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.

The μ-opioid receptor (MOR) is a class of opioid receptors characterized by a high affinity for β-endorphin and morphine. MOR is a G proteincoupled receptor (GPCR) that plays a role in reward and analgesic effects. While expression of MOR has been well established in neurons and microglia, astrocytic MOR expression has been less clear. Recently, we have reported that MOR is expressed in hippocampal astrocytes, and its activation has a critical role in the establishment of conditioned place preference. Despite this critical role, the expression and function of astrocytic MOR from other brain regions are still unknown. Here, we report that MOR is significantly expressed in astrocytes and GABAergic neurons from various brain regions including the hippocampus, nucleus accumbens, periaqueductal gray, amygdala, and arcuate nucleus. Using the MORmCherry reporter mice and Imaris analysis, we demonstrate that astrocytic MOR expression exceeded 60% in all tested regions. Also, we observed similar MOR expression of GABAergic neurons as shown in the previous distribution studies and it is noteworthy that MOR expression is particularly in parvalbumin (PV)-positive neurons. Furthermore, consistent with the normal MOR function observed in the MOR-mCherry mouse, our study also demonstrates intact MOR functionality in astrocytes through iGluSnFr-mediated glutamate imaging. Finally, we show the sex-difference in the expression pattern of MOR in PV-positive neurons, but not in the GABAergic neurons and astrocytes. Taken together, our findings highlight a substantial astrocytic MOR presence across various brain regions.

Parkinson’s disease (PD) is the most prevalent neurodegenerative motor disorder. While PD has been attributed to dopaminergic neuronal death in substantia nigra pars compacta (SNpc), accumulating lines of evidence have suggested that reactive astrogliosis is critically involved in PD pathology. These pathological changes are associated with α-synuclein aggregation, which is more prone to be induced by an A53T mutation. Therefore, the overexpression of A53T-mutated α-synuclein (A53T-α-syn) has been utilized as a popular animal model of PD. However, this animal model only shows marginal-to-moderate extents of reactive astrogliosis and astrocytic α-synuclein accumulation, while these phenomena are prominent in human PD brains. Here we show that Adeno-GFAP-GFP virus injection into SNpc causes severe reactive astrogliosis and exacerbates the A53Tα-syn-mediated PD pathology. In particular, we demonstrate that AAV-CMV-A53T-α-syn injection, when combined with Adeno-GFAP-GFP, causes more significant loss of dopaminergic neuronal tyrosine hydroxylase level and gain of astrocytic GFAP and GABA levels. Moreover, the combination of AAV-CMV-A53T-α-syn and Adeno-GFAP-GFP causes an extensive astrocytic α-syn expression, just as in human PD brains. These results are in marked contrast to previous reports that AAV-CMV-A53T-α-syn alone causes α-syn expression mostly in neurons but rarely in astrocytes. Furthermore, the combination causes a severe PD-like motor dysfunction as assessed by rotarod and cylinder tests within three weeks from the virus injection, whereas Adeno-GFAP-GFP alone or AAV-CMV-A53T-α-syn alone does not. Our findings implicate that inducing reactive astrogliosis exacerbates PD-like pathologies and propose the virus combination as an advanced strategy for developing a new animal model of PD.

Parkinson’s disease (PD) is the most prevalent neurodegenerative motor disorder. While PD has been attributed to dopaminergic neuronal death in substantia nigra pars compacta (SNpc), accumulating lines of evidence have suggested that reactive astrogliosis is critically involved in PD pathology. These pathological changes are associated with α-synuclein aggregation, which is more prone to be induced by an A53T mutation. Therefore, the overexpression of A53T-mutated α-synuclein (A53T-α-syn) has been utilized as a popular animal model of PD. However, this animal model only shows marginal-to-moderate extents of reactive astrogliosis and astrocytic α-synuclein accumulation, while these phenomena are prominent in human PD brains. Here we show that Adeno-GFAP-GFP virus injection into SNpc causes severe reactive astrogliosis and exacerbates the A53Tα-syn-mediated PD pathology. In particular, we demonstrate that AAV-CMV-A53T-α-syn injection, when combined with Adeno-GFAP-GFP, causes more significant loss of dopaminergic neuronal tyrosine hydroxylase level and gain of astrocytic GFAP and GABA levels. Moreover, the combination of AAV-CMV-A53T-α-syn and Adeno-GFAP-GFP causes an extensive astrocytic α-syn expression, just as in human PD brains. These results are in marked contrast to previous reports that AAV-CMV-A53T-α-syn alone causes α-syn expression mostly in neurons but rarely in astrocytes. Furthermore, the combination causes a severe PD-like motor dysfunction as assessed by rotarod and cylinder tests within three weeks from the virus injection, whereas Adeno-GFAP-GFP alone or AAV-CMV-A53T-α-syn alone does not. Our findings implicate that inducing reactive astrogliosis exacerbates PD-like pathologies and propose the virus combination as an advanced strategy for developing a new animal model of PD.

BACKGROUND: This study aimed to explore the associations of social-demographic, health-behavioral, and woman-specific factors with the non-screening status of the cervical cancer screening test in Korean adult women. METHODS: This study was a cross-sectional analysis of 9,698 Korean adult women aged 20 years or more who participated in the Korea National Health and Nutrition Examination Surveys V (2010–2012). Rao-Scott chi-square tests and survey logistic regression analyses were employed respectively to analyze the difference in the non-screening status of the cervical cancer screening test by each characteristic and to explore the factors related to the non-screening status. RESULTS: The rate of the non-screening status of the cervical cancer screening test was 53.5% over the previous 2 years. In the survey logistics regression analysis, age, marital status, educational levels, income levels, residential area, occupation, private health insurance, smoking, alcohol, obesity, menstrual status, pregnancy experience, and hormone replacement therapy were significantly associated with the non-screening status of the cervical cancer screening test. CONCLUSION On the basis of the results of this study, some social-demographic, health-behavioral, and woman-specific characteristics of Korean adult women seem to affect the non-screening status of the cervical cancer screening test. Therefore, appropriate health policies need to be designed, implemented, monitored, and evaluated to reduce the non-screening status of the cervical cancer screening test of them.