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.

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.

Purpose: To evaluate the effectiveness of an instrument devised for slit-lamp examination of donor corneas suspended in preservation medium. Methods: The study examined two donor corneas received at Yeouido St. Mary's Hospital in February 2023 and March 2023. The instrument has three main components: a plastic holder to hold the preservation medium bottle, a cube with a mirror for reflecting the slit beam, and a stand to attach the device to the slit-lamp. Using the instrument, the donor corneas were examined via slit-lamp: microscopy with the endothelium facing upward and downward. Specular microscopy and anterior segment optical coherence tomography (OCT) were also performed on the preserved donor corneas. Results: Slit-lamp examination of donor corneas in preservation medium using the instrument showed overall corneal buttoning and optical sections of the donor cornea. Using specular reflection and retroillumination, the endothelial layer was partially visible. However, specular microscopy and anterior segment OCT could not examine the donor cornea in preservation medium using the instrument. Conclusions The devised instrument facilitates slit-lamp examination of donor corneas in preservation medium, enabling a qualitative assessment of donor corneas before corneal transplantation surgery.

Background: Axitinib, a potent and selective inhibitor of vascular endothelial growth factor (VEGF) receptor (VEGFR) tyrosine kinase 1,2 and 3, is used in chemotherapy because it inhibits tumor angiogenesis by blocking the VEGF/VEGFR pathway. In veterinary medicine, attempts have been made to apply tyrosine kinase inhibitors with anti-angiogenic effects to tumor patients, but there are no studies on axitinib in canine mammary gland tumors (MGTs). Objectives: This study aimed to confirm the antitumor activity of axitinib in canine mammary gland cell lines. Methods: We treated canine MGT cell lines (CIPp and CIPm) with axitinib and conducted CCK, wound healing, apoptosis, and cell cycle assays. Additionally, we evaluated the expression levels of angiogenesis-associated factors, including VEGFs, PDGF-A, FGF-2, and TGF-β1, using quantitative real-time polymerase chain reaction. Furthermore, we collected canine peripheral blood mononuclear cells (PBMCs), activated them with concanavalin A (ConA) and lipopolysaccharide (LPS), and then treated them with axitinib to investigate changes in viability. Results: When axitinib was administered to CIPp and CIPm, cell viability significantly decreased at 24, 48, and 72 h (p < 0.001), and migration was markedly reduced (6 h, p < 0.05; 12 h, p < 0.005). The apoptosis rate significantly increased (p < 0.01), and the G2/M phase ratio showed a significant increase (p < 0.001). Additionally, there was no significant change in the viability of canine PBMCs treated with LPS and ConA. Conclusion In this study, we confirmed the antitumor activity of axitinib against canine MGT cell lines. Accordingly, we suggest that axitinib can be applied as a new treatment for patients with canine MGTs.

Plants belonging to the genus Juncus are widely distributed across North America, which are known to make a diverse array of bioactive natural products. In 2022, Juncus torreyi, a species of Juncus, was firstly found in Korea. Morphological and ecological characteristics of the species have been previously investigated;however, bioactive chemical potentials still remain to be explored. In the present work, we focused on the isolation and characterization of metabolites that harbor growth inhibitory activity against a fungal indicator, Candida albicans. Using activity-guided discovery method, we subsequently isolated and purified three metabolites from the most active methylene chloride-soluble fraction. Through NMR and high-resolution ESIOrbitrap-MS data analysis, the metabolites were structurally determined to be juncatrin B (1), ensifolin I (2), and juncusol (3). Metabolites 1–3 were evaluated for their C. albicans growth inhibitory activity and revealed inhibition with an IC 50 value of 74.3, 31.5, and 64.6 μg/mL, respectively.

Background/Aims: Optimal risk stratification based on simplified geriatric assessment to predict treatment-related toxicity and survival needs to be clarified in older patients with diffuse large B-cell lymphoma (DLBCL). Methods: This multicenter prospective cohort study enrolled newly diagnosed patients with DLBCL (≥ 65 yr) between September 2015 and April 2018. A simplified geriatric assessment was performed at baseline using Activities of Daily Living (ADL), Instrumental ADL (IADL), and Charlson’s Comorbidity Index (CCI). The primary endpoint was event-free survival (EFS). Results: The study included 249 patients, the median age was 74 years (range, 65-88), and 125 (50.2%) were female. In multivariable Cox analysis, ADL, IADL, CCI, and age were independent factors for EFS; an integrated geriatric score was derived and the patients stratified into three geriatric categories: fit (n = 162, 65.1%), intermediate-fit (n = 25, 10.0%), and frail (n = 62, 24.9%). The established geriatric model was significantly associated with EFS (fit vs. intermediate-fit, HR 2.61, p < 0.001; fit vs. frail, HR 4.61, p < 0.001) and outperformed each covariate alone or in combination. In 87 intermediate-fit or frail patients, the relative doxorubicin dose intensity (RDDI) ≥ 62.4% was significantly associated with worse EFS (HR, 2.15, 95% CI 1.30–3.53, p = 0.002). It was related with a higher incidence of grade ≥ 3 symptomatic non-hematologic toxicities (63.2% vs. 27.8%, p < 0.001) and earlier treatment discontinuation (34.5% vs. 8.0%, p < 0.001) in patients with RDDI ≥ 62.4% than in those with RDDI < 62.4%. Conclusions This model integrating simplified geriatric assessment can risk-stratify older patients with DLBCL and identify those who are highly vulnerable to standard dose-intensity chemoimmunotherapy.