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.

Purpose: Stem cell transplantation (SCT) has historically played a major role in the long-term remission of mantle cell lymphoma (MCL), an incurable hematological malignancy. Using data from the Korean Society of Bone and Marrow Transplantation registry, we retrospectively analyzed the role of autologous (auto) and allogeneic (allo) SCT in longterm MCL survival. Methods: This study analyzed data from 188 patients (age ≥ 19 years at the time of transplantation) who underwent a transplant for MCL from 2011 to 2020. Progression-free survival (PFS) was defined as the time from transplantation to disease progression, relapse, or death from any cause. Overall survival (OS) was defined as the time from transplan‑ tation to death from any cause or the last follow-up. Results: In total, 109 patients underwent consolidative SCT after first-line chemotherapy. The 3-year PFS and OS rates were 65.4% and 78.5%, respectively, in the auto-SCT group, and 66.7% and 71.4%, respectively, in the allo-SCT group. The PFS and OS did not differ significantly between the auto- and allo-SCT groups. As part of salvage treatment, 52 patients with relapsed or refractory disease underwent auto- or allo-SCT. Patients who underwent auto-SCT with complete remis‑ sion/partial remission status reported better outcomes. In patients with refractory status, allogeneic transplantation using human leukocyte antigen (HLA) fully matched donors was a significantly favorable factor for PFS and OS. Conclusion The long-term survival of patients who underwent consolidative transplantation was similar to that reported in previous studies. Auto-SCT may be beneficial in patients who respond to salvage therapy, whereas allo-SCT with HLA-matched donors may be an alternative for patients with refractory disease. 

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.