Background: Allergic bronchopulmonary aspergillosis/mycosis (ABPA/M) is a complex non-infectious pulmonary benign disease characterized by an immune response against aspergillus/fungus. Carcinoembryonic antigen (CEA), typically recognized as a tumor marker, also elevated in certain benign diseases. Few studies on ABPA/M cases presenting with elevated serum CEA levels have been reported.
View Article and Find Full Text PDFBackground: Ondansetron is widely used for prophylaxis of postoperative nausea and vomiting (PONV) after general anesthesia. While previous studies have emphasized its early use, the effects of ondansetron in intensive care unit (ICU) patients following cardiac surgery remain unclear. This study investigates the association between postoperative ondansetron exposure and the risk of mortality, acute kidney injury (AKI), and postoperative atrial fibrillation (POAF) in ICU patients after cardiac surgery.
View Article and Find Full Text PDFAiming at the difficulty of extracting vibration data under actual working conditions of rolling bearings, this paper proposes a bearing reliability evaluation method based on generative adversarial network sample enhancement and maximum entropy method under the condition of few samples. Based on generative adversarial network, data sample enhancement under few samples is carried out, and the reliability analysis model is established by using the maximum entropy principle and Poisson process. The reliability is evaluated according to the reliability variation frequency, variation speed and variation acceleration.
View Article and Find Full Text PDFBackground And Aims: Pancreas divisum (PD) is the most common developmental anatomic variant of pancreatic duct. The published data on the accuracy of the detection of pancreas divisum by linear-array endoscopic ultrasound (L-EUS) is limited. The current study aimed to assess the diagnostic accuracy of L-EUS compared with magnetic resonance cholangiopancreatography (MRCP) for identifying PD.
View Article and Find Full Text PDFThe remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have been made to enrich molecular representations by integrating data from diverse aspects. Nevertheless, current methodologies frequently compartmentalize geometric and semantic components, resulting in a fragmented approach that impairs the holistic integration of molecular attributes.
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