Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate urine microbiome differences among healthy women, women with recurrent uncomplicated cystitis (rUC), and those with sporadic/single uncomplicated cystitis (sUC) to challenge traditional beliefs about origins of these infections. Materials and Methods: Patients who underwent both conventional urine culture and next-generation sequencing (NGS) of urine were retrospectively reviewed. Symptom-free women with normal urinalysis results as a control group were also studied. Samples were collected via transurethral catheterization. Results: In the control group, urine microbiome was detected on NGS in 83.3%, with Lactobacillus and Prevotella being the most abundant genera. The sensitivity of urine NGS was significantly higher than that of conventional urine culture in both the sUC group (91.2% vs. 32.4%) and the rUC group (82.4% vs. 16.4%). In urine NGS results, Enterobacterales, Prevotella, and Escherichia/ Shigella were additionally found in the sUC group, while the recurrent urinary tract infection (rUTI)/rUC group exhibited the presence of Lactobacillus, Prevotella, Enterobacterales, Escherichia/Shigella, and Propionibacterium. Moreover, distinct patterns of urine NGS were observed based on menopausal status and ingestion of antibiotics or probiotics prior to NGS test sampling. Conclusions Urine microbiomes in control, sUC, and rUTI/rUC groups exhibited distinct characteristics. Notably, sUC and rUC might represent entirely separate pathological processes, given their distinct urine microbiomes. Consequently, the use of urine NGS might be essential to enhancing sensitivity compared to conventional urine culture in both sUC and rUTI/rUC groups.

Background/Aims: The prevalence and incidence of ulcerative colitis (UC) in Korea is increasing. Each patient has a different disease course and treatment response. Recently, with the development of biologic agents, histological remission has become a treatment goal. In this study, we aimed to identify the predictors of histological remission after first-line biologic agent treatment in patients with biologic agent-naïve UC. Methods: We retrospectively analyzed the medical records of 92 patients who had been diagnosed with UC and treated with first-line biologic agent treatment at our center, between 2015 and 2022. The clinical characteristics, laboratory test results, and endoscopic and biopsy findings were analyzed. Histological remission was defined as the absence of cryptitis, crypt abscesses, and inflammatory cells on histology. Univariate and multivariate logistic regression analyses were performed to identify the predictors of histological remission after first-line treatment. Results: Of the total 92 patients, 25 (27.2%) achieved histological remission. Each cohort had a varied body mass index (BMI) distribution, with a statistically significant overweight ratio, as defined by the Asian-Pacific BMI category of 23–25 kg/m2, of 48.0% in the histological remission cohort (P= 0.026). A causal correlation between the overweight category and histological remission was confirmed (odds ratio, 3.883; 95% confidence interval, 1.141–13.212; P= 0.030). Conclusions We confirmed that the overweight category was a predictor of histological remission after first-line treatment with a biological agent. However, as BMI does not account for skeletal muscle mass, future studies are required to confirm the correlation between skeletal muscle mass and histological remission.

Cancer cell heterogeneity is a serious problem in the control of tumor progression because it can cause chemoresistance and metastasis. Heterogeneity can be generated by various mechanisms, including genetic evolution of cancer cells, cancer stem cells (CSCs), and niche heterogeneity. Because the genetic heterogeneity of CSCs has been poorly characterized, the genetic mutation status of CSCs was examined using Exome-Seq and RNA-Seq data of liver cancer.Here we show that different surface markers for liver cancer stem cells (LCSCs) showed a unique propensity for genetic mutations. Cluster of differentiation 133 (CD133)-positive cells showed frequent mutations in the IRF2, BAP1, and ERBB3 genes. However, leucine-rich repeat-containing G protein-coupled receptor 5-positive cells showed frequent mutations in the CTNNB1, RELN, and ROBO1 genes. In addition, some genetic mutations were frequently observed irrespective of the surface markers for LCSCs. BAP1 mutations was frequently observed in CD133-, CD24-, CD13-, CD90-, epithelial cell adhesion molecule-, or keratin 19-positive LCSCs. ASXL2, ERBB3, IRF2, TLX3, CPS1, and NFATC2 mutations were observed in more than three types of LCSCs, suggesting that common mechanisms for the development of these LCSCs. The present study provides genetic heterogeneity depending on the surface markers for LCSCs. The genetic heterogeneity of LCSCs should be considered in the development of LCSC-targeting therapeutics.

Purpose: Hypoxaemia is a significant adverse event during endoscopic retrograde cholangiopancreatography (ERCP) under monitored anaesthesia care (MAC); however, no model has been developed to predict hypoxaemia. We aimed to develop and compare logistic regression (LR) and machine learning (ML) models to predict hypoxaemia during ERCP under MAC. Materials and Methods: We collected patient data from our institutional ERCP database. The study population was randomly divided into training and test sets (7:3). Models were fit to training data and evaluated on unseen test data. The training set was further split into k-fold (k=5) for tuning hyperparameters, such as feature selection and early stopping. Models were trained over k loops; the i-th fold was set aside as a validation set in the i-th loop. Model performance was measured using area under the curve (AUC). Results: We identified 6114 cases of ERCP under MAC, with a total hypoxaemia rate of 5.9%. The LR model was established by combining eight variables and had a test AUC of 0.693. The ML and LR models were evaluated on 30 independent data splits. The average test AUC for LR was 0.7230, which improved to 0.7336 by adding eight more variables with an l 1 regularisation-based selection technique and ensembling the LRs and gradient boosting algorithm (GBM). The high-risk group was discriminated using the GBM ensemble model, with a sensitivity and specificity of 63.6% and 72.2%, respectively. Conclusion We established GBM ensemble model and LR model for risk prediction, which demonstrated good potential for preventing hypoxaemia during ERCP under MAC.

Background: To evaluate workload in emergency rooms (ERs) among clinical specialties including neurology and investigate characteristics of neurological consultations in ER. Methods: A nationwide survey was conducted to evaluate the number of specialists, resident physicians/surgeons, and emergency consultations of each clinical specialty in Korean tertiary referral hospitals from 2018 to 2019. Characteristics of neurological emergency consultations during the same period were investigated in one of the hospitals that participated. Results: A total of 23 hospitals were included. Four irrelevant clinical specialties were excluded. The number of neurology specialists and resident physicians were 12.5/hospital (4.1% of all specialists) and 6.4/hospital (3.4% of all resident physicians/surgeons), respectively, while the mean numbers of specialists and resident physicians or surgeons per clinical specialty were 13.7/hospital and 8.6/hospital, respectively. Neurological consultations accounted for 11.0% of all ER consultations for severe patients (Korean Triage and Acuity Scale level 1-3). Annual ER consultations for severe patients per neurology specialist was 274.1, which was only second to pediatrics (290.0). Annual ER consultations for severe patients per resident physician was 406.6 which was 1.6 times higher than the second highest (internal medicine, 247.0). Frequent conditions for neurological ER consultation were dizziness (24.8%), motor weakness (23.9%), headache (10.6%), dysarthria (9.9%), and seizures (7.7%). Frequent primary diagnoses were cerebrovascular diseases (29.0%) and episodic and paroxysmal disorders (24.9%). Conclusions Workloads of neurology specialists for ER consultation were significantly heavy, and the workload of neurology resident physicians was the heaviest among all specialties. This should be considered in health care policies.