RESUMO
Although breakthroughs have been achieved with immune checkpoint inhibitors (ICI) therapy, some tumors do not respond to those therapies due to primary or acquired resistance. GARP, a type I transmembrane cell surface docking receptor mediating latent transforming growth factor-ß (TGF-ß) and abundantly expressed on regulatory T lymphocytes and platelets, is a potential target to render these tumors responsive to ICI therapy, and enhancing anti-tumor response especially combined with ICI. To facilitate these research efforts, we developed humanized mouse models expressing humanized GARP (hGARP) instead of their mouse counterparts, enabling in vivo assessment of GARP-targeting agents. We created GARP-humanized mice by replacing the mouse Garp gene with its human homolog. Then, comprehensive experiments, including expression analysis, immunophenotyping, functional assessments, and pharmacologic assays, were performed to characterize the mouse model accurately. The Tregs and platelets in the B-hGARP mice (The letter B is the first letter of the company's English name, Biocytogen.) expressed human GARP, without expression of mouse GARP. Similar T, B, NK, DCs, monocytes and macrophages frequencies were identified in the spleen and blood of B-hGARP and WT mice, indicating that the humanization of GARP did not change the distribution of immune cell in these compartments. When combined with anti-PD-1, monoclonal antibodies (mAbs) against GARP/TGF-ß1 complexes demonstrated enhanced in vivo anti-tumor activity compared to monotherapy with either agent. The novel hGARP model serves as a valuable tool for evaluating human GARP-targeting antibodies in immuno-oncology, which may enable preclinical studies to assess and validate new therapeutics targeting GARP. Furthermore, intercrosses of this model with ICI humanized models could facilitate the evaluation of combination therapies.
Assuntos
Anticorpos Monoclonais , Proteínas de Membrana , Neoplasias , Fator de Crescimento Transformador beta , Animais , Humanos , Camundongos , Anticorpos Monoclonais/uso terapêutico , Plaquetas/metabolismo , Modelos Animais de Doenças , Neoplasias/terapia , Linfócitos T Reguladores , Fator de Crescimento Transformador beta/metabolismo , Proteínas de Membrana/antagonistas & inibidores , Proteínas de Membrana/genética , Proteínas de Membrana/imunologia , Camundongos Endogâmicos C57BL , Inibidores de Checkpoint Imunológico/uso terapêuticoRESUMO
Food safety issues are inextricably linked to people's lives and, in extreme cases, endanger public safety and social stability. People are becoming increasingly concerned about food safety issues in a modern society with high-quality economic development. People's incomes are increasing day by day as the economy continues to grow, and the tourism industry has grown by leaps and bounds. However, many problems arose, such as the issue of food safety in tourism. Tourism food safety issues affect not only the development of the food industry but also the development of tourism. Food safety oversight of tourist attractions has always been a relatively concerning issue in the country, and it is also something that the general public is concerned about. It can be said that food safety supervision of tourist attractions is the most important thing in food safety supervision. In this context, it becomes an important task to evaluate the safety of tourist food. This work proposes a multiscale convolutional neural network (AMCNN) combined with neural networks and attention layers to realize the safety and quality evaluation of tourist food. The algorithm uses the lightweight Xception network as a basic model and utilizes multiscale depth-separable convolution modules of different sizes for feature extraction and fusion to extract richer food safety feature information. Furthermore, the convolutional attention module (CBAM) is embedded on the basis of the multiscale convolutional neural network, which makes the network model focus more on discriminative features.