The nuclear factor-κ B inhibitor SN50 enhances the efficacy of B-cell maturation antigen-targeted chimeric antigen receptor T-cell therapy

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Efficient Cancer Modeling Through CRISPR-Cas9/HDR-Based Somatic Precision Gene Editing in Mice

CRISPR-Cas9 has been used successfully to introduce indels in somatic cells of rodents; however, precise editing of single nucleotides has been hampered by limitations of flexibility and efficiency. Here, we report technological modifications to the CRISPR-Cas9 vector system that now allows homology-directed repair-mediated precise editing of any proto-oncogene in murine somatic tissues to generate tumor models with high flexibility and efficiency. Somatic editing of eithe

Improved CAR internalization and recycling through transmembrane domain optimization reduces CAR-T cytokine release and exhaustion

BackgroundAnti-CD19 chimeric antigen receptor T (CAR-T) cell therapy has proven effective for treating relapsed or refractory acute B cell leukemia. However, challenges such as cytokine release syndrome, T cell dysfunction, and exhaustion persist. Enhancing CAR-T cell efficacy through changing CAR internalization and recycling is a promising approach. The transmembrane domain is the easiest motif to optimize for modulating CAR internalization and recycling without introducing additional domains, and its impact on CAR internalization and recycling has not yet been thoroughly explored. In this study, we aim to enhance CAR-T cell function by focusing on the solely transmembrane domain design.MethodsUtilizing plasmid construction and lentivirus generation, we get two different transmembrane CAR-T cells [19CAR-T(1a) and 19CAR-T(8α)]. Through co-culture with tumor cells, we evaluate CAR dynamic change, activation levels, exhaustion markers, mitochondrial function, and differentiation in both CAR-T cells. Furthermore, immunofluorescence microscopy analysis is performed to reveal the localization of internalized CAR molecules. RNA sequencing is used to detect the transcriptome of activated CAR-T cells. Finally, a mouse study is utilized to verify the anti-tumor efficacy of 19CAR-T(1a) cells in vivo.ResultsOur findings demonstrate that 19CAR-T(1a) has lower surface CAR expression, faster internalization, and a higher recycling rate compared to 19CAR-T(8α). Internalized 19CAR(1a) co-localizes more with early and recycling endosomes, and less with lysosomes than 19CAR(8α). These features result in lower activation levels, less cytokine release, and reduced exhaustion markers in 19CAR-T(1a). Furthermore, CAR-T cells with CD1a transmembrane domain also exhibit a superior anti-tumor ability and reduced exhaustion in vivo.ConclusionOverall, we demonstrate that the transmembrane domain plays a critical role in CAR-T cell function. An optimized transmembrane domain can alleviate cytokine release syndrome and reduce CAR-T cell exhaustion, providing a direction for CAR design to enhance CAR-T cell function

Sensitivity Analysis of Dynamic Characteristics of Steel Plate Composite Girder Bridge with Supported Cross Beam System

Abstract A steel plate composite girder bridge with support beam system was selected in the case study and the software of SAP2000 was adopted to build its refined seismic finite element model. A sensitivity analysis about the dynamic characteristics of the bridge was carried out and the design parameters included relative stiffness ratio of beam to slab, shear stiffness of shear nails and steel beam. The results show that: the stiffness ratio of beam to plate has little influence on the low-order modes of structure, but more significant influence on the high-order modes. Furthermore, in the high order modes, the effect of the stiffness of concrete slabs on the dynamic characteristics of high order modes of structures is greater than that of the stiffness of steel girders. Large reduction of the stiffness of shear nails significantly affects the natural vibration period, the order of mode shapes and the mass participation coefficient. Its influence on the vertical dynamic characteristics of bridge is significantly greater than the dynamic characteristics in the longitudinal direction and the in the horizontal direction. Steel beams have little influence on the dynamic characteristics of structures.</jats:p

Adversarial Attention-Based Variational Graph Autoencoder

Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space. However, previous methods ignore the structure and properties of the reconstructed graph, or they do not consider the potential data distribution in the graph, which typically leads to unsatisfactory graph embedding performance. In this paper, we propose the adversarial attention variational graph autoencoder (AAVGA), which is a novel framework that incorporates attention networks into the encoder part and uses an adversarial mechanism in embedded training. The encoder involves node neighbors in the representation of nodes by stacking attention layers, which can further improve the graph embedding performance of the encoder. At the same time, due to the adversarial mechanism, the distribution of the potential features that are generated by the encoder are closer to the actual distribution of the original graph data; thus, the decoder generates a graph that is closer to the original graph. Experimental results prove that AAVGA performs competitively with state-of-the-art popular graph encoders on three citation datasets