Background Endometriosis developing in a cesarean section (CS) scar is an unusual event. Malignant transformation arising on the background of scar endometriosis in the abdominal wall is extremely rare. Herein we report a case of clear cell carcinoma (CCC) arising in the abdominal wall from endometriosis tissues following CS and review previous literature. Case Presentation A 48-year-old gravida 2 para 1 female presented with an abdominal wall mass at her CS scar, which increased in size and became painful in the last 2 years. Physical examination showed a multilocular solid mass of about 13 cm, at the previous CS scar. Computed tomography (CT) and magnetic resonance imaging (MRI) revealed a 12.8cm × 7.7cm multi-septate cystic lesion on the anterior abdominal wall, and histological examination showed that CCC was caused by the transformation of abdominal wall endometriosis (AWE). Conclusion An endometriosis-associated malignancy should be considered in the differential with any enlarging mass in the abdominal wall scar.

ObjectivesAbnormal brain function in ASD patients changes dynamically across developmental stages. However, no one has studied the brain function of prepubertal children with ASD. Prepuberty is an important stage for children’s socialization. This study aimed to investigate alterations in local spontaneous brain activity in prepubertal boys with ASD.Materials and MethodsMeasures of the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) acquired from resting-state functional magnetic resonance imaging (RS-fMRI) database, including 34 boys with ASD and 49 typically developing (TD) boys aged 7 to 10 years, were used to detect regional brain activity. Pearson correlation analyses were conducted on the relationship between abnormal ALFF and ReHo values and Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) scores.ResultsIn the ASD group, we found decreased ALFF in the left inferior parietal lobule (IPL) and decreased ReHo in the left lingual gyrus (LG), left superior temporal gyrus (STG), left middle occipital gyrus (MOG), and right cuneus (p < 0.05, FDR correction). There were negative correlations between ReHo values in the left LG and left STG and the ADOS social affect score and a negative correlation between ReHo values in the left STG and the calibrated severity total ADOS score.ConclusionBrain regions with functional abnormalities, including the left IPL, left LG, left STG, left MOG, and right cuneus may be crucial in the neuropathology of prepubertal boys with ASD. Furthermore, ReHo abnormalities in the left LG and left STG were correlated with sociality. These results will supplement the study of neural mechanisms in ASD at different developmental stages, and be helpful in exploring the neural mechanisms of prepubertal boys with ASD.

Objective. Glioma is one of the most fatal cancers in the world which has been divided into Low Grade Glioma (LGG) and High Grade Glioma (HGG), and its image grading has become a hot topic of contemporary research. Magnetic Resonance Imaging (MRI) is a vital diagnostic tool for brain tumor detection, analysis, and surgical planning. Accurate and automatic glioma grading is crucial for speeding up diagnosis and treatment planning. Aiming at the problems of 1) large number of parameters, 2) complex calculation, and 3) poor speed of the current glioma grading algorithms based on deep learning, this paper proposes a lightweight 3D UNet deep learning framework, which can improve classification accuracy in comparison with the existing methods. Approach. To improve efficiency while maintaining accuracy, existing 3D UNet has been excluded, and depthwise separable convolution has been applied to 3D convolution to reduce the number of network parameters. The weight of parameters on the basis of space and channel compression & excitation module has been strengthened to improve the model in the feature map, reduce the weight of redundant parameters, and strengthen the performance of the model. Main results. A total of 560 patients with glioma were retrospectively reviewed. All patients underwent MRI before surgery. The experiments were carried out on T1w, T2w, FLAIR, and CET1w images. Additionally, a way of marking tumor area by cube bounding box is presented which has no significant difference in model performance with the manually drawn ground truth. Evaluated on test data sets using the proposed model has shown good results (with accuracy of 89.29%). Significance. This work serves to achieve LGG/HGG grading by simple, effective, and non-invasive diagnostic approaches to provide diagnostic suggestions for clinical usage, thereby facilitating hasten treatment decisions.