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Eur J Cardiothorac Surg
January 2025
Division of Cardiovascular Surgery, The Labatt Family Heart Centre, The Hospital for Sick Children, Toronto, ON, Canada.
Objectives: This study aimed to assess the outcomes of heterotaxy patients undergone the Fontan operation, focusing on morphological features and surgical techniques.
Methods: Eighty-two consecutive heterotaxy patients who underwent the Fontan operation from 1985 to 2021 were compared to 150 patients with tricuspid atresia (TA) and 144 patients with hypoplastic left heart syndrome (HLHS). The Kaplan-Meier method and Cox proportional hazard model were used to analyze transplant-free survival and predictor of outcomes.
World J Urol
January 2025
Department of Urology, Azienda Socio Sanitaria Territoriale Lariana, Como, Italy.
Purpose: To compare the effect on sexual function of ejaculation-sparing enucleation of the prostate using Thulium: YAG laser (ES-ThuLEP) versus continuous-wave Thulium Fiber Laser (ES-ThuFLEP).
Methods: 112 patients with lower urinary tract symptoms secondary to benign prostatic hyperplasia who wished to preserve ejaculation were treated. 58 patients underwent ES-ThuLEP (Group A) using the Cyber TM generator.
Pediatr Cardiol
January 2025
Cardiothoracic Department, Children's Health Ireland at Crumlin, Dublin, Ireland.
Lead strangulation is a dangerous complication of epicardial pacemaker insertion. This complication has been increasingly highlighted lately. Our institution has recently identified four cases over the past five years.
View Article and Find Full Text PDFInt J Surg
January 2025
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Background: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. We aimed to develop a model with multi-modal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection.
Materials And Methods: A total of 1,092 participants were enrolled from 16 centers.
Background: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients.
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