No abstract

Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.

To investigate the role of ultrasound-targeted microbubbles in the homing effect of bone marrow-derived mesenchymal stem cells (BMSCs) and in the therapeutic efficacy of BMSCs on the ischemic stroke. A middle cerebral artery occlusion (MCAO) model was induced by plug wire preparation. Seventy-two hours after MCAO, the treatment of BMSCs with ultrasound-targeted microbubble was assessed via modified neurological severity score (mNSS), infarct volumes, and cerebral edema. In addition, immunofluorescence was performed to analyze the homing effect of BMSCs with ultrasound-targeted microbubble. We find that BMSCs with ultrasound-targeted microbubble (BMMSCs with ultrasound-targeted microbubble [USMM] group) could significantly ameliorate mNSS, infarct volumes, and cerebral J Cell Biochem. 2019;120:3315-3322.wileyonlinelibrary.com/journal/jcb

No abstract

No abstract