Mitochondria-associated endoplasmic reticulum membranes involve in oxidative stress-induced intestinal barrier injury and mitochondrial dysfunction under diquat exposing.

Many factors induced by environmental toxicants have made oxidative stress a risk factor for the intestinal barrier injury and growth restriction, which is serious health threat for human and livestock and induces significant economic loss. It is well-known that diquat-induced oxidative stress is implicated in the intestinal barrier injury. Although some studies have shown that mitochondria are the primary target organelle of diquat, the underlying mechanism remains incompletely understood. Recently, mitochondria-associated endoplasmic reticulum membranes (MAMs) have aroused increasing concerns among scholars, which participate in mitochondrial dynamics and signal transduction. In this study, we investigated whether MAMs involved in intestinal barrier injury and mitochondrial dysfunction induced by diquat-induced oxidative stress in piglets and porcine intestinal epithelial cells (IPEC-J2 cells). The results showed that diquat induced growth restriction and impaired intestinal barrier. The mitochondrial reactive oxygen species (ROS) was increased and mitochondrial membrane potential was decreased following diquat exposure. The ultrastructure of mitochondria and MAMs was also disturbed. Meanwhile, diquat upregulated endoplasmic reticulum stress marker protein and activated PERK pathway. Furthermore, loosening MAMs alleviated intestinal barrier injury, decrease of antioxidant enzyme activity and mitochondrial dysfunction induced by diquat in IPEC-J2 cells, while tightening MAMs exacerbated diquat-induced mitochondrial dysfunction. These results suggested that MAMs may be associated with the intestinal barrier injury and mitochondrial dysfunction induced by diquat in the jejunum of piglets.

DeepPulmoTB: A benchmark dataset for multi-task learning of tuberculosis lesions in lung computerized tomography (CT).

Tuberculosis (TB) remains a significant global health challenge, characterized by high incidence and mortality rates on a global scale. With the rapid advancement of computer-aided diagnosis (CAD) tools in recent years, CAD has assumed an increasingly crucial role in supporting TB diagnosis. Nonetheless, the development of CAD for TB diagnosis heavily relies on well-annotated computerized tomography (CT) datasets. Currently, the available annotations in TB CT datasets are still limited, which in turn restricts the development of CAD tools for TB diagnosis to some extent. To address this limitation, we introduce DeepPulmoTB, a CT multi-task learning dataset explicitly designed for TB diagnosis. To demonstrate the advantages of DeepPulmoTB, we propose a novel multi-task learning model, DeepPulmoTBNet (DPTBNet), for the joint segmentation and classification of lesion tissues in CT images. The architecture of DPTBNet comprises two subnets: SwinUnetR for the segmentation task, and a lightweight multi-scale network for the classification task. Furthermore, to enhance the model's capacity to capture TB lesion features, we introduce an improved iterative optimization algorithm that refines feature maps by integrating probability maps obtained in previous iterations. Extensive experiments validate the effectiveness of DPTBNet and the practicality of the DeepPulmoTB dataset.