Clinic Pathological Study of Aneurysmal Fibrous Histiocytoma

Aneurysmal fibrous histiocytoma (AFH) is a rare variant of benign fibrous histiocytoma (FH), Characterized by blood-filled spaces within the fibrohistiocytic tumor. AFH has a higher recurrence rate than FH. The diagnosis of AFH is often problematic due to its overlapping morphological features with other skin tumors. The diagnosis of AFH depends on histological features and immunohistochemistry. The aim of this study is to understand the clinical and histopathological diagnostic criteria for AFH.

Nrf-2 and HO-1 Expression in Medulloblastoma: A Clinicopathological Analysis

Medulloblastoma (MB) is one of the most common malignant tumors with poor survival in children. Nuclear factor erythroid 2-related factor2 (Nrf-2) and heme oxygenase-1 (HO-1) have been considered to play major roles in the pathogenesis of many tumors. There is no report about clinicopathological significance of Nrf-2 and HO-1 expression in medulloblastoma. In the present study, to explore the expression and potential function of Nrf-2 and HO-1 in MBs, immunohistochemistry was used to examine the Nrf-2 and HO-1 expression in 41 MBs and 27 control tissues adjacent to the tumor. The results showed that in the cases of MB, the positive expression rates of Nrf-2 and HO-1 (82.9% and 78.0%) were significantly increased compared with that (37.0% and 29.6%) in peritumoral control brain tissues. The difference was statistically significant (P 0.05). The abnormal expression of Nrf-2 and HO-1 in MB suggest that the Nrf-2/HO-1 pathway plays an important role in the formation and development of MB and may be a potential therapeutic target for MB.

MUS Model:A Deep Learning-Based Architecture for IoT Intrusion Detection

In the face of the effective popularity of the Internet of Things(IoT),but the frequent occurrence of cybersecurity incidents,various cybersecurity protection means have been proposed and applied.Among them,Intrusion Detection System(IDS)has been proven to be stable and efficient.However,traditional intrusion detection methods have shortcomings such as lowdetection accuracy and inability to effectively identifymalicious attacks.To address the above problems,this paper fully considers the superiority of deep learning models in processing highdimensional data,and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification accuracy.TheMarkov TransformField(MTF)method is used to convert 1Dnetwork traffic data into 2D images,and then the converted 2D images are filtered by UnsharpMasking to enhance the image details by sharpening;to further improve the accuracy of data classification and detection,unlike using the existing high-performance baseline image classification models,a soft-voting integrated model,which integrates three deep learning models,MobileNet,VGGNet and ResNet,to finally obtain an effective IoT intrusion detection architecture:the MUS model.Four types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT,and the results demonstrate that the accuracy of attack traffic detection is greatly improved,which is not only applicable to the IoT intrusion detection environment,but also to different types of attacks and different network environments,which confirms the effectiveness of the work done.