决定异体 CAR T 细胞排斥与扩增的细胞和分子机制
Cellular and molecular mechanisms determining allogeneic CAR T cell rejection and expansion.
我们评估了11例接受单一批次cemacabtagene ansegedleucel(cema-cel)治疗的大B细胞淋巴瘤患者,cemacabtagene ansegedleucel是一种异体抗CD19 CAR T产品。
英文原题:Breast cancer frontiers: mapping molecular signals, intelligent diagnostics, and adaptive therapies.
乳腺癌仍是全球范围内重要的健康问题,需要在早期检测、治疗方法和幸存者护理方面持续改进。
乳腺癌仍是全球重要的健康问题,需要持续改进早期检测、治疗方法和幸存者照护。整合蛋白质组学、代谢组学和基因组学等多组学技术,极大提升了对肿瘤异质性的认识,并使个体化治疗方案成为可能。新一代测序、液体活检和人工智能驱动的预测模型改变了精准肿瘤学,使靶向药物选择和实时疾病监测得以实现。机器人辅助乳房切除术、术中放射治疗和人工智能引导的自适应放疗等外科和放射肿瘤学创新,提高了治疗精度并减少长期并发症。TCR-T疗法、CAR-T细胞疗法和免疫检查点抑制剂等免疫治疗进展,正在改变乳腺癌治疗,尤其是三阴性乳腺癌等侵袭性亚型。可穿戴生物传感器、数字健康工具和心理健康人工智能平台通过改善患者生活质量应对生存者照护问题。人工智能驱动的药物发现及个体化治疗算法持续改善乳腺癌治疗,在优化疗效的同时最大限度降低毒性。尽管已有这些进展,治疗耐药、可及性和医疗卫生差异等问题依然存在。未来研究若结合实时分子分析、自适应临床决策和人工智能,有望显著改变乳腺癌治疗并改善患者结局。
Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements in early detection, therapeutic approaches, and care for survivors. The understanding of tumour heterogeneity has been greatly improved by the integration of multi-omics technologies, including proteomics, metabolomics, and genomics, which has made it possible to establish individualized treatment strategies. Precision oncology has been transformed by next-generation sequencing, liquid biopsy, and AI-driven predictive modelling, which enable targeted medication selection and real-time illness monitoring. Robotic-assisted mastectomy, intraoperative radiation therapy, and AI-guided adaptive radiotherapy are examples of innovations in radiation and surgical oncology that have increased treatment precision while reducing long-term complications. Advances in immunotherapy, such as TCR-T therapy, CAR-T cell therapy, and immune checkpoint inhibitors, are revolutionizing the treatment of breast cancer, especially aggressive subtypes like triple-negative breast cancer. By improving patient quality of life, developments in wearable biosensors, digital health tools, and mental health AI platforms are tackling survivorship issues. Breast cancer therapies are continually being enhanced by AI-powered drug discovery and personalized therapy algorithms, which optimize treatment effectiveness while minimizing toxicity. Issues, including therapeutic resistance, accessibility, and healthcare disparities, still exist despite these developments. Future studies that combine real-time molecular profiling, adaptive clinical decision-making, and artificial intelligence have the potential to significantly transform the treatment of breast cancer and enhance patient outcomes.
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