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Objective To explore the effects of lncRNA ANCR regulating miR-497-5p on vascular lumen formation, apoptosis and PI3K/AKT signaling pathway of gastric cancer cells. Methods Gastric cancer cells were collected and divided into gastric cancer cell (GC) group, gastric cancer cell +lncRNA ANCR-NC (AN) group, gastric cancer cell + lncRNA ANCR overexpressed plasmid (AM) group, gastric cancer cell + lncRNA ANCR siRNA (AI) group, gastric cancer cells + miR-497-5p-NC (MN) group, gastric cancer cells + miR-497-5p siRNA (MI) group, gastric cancer cells +miR-497-5p mimics (MM) group, and gastric cancer cells + lncRNA ANCR siRNA+miR-497-5p mimics (IM) group. Vascular lumen formation was observed by lumen formation assay, cell apoptosis was detected by PI staining method, and protein expression of PI3K and AKT was detected by western blotting. The interaction between lncRNA ANCR and miR-497-5p was confirmed by double luciferase reporting assay. Results Compared with AN group and MN group, the apoptosis rate of AM group and MI group was significantly decreased (P＜0.05), the number of vascular lumen formation and the expression of PI3K and AKT protein were significantly increased (P＜0.05). Compared with AM and MI groups, the apoptosis rate of AI and MM groups was significantly increased (P＜0.05), while the number of vascular lumen formation and the expression of PI3K and AKT protein were significantly decreased (P＜0.05). Compared with AI and MM groups, the apoptosis rate of IM group was significantly increased (P＜0.05), the number of vascular lumen formation and the expression of PI3K and AKT protein were significantly decreased (P＜0.05). The results of double luciferase report showed that transfection of lncRNA ANCR could significantly reduce the luciferase activity of miR-497-5p-3′-UTR-WT (P＜0.05), but had no significant effect on mutant genes (P＞0.05). Conclusion Inhibition of lncRNA ANCR can effectively inhibit the formation of vascular lumen, promote cell apoptosis, and inhibit the expression of PI3K/AKT signaling pathway in gastric cancer cells, which may be related to the targeted activation of miR-497-5p.

The fusion of hyperspectral and multispectral images presents a challenge as it involves blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral image (MSI) to produce a high-resolution hyperspectral image (HRI). A number of existing techniques have limitations; for instance, matrix decomposition-based approaches fail to retain adequate spatial and spectral image information during fusion, while tensor decomposition-based processes have high computational overhead. In this paper, we propose a novel method for fusing hyperspectral and multispectral images. Our method leverages the strong correlation among the spectral bands of hyperspectral images and employs the SVD technique to extract spectral feature subspaces. This approach results in a more compact and representative feature space for fusion. Secondly, the proposed method utilizes Elastic Net regularization in combination with ${L_1}$ L 1 and ${L_2}$ L 2 regularization for effective feature selection of highly covariate features. Weighted group sparse regularization is employed to enhance the fusion effect, enabling better representation of the image's structure and features. The algorithm is subsequently evaluated on multiple datasets to confirm its effectiveness. The results of the experiment suggest that the suggested algorithm greatly enhances the spatial resolution and visual clarity of HSI images while preserving the spectral characteristics when compared to conventional methods for fusion of hyperspectral images. Additionally, the constraint for regulation can competently reduce any noise or artefacts, thereby boosting image discrimination. To summarize, the utilization of a sparse tensor-based hyperspectral image fusion algorithm with subspace learning provides an efficacious approach for processing hyperspectral imagery. This method is capable of improving spatial resolution, extracting advantageous features from hyperspectral imagery, and ultimately supports the process of remote sensing image analysis and application. [ABSTRACT FROM AUTHOR]