Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: We aimed to investigate the characteristics of pediatric ulcerative colitis (UC) at diagnosis in Korea. Methods: This was a multicenter, registry-based, inception cohort study conducted in Korea between 2021 and 2023. Children and adolescents newly diagnosed with UC < 18 years were included. Baseline clinicodemographics, results from laboratory, endoscopic exams, and Paris classification factors were collected, and associations between factors at diagnosis were investigated. Results: A total 205 patients with UC were included. Male-to-female ratio was 1.59:1, and the median age at diagnosis was 14.7 years (interquartile range 11.9–16.2). Disease extent of E1 comprised 12.2% (25/205), E2 24.9% (51/205), E3 11.2% (23/205), and E4 51.7% (106/205) of the patients. S1 comprised 13.7% (28/205) of the patients. The proportion of patients with a disease severity of S1 was significantly higher in patients with E4 compared to the other groups (E1: 0% vs. E2: 2% vs. E3: 0% vs. E4: 24.5%, P < 0.001). Significant differences between disease extent groups were also observed in Pediatric Ulcerative Colitis Activity Index (median 25 vs. 35 vs. 40 vs. 45, respectively, P < 0.001), hemoglobin (median 13.5 vs.13.2 vs. 11.6 vs. 11.4 g/dL, respectively, P < 0.001), platelet count (median 301 vs. 324 vs. 372 vs. 377 × 103 /μL, respectively, P = 0.001), C-reactive protein (median 0.05 vs. 0.10 vs. 0.17 vs. 0.38 mg/dL, respectively, P < 0.001), and Ulcerative Colitis Endoscopic Index of Severity (median 4 vs. 4 vs. 4 vs. 5, respectively, P = 0.006). No significant differences were observed in factors between groups divided according to sex and diagnosis age. Conclusion This study represents the largest multicenter pediatric inflammatory bowel disease cohort in Korea. Disease severity was associated with disease extent in pediatric patients with UC at diagnosis.

Background: We aimed to investigate the characteristics of pediatric ulcerative colitis (UC) at diagnosis in Korea. Methods: This was a multicenter, registry-based, inception cohort study conducted in Korea between 2021 and 2023. Children and adolescents newly diagnosed with UC < 18 years were included. Baseline clinicodemographics, results from laboratory, endoscopic exams, and Paris classification factors were collected, and associations between factors at diagnosis were investigated. Results: A total 205 patients with UC were included. Male-to-female ratio was 1.59:1, and the median age at diagnosis was 14.7 years (interquartile range 11.9–16.2). Disease extent of E1 comprised 12.2% (25/205), E2 24.9% (51/205), E3 11.2% (23/205), and E4 51.7% (106/205) of the patients. S1 comprised 13.7% (28/205) of the patients. The proportion of patients with a disease severity of S1 was significantly higher in patients with E4 compared to the other groups (E1: 0% vs. E2: 2% vs. E3: 0% vs. E4: 24.5%, P < 0.001). Significant differences between disease extent groups were also observed in Pediatric Ulcerative Colitis Activity Index (median 25 vs. 35 vs. 40 vs. 45, respectively, P < 0.001), hemoglobin (median 13.5 vs.13.2 vs. 11.6 vs. 11.4 g/dL, respectively, P < 0.001), platelet count (median 301 vs. 324 vs. 372 vs. 377 × 103 /μL, respectively, P = 0.001), C-reactive protein (median 0.05 vs. 0.10 vs. 0.17 vs. 0.38 mg/dL, respectively, P < 0.001), and Ulcerative Colitis Endoscopic Index of Severity (median 4 vs. 4 vs. 4 vs. 5, respectively, P = 0.006). No significant differences were observed in factors between groups divided according to sex and diagnosis age. Conclusion This study represents the largest multicenter pediatric inflammatory bowel disease cohort in Korea. Disease severity was associated with disease extent in pediatric patients with UC at diagnosis.

Purpose: The aim of this study was to investigate the efficacy of ethanol extracts of Cornus alba (ECA) against benign prostatic hyperplasia (BPH) in vitro and in vivo. Materials and Methods: The prostate stromal cells (WPMY-1) and epithelial cells (RWPE-1) were used to examine the action mechanism of ECA in BPH in vitro. ECA efficacy was evaluated in vivo using a testosterone propionate (TP)-induced BPH rat model. Results: Treatment with ECA inhibited the proliferation of prostate cells by inducing G1-phase cell cycle arrest through the regulation of positive and negative proteins. Treatment of prostate cells with ECA resulted in alterations in the mitogen-activated protein kinases and protein kinase B signaling pathways. The transcriptional binding activity of the NF-κB motif was suppressed in both ECA-treated prostate cells. In addition, treatment with ECA altered the level of BPH-associated axis markers (5α-reductase, fibroblast growth factor-2, androgen receptor, epidermal growth factor, Bcl-2, and Bax) in both cell lines. Finally, the administration of ECA attenuated the enlargement of prostatic tissues in the TP-induced BPH rat model, accompanied by histology, immunoblot, and serum dihydrotestosterone levels. Conclusions These results demonstrated that ECA exerted beneficial effects on BPH both in vitro and in vivo and might provide valuable information in the development of preventive or therapeutic agents for improving BPH.

Purpose: The aim of this study was to investigate the efficacy of ethanol extracts of Cornus alba (ECA) against benign prostatic hyperplasia (BPH) in vitro and in vivo. Materials and Methods: The prostate stromal cells (WPMY-1) and epithelial cells (RWPE-1) were used to examine the action mechanism of ECA in BPH in vitro. ECA efficacy was evaluated in vivo using a testosterone propionate (TP)-induced BPH rat model. Results: Treatment with ECA inhibited the proliferation of prostate cells by inducing G1-phase cell cycle arrest through the regulation of positive and negative proteins. Treatment of prostate cells with ECA resulted in alterations in the mitogen-activated protein kinases and protein kinase B signaling pathways. The transcriptional binding activity of the NF-κB motif was suppressed in both ECA-treated prostate cells. In addition, treatment with ECA altered the level of BPH-associated axis markers (5α-reductase, fibroblast growth factor-2, androgen receptor, epidermal growth factor, Bcl-2, and Bax) in both cell lines. Finally, the administration of ECA attenuated the enlargement of prostatic tissues in the TP-induced BPH rat model, accompanied by histology, immunoblot, and serum dihydrotestosterone levels. Conclusions These results demonstrated that ECA exerted beneficial effects on BPH both in vitro and in vivo and might provide valuable information in the development of preventive or therapeutic agents for improving BPH.

Background/Aims: Chronic hepatitis B (CHB) is related to an increased risk of extrahepatic malignancy (EHM), and antiviral treatment is associated with an incidence of EHM comparable to controls. We compared the risks of EHM and intrahepatic malignancy (IHM) between entecavir (ETV) and tenofovir disoproxil fumarate (TDF) treatment. Methods: Using data from the National Health Insurance Service of Korea, this nationwide cohort study included treatment-naïve CHB patients who initiated ETV (n=24,287) or TDF (n=29,199) therapy between 2012 and 2014. The primary outcome was the development of any primary EHM. Secondary outcomes included overall IHM development. E-value was calculated to assess the robustness of results to unmeasured confounders. Results: The median follow-up duration was 5.9 years, and all baseline characteristics were well balanced after propensity score matching. EHM incidence rate differed significantly between within versus beyond 3 years in both groups (P<0.01, Davies test). During the first 3 years, EHM risk was comparable in the propensity score-matched cohort (5.88 versus 5.84/1,000 person-years; subdistribution hazard ratio [SHR]=1.01, 95% confidence interval [CI]=0.88–1.17, P=0.84). After year 3, however, TDF was associated with a significantly lower EHM incidence compared to ETV (4.92 versus 6.91/1,000 person-years; SHR=0.70, 95% CI=0.60–0.81, P<0.01; E-value for SHR=2.21). Regarding IHM, the superiority of TDF over ETV was maintained both within (17.58 versus 20.19/1,000 person-years; SHR=0.88, 95% CI=0.81–0.95, P<0.01) and after year 3 (11.45 versus 16.20/1,000 person-years; SHR=0.68, 95% CI=0.62–0.75, P<0.01; E-value for SHR=2.30). Conclusions TDF was associated with approximately 30% lower risks of both EHM and IHM than ETV in CHB patients after 3 years of antiviral therapy.