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Immunotherapy is a promising approach for treating metastatic breast cancer (MBC), offering new possibilities for therapy. While checkpoint inhibitors have shown great progress in the treatment of metastatic breast cancer, their effectiveness in patients with bone metastases has been disappointing. This lack of efficacy seems to be specific to the bone environment, which exhibits immunosuppressive features. In this study, we elucidate the multiple roles of the sialic acid-binding Ig-like lectin (Siglec)-15/sialic acid glyco-immune checkpoint axis in the bone metastatic niche and explore potential therapeutic strategies targeting this glyco-immune checkpoint. Our research reveals that elevated levels of Siglec-15 in the bone metastatic niche can promote tumor-induced osteoclastogenesis as well as suppress antigen-specific T cell responses. Next, we demonstrate that antibody blockade of the Siglec-15/sialic acid glyco-immune checkpoint axis can act as a potential treatment for breast cancer bone metastasis. By targeting this pathway, we not only aim to treat bone metastasis but also inhibit the spread of metastatic cancer cells from bone lesions to other organs. [ABSTRACT FROM AUTHOR]

Image segmentation is a significant step in image analysis and computer vision. Many entropy-based approaches have been presented on this topic. Among them, Tsallis entropy isone of the best-performing methods. In this paper, the surface defect images of galvanised steel sheets were studied. Atwo-dimensional asymmetric Tsallis cross-entropy image segmentation algorithm based on chaotic bee colony algorithm optimisation was used to investigate the segmentation of surface defects under complex texture backgrounds. On the basis of Tsallis entropy threshold segmentation, a more concise expression form was used to define the asymmetric Tsallis cross-entropy in order to reduce the calculation complexity of the algorithm. The chaotic algorithm was combined with the artificial bee colony (ABC) algorithm to construct the chaotic bee colony algorithm, so that the optimal threshold of Tsallis entropy could be quickly identified. The experimental results showed that compared with the maximum Shannon entropy algorithm, the calculation time of this algorithm decreased by about 58% and the threshold value increased by about (26%, 54%). Compared with the two-dimensional Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 35% and about 20% was improved in the g-axis direction only. Compared with the two-dimensional asymmetric Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 30% and the threshold values of the two algorithms were almost the same. The algorithm proposed in this paper can rapidly and effectively segment defect targets, making it a more suitable method for detecting surface defects in factories with a rapid production pace. [ABSTRACT FROM AUTHOR]