Single-center retrospective study on the clinical application of multi-arm robotic-assisted uniportal video-assisted thoracic surgery.

This research aims to consolidate clinical experiences with hybrid multi-arm robotic-assisted uniportal thoracoscopic surgery (H-URATS) employing endoscopic staplers and assess its safety and feasibility. A retrospective review was conducted on the clinical records of 128 patients treated with H-URATS at the Department of Thoracic Surgery II, Xinjiang Medical University Affiliated Tumor Hospital, between January and December 2024. The cohort included 52 men and 76 women, with an average age of 57.

Analysis of Chemical Composition of Long-Chain Linear Alkylbenzenes Using High-Performance Liquid Chromatography Coupled with Microwave Plasma Torch Mass Spectrometry.

Long-chain linear alkylbenzenes (LABs) are essential precursors to produce alkylbenzene sulfonates, which have high surfactant properties and are readily biodegradable, widely used in various detergents. However, an effective and rapid method for the direct analysis and quality control of LAB chemical composition still needs to be improved. This study uses high-performance liquid chromatography coupled with microwave plasma torch mass spectrometry (HPLC-MPT-MS) to analyze the composition of long-chain LABs and quantify nine of these components.

Right Atrial Function and Right Ventricular Diastolic Function and Their Relation With Exercise Capacity in OSAS.

Background: Right ventricular (RV) diastolic function and right atrial (RA) function are poorly characterized in patients with obstructive sleep apnea syndrome (OSAS), but may influence exercise capacity. We aimed to evaluate RA function and RV diastolic function in OSAS and study their relationship with exercise capacity.

Methods: Fifty-three patients with OSAS and thirty age- and gender-matched controls prospectively underwent echocardiography and cardiopulmonary exercise testing to investigate RV diastolic function, RA function, and exercise capacity.

Use machine learning to predict treatment outcome of early childhood caries.

Background: Early childhood caries (ECC) is a major oral health problem among preschool children that can significantly influence children's quality of life. Machine learning can accurately predict the treatment outcome but its use in ECC management is limited. The aim of this study is to explore the application of machine learning in predicting the treatment outcome of ECC.