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.

Background: and Purpose The risk factors for developing epilepsy following febrile convulsion (FC) have been studied extensively, but the underlying genetic components remain largely unexplored. Our objective here was to identify the risk loci related to FC through a genomewide association study of Korean epilepsy patients. Methods: We examined associations between a history of FC and single-nucleotide polymorphisms (SNPs) in data obtained from 125 patients with focal epilepsy: 28 with an FC history and 97 without an FC history. Results: Among 288,394 SNPs, 5 candidate SNPs showed p<1×10-4 . Regional association plots of these SNPs identified a novel locus adjacent to PROX1 that is implicated in hippocampal neurogenesis and epileptogenesis. The allele frequencies of the SNPs upstream of PROX1 including two candidate SNPs (rs1159179 and rs7554295 on chromosome 1) differed significantly between the groups with and without an FC history. In contrast, the allele frequencies of the SNPs inside PROX1 showed no differences, indicating dysregulated expression of PROX1 rather than a functional alteration in the PROX1 protein. Conclusions This novel discovery of SNPs upstream of PROX1 suggests that the dysregulated expression of PROX1 contributes to the development of focal epilepsy following FC. We propose that these SNPs are potential genetic markers for focal epilepsy following FC, and that PROX1 represents a potential therapeutic target of antiseizure medications.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: Developmentally regulated GTP-binding protein 2 (DRG2) regulates microtubule dynamics and G2/M arrest during docetaxel treatment. Poly ADP-ribose polymerase (PARP) acts as an important repair system for DNA damage caused by docetaxel treatment. This study investigated whether DRG2 expression affects response to PARP inhibitors (olaparib) using prostate cancer cell lines PC3, DU145, LNCaP-FGC, and LNCaP-LN3. Materials and Methods: The cell viability and DRG2 expression levels were assessed using colorimetric-based cell viability assay and western blot. Cells were transfected with DRG2 siRNA, and pcDNA6/V5-DRG2 was used to overexpress DRG2. Flow cytometry was applied for cell cycle assay and apoptosis analysis using the Annexing V cell death assay. Results: The expression of DRG2 was highest in LNCaP-LN3 and lowest in DU145 cells. Expressions of p53 in PC3, DU145, and the two LNCaP cell lines were null-type, high-expression, and medium-expression, respectively. In PC3 (DRG2 high, p53 null) cells, docetaxel increased G2/M arrest without apoptosis; however, subsequent treatment with olaparib promoted apoptosis. In DU145 and LNCaP-FGC (DRG2 low), docetaxel increased sub-G1 but not G2/M arrest and induced apoptosis, whereas olaparib had no additional effect. In LNCaP-LN3 (DRG2 high, p53 wild-type), docetaxel increased sub-G1 and G2/M arrest, furthermore olaparib enhanced cell death. Docetaxel and olaparib combination treatment had a slight effect on DRG2 knockdown PC3, but increased apoptosis in DRG2-overexpressed DU145 cells. Conclusions DRG2 and p53 expressions play an important role in prostate cancer cell lines treated with docetaxel, and DRG2 levels can predict the response to PARP inhibitors.