ObjectiveTo investigate the role of Aspergillus in the severe refractory exacerbations of chronic obstructive pulmonary disease (COPD).MethodsThe clinical data of two COPD patients suffering from refractory acute exacerbations were analyzed and the relevant literature were reviewed.ResultsTwo patients were male, aging 72 and 64 years respectively. Both of them had a history of frequent acute exacerbations with severe COPD recently. Meanwhile, they received intravenous use of antibiotics repeatedly, one of them took oral corticosteroids to control wheezing, but failed. Their serum Aspergillus-specific IgG antibody was weakly positive. Besides traditional treatment, they received additional antifungal therapy, and the symptoms alleviated. There was no acute exacerbation in the half a year follow-up period after appropriate therapy.ConclusionsAspergillus colonization, sensitization, infection should be considered in patients with severe COPD. When Aspergillus-associated evidence are acquired, antifungal therapy will be unexpected helpful.
Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.