Sixteen patients with congenitally corrected transposition of the great arteries (CTGA) underwent operative closure of ventricular septal defects (VSD). Ages of the patients ranged from 10 months to 25 years. Three different approaches were employed to access to the defect: through right ventriculotomy 3, through left ventriculotomy 5, and de Leval's maneuver 8. Here, right or left ventricle refers to its anatomic morphology. Early postoperative death occurred in a patient who concomitantly underwent extracardiac couduit repair between left ventricle and pulmonary trunk. Late death occourred in 5 (left ventriculotomy in 1 and righ ventriculotomy in 4), among whom 2 expired suddenly of unknown cause (one in each of the right and left ventriculotomy), and 1 expired of pneumonia. Two other deaths were related to their reoperations for replacement of the incompetent left atrioventricular (AV) valve. Another patient who had been repaired by de Leval's maneuver also underwent replacement of the left AV valve and survived. Two patients who had undergone left ventriculotomy developed com-plete heart block leading to implantation of permanent pacemaker. Postoperative complete heart block was temporarily noted in a patient who had been repaired by de Leval's maneuver but returned to sinus rhythm on the 10th postoperative day. Late postoperative function of the systemic ventricle was assessed in 8 by gated radionuclide ventriculography. Calculated ejection fractions in each of the methods were the followings. Left ventriculotomy: 0.38, 0.47. Right ventriculotomy: 0.13. de Leval's maneuver: 0.29, 0.54, 0.66, 0.47, 0.36. These results draw us to the following conclusions that either ventriculotomy holds its drawbacks, that is, left ventriculotomy is apt to develop complete heart block and right ventriculotomy can predispose incompetent left AV valve ultimately leading to the fatal congestive heart failure. de Leval's maneuver, however, is rare to be complicated by the above morbidity and is considered to be the best operative method currently available.

Objective: To determine the appropriate time points to start regular exercise which could reduce age-related anxiety and impaired social behavior. Methods: For this study, 8-week-old male Wistar rats were divided into three groups: no exercise (NoEX), short-term exercise (S-Ex), and long-term exercise (L-Ex) groups. S-Ex-group rats started treadmill exercise at 12 months of age, while L-Ex rats started from at 2 months of age. Exercise rats were forced to walk on the treadmill three times per week, with 1- to 2-day intervals for 10 minutes during the first 2 weeks, at 10 m/min until 17 months of age, and at 8 m/min thereafter. At 19 months of age, behavioral tests were performed to assess the effects of exercise on age-induced behavioral change as well as quantitative polymerase chain reaction were done to uncover the mechanism behind the behavioral changes. Results: Anxiety-like behavior was improved by long-term exercise. Additionally, rats belonging to the S-Ex and L-Ex groups showed improved social behavior and increased curiosity about interesting objects. The qPCR data showed that treadmill exercise suppressed the expression of immediate-early genes in the prefrontal cortex of the aged rats. Conclusion This study suggests that long-term exercise represses early response genes, and in this way, it increases resistance to stress, diminishes anxiety-related behavior, and improves social behavior. These findings underscore the need to consider appropriate time to start exercise to prevent stress induced anxiety related behavior.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Hydrophilic polymer embolism (HPE) associated with endovascular therapy has steadily gained attention. We report a case of a 70-year-old man who had undergone one-debranched TEVAR. He had a history of distal arch replacement for dissecting aortic aneurysm 14 years earlier. Pseudoaneurysm at the proximal site of graft anastomosis was found on computed tomography (CT) angiogram during the follow-up. 1 debranching TEVAR was performed using the pull-through technique. Fourth days after the procedure, a skin rash appeared in the right lower extremity around the access site. Skin biopsy with pathological examination revealed HPE. We decided to observe a patient because there was no symptom of limb ischemia. Skin lesions improved and he was discharged on the 27th postoperative day. Hydrophilic polymers are widely used in the endovascular devices and there is an urgent need to better understand the complication of HPE.