A pyramidal molar is which has completely fused roots with a solitary enlarged canal. The purpose of this retrospective study was to assess the prevalence and characteristics of pyramidal molars among adolescent.
A total of 1,612 patients’ panoramic radiographs were screened. A total of 12,896 first and second molars were evaluated. The relative incidence and the correlations regarding the location of pyramidal molar (maxillary versus mandibular) and gender were analyzed using the chi-square test.
The overall incidence of patients with pyramidal molars was 1.49%. 24 patients were found to have a pyramidal molar and it was more prevalent in women (18 women and 6 men). The prevalence of pyramidal molars from all first and second molars examined was 0.31%. 88 percent of pyramidal molars occurred in maxilla. All pyramidal molars were second molar.
Pyramidal molar has a relatively poor periodontal prognosis compared with common multi-rooted teeth and it is important to understand the structural characteristics of root canal during pulp treatment. Clinicians should be able to understand the anatomical properties of pyramidal molar and apply it to treatment and prognostic evaluation.

BACKGROUND/OBJECTIVES: The inhibitory effect of Hijikia fusiforme (HF) extracts on degenerative osteoarthritis was examined in primary cultured rat cartilage cells and a monosodium iodoacetate (MIA)-induced osteoarthritis rat model. MATERIALS/METHODS: In vitro, cell survival and the expression of matrix metalloproteinases (MMPs), collagen type I, collagen type II, aggrecan, and tissue inhibitor of metalloproteinases (TIMPs) was measured after H2O2 (800 µM, 2 hr) treatment in primary chondrocytes. In vivo animal study, osteoarthritis was induced by intra-articular injection of MIA into knee joints of rats, and then RH500, HFE250 and HFE500 were administered orally once a day for 28 days. To determine the anti-inflammatory effects of HFE, nitric oxide (NO), prostaglandin E2 (PGE2) expression were measured. In addition, real-time PCR was performed to measure the genetic expression of MMPs, collagen type I, collagen type II, aggrecan, and TIMPs. RESULTS: In the in vitro assay, cell survival after H2O2 treatment was increased by HFE extract (20% EtOH). In addition, anabolic factors (genetic expression of collagen type I, II, and aggrecan) were increased by HFE extract (20% EtOH). However, the genetic expression of MMP-3 and 7, known as catabolic factors were significantly inhibited by treatment with HFE extract (20% EtOH). In the in vivo assay, anabolic factors (genetic expression of collagen type I, II, aggrecan, and TIMPs) were increased by oral administration of HFE extract. However, the genetic expression of MMP-3 and 7, known as catabolic factors, and production of NO and PGE2 were significantly inhibited by treatment with oral administration of HFE extract. CONCLUSIONS: HFE extract inhibited articular cartilage degeneration through preventing extracellular matrix degradation and chondrocyte injury.
Administration, Oral ; Aggrecans ; Animals ; Cartilage ; Cartilage, Articular ; Cell Survival ; Chondrocytes ; Collagen ; Collagen Type I ; Collagen Type II ; Dinoprostone ; Extracellular Matrix ; In Vitro Techniques* ; Injections, Intra-Articular ; Knee Joint ; Matrix Metalloproteinases ; Models, Animal ; Nitric Oxide ; Osteoarthritis* ; Rats ; Real-Time Polymerase Chain Reaction ; Tissue Inhibitor of Metalloproteinases

BACKGROUND/OBJECTIVES: The inhibitory effect of Hijikia fusiforme (HF) extracts on degenerative osteoarthritis was examined in primary cultured rat cartilage cells and a monosodium iodoacetate (MIA)-induced osteoarthritis rat model. MATERIALS/METHODS: In vitro, cell survival and the expression of matrix metalloproteinases (MMPs), collagen type I, collagen type II, aggrecan, and tissue inhibitor of metalloproteinases (TIMPs) was measured after H2O2 (800 µM, 2 hr) treatment in primary chondrocytes. In vivo animal study, osteoarthritis was induced by intra-articular injection of MIA into knee joints of rats, and then RH500, HFE250 and HFE500 were administered orally once a day for 28 days. To determine the anti-inflammatory effects of HFE, nitric oxide (NO), prostaglandin E2 (PGE2) expression were measured. In addition, real-time PCR was performed to measure the genetic expression of MMPs, collagen type I, collagen type II, aggrecan, and TIMPs. RESULTS: In the in vitro assay, cell survival after H2O2 treatment was increased by HFE extract (20% EtOH). In addition, anabolic factors (genetic expression of collagen type I, II, and aggrecan) were increased by HFE extract (20% EtOH). However, the genetic expression of MMP-3 and 7, known as catabolic factors were significantly inhibited by treatment with HFE extract (20% EtOH). In the in vivo assay, anabolic factors (genetic expression of collagen type I, II, aggrecan, and TIMPs) were increased by oral administration of HFE extract. However, the genetic expression of MMP-3 and 7, known as catabolic factors, and production of NO and PGE2 were significantly inhibited by treatment with oral administration of HFE extract. CONCLUSIONS: HFE extract inhibited articular cartilage degeneration through preventing extracellular matrix degradation and chondrocyte injury.
Administration, Oral ; Aggrecans ; Animals ; Cartilage ; Cartilage, Articular ; Cell Survival ; Chondrocytes ; Collagen ; Collagen Type I ; Collagen Type II ; Dinoprostone ; Extracellular Matrix ; In Vitro Techniques* ; Injections, Intra-Articular ; Knee Joint ; Matrix Metalloproteinases ; Models, Animal ; Nitric Oxide ; Osteoarthritis* ; Rats ; Real-Time Polymerase Chain Reaction ; Tissue Inhibitor of Metalloproteinases

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.