Background/Aims: Fibronectin (FN) has recently been identified as being overexpressed in patients with hepatocellular carcinoma (HCC) and deemed a promising biomarker of vascular invasion. The aim of this study was to examine the patterns of FN expression in HCC cells and their clinicopathological significance, such as their association with vascular invasion and angiogenesis patterns. Methods: Immunohistochemical analysis of FN was conducted using tissue microarrays from 258 surgically resected HCCs and matched nontumorous liver tissues. Three distinct FN expression patterns were observed: cytoplasmic, membranous, and sinusoidal. Moderate or strong expression was considered FN-positive. Results: Cytoplasmic or sinusoidal FN expression was significantly more common in HCC cells than in the adjacent liver tissue (p<0.001). FN expression was detected in the membranes of HCC cells and absent in nonneoplastic hepatocytes (p<0.001). Overall survival and disease-free survival in patients with HCC cells with membranous FN expression were significantly shorter than those in patients without membranous FN expression. Membranous FN expression in HCC was significantly associated with high serum alpha-fetoprotein (AFP) and protein induced by vitamin K absenceII (PIVKA-II) levels, infiltrative gross type, poor Edmondson-Steiner grade, major vessel invasion, microvascular invasion, macrotrabecular massive subtype, advanced T stage, and vessel-encapsulating tumor cluster pattern. Sinusoidal pattern of FN expression in HCC was significantly associated with high serum AFP and PIVKA-II levels, infiltrative gross type, large tumor size, microvascular invasion, macrotrabecular massive subtype, and vessel-encapsulating tumor cluster patterns. Conclusions Evaluating FN expression in HCC cells may be useful for identifying aggressive cases of HCC with vascular invasion via biopsy.

Background/Aims: Fibronectin (FN) has recently been identified as being overexpressed in patients with hepatocellular carcinoma (HCC) and deemed a promising biomarker of vascular invasion. The aim of this study was to examine the patterns of FN expression in HCC cells and their clinicopathological significance, such as their association with vascular invasion and angiogenesis patterns. Methods: Immunohistochemical analysis of FN was conducted using tissue microarrays from 258 surgically resected HCCs and matched nontumorous liver tissues. Three distinct FN expression patterns were observed: cytoplasmic, membranous, and sinusoidal. Moderate or strong expression was considered FN-positive. Results: Cytoplasmic or sinusoidal FN expression was significantly more common in HCC cells than in the adjacent liver tissue (p<0.001). FN expression was detected in the membranes of HCC cells and absent in nonneoplastic hepatocytes (p<0.001). Overall survival and disease-free survival in patients with HCC cells with membranous FN expression were significantly shorter than those in patients without membranous FN expression. Membranous FN expression in HCC was significantly associated with high serum alpha-fetoprotein (AFP) and protein induced by vitamin K absenceII (PIVKA-II) levels, infiltrative gross type, poor Edmondson-Steiner grade, major vessel invasion, microvascular invasion, macrotrabecular massive subtype, advanced T stage, and vessel-encapsulating tumor cluster pattern. Sinusoidal pattern of FN expression in HCC was significantly associated with high serum AFP and PIVKA-II levels, infiltrative gross type, large tumor size, microvascular invasion, macrotrabecular massive subtype, and vessel-encapsulating tumor cluster patterns. Conclusions Evaluating FN expression in HCC cells may be useful for identifying aggressive cases of HCC with vascular invasion via biopsy.

Background/Aims: Fibronectin (FN) has recently been identified as being overexpressed in patients with hepatocellular carcinoma (HCC) and deemed a promising biomarker of vascular invasion. The aim of this study was to examine the patterns of FN expression in HCC cells and their clinicopathological significance, such as their association with vascular invasion and angiogenesis patterns. Methods: Immunohistochemical analysis of FN was conducted using tissue microarrays from 258 surgically resected HCCs and matched nontumorous liver tissues. Three distinct FN expression patterns were observed: cytoplasmic, membranous, and sinusoidal. Moderate or strong expression was considered FN-positive. Results: Cytoplasmic or sinusoidal FN expression was significantly more common in HCC cells than in the adjacent liver tissue (p<0.001). FN expression was detected in the membranes of HCC cells and absent in nonneoplastic hepatocytes (p<0.001). Overall survival and disease-free survival in patients with HCC cells with membranous FN expression were significantly shorter than those in patients without membranous FN expression. Membranous FN expression in HCC was significantly associated with high serum alpha-fetoprotein (AFP) and protein induced by vitamin K absenceII (PIVKA-II) levels, infiltrative gross type, poor Edmondson-Steiner grade, major vessel invasion, microvascular invasion, macrotrabecular massive subtype, advanced T stage, and vessel-encapsulating tumor cluster pattern. Sinusoidal pattern of FN expression in HCC was significantly associated with high serum AFP and PIVKA-II levels, infiltrative gross type, large tumor size, microvascular invasion, macrotrabecular massive subtype, and vessel-encapsulating tumor cluster patterns. Conclusions Evaluating FN expression in HCC cells may be useful for identifying aggressive cases of HCC with vascular invasion via biopsy.

Background/Aims: Fibronectin (FN) has recently been identified as being overexpressed in patients with hepatocellular carcinoma (HCC) and deemed a promising biomarker of vascular invasion. The aim of this study was to examine the patterns of FN expression in HCC cells and their clinicopathological significance, such as their association with vascular invasion and angiogenesis patterns. Methods: Immunohistochemical analysis of FN was conducted using tissue microarrays from 258 surgically resected HCCs and matched nontumorous liver tissues. Three distinct FN expression patterns were observed: cytoplasmic, membranous, and sinusoidal. Moderate or strong expression was considered FN-positive. Results: Cytoplasmic or sinusoidal FN expression was significantly more common in HCC cells than in the adjacent liver tissue (p<0.001). FN expression was detected in the membranes of HCC cells and absent in nonneoplastic hepatocytes (p<0.001). Overall survival and disease-free survival in patients with HCC cells with membranous FN expression were significantly shorter than those in patients without membranous FN expression. Membranous FN expression in HCC was significantly associated with high serum alpha-fetoprotein (AFP) and protein induced by vitamin K absenceII (PIVKA-II) levels, infiltrative gross type, poor Edmondson-Steiner grade, major vessel invasion, microvascular invasion, macrotrabecular massive subtype, advanced T stage, and vessel-encapsulating tumor cluster pattern. Sinusoidal pattern of FN expression in HCC was significantly associated with high serum AFP and PIVKA-II levels, infiltrative gross type, large tumor size, microvascular invasion, macrotrabecular massive subtype, and vessel-encapsulating tumor cluster patterns. Conclusions Evaluating FN expression in HCC cells may be useful for identifying aggressive cases of HCC with vascular invasion via biopsy.

Objectives: The aim of this study was to assess the impact of a multi-component program, including partially hydrolyzed guar gum (PHGG, Sunfiber®) supplementation, on glycemic control, gut health, and nutritional status to support diabetes prevention and management among Korean adults. Methods: A single-arm trial was conducted with 29 adults (aged 20-55 years) with fasting plasma glucose (FPG) ≥ 100 mg/dL. Over a six-week period, participants engaged in a multi-component program that incorporated the supplementation of PHGG (Sunfiber®, 12.5 g/day), weekly nutritional coaching, and the use of continuous glucose monitoring devices. The program’s effectiveness was evaluated by measuring FPG and glycated hemoglobin (HbA1c) levels through blood tests conducted before and after the intervention. Improvements in gut health were gauged using the Korean Gut Quotient Measurement Scales, while enhancements in nutritional status were assessed using the Nutrition Quotient (NQ) and surveys that evaluated improvements in gut health and nutritional status. Results: Participants’ average age was 43.89 years, with approximately 80% being male. Most participants (about 75%) were classified as overweight or obese. After six-weeks, 17 participants who adhered closely to the program (meeting certification criteria) exhibited significant reductions in key blood glucose markers. FPG levels decreased from 113.06 ± 23.16 mg/dL to 106.24 ± 16.33 mg/dL (P < 0.05), and HbA1c levels decreased from 6.08% ± 0.81% to 5.87% ± 0.53% (P < 0.05). The NQ evaluation revealed significant increases in comprehensive nutrition scores, and in the balance and practice domain scores for all participants (P < 0.05). Furthermore, in the gut health survey, approximately 82.1% of all participants reported experiencing positive changes. Conclusion Among adults with elevated FPG levels, a multi-component intervention program that included PHGG (Sunfiber®) supplementation, structured dietary management, and the use of health-monitoring devices showed significant benefits in improving glycemic control, overall nutritional status, and gut health.Trial Registration: Clinical Research Information Service Identifier: KCT0010049.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.