ABSTRACT
Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based algorithms include pooling operations, which are a type of subsampling used to enlarge the receptive field. However, pooling operations degrade the image details in terms of signal processing theory, which is significantly sensitive to small objects in an image. Therefore, in this study, we designed a Rense block and edge conservative module to effectively manipulate previous feature information in the feed-forward learning process. Specifically, a Rense block, an optimal design that incorporates skip connections of residual and dense blocks, was demonstrated through mathematical analysis. Furthermore, we avoid blurring of the features in the pooling operation through a compensation path in the edge conservative module. Two independent CT datasets of kidney stones and lung tumors, in which small lesions are often included in the images, were used to verify the proposed RenseNet. The results of the classification and explanation heatmaps show that the proposed RenseNet provides the best inference and interpretation compared to current state-of-the-art methods. The proposed RenseNet can significantly contribute to efficient diagnosis and treatment because it is effective for small lesions that might be misclassified or misinterpreted.
ABSTRACT
ETHNOPHARMACOLOGICAL RELEVANCE: Guarea genus comprises tropical and subtropical terrestrial herbs inhabiting Central and South America. These plants, including Guarea guidonia (L.) Sleumer, have anti-inflammatory, analgesic, antibacterial, antiviral, and immune-enhancing properties. AIM OF THE STUDY: Although various species of the Guarea genus are known for their medicinal properties, comprehensive data on their anti-inflammatory effects remain limited. Therefore, we investigated the NLRP3 inflammasome-inhibiting effects of the Guarea genus in this study. MATERIALS AND METHODS: To evaluate the anti-inflammatory activities of 18 members of the Guarea genus, we treated NLRP3 inflammasome activators with their extracts in LPS-primed J774A.1 and THP-1 cells. Cell viability was determined by water soluble tetrazolium salt (WST) and cytokine production, protein expression, and nuclear fractionation were determined by western blotting. Reactive oxygen species (ROS) production and apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) oligomerization were measured using confocal microscopic analysis. Inflammation-induced zebrafish was used in the in vivo experiments. RESULTS: Among the 18 Guarea members tested, Guarea microcarpa C. DC. extract (GM) exhibited no cytotoxicity and specifically suppressed the activation of the NLRP3 inflammasome, but not of the AIM2 or NLRC4 inflammasomes, by inhibiting the ATPase activity of NLRP3. This was achieved without affecting NF-κB signaling, potassium efflux, or intracellular ROS production, all of which are involved in NLRP3 activation. The reduced ATPase activity of NLRP3 led to decreased ASC oligomerization. Furthermore, GM exhibited anti-inflammatory effects in vivo. Additionally, GM treatment alleviated inflammation at the organismal level in an LPS-induced inflammation model using zebrafish embryos. CONCLUSION: Our results demonstrate the anti-inflammatory effects of GM via suppressing the NLRP3 inflammasome. Therefore, GM can be a potential therapeutic candidate for various inflammatory diseases caused by aberrant NLRP3 inflammasome activation.
