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11.
Ping Xiao Jue Wang Lei Fang Zitong Zhao Xiangshi Sun Xiaochen Liu Haiqiang Cao Pengcheng Zhang Dangge Wang Yaping Li 《Advanced functional materials》2021,31(36):2104068
Neoantigen vaccines and adoptive dendritic cell (DC) transfer are major clinical approaches to initiate personalized immunity in cancer patients. However, the immunization efficacy is largely limited by the in vivo trajectory including neoantigens’ access to resident DCs and DCs’ access to lymph nodes (LNs). Herein, an innovative strategy is proposed to improve personalized immunization through neoantigen-loaded nanovaccines synergized with adoptive DC transfer. It is found that it enables selective delivery of neoantigens to resident DCs and macrophages by coating cancer cell membranes onto neoantigen-loaded nanoparticles. In addition, the nanovaccines promote the secretion of chemokine C-C motif ligand 2 (CCL2), CCL3, and C-X-C motif ligand 10 from macrophages, thus potentiating the access of transferred DCs to LNs. This immunization strategy enables coordinated delivery of identified neoantigens and autologous tumor lysate-derived undefined antigens, leading to initiation of antitumor T cell immunity in a personalized manner. It significantly inhibits tumor growth in prophylactic and established mouse tumor models. The findings provide a new vision for potentiating adoptive cell transfer by nanovaccines, which may open the door to a transformative possibility for improving personalized immunization. 相似文献
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Ava Kwong Cecilia Y. S. Ho Vivian Y. Shin Chun Hang Au Tsun Leung Chan Edmond S. K. Ma 《International journal of molecular sciences》2021,22(2)
The germline carrier of the BRCA1 pathogenic mutation has been well proven to confer an increased risk of breast and ovarian cancer. Despite BRCA1 biallelic pathogenic mutations being extremely rare, they have been reported to be embryonically lethal or to cause Fanconi anemia (FA). Here we describe a patient who was a 48-year-old female identified with biallelic pathogenic mutations of the BRCA1 gene, with no or very subtle FA-features. She was diagnosed with ovarian cancer and breast cancer at the ages of 43 and 44 and had a strong family history of breast and gynecological cancers. 相似文献
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Qingna Gong Songtao Cao Yu Zhou Ruixin Wang Weizhou Jiao 《International Journal of Hydrogen Energy》2021,46(41):21442-21453
Ni2P nanoparticles and CdS nanorods were grew together on a mesoporous g-C3N4 through a facile in-situ solvothermal approach. Under visible light (λ > 400 nm), the as-prepared ternary PCN–CdS-5% Ni2P composite displays a high H2 evolution rate with 2905.86 μmol g?1 h?1, which is about 14, 18 and 279 times that of PCN–CdS, PCN–Ni2P and PCN, respectively. The enhanced photocatalytic activity is mainly attributed to the improved separation efficiency of the photocarriers by the type II PCN–CdS heterojunction and the effective extraction of photogenerated electrons by Ni2P. Meanwhile, Ni2P acts as co-catalyst to provide the photocatalytic active site for hydrogen reduction. In addition, PCN–CdS-5% Ni2P composite exerts good stability in 12-h cycles. 相似文献
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Zhang Keguo Cao Yang An Jianlin Wang Keyi 《Journal of Mechanical Science and Technology》2021,35(12):5405-5413
Journal of Mechanical Science and Technology - The flow stress increases with the increase in strain rate. This phenomenon is the strain rate effect of plastic deformation. Hopkinson experiment... 相似文献
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Zhijiang Li Yingping Zheng Liqin Cao Lei Jiao Yanfei Zhong Caiyi Zhang 《Color research and application》2020,45(4):656-670
Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results. 相似文献
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