Background: Humans are exposed to mercury primarily in its highly toxic form, methyl mercury, which is known to have adverse effects on various organs and systems. The negative impact of mercury exposure on the growth, development, and mental health of children, from infancy to adolescence, is well-documented. However, there are no internationally standardized safe limits for mercury exposure. This study investigated the impact of dietary habits and higher body mass index (BMI) on blood mercury levels in adolescents. Methods: This study analyzed the data from the 4th Korean National Environmental Health Survey (KoNEHS) 2018–2020. The focus was on 825 middle and high school students aged 13–18 years, whose blood mercury levels were measured. A survey on dietary and lifestyle habits was also conducted. Blood mercury levels were categorized by geometric median values, and associations with overweight status and seafood consumption were examined using a generalized linear model. Results: The geometric mean blood mercury level for the entire sample was 1.37 μg/L, with levels of 1.31 μg/L in normal-weight individuals and 1.43 μg/L in overweight individuals, showing a statistically significant difference between the two groups. After adjusting for other variables, blood mercury levels were significantly associated with overweight status (estimate: 0.084; p = 0.018; 95% confidence interval [CI]: 0.015–0.153), consumption of large fish and tuna more than once a week (estimate: 0.18; p = 0.001; 95% CI: 0.077–0.284), and consumption of fish once a week or more (estimate: 0.147; p = 0.004; 95% CI: 0.043–0.250). Conclusions In adolescents, a higher BMI and an increased consumption of large fish, tuna, and fish were associated with higher blood mercury levels. Notably, a stronger association was found between large fish consumption and blood mercury levels in the overweight group. These findings suggest the need to moderate seafood consumption and establish more proactive mercury exposure standards for adolescents.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Background: Prevention of contrast-induced nephropathy (CIN) is crucial in acute myocardial infarction (AMI) patients undergoing coronary interventions. Previous studies suggest that high-dose statins may aid in CIN prevention, yet comparative studies among different statin types using cystatin C (cysC) as a biomarker for CIN are absent. This study evaluated the effectiveness of high-dose rosuvastatin versus atorvastatin in preventing cysC-based CIN (cysC-CIN) in AMI patients. Methods: This multicenter registry included 431 patients (rosuvastatin 20 mg: n = 231, atorvastatin 40 mg: n = 200). The primary endpoint was cysC-CIN incidence within 48 hours post contrast; the secondary endpoints were creatinine-based CIN (cr-CIN) incidence within 72 hours post contrast and post 30 days adverse events. Results: The incidences of cysC-CIN (12.1% vs. 7.5%, P = 0.103) and cr-CIN (6.2% vs. 3.5%, P = 0.103) were higher in the atorvastatin group without significant statistical differences.Multivariable regression analysis, which was adjusted for CIN risk factors and the variables with univariate association, showed no increased odds ratio (OR) (OR, 2.185; 95% confidence interval [CI], 0.899, 5.315; P = 0.085) for cysC-CIN in the atorvastatin group compared to the rosuvastatin group. However, statin-naïve atorvastatin subgroup had significantly increased odds of cysC-CIN compared to the rosuvastatin group (OR, 2.977; 95% CI, 1.057, 8.378; P = 0.039). At post 30 days renal, cardiovascular, and mortality event rates were both low and similar between the two groups. Conclusion No significant difference in cysC-CIN incidence was found between the highdose rosuvastatin and atorvastatin groups in AMI patients and cysC was more sensitive to the early detection of CIN than creatinine.

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.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Background: Prevention of contrast-induced nephropathy (CIN) is crucial in acute myocardial infarction (AMI) patients undergoing coronary interventions. Previous studies suggest that high-dose statins may aid in CIN prevention, yet comparative studies among different statin types using cystatin C (cysC) as a biomarker for CIN are absent. This study evaluated the effectiveness of high-dose rosuvastatin versus atorvastatin in preventing cysC-based CIN (cysC-CIN) in AMI patients. Methods: This multicenter registry included 431 patients (rosuvastatin 20 mg: n = 231, atorvastatin 40 mg: n = 200). The primary endpoint was cysC-CIN incidence within 48 hours post contrast; the secondary endpoints were creatinine-based CIN (cr-CIN) incidence within 72 hours post contrast and post 30 days adverse events. Results: The incidences of cysC-CIN (12.1% vs. 7.5%, P = 0.103) and cr-CIN (6.2% vs. 3.5%, P = 0.103) were higher in the atorvastatin group without significant statistical differences.Multivariable regression analysis, which was adjusted for CIN risk factors and the variables with univariate association, showed no increased odds ratio (OR) (OR, 2.185; 95% confidence interval [CI], 0.899, 5.315; P = 0.085) for cysC-CIN in the atorvastatin group compared to the rosuvastatin group. However, statin-naïve atorvastatin subgroup had significantly increased odds of cysC-CIN compared to the rosuvastatin group (OR, 2.977; 95% CI, 1.057, 8.378; P = 0.039). At post 30 days renal, cardiovascular, and mortality event rates were both low and similar between the two groups. Conclusion No significant difference in cysC-CIN incidence was found between the highdose rosuvastatin and atorvastatin groups in AMI patients and cysC was more sensitive to the early detection of CIN than creatinine.

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.

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.