Objective: To improve our understanding of EPEC, this study focused on analyzing and comparing the genomic characteristics of EPEC isolates from humans and companion animals in Korea. Methods: The whole genome of 26 EPEC isolates from patients with diarrhea and 20 EPEC isolates from companion animals in Korea were sequenced using the Illumina HiSeq X (Illumina, USA) and Oxford Nanopore MinION (Oxford Nanopore Technologies, UK) platforms. Results: Most isolates were atypical EPEC, and did not harbor the bfpA gene. The most prevalent virulence genes were found to be ompT (humans: 61.5%; companion animals:60.0%) followed by lpfA (humans: 46.2%; companion animals: 60.0%). Although pangenome analyses showed no apparent correlation among the origin of the strains, virulence profiles, and antimicrobial resistance profiles, isolates included in clade A obtained from both humans and companion animals exhibited high similarity. Additionally, all the isolates included in clade A encoded the ompT gene and did not encode the hlyE gene. The two isolates from companion animals harbored an incomplete bundle-forming pilus region encoding bfpA and bfpB. Moreover, the type IV secretion system-associated genes tra and trb were found in the bfpA-encoding isolates from humans. Conclusions and Relevance: Whole-genome sequencing enabled a more accurate analysis of the phylogenetic structure of EPEC and provided better insights into the understanding of EPEC epidemiology and pathogenicity.

Objective: To improve our understanding of EPEC, this study focused on analyzing and comparing the genomic characteristics of EPEC isolates from humans and companion animals in Korea. Methods: The whole genome of 26 EPEC isolates from patients with diarrhea and 20 EPEC isolates from companion animals in Korea were sequenced using the Illumina HiSeq X (Illumina, USA) and Oxford Nanopore MinION (Oxford Nanopore Technologies, UK) platforms. Results: Most isolates were atypical EPEC, and did not harbor the bfpA gene. The most prevalent virulence genes were found to be ompT (humans: 61.5%; companion animals:60.0%) followed by lpfA (humans: 46.2%; companion animals: 60.0%). Although pangenome analyses showed no apparent correlation among the origin of the strains, virulence profiles, and antimicrobial resistance profiles, isolates included in clade A obtained from both humans and companion animals exhibited high similarity. Additionally, all the isolates included in clade A encoded the ompT gene and did not encode the hlyE gene. The two isolates from companion animals harbored an incomplete bundle-forming pilus region encoding bfpA and bfpB. Moreover, the type IV secretion system-associated genes tra and trb were found in the bfpA-encoding isolates from humans. Conclusions and Relevance: Whole-genome sequencing enabled a more accurate analysis of the phylogenetic structure of EPEC and provided better insights into the understanding of EPEC epidemiology and pathogenicity.

Purpose: Finasteride and dutasteride are used to treat benign prostatic hyperplasia (BPH) and reduce the risk of developing prostate cancer. Finasteride blocks only the type 2 form of 5-alpha-reductase, whereas dutasteride blocks both type 1 and 2 forms of the enzyme. Previous studies suggest the possibility that dutasteride may be superior to finasteride in preventing prostate cancer. We directly compared the effects of finasteride and dutasteride on the risk of prostate cancer in patients with BPH using a pooled analysis of 15 real-world databases. Materials and Methods: We conducted a multicenter, cohort study of new-users of finasteride and dutasteride. We include patients who were prescribed 5 mg finasteride or dutasteride for the first time to treat BPH and had at least 180 days of prescription. We excluded patients with a history of prostate cancer or a prostate-specific antigen level ≥ 4 ng/mL before the study drug prescription. Cox regression analysis was performed to examine the hazard ratio (HR) for prostate cancer after propensity score (PS) matching. Results: A total of 8,284 patients of new-users of finasteride and 8,670 patients of new-users of dutasteride were included across the 15 databases. In the overall population, compared to dutasteride, finasteride was associated with a lower risk of prostate cancer in both on-treatment and intent-to-treat time-at-risk periods. After 1:1 PS matching, 4,897 patients using finasteride and 4,897 patients using dutasteride were enrolled in the present study. No significant differences were observed for risk of prostate cancer between finasteride and dutasteride both on-treatment (HR=0.66, 95% confidence interval [CI]: 0.44–1.00; p=0.051) and intent-to-treat time-at-risk periods (HR=0.87, 95% CI: 0.67–1.14; p=0.310). Conclusions Using real-world databases, the present study demonstrated that dutasteride was not associated with a lower risk of prostate cancer than finasteride in patients with BPH.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

Purpose: Finasteride and dutasteride are used to treat benign prostatic hyperplasia (BPH) and reduce the risk of developing prostate cancer. Finasteride blocks only the type 2 form of 5-alpha-reductase, whereas dutasteride blocks both type 1 and 2 forms of the enzyme. Previous studies suggest the possibility that dutasteride may be superior to finasteride in preventing prostate cancer. We directly compared the effects of finasteride and dutasteride on the risk of prostate cancer in patients with BPH using a pooled analysis of 15 real-world databases. Materials and Methods: We conducted a multicenter, cohort study of new-users of finasteride and dutasteride. We include patients who were prescribed 5 mg finasteride or dutasteride for the first time to treat BPH and had at least 180 days of prescription. We excluded patients with a history of prostate cancer or a prostate-specific antigen level ≥ 4 ng/mL before the study drug prescription. Cox regression analysis was performed to examine the hazard ratio (HR) for prostate cancer after propensity score (PS) matching. Results: A total of 8,284 patients of new-users of finasteride and 8,670 patients of new-users of dutasteride were included across the 15 databases. In the overall population, compared to dutasteride, finasteride was associated with a lower risk of prostate cancer in both on-treatment and intent-to-treat time-at-risk periods. After 1:1 PS matching, 4,897 patients using finasteride and 4,897 patients using dutasteride were enrolled in the present study. No significant differences were observed for risk of prostate cancer between finasteride and dutasteride both on-treatment (HR=0.66, 95% confidence interval [CI]: 0.44–1.00; p=0.051) and intent-to-treat time-at-risk periods (HR=0.87, 95% CI: 0.67–1.14; p=0.310). Conclusions Using real-world databases, the present study demonstrated that dutasteride was not associated with a lower risk of prostate cancer than finasteride in patients with BPH.

Purpose: Finasteride and dutasteride are used to treat benign prostatic hyperplasia (BPH) and reduce the risk of developing prostate cancer. Finasteride blocks only the type 2 form of 5-alpha-reductase, whereas dutasteride blocks both type 1 and 2 forms of the enzyme. Previous studies suggest the possibility that dutasteride may be superior to finasteride in preventing prostate cancer. We directly compared the effects of finasteride and dutasteride on the risk of prostate cancer in patients with BPH using a pooled analysis of 15 real-world databases. Materials and Methods: We conducted a multicenter, cohort study of new-users of finasteride and dutasteride. We include patients who were prescribed 5 mg finasteride or dutasteride for the first time to treat BPH and had at least 180 days of prescription. We excluded patients with a history of prostate cancer or a prostate-specific antigen level ≥ 4 ng/mL before the study drug prescription. Cox regression analysis was performed to examine the hazard ratio (HR) for prostate cancer after propensity score (PS) matching. Results: A total of 8,284 patients of new-users of finasteride and 8,670 patients of new-users of dutasteride were included across the 15 databases. In the overall population, compared to dutasteride, finasteride was associated with a lower risk of prostate cancer in both on-treatment and intent-to-treat time-at-risk periods. After 1:1 PS matching, 4,897 patients using finasteride and 4,897 patients using dutasteride were enrolled in the present study. No significant differences were observed for risk of prostate cancer between finasteride and dutasteride both on-treatment (HR=0.66, 95% confidence interval [CI]: 0.44–1.00; p=0.051) and intent-to-treat time-at-risk periods (HR=0.87, 95% CI: 0.67–1.14; p=0.310). Conclusions Using real-world databases, the present study demonstrated that dutasteride was not associated with a lower risk of prostate cancer than finasteride in patients with BPH.

Background: Gout is a type of inflammatory arthritis caused by monosodium urate crystal deposits, and the prevalence of this condition has been increasing. This study aimed to determine the combined effects of genetic risk factors and lifestyle habits on gout, using data from a Korean cohort study. Identifying high-risk individuals in advance can help prevent gout and its associated disorders. Methods: We analyzed data from the Korean Genome and Epidemiology Study-Urban Health Examinees cohort (KoGES-HEXA). Genetic information of the participants was collected at baseline, and gout cases were identified based on patient statements. The polygenic risk score (PRS) was calculated using nine independent genome-wide association study datasets, and lifestyle factors and metabolic syndrome status were measured for each participant using the KoGES. Logistic regression models were used to estimate the odds ratios (ORs) for gout in relation to genetic risk, lifestyle habits, and metabolic health status, after adjusting for age and sex. Results: Among 44,605 participants, 617 were diagnosed with gout. Gout was associated with older age, higher body mass index, and higher prevalence of hypertension, diabetes, and hypertriglyceridemia. High PRS, unfavorable lifestyle habits, and poor metabolic profiles were significantly associated with an increased risk of gout. Compared with that in the low-genetic-risk and healthy lifestyle group or ideal metabolic profile group, the risk of gout was increased in the high-genetic-risk plus unfavorable lifestyle (OR, 3.64; 95% confidence interval [CI], 2.32–6.03) or poor metabolic profile (OR, 7.78; 95% CI, 4.61–13.40) group.Conversely, adherence to favorable lifestyle habits significantly reduced gout risk, especially in high-genetic-risk groups. Conclusion Genetic predisposition and unhealthy lifestyle habits significantly increase the risk of gout. Promoting healthy lifestyle habits is crucial to prevent the development of gout, particularly in individuals with high genetic susceptibility.

Background: We evaluated severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-specific humoral and cellular responses for up to 6 months after the 3rd dose of ancestral coronavirus disease 2019 (COVID-19) vaccination in people living with HIV (PLWH) and healthy controls (HCs) who were not infected with COVID-19. Methods: Anti-spike receptor-binding domain IgG (anti-RBD IgG) concentrations using chemiluminescence immunoassay and neutralizing antibodies using focus reduction neutralization test (FRNT) were assessed at 1 week after each dose of vaccination, and 3 and 6 months after the 3rd dose in 62 PLWH and 25 HCs. T-cell responses using intracellular cytokine stain were evaluated at 1 week before, and 1 week and 6 months after the 3rd dose. Results: At 1 week after the 3rd dose, adequate anti-RBD IgG (> 300 binding antibody unit /mL) was elicited in all PLWH except for one patient with 36 CD4 T-cell count/mm3 . The geometric mean titers of 50% FRNT against wild type (WT) and omicron BA.5 strains of SARS-CoV-2 in PLWH with CD4 T-cell count ≥ 500 cells/mm3(high CD4 recovery, HCDR) were comparable to HC, but they were significantly decreased in PLWH with CD4 T-cell count < 500/mm3 (low CD4 recovery, LCDR). After adjusting for age, gender, viral suppression, and number of preexisting comorbidities, CD4 T-cell counts < 500/mm3 significantly predicted a poor magnitude of neutralizing antibodies against WT, omicron BA.5, and XBB 1.5 strains among PLWH. Multivariable linear regression adjusting for age and gender revealed that LCDR was associated with reduced neutralizing activity (P = 0.017) and interferon-γ-producing T-cell responses (P = 0.049 for CD T-cell; P = 0.014 for CD8 T-cell) against WT, and strongly associated with more decreased cross-neutralization against omicron BA.5 strains (P < 0.001). Conclusion HCDR demonstrated robust humoral and cell-mediated immune responses after a booster dose of ancestral SARS-CoV-2 vaccine, whereas LCDR showed diminished immune responses against WT virus and more impaired cross-neutralization against omicron BA.5 strain.

Background: Gout is a type of inflammatory arthritis caused by monosodium urate crystal deposits, and the prevalence of this condition has been increasing. This study aimed to determine the combined effects of genetic risk factors and lifestyle habits on gout, using data from a Korean cohort study. Identifying high-risk individuals in advance can help prevent gout and its associated disorders. Methods: We analyzed data from the Korean Genome and Epidemiology Study-Urban Health Examinees cohort (KoGES-HEXA). Genetic information of the participants was collected at baseline, and gout cases were identified based on patient statements. The polygenic risk score (PRS) was calculated using nine independent genome-wide association study datasets, and lifestyle factors and metabolic syndrome status were measured for each participant using the KoGES. Logistic regression models were used to estimate the odds ratios (ORs) for gout in relation to genetic risk, lifestyle habits, and metabolic health status, after adjusting for age and sex. Results: Among 44,605 participants, 617 were diagnosed with gout. Gout was associated with older age, higher body mass index, and higher prevalence of hypertension, diabetes, and hypertriglyceridemia. High PRS, unfavorable lifestyle habits, and poor metabolic profiles were significantly associated with an increased risk of gout. Compared with that in the low-genetic-risk and healthy lifestyle group or ideal metabolic profile group, the risk of gout was increased in the high-genetic-risk plus unfavorable lifestyle (OR, 3.64; 95% confidence interval [CI], 2.32–6.03) or poor metabolic profile (OR, 7.78; 95% CI, 4.61–13.40) group.Conversely, adherence to favorable lifestyle habits significantly reduced gout risk, especially in high-genetic-risk groups. Conclusion Genetic predisposition and unhealthy lifestyle habits significantly increase the risk of gout. Promoting healthy lifestyle habits is crucial to prevent the development of gout, particularly in individuals with high genetic susceptibility.