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.

TNF, a pleiotropic proinflammatory cytokine, is important for protective immunity and immunopathology during Mycobacterium tuberculosis (Mtb) infection, which causes tuberculosis (TB) in humans. TNF is produced primarily by phagocytes in the lungs during the early stages of Mtb infection and performs diverse physiological and pathological functions by binding to its receptors in a context-dependent manner. TNF is essential for granuloma formation, chronic infection prevention, and macrophage recruitment to and activation at the site of infection. In animal models, TNF, in cooperation with chemokines, contributes to the initiation, maintenance, and clearance of mycobacteria in granulomas. Although anti-TNF therapy is effective against immune diseases such as rheumatoid arthritis, it carries the risk of reactivating TB. Furthermore, TNF-associated inflammation contributes to cachexia in patients with TB. This review focuses on the multifaceted role of TNF in the pathogenesis and prevention of TB and underscores the importance of investigating the functions of TNF and its receptors in the establishment of protective immunity against and in the pathology of TB.Such investigations will facilitate the development of therapeutic strategies that target TNF signaling, which makes beneficial and detrimental contributions to the pathogenesis of TB.

Background: The quality of a vaccine depends strongly on the effects of the adjuvants applied simultaneously with the antigen in the vaccine. The adjuvants enhance the protective effect of the vaccine against a viral challenge. Conversely, oil-type adjuvants leave oil residue inside the bodies of the injected animals that can produce a local reaction in the muscle. The longterm immunogenicity of mice after vaccination was examined. ISA206 or ISA15 oil adjuvants maintained the best immunity, protective capability, and safety among the oil adjuvants in the experimental group. Objectives: This study screened the adjuvant composites aimed at enhancing foot-andmouth disease (FMD) immunity. The C-type lectin or toll-like receptor (TLR) agonist showed the most improved protection rate. Methods: Experimental vaccines were fabricated by mixing various known oil adjuvants and composites that can act as immunogenic adjuvants (gel, saponin, and other components) and examined the enhancement effect on the vaccine. Results: The water in oil (W/O) and water in oil in water (W/O/W) adjuvants showed better immune effects than the oil in water (O/W) adjuvants, which have a small volume of oil component. The W/O type left the largest amount of oil residue, followed by W/O/W and O/W types. In the mouse model, intramuscular inoculation showed a better protection rate than subcutaneous inoculation. Moreover, the protective effect was particularly weak in the case of inoculation in fatty tissue. The initial immune reaction and persistence of long-term immunity were also confirmed in an immune reaction on pigs. Conclusions The new experimental vaccine with immunostimulants produces improved immune responses and safety in pigs than general oil-adjuvanted vaccines.

Hand-foot-and-mouth disease, a highly contagious viral infection, occurs more common in children than in adults. However, there was a recent outbreak of Coxsackievirus A6-induced infection with an atypical presentation among the adult population. Stevens– Johnson syndrome is a severe mucocutaneous disease characterized by extensive necrosis and detachment of the epidermis, and this condition is commonly caused by medications.Herein, we describe a 30-year-old male patient taking allopurinol for the management of gout. The patient presented with numerous erythematous papules, vesicles, and patches with mucosal eruptions on the whole body, oral mucositis, and fever, and he was finally diagnosed with hand-foot-and-mouth disease.

Hand-foot-and-mouth disease, a highly contagious viral infection, occurs more common in children than in adults. However, there was a recent outbreak of Coxsackievirus A6-induced infection with an atypical presentation among the adult population. Stevens– Johnson syndrome is a severe mucocutaneous disease characterized by extensive necrosis and detachment of the epidermis, and this condition is commonly caused by medications.Herein, we describe a 30-year-old male patient taking allopurinol for the management of gout. The patient presented with numerous erythematous papules, vesicles, and patches with mucosal eruptions on the whole body, oral mucositis, and fever, and he was finally diagnosed with hand-foot-and-mouth disease.

BACKGROUND/AIMS: The serum aminotransferase level is usually elevated in rhabdomyolysis, and these enzymes originate from the skeletal muscle. On the other hand, there is limited data showing whether the degree of elevation of these enzymes differs according to the concurrent liver disease. METHODS: Patients with rhabdomyolysis were selected when their serum creatinine kinase level was >1,000 U/L. They were categorized as the group with and without concurrent liver disease. The AST and ALT levels in both groups were compared. In addition, the aminotransferase level was compared between those with rhabdomyolysis and those with alcoholic liver disease. RESULTS: Among the 165 patients with rhabdomyolysis, 19 had concurrent liver disease. The median peak AST was higher in the group with concurrent liver disease (332 U/L [interquartile range (IQR), 127–1,604] vs. 219 U/L [IQR, 115–504]). In addition, the median peak ALT was higher in the group with concurrent liver disease (107 U/L [IQR, 74–418] vs. 101 U/L [IQR, 56–218]). On the other hand, there was no significant difference in both enzymes between the two groups. The median peak AST level was significantly higher in those with rhabdomyolysis than in those with alcoholic liver disease (221 U/L [IQR, 118–553] vs. 103 U/L [IQR, 59–206]), but the median peak ALT was not significantly different (102 U/L [IQR, 58–222] vs. 51 U/L [IQR, 26–117]). CONCLUSIONS: Rhabdomyolysis showed an elevated AST-dominant aminotransferase level, which is not different according to concurrent liver disease. Therefore, it is recommended that rhabdomyolysis be considered first in cases of elevated aminotransferase levels in patients with a suspicious skeletal muscle injury.
Alanine Transaminase ; Aspartate Aminotransferases ; Creatinine ; Hand ; Humans ; Liver Diseases ; Liver Diseases, Alcoholic ; Liver ; Muscle, Skeletal ; Phosphotransferases ; Rhabdomyolysis