remyelination-enhancing effects in the central nervous system, has not been thoroughly explored for its protective role in peripheral myelinating cells under oxidative stress. In this study, we investigated the time-dependent protective effects of Clemastine in S16 Schwann cells exposed to hydrogen peroxide (H2O2) as a model of oxidative injury. Treatment with Clemastine significantly increased the expression of myelin-related proteins such as myelin protein zero (MPZ), alongside in increase in AMPK phosphorylation at Thr172. However, co-treatment with H2O2 ensued oxidative damage, leading to reduced pAMPK(T172) and MPZ expression, elevated ROS levels, and increased lipid accumulation. These results suggest that oxidative stress can attenuate Clemastine’s effects in association with disrupted redox balance and energy metabolism. Subsequent treatment with Metformin (Met), a pharmacological activator of AMPK, was associated with partial recovery from H2O2-induced oxidative damage. Overall, our findings support the potential of a combinatorial approach using Clemastine and Met to promote myelin-related protein expression and lipid metabolic balance in Schwann cells under oxidative stress, rather than establishing a definitive synergistic or causal mechanism.

Purpose: We aimed to investigate how genetic predisposition to drinking and gastric cancer (GC) modifies the association between alcohol consumption and GC risk in the Korean population. Materials and Methods: Polygenic risk scores for GC (PRS-GC) and alcohol consumption (PRS-Alcohol) were formulated using genome-wide association results from BioBank Japan. Validation was performed using Korean cohorts (SNUBH-GENIE cohort), incorporating 8,846 controls and 531 patients with GC. Subsequently, these PRSs were applied to an independent Korean cohort of 67,771 participants, including 313 patients with GC during the follow-up for 14 years (KoGES cohort). Results: In KoGES cohort, the influence of alcohol consumption on GC risk was significantly altered by the PRS-GC and exhibited a synergistic interaction effect. PRS-Alcohol itself shows a negative correlation with GC risk. However, when actual alcohol consumption significantly exceeded genetically predicted levels, the risk of alcohol-related GC was notably increased (adjusted hazard ratio, 1.32; 95% confidence interval, 1.01 to 1.72). Heavy drinkers in the high–PRS-GC/low–PRS-Alcohol group had a 2.16 times higher risk of GC than non-to-light drinkers, which was prominent in males. Conclusion Korean drinkers with higher PRS-GC who consume alcohol more than genetically predicted levels are susceptible to GC. PRS-GC and PRS-Alcohol may be beneficial for assessing the impact of alcohol consumption on GC risk in Koreans.

Purpose: This study aimed to develop and implement an antimicrobial stewardship education program for physician assistant nurses and to evaluate its effects on their knowledge and clinical performance. Methods: A quasi-experimental, single-group pre-post design was conducted with 50 physician assistant nurses at a university hospital in Seoul, Republic of Korea. The antimicrobial stewardship education program, developed using the ADDIE model, consisted of 12 sessions including lectures and case-based learning (CBL)-based discussions.Knowledge was measured before and immediately after the intervention, while performance was assessed pre-intervention and four weeks post-program. Data were analyzed using paired t-tests, Wilcoxon signed-rank tests, and analysis of covariance (ANCOVA). Results: Knowledge scores significantly improved from 44.65±7.45 to 58.50±10.11 (p<.001), and all subdomains showed significant increases (p<.001). Performance scores increased from 3.68±0.77 to 4.28±0.68 (p<.001). Knowledge gain did not differ significantly between the medical and surgical departments (p=.710). Likewise, after adjusting for pre-test scores, no significant difference in performance improvement was observed between the two departments (ANCOVA, p=.170). These results indicate that the program was effective across both departments regardless of their characteristics. Conclusion The antimicrobial stewardship education program improved both knowledge and performance among physician assistant nurses. This program may contribute to the standardization of antimicrobial stewardship education and to appropriate antimicrobial use and the reduction of antimicrobial resistance.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

Background: Comprehensive studies on the tumor burden of soft-tissue sarcoma (STS) by anatomical site are lacking in Asian populations. Objective: To investigate the anatomical distribution of STS via relative tumor density (RTD) in a Korean cohort. Methods: The RTDs of patients with STS at a single-institution from 2007–2022 were retrospectively analyzed. To describe the STS locations, the body was divided into 4 anatomical sites, and the RTD of each was calculated to the compare topographic tumor burden. Results: Fifty-nine cases in 58 individuals, 35 male (60.3%) and 23 female (39.7%), with a mean age of 56.5±20.4 were analyzed. Overall, the most frequent STS site was the lower extremity (LE, n=22, 37.3%), and the highest RTD was in the head and neck (H&N, 2.44; 95% confidence interval, 1.39–3.77). Dermatofibrosarcoma protuberans (DFSP), Kaposi’s sarcoma (KS), and angiosarcoma (AS) accounted for 76.3% of all the cases. DFSP, KS, and AS showed significantly higher RTD on the trunk (2.55, p=0.025), LE (3.88, p<0.001), and H&N (7.42, p<0.001), respectively, than elsewhere. Conclusion Each STS displays topographic variability and produces different topographic tumor burdens by body site in an Asian population.

Purpose: Stem cell transplantation (SCT) has historically played a major role in the long-term remission of mantle cell lymphoma (MCL), an incurable hematological malignancy. Using data from the Korean Society of Bone and Marrow Transplantation registry, we retrospectively analyzed the role of autologous (auto) and allogeneic (allo) SCT in longterm MCL survival. Methods: This study analyzed data from 188 patients (age ≥ 19 years at the time of transplantation) who underwent a transplant for MCL from 2011 to 2020. Progression-free survival (PFS) was defined as the time from transplantation to disease progression, relapse, or death from any cause. Overall survival (OS) was defined as the time from transplan‑ tation to death from any cause or the last follow-up. Results: In total, 109 patients underwent consolidative SCT after first-line chemotherapy. The 3-year PFS and OS rates were 65.4% and 78.5%, respectively, in the auto-SCT group, and 66.7% and 71.4%, respectively, in the allo-SCT group. The PFS and OS did not differ significantly between the auto- and allo-SCT groups. As part of salvage treatment, 52 patients with relapsed or refractory disease underwent auto- or allo-SCT. Patients who underwent auto-SCT with complete remis‑ sion/partial remission status reported better outcomes. In patients with refractory status, allogeneic transplantation using human leukocyte antigen (HLA) fully matched donors was a significantly favorable factor for PFS and OS. Conclusion The long-term survival of patients who underwent consolidative transplantation was similar to that reported in previous studies. Auto-SCT may be beneficial in patients who respond to salvage therapy, whereas allo-SCT with HLA-matched donors may be an alternative for patients with refractory disease.