Search

Primary hepatocellular carcinoma (PHC) is the sixth most common cancer and the third leading cause of cancer death worldwide. Hepatocellular carcinoma (HCC) accounts for 75%-85% of PHC. LARP3 is aberrantly expressed in multiple cancers. We found that it is significantly highly expressed in the liver cancer tissues of HCC patients, but the exact role and specific mechanism of this abnormal expression are not yet clear. In this study, through bioinformatics analysis, we concluded that LARP3 expression is associated with a poor prognosis for patients with HCC. Through cellular experiments such as gene editing and phenotypic functions, we found that LARP3 promotes the occurrence and development of HCC and inhibits apoptosis. Finally, through biological means such as RNA sequencing, flow cytometry, western blotting, and the construction of a subcutaneous tumorigenesis model in nude mice, we concluded that inhibition of HCC apoptosis by LARP3 is related to LARP3 negatively regulating ROS level and inhibiting the PI3K/c-Fos/apoptosis axis. This study will provide potential targets for the treatment of HCC. [ABSTRACT FROM AUTHOR]

Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution. [ABSTRACT FROM AUTHOR]