Microglia are the resident macrophages in the central nervous system (CNS) and play essential roles in neuronal homeostasis and neuroinflammatory pathologies. Recently, microglia have been shown to contribute decisively to neuropathologic processes after ischemic stroke. Furthermore, natural compounds have been reported to attenuate inflammation and pathologies associated with neuroinflammation. Tryptanthrin (indolo[2,1-b]quinazoline-6,12-dione) is a phytoalkaloid with known anti-inflammatory effects in cells. In present study, the authors confirmed middle cerebral artery occlusion (MCAO) injury triggers the activation of microglia in brain tissue, and investigated whether tryptanthrin influences the function of mouse murine BV2 microglia under LPS-induced inflammatory conditions in vitro. It was found tryptanthrin protected BV2 microglia cells against LPS-induced inflammation and inhibited the induction of M1 phenotype microglia under inflammatory conditions. In addition, tryptanthrin reduced the production of pro-inflammatory cytokines in BV2 microglia cells via nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase 1 (HO-1) signaling and NF-κB signaling. The authors suggest that tryptanthrin might alleviate the progress of neuropathologies by controlling microglial functions under neuroinflammatory conditions.

Abstract. BACKGROUND: Artificial neural networks is one of pattern analyzer method which are rapidly applied on a bio-medical field. OBJECTIVE: The aim of this research was to propose an appendicitis diagnosis system using artificial neural networks (ANNs). METHODS: Data from 801 patients of the university hospital in Dongguk were used to construct artificial neural networks for diagnosing appendicitis and acute appendicitis. A radial basis function neural network structure (RBF), a multilayer neural network structure (MLNN), and a probabilistic neural network structure (PNN) were used for artificial neural network models. The Alvarado clinical scoring system was used for comparison with the ANNs. RESULTS: The accuracy of the RBF, PNN, MLNN, and Alvarado was 99.80%, 99.41%, 97.84%, and 72.19%, respectively. The area under ROC (receiver operating characteristic) curve of RBF, PNN, MLNN, and Alvarado was 0.998, 0.993, 0.985, and 0.633, respectively. CONCLUSIONS: The proposed models using ANNs for diagnosing appendicitis showed good performances, and were significantly better than the Alvarado clinical scoring system (p < 0.001). With cooperation among facilities, the accuracy for diagnosing this serious health condition can be improved.

Abstract. Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

The ideal treatment conditions for skin rejuvenation were 8 mm diameter with 30 J/cm2 and 10 mm diameter with 26 J/cm2 for the 1064 nm laser, and 8 mm diameter with 36 J/cm2 and 10 mm diameter with 26 J/cm2 for the 755 nm laser.

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