Objective To study the influence of Suspension Pancreatic-Duct-Jejunum End-to-Side Continuous Suture Anastomosis （SPDJCS） on the incidence of pancreatic fistula after pancreaticoduodenectomy, and to analyze its appl...Objective To study the influence of Suspension Pancreatic-Duct-Jejunum End-to-Side Continuous Suture Anastomosis （SPDJCS） on the incidence of pancreatic fistula after pancreaticoduodenectomy, and to analyze its applicability, safety, and efficacies. Methods A prospective controlled trial was conducted with 165 cases receiving pancreati- coduodenectomy in the Department of Hepatopancreatobiliary Surgery from January 2010 to May 2012. The patients were divided into Group A （end-to-end/end-to-side invaginated anastomosis, n=52）, Group B （end-to-side mucosal anastomosis, n=48）, and Group C （SPDJCS, n=65）. The preoperative data, intra- operative data, and operative outcomes （incidence of pancreatic fistula, operation time, intraoperative blood loss, peritoneal drainage, peritoneal hemorrhage, peritoneal abscess, delayed gastric emptying, pulmonary infection, postoperative infection, blood transfusion, and perioperative mortality） were com- pared among the 3 groups. Results The total incidence of pancreatic fistula was 13.9% （23/165） in all the 165 patients. The inci- dence in Group A and Group B was 23.1% （12/52） and 18.8% （9/48）, both higher than that in Group C [3.1% （2/65）, both P〈0.05]. Group C showed significantly better outcomes than group A and B in terms of the opera- tion time （5.5±1.2 hours vs. 6.1±1.1 hours, 5.5±1.2 hours vs. 6.3±1.5 hours）, volume of blood loss （412.0±205.0 mL vs. 525.0±217.0 mL, 412.0±205.0 mL vs. 514.0±217.0 mL）, and postoperative drainage amount of plasma tubes （175.0±65.0 mE vs. 275.0±80.0 mL, 175.0±65.0 mL vs. 255.0±75.0 mL） （all P〈0.05）, while Group A and Group B displayed no difference in these aspects （P〉0.05）. As complications other than pancreatic fistula were concerned, the three groups were not different from each other （P〉0.05）. Conclusions SPDJCS may have the effect of reducing the incidence of pancreatic fistula after pan- creaticoduodenectomy. It could be safe, practical and convenient technique of anastomosis for pancreaticoje- junostomy.展开更多
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi...With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.展开更多
文摘Objective To study the influence of Suspension Pancreatic-Duct-Jejunum End-to-Side Continuous Suture Anastomosis （SPDJCS） on the incidence of pancreatic fistula after pancreaticoduodenectomy, and to analyze its applicability, safety, and efficacies. Methods A prospective controlled trial was conducted with 165 cases receiving pancreati- coduodenectomy in the Department of Hepatopancreatobiliary Surgery from January 2010 to May 2012. The patients were divided into Group A （end-to-end/end-to-side invaginated anastomosis, n=52）, Group B （end-to-side mucosal anastomosis, n=48）, and Group C （SPDJCS, n=65）. The preoperative data, intra- operative data, and operative outcomes （incidence of pancreatic fistula, operation time, intraoperative blood loss, peritoneal drainage, peritoneal hemorrhage, peritoneal abscess, delayed gastric emptying, pulmonary infection, postoperative infection, blood transfusion, and perioperative mortality） were com- pared among the 3 groups. Results The total incidence of pancreatic fistula was 13.9% （23/165） in all the 165 patients. The inci- dence in Group A and Group B was 23.1% （12/52） and 18.8% （9/48）, both higher than that in Group C [3.1% （2/65）, both P〈0.05]. Group C showed significantly better outcomes than group A and B in terms of the opera- tion time （5.5±1.2 hours vs. 6.1±1.1 hours, 5.5±1.2 hours vs. 6.3±1.5 hours）, volume of blood loss （412.0±205.0 mL vs. 525.0±217.0 mL, 412.0±205.0 mL vs. 514.0±217.0 mL）, and postoperative drainage amount of plasma tubes （175.0±65.0 mE vs. 275.0±80.0 mL, 175.0±65.0 mL vs. 255.0±75.0 mL） （all P〈0.05）, while Group A and Group B displayed no difference in these aspects （P〉0.05）. As complications other than pancreatic fistula were concerned, the three groups were not different from each other （P〉0.05）. Conclusions SPDJCS may have the effect of reducing the incidence of pancreatic fistula after pan- creaticoduodenectomy. It could be safe, practical and convenient technique of anastomosis for pancreaticoje- junostomy.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011).
文摘With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.
