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.

Background: Remimazolam is an ultrashort-acting benzodiazepine. Few studies have evaluated the effects of remimazolam-based total intravenous anesthesia (TIVA) on emergence agitation (EA). This study aimed to compare the incidence and severity of EA between TIVA using remimazolam and desflurane. Methods: This prospective randomized controlled study enrolled 76 patients who underwent nasal surgery under general anesthesia. Patients were randomized into two groups of 38 each: desflurane-nitrous oxide (N2O) (DN) and remimazolam-remifentanil (RR) groups. The same protocol was used for each group from induction to emergence, except for the use of different anesthetics during maintenance of anesthesia according to the assigned group: desflurane and nitrous oxide for the DN group and remimazolam and remifentanil for the RR group. The incidence of EA as the primary outcome was evaluated using three scales: Ricker Sedation-Agitation Scale, Richmond Agitation-Sedation Scale, and Aono’s four-point agitation scale. Additionally, hemodynamic changes during emergence and postoperative sense of suffocation were compared. Results: The incidence of EA was significantly lower in the RR group than in the DN group in all three types of EA assessment scales (all P < 0.001). During emergence, the change in heart rate differed between the two groups (P = 0.002). The sense of suffocation was lower in the RR group than in the DN group (P = 0.027). Conclusions RR reduced the incidence and severity of EA in patients undergoing nasal surgery under general anesthesia. In addition, RR was favorable for managing hemodynamics and postoperative sense of suffocation.

Methods: Seven human lumbopelvic spines were used, each affixed to six-degrees-of-freedom testing apparatus; 8.5-Nm pure unconstrained bending moments applied in flexion-extension, lateral bending, and axial rotation. The ROM of left and right SIJ was measured using a motion analysis system. Each specimen tested as (1) intact, (2) injury (left), (3) L5–S1 fixation, (4) unilateral stabilization (left), (5) unilateral stabilization+L5–S1 fixation, (6) bilateral stabilization, and (7) bilateral stabilization+L5–S1 fixation. Both left-sided iliosacral and posterior ligaments were cut for injury condition to model SIJ instability before surgery. Results: There were no statistical differences between fixated and contralateral nonfixated SIJ ROM following unilateral stabilization with/without L5–S1 fixation for all loading directions (p>0.930). Injured condition and L5–S1 fixation provided the largest motion increases across both joints; no significant differences were recorded between SIJs in any loading direction (p>0.850). Unilateral and bilateral stabilization with/without L5–S1 fixation reduced ROM compared with the injured condition for both SIJs, with bilateral stabilization providing maximum stability. Conclusions In the cadaveric model, unilateral SIJ stabilization with/without lumbosacral fixation did not lead to significant contralateral SIJ hypermobility; long-term changes and in vivo response may differ.

Background: Accurate diagnosis of diseases in animals is crucial for their treatment, and imaging evaluations such as radiographs, computed tomography (CT), and magnetic resonance imaging (MRI) are important tools for this purpose. However, a cross-sectional anatomical atlas of normal skeletal and internal organs of long-tailed gorals (Naemorhedus caudatus) has not yet been prepared for diagnosing their diseases. Objectives: The objective of this study was to create an anatomical atlas of gorals using CT and MRI, which are imaging techniques that have not been extensively studied in this type of wild animal in Korea. Methods: The researchers used CT and MRI to create an anatomical atlas of gorals, and selected 37 cross-sections from the head, thoracic, lumbar, and sacrum parts of gorals to produce an average cross-sectional anatomy atlas. Results: This study successfully created an anatomical atlas of gorals using CT and MRI. Conclusions The atlas provides valuable information for the diagnosis of diseases in gorals, which can improve their treatment and welfare. The study highlights the importance of developing cross-sectional anatomical atlases of gorals to diagnose and treat their diseases effectively.

Background: The use of translaminar screws may serve as a viable salvage method for complicated cases. To our understanding, the study of the feasibility of translaminar screw insertion in the actual entire subaxial cervical spine has not been carried out yet. The purpose of this study was to report the feasibility of translaminar screw insertion in the entire subaxial cervical spine. Methods: Eighteen cadaveric spines were harvested from C3 to C7 and 1-mm computed tomography (CT) scans and three-dimensional reconstructions were created to exclude any bony anomaly. Thirty anatomically intact segments were collected (C3, 2; C4, 3; C5, 3; C6, 8; and C7, 14), and randomly arranged. Twenty-one segments were physically separated at each vertebral level (group S), while 9 segments were not separated from the vertebral column and left in situ (group N–S). CT measurement of lamina thickness was done for both group S and group N–S, and manual measurement of various length and angle was done for group S only. Using the trajectory proposed by the previous studies, translaminar screws were placed at each level. Screw diameter was the same or 0.5 mm larger than the proposed diameter based on CT measurement. Post-insertion CT was performed. Cortical breakage was checked either visually or by CT. Results: When 1° and 2° screws of the same size were used, medial cortex breakage was found 13% and 33% of the time, respectively. C7 was relatively safer than the other levels. With larger-sized screws, medial cortex breakage was found in 47% and 46% of 1° and 2° screws, respectively. There were no facet injuries due to the screws in group N–S. Conclusions Translaminar screw insertion in the subaxial cervical spine is feasible only when the lamina is thick enough to avoid any breakage that could lead to further complications. The authors do not recommend inserting translaminar screws in the subaxial cervical spine except in some salvage cases in the presence of a thick lamina.

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.