Current Status and Perspectives of External Versus Internal Pancreatic Duct Drainage during the Learning Curve of Laparoscopic Pancreaticoduodenectomy

Objectives: To summarize the current status and outlook of pancreatic duct drainage in the learning curve period of laparoscopic pancreaticoduodenectomy (LPD). Methods: By searching the literature related to the efficacy analysis of internal versus external pancreatic duct drainage in pancreaticoduodenectomy (OPD) and the learning curve period of laparoscopic pancreaticoduodenectomy in recent years at home and abroad and making a review. Results: Because of the complexity of the LPD surgical procedure, the high technical requirements and the high complication rate, it is necessary for the operator and his/her team to carry out a certain number of cases to pass through the learning curve in order to have a basic mastery of the procedure. In recent years, more and more pancreatic surgeons have begun to promote and use pancreatic duct drains. However, no consensus conclusion has been reached on whether to choose internal or external drainage for pancreatic duct placement and drainage in LPD. Conclusions: Intraoperative application of pancreatic duct drainage reduces the incidence of pancreatic fistula during the learning curve of laparoscopic pancreaticoduodenectomy. However, external pancreatic duct drainage and internal pancreatic duct drainage have both advantages and disadvantages, so when choosing the drainage method, one should choose the appropriate drainage method in conjunction with one’s own conditions, so as to reduce the incidence of complications.

Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

Kinetic energy(KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density.