The Correlation Between IFNG Gene Methylation and Th1|Th2 Cell Balance in ROU and the Interventional Study of Jiaweidaochi Powder

Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images

Background/purpose

The application of artificial intelligence diagnosis based on deep learning in the medical field has been widely accepted. We aimed to evaluate convolutional neural networks (CNNs) for automated classification and detection of recurrent aphthous ulcerations (RAU), normal oral mucosa, and other common oral mucosal diseases in clinical oral photographs.

Materials and methods

The study included 785 clinical oral photographs, which was divided into 251 images of RAU, 271 images of the normal oral mucosa, and 263 images of other common oral mucosal diseases. Four and three CNN models were used for the classification and detection tasks, respectively. 628 images were randomly selected as training data. In addition, 78 and 79 images were assigned as validating and testing data. Main outcome measures included precision, recall, F1, specificity, sensitivity and area under the receiver operating characteristics curve (AUC).

Results

In the classification task, the Pretrained ResNet50 model had the best performance with a precision of 92.86%, a recall of 91.84%, an F1 score of 92.24%, a specificity of 96.41%, a sensitivity of 91.84% and an AUC of 98.95%. In the detection task, the Pretrained YOLOV5 model had the best performance with a precision of 98.70%, a recall of 79.51%, an F1 score of 88.07% and an AUC of Precision-Recall curve 90.89%.

Conclusion

The Pretrained ResNet50 and the Pretrained YOLOV5 algorithms were shown to have superior performance and acceptable potential in the classification and detection of RAU lesions based on non-invasive oral images, which may prove useful in clinical practice.

The Renin-Angiotensin System: The Challenge behind Autoimmune Dermatological Diseases

Autoimmune dermatological diseases (AIDD) encompass a diverse group of disorders characterized by aberrant immune responses targeting the skin and its associated structures. In recent years, emerging evidence suggests a potential involvement of the renin-angiotensin system (RAS) in the pathogenesis and progression of these conditions. RAS is a multicomponent cascade, primarily known for its role in regulating blood pressure and fluid balance. All of the RAS components play an important role in controlling inflammation and other immune responses. Angiotensin II, the main effector, acts on two essential receptors: Angiotensin Receptor 1 and 2 (AT1R and AT2R). A disturbance in the axis can lead to many pathological processes, including autoimmune (AI) diseases. AT1R activation triggers diverse signaling cascades involved in inflammation, fibrosis and tissue remodeling. Experimental studies have demonstrated the presence of AT1R in various cutaneous cells and immune cells, further emphasizing its potential contribution to the AI processes in the skin. Furthermore, recent investigations have highlighted the role of other RAS components, beyond angiotensin-converting enzyme (ACE) and Ang II, that may contribute to the pathophysiology of AIDD. Alternative pathways involving ACE2, Ang receptors and Ang-(1-7) have been implicated in regulating immune responses and tissue homeostasis within the skin microenvironment. Understanding the intricate involvement of the RAS in AIDD may provide novel therapeutic opportunities. Targeting specific components of the RAS, such as angiotensin receptor blockers (ARBs), ACE inhibitors (ACEIs) or alternative RAS pathway modulators, could potentially ameliorate inflammatory responses, reduce tissue damage and lessen disease manifestations. Further research is warranted to outline the exact mechanisms underlying RAS-mediated immune dysregulation in AIDD. This abstract aims to provide a concise overview of the intricate interplay between the RAS and AIDD. Therefore, we elaborate a systematic review of the potential challenge of RAS in the AIDD, including psoriasis, systemic sclerosis, vitiligo, lupus erythematosus and many more.