Background: Flow cytometric immunophenotyping of hematolymphoid neoplasms (FCIHLN) is essential for diagnosis, classification, and minimal residual disease (MRD) monitoring. FCI-HLN is typically performed using in-house protocols, raising the need for standardization. Therefore, we surveyed the current status of FCI-HLN in Korea to obtain fundamental data for quality improvement and standardization. Methods: Eight university hospitals actively conducting FCI-HLN participated in our survey.We analyzed responses to a questionnaire that included inquiries regarding test items, reagent antibodies (RAs), fluorophores, sample amounts (SAs), reagent antibody amounts (RAAs), acquisition cell number (ACN), isotype control (IC) usage, positiveegative criteria, and reporting. Results: Most hospitals used acute HLN, chronic HLN, plasma cell neoplasm (PCN), and MRD panels. The numbers of RAs were heterogeneous, with a maximum of 32, 26, 12, 14, and 10 antibodies used for acute HLN, chronic HLN, PCN, ALL-MRD, and multiple myeloma-MRD, respectively. The number of fluorophores ranged from 4 to 10. RAs, SAs, RAAs, and ACN were diverse. Most hospitals used a positive criterion of 20%, whereas one used 10% for acute and chronic HLN panels. Five hospitals used ICs for the negative criterion. Positiveegative assignments, percentages, and general opinions were commonly reported. In MRD reporting, the limit of detection and lower limit of quantification were included. Conclusions This is the first comprehensive study on the current status of FCI-HLN in Korea, confirming the high heterogeneity and complexity of FCI-HLN practices. Standardization of FCI-HLN is urgently needed. The findings provide a reference for establishing standard FCI-HLN guidelines.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Background: Tuberculosis (TB) survivors have an increased risk of developing chronic obstructive pulmonary disease (COPD). This study assessed the risk of COPD development and COPD-related hospitalization in TB survivors compared to controls. Methods: We conducted a population-based cohort study of TB survivors and 1:1 age- and sex-matched controls using data from the Korean National Health Insurance Service database collected from 2010 to 2017. We compared the risk of COPD development and COPD-related hospitalization between TB survivors and controls. Results: Of the subjects, 9.6% developed COPD, and 2.8% experienced COPD-related hospitalization. TB survivors had significantly higher COPD incidence rates (36.7/1,000 vs. 18.8/1,000 person-years, P < 0.001) and COPD-related hospitalization (10.7/1,000 vs.4.3/1,000 person-years, P < 0.001) than controls. Multivariable Cox regression analyses revealed higher risks of COPD development (adjusted hazard ratio [aHR], 1.63; 95% confidence interval [CI], 1.54–1.73) and COPD-related hospitalization (aHR, 2.03; 95% CI, 1.81–2.27) in TB survivors. Among those who developed COPD, the hospitalization rate was higher in individuals with post-TB COPD compared to those with non-TB COPD (10.7/1,000 vs. 4.9/1,000 person-years, P < 0.001), showing an increased risk of COPD-related hospitalization (aHR, 1.84; 95% CI, 1.17–2.92). Conclusion TB survivors had higher risks of incident COPD and COPD-related hospitalization compared to controls. These results suggest that previous TB is an important COPD etiology associated with COPD-related hospitalization.

Objective: This study was performed to evaluate the efficacy and safety of lurasidone (160 mg/day) compared to quetiapine XR (QXR; 600 mg/day) in the treatment of acutely psychotic patients with schizophrenia. Methods: Patients were randomly assigned to 6 weeks of double-blind treatment with lurasidone 160 mg/day (n=105) or QXR 600 mg/day (n=105). Primary efficacy measure was the change from baseline to week 6 in Positive and Negative Syndrome Scale (PANSS) total score and Clinical Global Impressions severity (CGI-S) score. Adverse events, body measurements, and laboratory parameters were assessed. Results: Lurasidone demonstrated non-inferiority to QXR on the PANSS total score. Adjusted mean±standard error change at week 6 on the PANSS total score was -26.42±2.02 and -27.33±2.01 in the lurasidone and QXR group, respectively. The mean difference score was -0.91 (95% confidence interval -6.35–4.53). The lurasidone group showed a greater reduction in PANSS total and negative subscale on week 1 and a greater reduction in end-point CGI-S score compared to the QXR group. Body weight, body mass index, and waist circumference in the lurasidone group were reduced, with significantly lower mean change compared to QXR. Endpoint changes in glucose, cholesterol, triglycerides, and low-density lipoprotein levels were also significantly lower. The most common adverse drug reactions with lurasidone were akathisia and nausea. Conclusion Lurasidone 160 mg/day was found to be non-inferior to QXR 600 mg/day in the treatment of schizophrenia with comparable efficacy and tolerability. Adverse effects of lurasidone were generally tolerable, and beneficial effects on metabolic parameters can be expected.

Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification. Methods: We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression. Results: We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667–1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667–1.000) showed the highest discriminatory ability to predict IgAN disease progression. Conclusion: This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.

Several studies have reported that depression is prevalent in patients with diabetes or chronic kidney disease. However, the relationship between weight changes and the risk of depression has not been elucidated in patients with diabetic kidney disease (DKD). Methods: From the Korean National Health Insurance Service database, we selected 67,866 patients with DKD and body weight data from two consecutive health examinations with a 2-year interval between 2009 and 2012. Weight change over 2 years was categorized into five groups: ≥–10%, <–10% to ≥–5%, <–5% to <5%, ≥5% to <10%, and ≥10%. The occurrence of depression was monitored via the codes of International Statistical Classification of Diseases, 10th revision through the end of 2018. Results: During the 5.24-year follow-up, 17,023 patients with DKD developed depression. Weight change and the risk of depression had a U-shaped relationship: patients with ≥–10% weight change (hazard ratio [HR], 1.12) and those with ≥10% weight change (HR, 1.11) showed higher HRs for depression than those with <–5% to <5% weight change, even after adjusting for several confounding factors. In the subgroup analyses, the risk of depression tended to increase as weight gain or weight loss increased in all subgroups. Conclusion: Both weight loss and weight gain increased the risk of depression in patients with DKD.