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.

Purpose: We compared the cataract grades with slit lamp examination and anterior segment photography using the Lens Opacities Classification System (LOCS) III criteria. We also explored the effect of a yellow filter on the photographic results. Methods: Eighty eyes with cataracts were examined by three inspectors (1, 2, and 3). Anterior segment photographs taken by inspector 1 were divided into two groups depending on whether cortical opacity or nuclear sclerosis predominated. In each group, the cataract grades determined by inspector 1 on slit lamp examination and anterior segment photography were compared. Also, after randomly assigning the anterior segment photographs taken by inspector 1 to inspectors 2 and 3, the cataract grades of these photographs were compared to the grades of photographs taken by all inspectors using a yellow filter. Results: The average cortical opacity evaluated by inspector 1 on slit lamp examination (3.48 ± 0.91) was significantly higher than that apparent on anterior segment photographs (2.35 ± 0.77) (p < 0.001). In the photographs, the average cortical opacity when a yellow filter was used was significantly higher for both inspectors 1 (p < 0.001) and 2 (p = 0.022) than when the filter was absent. The average extent of nuclear sclerosis evaluated by inspector 1 on slit lamp examination (4.08 ± 0.94) was significantly higher than that of anterior segment photography (3.73 ± 1.24) (p = 0.042). Conclusions Cataract evaluation via anterior segment photography underestimates the extent of damage compared to direct slit lamp examination. However, use of a yellow filter during photography aids cataract evaluation, especially cortical opacity.

BACKGROUND: The aim of the current study was to investigate the factors related to the non-practice of cancer screening in cancer survivors, who are at high risk of developing second cancers. METHODS: This study is a cross-sectional analysis of 1,125 cancer survivors > or =19 years old who participated in the Korean National Health and Nutrition Examination Surveys IV and V (2007-2012). A Rao-scott chi-square test and a survey logistic regression analysis were employed respectively to analyze the difference of cancer survivors in cancer screening by each characteristic and the factors related to the non-practice of cancer screening in cancer survivors. RESULTS: Among total subjects, 33.5% did not participate in cancer screening in the last two years. Results from a fully adjusted logistic model showed that the non-practice of cancer screening in cancer survivors was significantly associated with variables such as sex, age, marital status, education level, monthly income, and drinking a alcoholic beverage Specifically, the odds ratio of non-practice of cancer screening was higher in males than in females, in the younger group than in older group, in the group with no spouse than in the group with a spouse; in a group with a low level of education than in a group with a high level of education; in a group with the lowest income level than in a group with the other levels of income; or in non-drinkers than in drinkers. CONCLUSION: Health policies to reduce the non-practice rate of cancer screening in cancer survivors should be designed and implemented with close attention to cancer survivors\' socio-economic characteristics such as sex, age, marital status, education, and income, along with a health behavioral characteristic as drinking.
Alcoholic Beverages ; Cross-Sectional Studies ; Drinking ; Early Detection of Cancer* ; Education ; Female ; Health Behavior ; Health Policy ; Humans ; Logistic Models ; Male ; Marital Status ; Neoplasms, Second Primary ; Nutrition Surveys* ; Odds Ratio ; Spouses ; Survivors*

OBJECTIVE: To investigate the energy expenditure (EE) of Korean young adults based on activities refined to a deskbound lifestyle. METHODS: Sixty-four healthy office workers aged between 25 and 46 years participated in this study. EE was expressed as metabolic equivalent of task (MET). Participants were evaluated in terms of their EE during physical activities of sleeping (n=22), typing (n=37), folding laundry (n=34), dishwashing (n=32), studying (n=18), mopping (n=35), walking (n=33), stair climbing (n=23), and running (n=29). Volume of oxygen consumption was measured by indirect calorimetry K4b² (COSMED). The results were compared to the established Compendium MET. RESULTS: The MET of activities were: sleeping, 1.24±0.43; typing, 1.35±0.25; folding laundry, 1.58±0.51; dishwashing, 2.20±0.51; studying, 2.11±0.90; mopping, 2.72±0.69; walking at 4 km/hr, 3.48±0.65; stair climbing of five stories, 6.18±1.08; and running at 8 km/hr, 7.57±0.57. The values of typing and mopping were similar to those in the Compendium, whereas those of sleeping, folding laundry, dishwashing, studying, walking, stair climbing and running were different. CONCLUSION: To our knowledge, this estimation of EE in MET during activities of daily living is the first data of young adults in Korea. These data could be used as a reference to modify the guidelines of physical activities for the age group examined in this study.
Activities of Daily Living* ; Calorimetry, Indirect ; Energy Metabolism* ; Humans ; Korea ; Life Style ; Metabolic Equivalent ; Motor Activity ; Oxygen Consumption ; Physical Exertion ; Running ; Walking ; Young Adult*

Proximal junctional problems are among the potential complications of surgery for adult spinal deformity (ASD) and are associated with higher morbidity and increased rates of revision surgery. The diverse manifestations of proximal junctional problems range from proximal junctional kyphosis (PJK) to proximal junctional failure (PJF). Although there is no universally accepted definition for PJK, the most common is a proximal junctional angle greater than 10° that is at least 10° greater than the preoperative measurement. PJF represents a progression from PJK and is characterized by pain, gait disturbances, and neurological deficits. The risk factors for PJK can be classified according to patient-related, radiological, and surgical factors. Based on an understanding of the modifiable factors that contribute to reducing the risk of PJK, prevention strategies are critical for patients with ASD.

Adult spinal deformity (ASD) is characterized by three-dimensional abnormalities of the thoracic or thoracolumbar spine that exerts significant impacts on the health-related quality of life (HRQoL). With the important effects that deformity of the sagittal plane exerts on the HRQoL, there have been paradigm shifts in ASD evaluation and management. Loss of lumbar lordosis is recognized as a key driver of ASD followed by reducing kyphosis, pelvic retroversion, and knee flexion. The Scoliosis Research Society (SRS)– Schwab classification reflects the sagittal spinopelvic parameters that correlate pain and disability in ASD patients. Although the SRS–Schwab classification provides a realignment target framework for surgeons, a structured patient-specific systemic approach is crucial for the process of decision-making. ASD management should be focused on restoring age-specific harmonious alignment and should consider the comorbidities and risk factors of each patient to prevent catastrophic complications and enhance the HRQoL.

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.