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.

Ehlers-Danlos syndrome (EDS) is a rare genetic disorder in dogs and cats and has been mostly reported in purebred cats. In this study, we report a case of a 1-year-old castrated male Korean shorthair cat, who presented with multiple small skin tears and bruises distributed over the entire trunk area. The cat’s skin was hyperextensible and easily torn with gentle touch. The skin extensibility index of the cat was 25%, indicating the possibility of EDS. The cat exhibited no signs of pruritus or inflammation, and no underlying disease was found.However, radiography revealed hip joint subluxation and arthritis. Following this, biopsy of the lacerated skin was performed. Histopathological examination of the skin revealed that in the dermis adjacent to the lesions, the collagen fibers were irregular in size and width, with a slightly thinner epidermis, and increased interfibrillar spaces containing low numbers of scattered well-differentiated fibroblasts and mast cells. Histopathological examination of the skin confirmed EDS. The symptoms observed in the cat, including skin hyperextensibility, multiple bruising, hip joint subluxation, and arthritis, corresponded to the classical subtype of EDS in humans. Thus, this study is a rare report of a classical EDS case in a Korean shorthair cat. This study suggests that skin extensibility index and biopsy are useful diagnostic procedures for confirming EDS in animals until a more definitive genetic test is established.

A 6-year-old spayed female French Bulldog presented with a left-sided chest wall tumor. Physical examination revealed that the tumor was firmly adhered to the chest wall. A preoperative punch biopsy of the tumor revealed a grade 2 soft tissue sarcoma (STS). On computed tomography, the tumor’s dimensions were assessed as 6.5 × 5.7 × 3.5 cm, and it exhibited invasiveness near the tissue surrounding the ninth rib. The tumor size was large in comparison to the dog’s chest wall area. Hence, if the traditional wide-margin resection surgery were to be performed, primary wound closure seemed impractical and could potentially result in respiratory function complications. Therefore, considering the extent of tumor invasion and grade, deep margins were established to include the removal of the eighth to tenth ribs, and a 1-cm lateral margin was designated to enable primary wound closure. To reconstruct the chest wall, polypropylene mesh was attached to the adjacent ribs and the remaining muscles were sutured and covered over the mesh. The dog exhibited a rapid recovery beginning the day after the operation. Postoperative biopsy confirmed that the tumor was a grade 2 STS, and the surgical margins were evaluated as incomplete. The owner chose to pursue follow-up observation instead of chemotherapy. In this study, the surgical approach was chosen based on the importance of functional recovery after surgery. Recent research indicates that the tumor grade is more critical for postoperative prognosis than the extent of surgical margins when removing an STS.

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.

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.

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.