Sepsis is a common critical clinical disease with high mortality that can cause approximately 10 million deaths worldwide each year. Acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) is a common clinical complication of sepsis, which occurs primarily as diffuse alveolar injury, hypoxemia, and respiratory distress. The mortality rate of ALI/ARDS is as high as 30%-40%, which greatly endangers human health. Due to the unclear pathogenesis of ALI/ARDS, its treatment is still a worldwide problem. At present, clinical treatment mainly relies on lung-protective ventilation, prone position ventilation, and fluid management. However, there is a lack of effective and specific treatment measures. In recent years, domestic and foreign scholars have committed to basic research on ALI/ARDS, trying to further clarify its pathogenesis and find new targets and methods for the treatment of ALI/ARDS. In this review, we summarize the signaling pathways related to alveolar injury and repair in sepsis-induced ALI/ARDS and their latest research progress. They include the NF-κB, JAK2/STAT3, mitogen-activated protein kinase (MAPK), mTOR, and Notch signaling pathways. Understanding the molecular mechanisms of these signaling pathways in sepsis-induced ALI/ARDS may provide new targets and ideas for the clinical treatment of this disease.
In recent years, segmentation details and computing efficiency have become more important in medical image segmentation for clinical applications. In deep learning, UNet based on a convolutional neural network is one of the most commonly used models. UNet 3+ was designed as a modified UNet by adopting the architecture of full-scale skip connections. However, full-scale feature fusion can result in excessively redundant computations. This study aimed to reduce the network parameters of UNet 3+ while further improving the feature extraction capability. First, to eliminate redundancy and improve computational efficiency, we prune the full-scale skip connections of UNet 3+. In addition, we use the attention module called Convolutional Block Attention Module (CBAM) to capture more essential features and thus improve the feature expression capabilities. The performance of the proposed model was validated by three different types of datasets: skin cancer segmentation, breast cancer segmentation, and lung segmentation. The parameters are reduced by about 36% and 18% compared to UNet and UNet 3+, respectively. The results show that the proposed method not only outperformed the comparison models in a variety of evaluation metrics but also achieved more accurate segmentation results. The proposed models have lower network parameters that enhance feature extraction and improve segmentation performance efficiently. Furthermore, the models have great potential for application in medical imaging computer-aided diagnosis.
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