Background: Korea National Health and Nutrition Examination Survey (KNHANES) triglyceride testing changed from the glycerol blanking method (2005–2021) to the glycerol nonblanking method (2022). We converted triglyceride data from 2005–2021 to that obtained since 2022 with different analytical methods. Methods: To develop a conversion equation, 98 fresh serum specimen pairs were compared using Passing–Bablok regression analysis. Implications of the conversion equation on epidemiological data were evaluated using KNHANES data from 2019–2021. Bias estimations determined using the Lipid Standardization Program (LSP) of the United States Centers for Disease Control and Prevention (CDC) enhanced the accuracy and comparability of the triglyceride results. Results: Triglyceride concentrations measured via the glycerol non-blanking method were 10.7 mg/dL (0.12 mmol/L, 10.0%) higher than those from the glycerol blanking method, with a 9.9 mg/dL (0.11 mmol/L, 5.0%) difference at a concentration of 200 mg/dL (2.26 mmol/L, N = 98). The conversion equation y (glycerol non-blanking, 2022) = 11.94+0.99x (glycerol blanking, 2005–2021) changed the mean triglyceride concentrations of the KNHANES 2019–2021 data (N = 16,015) from 123.7 mg/dL (1.40 mmol/L, 95% confidence interval [CI]: 122.2–125.1 mg/dL [1.38–1.41 mmol/L]) to 134.3 mg/dL (1.52 mmol/L, 95% CI: 132.9–135.8 mg/dL [1.50–1.53 mmol/L]). Since 2022, bias monitoring using the CDC’s LSP has remained within a 5.0% limit. Conclusions KNHANES triglyceride values in 2022 (non-blanking) were substantially higher than those from 2005–2021 (blanking). Conversion equations helped effectively adjust 2005–2021 data. Researchers should consider adjusting the KNHANES triglyceride data based on their study characteristics.

Background: Ensuring reference material (RM) commutability is crucial for evaluating measurement traceability in order to standardize laboratory tests. However, commutability assessment is not routinely performed. We assessed whether RMs prepared following CLSI C37-A guidelines could be used without assessing commutability by evaluating their commutability for four lipid measurements using the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and CLSI EP14 protocols. Methods: We analyzed total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in frozen sera from 20 individuals and 11 RMs, prepared by the Korea Disease Control and Prevention AgencyLaboratory Standardization Project (per CLSI C37-A), using six routine measurement procedures (MPs). Regression equations and 95% prediction intervals derived from single-donor sera were analyzed following CLSI EP14. The IFCC protocol was used to assess differences in inter-MP biases between RM and clinical samples. The effect of the TG concentration on commutability was evaluated by analyzing biases between MP results and reference procedure-assigned values. Results: RMs were commutable for most MP pairs for TC and TG. Commutability for HDL-C and LDL-C varied across RMs, with RM10 and RM11 showing higher TG levels (2.38 and 2.95 mmol/L, respectively) and lower commutability. Increased bias percentages from assigned values were observed for RMs with higher TG levels. Conclusions RMs prepared per CLSI C37-A were commutable with most MP pairs for TC and TG. Elevated TG levels affected HDL-C and LDL-C commutability, highlighting the need to consider TG concentrations during RM preparation and assess commutability to standardize laboratory tests.

Background: The utility of the QuantiFERON-Monitor (QFM, Qiagen), a tool developed to assess general immune function, remains insufficiently explored in critically ill patients with acute respiratory failure (ARF). Therefore, we used the QFM to evaluate the immune function of patients with ARF at intensive care unit (ICU) admission and monitored QFM changes based on disease severity and clinical outcome correlations. Methods: We evaluated the immune function of 99 patients with ARF in an ICU setting.The QFM was evaluated upon ICU admission, day 7 post-ICU admission, and discharge.Their results were compared with those of five healthy controls. Results: The QFM levels at ICU admission were significantly lower in patients with ARF than in healthy controls (median IUs/mL: 5.5 vs. 465.0, respectively). The QFM levels in patients with coronavirus disease 2019 or pneumonia (9.2 and 7.9 IUs/mL, respectively) were higher than those in patients with acute respiratory distress syndrome or septic shock (4.9 and 3.6 IUs/mL, respectively). On day 7, the QFM levels increased to 8.3 IUs/ mL and reached 16.7 IUs/mL at discharge. At ICU admission, patients requiring ventilator support had lower QFM levels than those requiring nasal prong or high-flow nasal cannula support. Those who died in the ICU had significantly lower QFM levels (4.0 IUs/mL) at ICU admission than those who survived (5.8 IUs/mL). Conclusions Reduced QFM levels among patients with severe ARF reflect impaired cellular immune responses and suggest that QFM may serve as a practical tool for early risk stratification and immune monitoring in ICU settings.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: Dental caries is a prevalent public health problem among adolescents that significantly affects their quality of life. The Caries Management by Risk Assessment (CAMBRA) model provides a structured, risk-based approach for caries prevention and management. This study evaluated the effectiveness of the CAMBRA-students mobile application, integrated with an information and communication technology (ICT) based intervention, in modifying caries risk factors in middle school students. Methods: A non-equivalent control group pretest-posttest design was used. A total of 131 middle school students aged 13∼14 years participated. The experimental group (n=68) received a risk-based intervention using the CAMBRA-student mobile application for 1 year, while the control group (n=63) received only standard oral health education and underwent pre- and post-tests. Data on caries risk factors; protective factors; disease indicators; and decayed, missing, and filled teeth rates were collected. Paired and independent t-tests were conducted to analyze changes within and between groups. Results: The rate of change in the caries risk classification before and after the intervention differed between the groups. In the control group, 50% of the low-risk participants were at a high-risk after 12 months. In the intervention group, 90% of the low-risk participants shifted to the high-risk category, and those classified as extremely high-risk moved to the high-risk category. When comparing changes in the caries risk assessment before and after the intervention, both groups demonstrated a significant increase in protective factors (p＜0.001). However, disease indicators increased significantly in the control, whereas no significant change was noted in the intervention group. Conclusion This study evaluated the effectiveness of an ICT-based caries management program for middle school students and confirmed changes in protective factors and specific oral health behaviors through personalized interventions. Future studies should investigate the long-term sustainability of such interventions across diverse populations to further establish their effectiveness.

Previous studies have shown that testosterone activates the GPRC6A-Duox1 axis, resulting in the production of H 2O 2 which leads to the apoptosis of keratinocytes and ultimately hair loss. Here, we elucidated a molecular mechanism by which the non-genomic action of testosterone regulates cellular redox status in androgenetic alopecia (AGA). Building upon this molecular understanding, we conducted a high-throughput screening assay of Nox inhibitors from a natural compounds library. This screening identified diterpenoid compounds, specifically Tanshinone I, Tanshinone IIA, Tanshinone IIB, and Cryptotanshinone, derived from Salviae Miltiorrhizae Radix. The IC50 values for Nox isozymes were found to be 2.6-12.9 μM for Tanshinone I, 1.9-7.2 μM for Tanshinone IIA, 5.2-11.9 μM for Tanshinone IIB, and 2.1-7.9 μM for Cryptotanshinone. Furthermore, 3D computational docking analysis confirmed the structural basis by which Tanshinone compounds inhibit Nox activity. These compounds were observed to substitute for NADPH at the π-π bond site between NADPH and FAD, leading to the suppression of Nox activity. Notably, Tanshinone I and Tanshinone IIA effectively inhibited Nox activity heightened by testosterone, consequently reducing the production of intracellular H2O2 and preventing cell apoptosis. In an animal study involving the application of testosterone to the back skin of 8-week-old C57BL/6J mice to inhibit hair growth, subsequent treatment with Tanshinone I or Tanshinone IIA alongside testosterone resulted in a substantial increase in hair follicle length compared to testosterone treatment alone. These findings underscore the potential efficacy of Tanshinone I and Tanshinone IIA as therapeutic agents for AGA by inhibiting Nox activity.