Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Excessive lipid peroxidation and renin–angiotensin system dysregulation are central mechanisms linking oxidative stress to hypertension. In this study, a bioactive tetrapeptide, CAAP (Cys-Ala-Ala-Pro), isolated from the muscle hydrolysate of Paralichthys olivaceus (olive flounder), was evaluated for its dual antioxidant and antihypertensive actions. In Vero cells, CAAP significantly suppressed 2,2′-azobis(2-amidinopropane)-induced lipid peroxidation, reduced malondialdehyde accumulation, and preserved cell viability. Fluorescent imaging and flow cytometric analyses revealed that CAAP mitigated nuclear fragmentation and apoptotic sub-G1 cell populations through modulation of the Bcl-xL/Bax/caspase-3 axis, indicating cell protection. Molecular docking analysis demonstrated that CAAP interacts with the S′₂ pocket of angiotensin-converting enzyme (ACE) via hydrogen bonding with Glu281, His513, Lys511, and Tyr520, supporting a strong inhibitory potential (IC50 = 39.3 µg/mL). In spontaneously hypertensive rats, oral administration of CAAP (40 mg/kg) elicited a sustained decrease in systolic blood pressure within 2–6 h post-treatment, comparable to that of captopril. Collectively, these findings highlight CAAP as a multifunctional marine peptide capable of attenuating oxidative and hypertensive injury through cell stabilization and ACE inhibition, suggesting its potential as a natural therapeutic or functional food ingredient for cardiovascular protection.

Purpose: This study aims to prospectively analyze the effects of anticholinergic therapy using imidafenacin on detrusor overactivity occurring after robot-assisted radical prostatectomy (RARP). Materials and Methods: Patients were followed-up at outpatient visits 2–4 weeks post-surgery (visit 2) to confirm the presence of urinary incontinence. Those confirmed with urinary incontinence were randomly assigned in a 1:1 ratio to the anticholinergic medication group (imidafenacin 0.1 mg twice daily) or the control group. Patients were followed-up at 1, 3, and 6 months post-surgery for observational assessments, including the International Prostate Symptom Score (IPSS) and Overactive Bladder Symptom Score (OABSS). Results: A total of 49 patients (25 in the treatment group and 24 in the control group) were randomized for the study. There were no differences observed between the groups in terms of age, comorbidities, prostate size, or pathological staging. According to the IPSS questionnaire results, there was no statistically significant difference between the medication and control groups (p=0.161).However, when comparing storage and voiding symptoms separately, there was a statistically significant improvement in storage symptom scores (p=0.012). OABSS also revealed statistically significant improvement in symptoms from 3 months post-surgery (p=0.005), which persisted until 6 months post-surgery (IPSS storage: p=0.023, OABSS: p=0.013). Conclusions In the case of urinary incontinence that occurs after RARP, even if the function of the intrinsic sphincter is sufficiently preserved, if urinary incontinence persists due to changes in the bladder, pharmacological therapy using imidafenacin can be beneficial in managing urinary incontinence.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: In the Rivaroxaban Once-daily oral direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) trial, rivaroxaban 20 mg was the on-label dose, and the dose-reduction criterion for rivaroxaban was a creatinine clearance of < 50 mL/min. Some Asian countries are using reduced doses label according to the J-ROCKET AF trial. The aim of this study was to assess the safety and efficacy of a high-dose rivaroxaban regimen (HDRR, 20/15 mg) and low-dose rivaroxaban regimen (LDRR, 15/10 mg) among elderly East Asian patients with atrial fibrillation (AF) in real-world practice. Methods: This study was a multicenter, prospective, non-interventional observational study designed to evaluate the efficacy and safety of rivaroxaban in AF patients > 65 years of age with or without renal impairment. Results: A total of 1,093 patients (mean age, 72.8 ± 5.8 years; 686 [62.9%] men) were included in the analysis, with 493 patients allocated to the HDRR group and 598 patients allocated to the LDRR group. A total of 765 patients received 15 mg of rivaroxaban (203 in the HDRR group and 562 in the LDRR group). There were no significant differences in the incidence rates of major bleeding (adjusted hazard ratio [HR], 0.64; 95% confidential interval [CI], 0.21–1.93), stroke (adjusted HR, 3.21; 95% CI, 0.54–19.03), and composite outcomes (adjusted HR, 1.13;95% CI, 0.47–2.69) between the HDRR and LDRR groups. Conclusion This study revealed the safety and effectiveness of either dose regimen of rivaroxaban in an Asian population for stroke prevention of AF. Considerable numbers of patients are receiving LDRR therapy in real-world practice in Asia. Both regimens were safe and effective for these patients.

Purpose: Junctional adhesion molecule (JAM)-A is an immunoglobulin-like molecule that colocalizes with tight junctions (TJs) in the endothelium and epithelium. It is also found in blood leukocytes and platelets. The biological significance of JAM-A in asthma, as well as its clinical potential as a therapeutic target, are not well understood. The aim of this study was to elucidate the role of JAM-A in a mouse model of asthma, and to determine blood levels of JAM-A in asthmatic patients. Materials and Methods: Mice sensitized and challenged with ovalbumin (OVA) or saline were used to investigate the role of JAM-A in the pathogenesis of bronchial asthma. In addition, JAM-A levels were measured in the plasma of asthmatic patients and healthy controls. The relationships between JAM-A and clinical variables in patients with asthma were also examined. Results: Plasma JAM-A levels were higher in asthma patients (n=19) than in healthy controls (n=12). In asthma patients, the JAM-A levels correlated with forced expiratory volume in 1 second (FEV1%), FEV1/forced vital capacity (FVC), and the blood lymphocyte proportion. JAM-A, phospho-JNK, and phospho-ERK protein expressions in lung tissue were significantly higher in OVA/OVA mice than in control mice. In human bronchial epithelial cells treated with house dust mite extracts for 4 h, 8 h, and 24 h, the JAMA, phospho-JNK, and phospho-ERK expressions were increased, as shown by Western blotting, while the transepithelial electrical resistance was reduced. Conclusion These results suggest that JAM-A is involved in the pathogenesis of asthma, and may be a marker for asthma.