Background This study aimed to establish a deep learning system for detecting the active and inactive phases of thyroid-associated ophthalmopathy (TAO) using magnetic resonance imaging (MRI). This system could provide faster, more accurate, and more objective assessments across populations. Methods A total of 160 MRI images of patients with TAO, who visited the Ophthalmology Clinic of the Ninth People’s Hospital, were retrospectively obtained for this study. Of these, 80% were used for training and validation, and 20% were used for testing. The deep learning system, based on deep convolutional neural network, was established to distinguish patients with active phase from those with inactive phase. The accuracy, precision, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve were analyzed. Besides, visualization method was applied to explain the operation of the networks. Results Network A inherited from Visual Geometry Group network. The accuracy, specificity and sensitivity were 0.863±0.055, 0.896±0.042 and 0.750±0.136 respectively. Due to the recurring phenomenon of vanishing gradient during the training process of network A, we added parts of Residual Neural Network to build network B. After modification, network B improved the sensitivity (0.821±0.021) while maintaining a good accuracy (0.855±0.018) and a good specificity (0.865±0.021). Conclusions The deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. This system could standardize the diagnostic process and speed up the treatment decision making for TAO.
Inflammatory bowel disease (IBD) is an incurable disease of the gastrointestinal tract with a lack of effective therapeutic strategies. The proinflammatory microenvironment plays a significant role in both amplifying and sustaining inflammation during IBD progression. Herein, biocompatible drug-free ceria nanoparticles (CeNP-PEG) with regenerable scavenging activities against multiple reactive oxygen species (ROS) were developed. CeNP-PEG exerted therapeutic effect in dextran sulfate sodium (DSS)-induced colitis murine model, evidenced by corrected the disease activity index, restrained colon length shortening, improved intestinal permeability and restored the colonic epithelium disruption. CeNP-PEG ameliorated the proinflammatory microenvironment by persistently scavenging ROS, down-regulating the levels of multiple proinflammatory cytokines, restraining the proinflammatory profile of macrophages and Th1/Th17 response. The underlying mechanism may involve restraining the co-activation of NF-κB and JAK2/STAT3 pathways. In summary, this work demonstrates an effective strategy for IBD treatment by ameliorating the self-perpetuating proinflammatory microenvironment, which offers a new avenue in the treatment of inflammation-related diseases. Graphical Abstract
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