eGastroenterology最新文献

Colorectal cancer is a significant public health concern, and large non-pedunculated colorectal polyps pose a substantial risk for malignancy and incomplete resection, which may lead to interval cancer. The choice of resection technique is influenced by various factors, including polyp size, morphology, location, submucosal invasion depth and endoscopist expertise. For non-cancerous superficial large non-pedunculated polyps, conventional hot or cold snare polypectomy, endoscopic mucosal resection and endoscopic submucosal dissection are common techniques for non-surgical therapeutic endoscopic resection of these polyps. This manuscript provides a comprehensive review of literature on current endoscopic resection techniques for large non-pedunculated colorectal polyps, emphasising indications, advantages, limitations and outcomes.

Bile acids are physiological detergents and signalling molecules that are critically implicated in liver health and diseases. Dysregulation of bile acid homeostasis alters cell function and causes cell injury in chronic liver diseases. Therapeutic agents targeting bile acid synthesis, transport and signalling hold great potential for treatment of chronic liver diseases. The broad cellular and physiological impacts of pharmacological manipulations of bile acid metabolism are still incompletely understood. Recent research has discovered new links of bile acid signalling to the regulation of autophagy and lysosome biology, redox homeostasis and endoplasmic reticulum stress. These are well-conserved mechanisms that allow cells to adapt to nutrient and organelle stresses and play critical roles in maintaining cellular integrity and promoting survival. However, dysregulation of these cellular pathways is often observed in chronic liver diseases, which exacerbates cellular dysfunction to contribute to disease pathogenesis. Therefore, identification of these novel links has significantly advanced our knowledge of bile acid biology and physiology, which is needed to understand the contributions of bile acid dysregulation in disease pathogenesis, establish bile acids as diagnostic markers and develop bile acid-based pharmacological interventions. In this review, we will first discuss the roles of bile acid dysregulation in the pathogenesis of chronic liver diseases, and then discuss the recent findings on the crosstalk of bile acid signalling and cellular stress responses. Future investigations are needed to better define the roles of these crosstalks in regulating cellular function and disease processes.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. This review explores the recent progress in the application of artificial intelligence (AI) in radiological diagnosis of HCC. The Barcelona Classification of Liver Cancer criteria guides treatment decisions based on tumour characteristics and liver function indicators, but HCC often remains undetected until intermediate or advanced stages, limiting treatment options and patient outcomes. Timely and accurate diagnostic methods are crucial for enabling curative therapies and improving patient outcomes. AI, particularly deep learning and neural network models, has shown promise in the radiological detection of HCC. AI offers several advantages in HCC diagnosis, including reducing diagnostic variability, optimising data analysis and reallocating healthcare resources. By providing objective and consistent analysis of imaging data, AI can overcome the limitations of human interpretation and enhance the accuracy of HCC diagnosis. Furthermore, AI systems can assist healthcare professionals in managing the increasing workload by serving as a reliable diagnostic tool. Integration of AI with information systems enables comprehensive analysis of patient data, facilitating more informed and reliable diagnoses. The advancements in AI-based radiological diagnosis hold significant potential to improve early detection, treatment selection and patient outcomes in HCC. Further research and clinical implementation of AI models in routine practice are necessary to harness the full potential of this technology in HCC management.