2,989 research outputs found

    人造血管在血液透析造瘘的应用及护理

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    Objective: To study the application of artificial blood vessels to build alternative standard arteriovenous fistula blood vessels for hemodialysis maintenance nursing. Methods: 17 cases of patients underwent artificial blood vessel transplantation, material for ptfe (PTEE), 30 cm in length, inner diameter 5mm, from 2012 to 2013 in Jinan military region general hospital. Artificial blood vessel transplantation is more operated on brachial vein, cephalic vein, median cubital vein and basilic vein. When the anastomosis among artificial blood vessels and brachial artery and vein anastomosis was established, the arterial end was on the inner side. When the anastomosis among brachial vein, median cubital vein and basilic vein was established, the arterial end was on the outside for the extension of vascular access. Results: 15 cases have hemodialysis after two months’ treatment, 1 case have artificial blood vessel fistula after swelling for 4 months, infection of thrombosis occurred one month after the operation in 1 case. Conclusion: Artificial blood vessel show good biocompatibility, high long-term patency rate, blood flow, and convenient puncture point. It builds the “lifeline” for the patients who are unable to establish a good vascular access. It is of great significance to improve the management of artificial blood vessel fistula for maintenance hemodialysis patients.目的  研究应用人造血管造瘘替代标准动静脉血管维持血液透析的护理。方法  本院自2012—2013年对17例血液透析患者进行人造血管移植术,材料为聚四氟乙烯(PTEE)材料,长度30cm,内径5mm。人造血管移植术多选择与肱动脉和头静脉或肘正中静脉、贵要静脉U型吻合。人造血管与肱动脉和头静脉吻合时,动脉端在内侧。但肱动脉与肘正中静脉、贵要静脉吻合时,为延长有效性血管通路,动脉端则在外侧。结果  15例术后2个月成熟顺利实施血液透析,1例持续肿胀4个月后开始应用,1例术后1个月出现感染血栓形成。结论  人造血管具有生物相容性好、长期通畅率高、血流量大、穿刺方便、穿刺部位充足等优点,为自身血管条件差、无法建立良好血管通路的透析患者提供了可靠的血管通路,为维持性血液透析患者建立了“生命线”。加强透析过程中的人造血管内瘘管理对于维持透析患者良好的血管通路具有重要意义

    Bis(benzimidazol-1-yl)methane dihydrate

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    The bis­(benzimidazol-1-yl)methane mol­ecule of the title compound, C15H12N4·2H2O, displays a trans conformation with a twofold axis running through the methylene C atom. Two adjacent water mol­ecules are bonded to this mol­ecule through O—H⋯N hydrogen bonds, forming a trimer. Adjacent trimers are connected together via C—H⋯O inter­actions, forming a chain running along the b-axis direction. Two such chains are joined together via π–π inter­actions [centroid–centroid distance = 3.556 (2) Å], forming double chains, which are connected via the water mol­ecules through C—H⋯O associations, forming a sheet structure. The sheets are stacked on top of each other along the a-axis direction and connected through O—H⋯O and C—H⋯O inter­actions, forming a three-dimensional ABAB layer network structure

    Oxonium picrate

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    The title compound, H3O+·C6H2N3O7 −, consists of one picrate anion and one oxonium cation. The oxonium cation is located on a crystallographic twofold axis and both its H atoms are disordered, each over two symmetry-equivalent positions with occupancy ratios of 0.75. The picrate anions are also located on twofold axes bis­ecting the phenolate and p-nitro groups. π–π inter­actions between the rings of the picrates [centroid-to-centroid distances of 3.324 (2) Å] connect the anions to form stacks along the a-axis direction. The stacks are further joined together by the protonated water mol­ecules through hydrogen bonds to form two-dimensional sheets extending parallel to the ab plane. The sheets are stacked on top of each other along the c-axis direction and connected through C—H⋯O inter­actions between the CH groups of the benzene rings and the picrate nitro groups, with C⋯O distances of 3.450 (2) Å

    Self-planning Code Generation with Large Language Models

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    Although large language models have demonstrated impressive ability in code generation, they are still struggling to address the complicated intent provided by humans. It is widely acknowledged that humans typically employ planning to decompose complex problems and schedule the solution steps prior to implementation. Thus we introduce planning into code generation to help the model understand complex intent and reduce the difficulty of problem solving. This paper proposes a self-planning code generation method with large language model, which consists of two phases, namely planning phase and implementation phase. Specifically, in the planning phase, the language model plans out the solution steps from the intent combined with in-context learning. Then it enters the implementation phase, where the model generates code step by step, guided by the solution steps. The effectiveness of self-planning code generation has been rigorously evaluated on multiple code generation datasets and the results have demonstrated a marked superiority over naive direct generation approaches with language model. The improvement in performance is substantial, highlighting the significance of self-planning in code generation tasks
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