Chylopericardium is a rare disease entity characterized by the accumulation of chylous fluid in the pericardial sac. It usually arises from mediastinal neoplasms, thrombosis of the subclavian vein, tuberculosis, nonsurgical trauma, thoracic or cardiac surgery. The spectrum of symptoms for chylopericardium varies from an incidental finding of cardiomegaly to dyspnea, upper abdominal discomfort, cough, chest pain, palpitation, fatigue. However, most of the patients are asymptomatic. The main purpose of treatment of chylopericardium is the prevention of cardiac tamponade and prevention of metabolic, nutritional, and immunological compromise due to chyle leak. Here, we report a case of chylopercardium secondary to lymphangiomyoma with review of the literature.
Cardiac Tamponade ; Cardiomegaly ; Chest Pain ; Chyle ; Cough ; Dyspnea ; Fatigue ; Humans ; Incidental Findings ; Lymphangioma ; Lymphangiomyoma ; Mediastinal Neoplasms ; Pericardial Effusion ; Rare Diseases ; Subclavian Vein ; Thoracic Surgery ; Thorax ; Thrombosis ; Tuberculosis

OBJECTIVE: The purpose of the study was to examine a possible physiological function of p32-mediated apoptosis signaling in ovarian cancer cells. METHODS: SK-OV-3 cells were transfected with respective plasmid DNAs, and cell viability was measured. By immunoprecipitation and immunofluorescence staining analysis, we confirmed that p32 interacts with Harakiri in ovarian cancer cells. RESULTS: In SK-OV-3 cells, p32 interacted with Harakiri and both p32 and Harakiri were colocalized in the mitochondria. In addition, overexpression of p32 induced apoptosis of ovarian cancer cells and augmented Harakiri-mediated apoptosis. CONCLUSION: Our results demonstrated p32 as an apoptosis inducer and helped to provide the better understanding of the function of p32 in ovarian cancer cells and a possibility of p32 in the application of cancer therapeutics.
Apoptosis ; Cell Death ; Cell Survival ; DNA ; Fluorescent Antibody Technique ; Humans ; Immunoprecipitation ; Mitochondria ; Ovarian Neoplasms ; Plasmids

OBJECTIVE: The aim of this study was to compare and determine the feasibility, surgical outcomes, learning curves of robotic radical hysterectomy with lymph node dissection (RRHND) to conventional laparoscopic radical hysterectomy with lymph node dissection (LRHND) performed by a single surgeon, in patients with cervical cancer. METHODS: Between April 2009 and March 2013, 22 patients underwent LRHND and 19 patients underwent RRHND. Variables such as age, body mass index, International Federation of Gynecology and Obstetrics stage, histological results, number of dissected lymph nodes, operative time, estimated blood loss, days of hospitalization and complications were reviewed. Learning curves of operation time was obtained using cumulative sum (CUSUM) method. RESULTS: Both groups showed similar patient and tumor characteristics. In surgical outcome analysis, RRHND (51.8±10.4 minutes) showed longer preparing time than LRHND (42.5±14.1 minutes). In the LRHND group, 8 patients experienced postoperative complications (5 void difficulty, 1 postoperative bleeding, 1 right basal ganglia infarction, 1 fever). On the other hand, in the RRHND group, 4 patients experienced a postoperative complication (2 bleeding, 1 peritonitis, 1 dehiscence of trocar site). Using CUSUM method, the learning curves were obtained by plotting the cumulative sequential differences between each data point and the average operation time, and showed two distinct phases in both type of operations. CONCLUSION: RRHND would be appropriate surgical approach in patients with cervical cancer with favorable outcome of less voiding difficulty. A minimum of 13 cases of robotic radical hysterectomies are required to achieve surgical improvement in the treatment of cervical cancer.
Basal Ganglia ; Body Mass Index ; Gynecology ; Hand ; Hemorrhage ; Hospitalization ; Humans ; Hysterectomy* ; Infarction ; Laparoscopy* ; Learning Curve* ; Learning* ; Lymph Node Excision ; Lymph Nodes ; Methods ; Minimally Invasive Surgical Procedures ; Obstetrics ; Operative Time ; Peritonitis ; Postoperative Complications ; Surgical Instruments ; Uterine Cervical Neoplasms*

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.