Objective: To develop a smart blood donation service assistant for popularizing donation-related knowledge to blood donors via intelligent Q&A support, thereby enabling precise service delivery. Methods: Based on the operational scenarios of the Zhejiang Provincial Blood Center, the system utilized the open-source Dify platform for agent orchestration, and integrated with the DeepSeek model as the language processing engine to support online real-time interaction. External tools, including the Amap API and MySQL database queries, were encapsulated via the Model Context Protocol (MCP). A professional blood knowledge base for Retrieval-Augmented Generation (RAG) was constructed using the BGE-M3 embedding model. An innovative dual-large language model collaborative verification mechanism was introduced to design the overall framework. The system was deployed privately using Docker containerization, and offline closed-loop optimization was achieved through customized Python scripts. Results: An interactive interface for blood donors was developed by integrating the chatflow Web component from Dify. The intelligent assistant Agent can recommend optimal blood donation sites and navigation routes by invoking the Amap API based on the donor's location. The Blood Donation Knowledge Agent enables timely responses to inquiries, along with reasonable suggestions and reminders. This agent specializes in the field of voluntary blood donation, empowering the assistant to answer doubts and questions for blood donors in the form of intelligent question-and-answer interaction. It also guides users through preliminary self-assessments, helping potential donors identify eligibility issues beforehand, thereby effectively increasing the on-site success rate of blood donation and reducing resource waste. Conclusion: The smart blood donation assistant validates the feasibility of the "Dify+MCP+RAG" technical architecture within the blood transfusion informatization field. The assistant not only improves the service experience for blood donors, but also, ensures the sustainable evolution of the system through its modular design and closed-loop optimization mechanism, thus providing valuable insights for the intelligent transformation of traditional blood donation service systems.

Objective To investigate the clinical application and nursing effect of damage control surgery ( DCS ) in treating patients with severe liver trauma .Methods A retrospective study was used to investigate 83 patients with severe liver trauma ( grade Ⅳ, and Ⅴ) from January 2008 to December 2012 associated with the lethal triad of coagulopathy , hypothermia , and acidosis treated by DCS .Nursing intervention included fluid resuscitation , care of lethal triad and postoperative complications .And the control group was 31 patients with liver trauma of grade Ⅳand Ⅴwho had be treated without DCS .Results The mortality rate was 25.3%and 51.6% in the DCS group and the control group , and the difference was statistically significant (χ2 =7.13, P<0.01).The main cause of death was coagulopathy .The incidence of abdominal compartment syndrome was respectively 6.0% and 19.4% in the DCS group and the control group , the incidence of subdiaphragmatic abscess was 7.2%and 22.6%, and the differences were statistically significant (χ2 =4.60, 5.27, respectively;P<0.05).Conclusions DCS for patients with severe liver trauma can obviously increase their survival rate .The point of nursing intervention was limited fluid resuscitation before the surgery , correction of coagulopathy , hypothermia and acidosis during ICU as well as prevention and management of postoperative complications after the surgery .