ABSTRACT
Objective To compare the dose distribution of postoperative plans with preoperative plans for 3D printing coplanar template assisted radioactive seed implantation,and to explore the accuracy of the technique in seed implantation.Methods From November 2015 to December 2016 a total of 32 patients were selected and underwent 3D printing coplanar template assisted radioactive seed implantation in Tengzhou Central People's Hospital of Shandong province.There were 36 implanted lesions,including l0 in the lungs,5 in neck lymphs,3 in pelvic cavities,3 in vertebral body,2 in pancreas,2 in abdominal lymph nodes,2 in portal veins,and 9 in the other parts.All patients were given preoperative planning and guided by the coplanar templates.Compared with the preoperative plan,all levels needles inserted at the same time.According to preoperative planning the implantation surgery was completed accurately.The postoperative dosimetry was evaluated.The preoperative and postoperative dosimetry parameters were compared,including Dg0,D100,V90,V100.V150,V200 and conformal index (CI),external index (EI),and homogeneity index(HI).The paired t test was used to perform the statistical analysis.Result There was no significant differences in Dg0,D100,V90,V100,V150,V200,CI,EI and HI between before and after operation(P ＞ 0.05).Conclusions The dose parameters in postplan showed no difference compared with preplan in this study.For fixed and moving organ tumors,3D printing coplanar template assisted radioactive seed implantation has good therapeutic accuracy,and may be a standardized surgicalmethod for seed implantation in the future.
ABSTRACT
Construction of artificial cell factory to produce specific compounds of interest needs wild strain to be genetically engineered. In recent years, with the reconstruction of many genome-scale metabolic networks, a number of methods have been proposed based on metabolic network analysis for predicting genetic modification targets that lead to overproduction of compounds of interest. These approaches use constraints of stoichiometry and reaction reversibility in genome-scale models of metabolism and adopt different mathematical algorithms to predict modification targets, and thus can discover new targets that are difficult to find through traditional intuitive methods. In this review, we introduce the principle, merit, demerit and application of various strain optimization methods in detail. The main problems in existing methods and perspectives on this emerging research field are also discussed, aiming to provide guidance to choose the appropriate methods according to different types of products and the reliability of the predicted results.