Objective: Thoracic pedicle screw fixation is technically demanding because of the natural variability in pedicle anatomy, a challenge that is further exacerbated in patients with spinal tumors who often have compromised pedicles. The L-point entry, juxtapedicular, and endplate-parallel trajectory (L-JET) technique was developed to provide a uniform entry point, a predictable screw trajectory, and the capacity to accommodate larger-diameter screws at levels T3–10. This study aimed to evaluate the safety and feasibility of the L-JET technique in comparison with the conventional transpedicular method. Methods: A comparative analysis of consecutively collected data was performed to evaluate outcomes associated with the L-JET technique in patients undergoing surgery for thoracic extradural spinal tumors. The L-point was defined as the intersection of a vertical line along the lateral margin of the facet joint and a horizontal line along the upper edge of the transverse process. The screw trajectory was planned with 30° of medial convergence in the axial plane, creating a juxtapedicular path, and a straightforward trajectory in the sagittal plane. Primary outcome measures included screw diameter, screw length, cortical breach rate, and the need for revision surgery, as assessed using computed tomography scans and medical records. Results: A total of 108 screws were placed in 22 patients using the L-JET technique, while 98 screws were placed in 18 patients using the conventional technique. The L-JET group used significantly larger-diameter screws (6.06±0.65 mm) than the conventional group (5.74±0.80 mm, p=0.02), with no significant difference observed in screw length between groups. Medial cortical breach occurred in one screw in each group, and no supra- or infrapedicular breaches were identified. No screw-related neurovascular complications or revision surgeries occurred in either group. Conclusion The L-JET technique enables consistent screw placement with larger-diameter screws, even in compromised pedicles associated with thoracic spinal tumors, without increasing the risk of complications or the need for revision surgery.

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.

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