Objectives: This study evaluated the feasibility of voice recognition-based electronic medical record (EMR) documentation for periodontal probing in dentistry, particularly emphasizing Korean-English bilingual speech patterns and real-world clinical conditions. Methods: Experiments were conducted in a dental chair setting during routine clinical hours. Environmental noise levels were measured, and two microphone types (stationary and pin-type) were evaluated. Periodontal probing phrases composed of three-digit numbers and positional terms were used for speech recognition. Consistent with common clinical practice in Korea, numerical values were spoken in Korean, whereas positional terms were spoken in English. Two speech-to-text application programming interfaces, Google Cloud Speech-to-Text and Naver Clova Speech Recognition, were assessed. Recognition accuracy was evaluated for both numerical components and complete bilingual phrases. Results: The mean environmental noise level was 60.65 dB and was minimally influenced by activity at adjacent dental chairs. The stationary microphone failed to capture speech effectively, whereas the pin-type microphone demonstrated stable recognition performance. For three-digit number recognition, accuracy was 88.3% with Google and 96.8% with Naver. For full-phrase recognition, complete matching was achieved in 36.7% of cases for Google and 52.5% for Naver. Partial recognition occurred more frequently for numerical components than for English positional terms. Conclusions Voice recognition-based EMR documentation for periodontal probing demonstrated preliminary feasibility in a dental clinical environment; however, performance was influenced by Korean-English bilingual speech patterns. These findings suggest that bilingual speech characteristics should be considered when implementing voice recognition systems in dental EMR workflows. Further optimization is required before routine clinical application.

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: Acinar adenocarcinoma with mucinous components is a rare histologic variant of prostate cancer (PC) that was previously reported to exhibit more aggressive behavior than typical PC. However, recent studies have suggested that PC with mucinous components may not be more aggressive and could even have a more favorable prognosis. Therefore, this study investigated the clinical outcomes of PC with mucinous components. Materials and Methods: We reviewed 7,983 patients with PC who underwent radical prostatectomy between 2006 and 2019. Propensity score matching and Kaplan-Meier analyses were performed to compare outcomes between patients with typical PC (group 1) and those with PC containing mucinous components (group 2). Matching variables included age, initial prostate-specific antigen level, clinical stage, and pathological Gleason score (GS). Biochemical recurrence-free survival (BCRFS) and cancer-specific survival (CSS) were analyzed using Cox regression analysis to identify survival predictors. Results: Sixty-one patients (0.76%) had PC with mucinous components. No significant differences were observed in matched variables between the 2 groups. Pathological stage, lymph node invasion (LNI), and positive surgical margin rates were also comparable. At a median follow-up of 53 (interquartile range, 24–80) months, biochemical recurrence occurred in 29 patients in group 1 and 24 in group 2 (p=0.361). Two patients in group 1 and 3 in group 2 died from PC (p>0.999). BCRFS and CSS did not differ significantly between the 2 groups (p=0.676 and p=0.458, respectively). High GS (≥8) (p=0.007) and pT3b stage (p=0.035) were independent risk factors for BCRFS, while LNI (p=0.001) predicted CSS. The presence of mucinous components was not a significant predictor of either BCRFS or CSS (p=0.127 and p=0.561, respectively). Conclusion PC with mucinous components demonstrated clinical outcomes comparable to those of typical PC and was not an independent prognostic factor for survival. PC with mucinous components may not be as aggressive as previously believed.

Purpose: This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies. Methods: The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration. Results: The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible. Conclusions This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.

Purpose: This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies. Methods: The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration. Results: The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible. Conclusions This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.

Purpose: This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies. Methods: The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration. Results: The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible. Conclusions This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.