Research Progress on the Antitumor Molecular Mechanism of Ginsenoside Rh2.

Cancer has evolved into a substantial public health concern as the second-leading cause of mortality globally. Radiotherapy and chemotherapy have been the two most widely used cancer therapies in recent years; however, both have drawbacks. Therefore, the focus has shifted to the creation of herbal medicines, the extraction of active ingredients, replacement therapy, and the adverse effects of these medications. Ginsenoside Rh2, which is extracted from ginseng, has been identified in many cancer cells. The immune system of the body is strengthened by ginsenoside Rh2, which can also cause the proliferation, death, and differentiation of tumor cells through various pathways. For instance, it inhibits the expression of the NF-[Formula: see text]B signaling pathway and induces cell apoptosis, affects the expression levels of mitochondrial apoptosis proteins Bcl-2 and Bax, and cooperates with the PD-1 blockade to reactivate T cells to promote an antitumor immune response. Furthermore, ginsenosides Rh2 has the effect of reversing the toxic effect of chemotherapy drugs on normal cells, reducing myocardial damage, and relieving bone marrow function suppression. For clinical applications, it is mainly used as an adjuvant drug for preoperative neoadjuvant chemotherapy, postoperative adjuvant chemotherapy, and rescue treatment of advanced cancer. This paper summarizes the pharmacological action and mechanism of ginsenosides Rh2 in all kinds of cancer and looks forward to its future development and application.

Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism.

Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with complex cropland feature types in remote sensing images that resulted in blurred boundaries and low accuracy in plot partitioning. The proposed network contains different modules, such as spatial gradient guidance and dilated semantic fusion. It employs the image gradient attention guidance module to fully extract cropland plot features. This causes the feature to be transferred from the encoding layer to the decoding layer, creating layers full of key features within the cropland and making the extracted cropland information more accurate. In addition, this study also solves the problem caused by a large amount of spatial feature information, which losses easily during the downsampling process of continuous convolution in the coding layer. Aiming to solve this issue, we put forward a model for consensus fusion of multiscale spatial features to fuse each-layer feature of the coding layer through dilated convolution with different dilated ratios. This approach was proposed to make the segmentation results more comprehensive and complete. The lab findings showed that the Precision, Recall, MIoU, and F1 score of the multiscale fusion segmentation SSGNet network based on the attention mechanism had achieved 93.46%, 90.91%, 85.54%, and 92.73%, respectively. Its segmentation effect on cropland was better than other semantic segmentation networks and can effectively promote cropland semantic extraction.