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With the rapid development of medical imaging technology, 3D image segmentation technology has gradually become a mainstream method, especially in brain tumour detection and diagnosis showing its unique advantages. The technique makes full use of 3D spatial information to locate and analyze tumours more accurately, thus playing an important role in improving diagnostic accuracy, optimising treatment planning and promoting research. However, it also suffers from significant computational expenditure and delayed processing pace. In this paper, we propose an innovative optimisation scheme to address this problem. We thoroughly investigate the MedNeXt network and propose Fast‐MedNeXt, which aims to increase the processing speed while maintaining accuracy. First, we introduce the partial convolution (PConv) technique, which replaces the deep convolutional layers in the network. This improvement effectively reduces computation and memory requirements while maintaining efficient feature extraction. Second, based on PConv, we propose PConv‐Down and PConv‐Up modules, which are applied to the up‐sampling and down‐sampling modules to further optimise the network structure and improve efficiency. To confirm the efficacy of the approach, we carried out a sequence of tests in the multimodal brain tumour segmentation challenge 2021 (BraTS2021). By comparing with the MedNeXt series network, the Fast‐MedNeXt reduced the latency by 22.1%, 20.5%, 15.8%, and 11.4% respectively, while the average accuracy also increased by 0.475% and 0.2% respectively. These significant performance improvements demonstrate the effectiveness of Fast‐MedNeXt in 3D medical image segmentation tasks and provide a new and more efficient solution for the field. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) images play an indispensable role in many key fields such as environmental monitoring, precision agriculture, and urban resource management. Traditional deep convolutional neural networks have the problem of limited receptive fields. To address this problem, this paper introduces a hybrid network model that combines the advantages of CNN and Transformer, called MBT-UNet. First, a multi-branch encoder design based on the pyramid vision transformer (PVT) is proposed to effectively capture multi-scale feature information; second, an efficient feature fusion module (FFM) is proposed to optimize the collaboration and integration of features at different scales; finally, in the decoder stage, a multi-scale upsampling module (MSUM) is proposed to further refine the segmentation results and enhance segmentation accuracy. We conduct experiments on the ISPRS Vaihingen dataset, the Potsdam dataset, the LoveDA dataset, and the UAVid dataset. Experimental results show that MBT-UNet surpasses state-of-the-art algorithms in key performance indicators, confirming its superior performance in high-precision remote sensing image segmentation tasks. [ABSTRACT FROM AUTHOR]

The Ras association domain family 7 (RASSF7, also named HRC1), a potential tumor-related gene, located on human chromosome 11p15, has been identified as an important member of the N-terminal RASSF family. Whereas, the molecular biological mechanisms of RASSF7 in tumorigenesis remain to be further established. We perform a systematic review of the literature and assessment from PUBMED and MEDLINE databases in this article. RASSF7 plays a significant role in mitosis, microtubule growth, apoptosis, proliferation and differentiation. Many research literature shows that the RASSF7 could promote the occurrence and advance of human tumors by regulating Aurora B, MKK4, MKK7, JNK, YAP, MEK, and ERK, whereas, it might inhibit c-Myc and thus lead to the suppression of tumorigenesis. The pregulation of RASSF7 often occurs in various malignancies such as lung cancer, neuroblastoma, thyroid neoplasm, hepatocellular cancer, breast cancer and gastric cancer. The expression stage of RASSF7 is positively correlated with the tumor TNM stage. In this review, we primarily elaborate on the acknowledged structure and progress in the various biomechanisms and research advances of RASSF7, especially the potential relevant signaling pathways. We hope that RASSF7 , a prospective therapeutic target for human malignancies, could play an available role in future anti-cancer treatment., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)