Objective To provide a reference for the individualized medication of tacrolimus in children after living related liver transplantation,according to the effect of CYP3A5 genotyping on the concentration/dose ratio of tacrolimus in children with living related liver transplantation.Methods Peripheral blood samples were collected from children with living related liver transplantation in the transplant center.The CYP3A5 genotype was determined by polymerase chain reaction (PCR)pyrosequencing.Related indicators such as tacrolimus dose and concentration in children with living related liver transplantation were collected within 3 months after operation.According to the donor/receptor genotype,the donor/receptor expression group,the donor/receptor single expression group,and the donor/receptor non-expression group were set up.Tacrolimus concentration/dose (C0/D) ratio was statistically analyzed at 5th day,7th day,14th day,28th day,2nd month and 3rd month after administration.Results Among the 76 patients,there were 21 patients (27.63％) in CYP3A5 donor/receptor non-expression group,27 patients (35.53％) in donor/receptor single expression group,and 28 patients (36.84％) in the donor/receptor expression group.The time to the target concentration range (C0＞8 ng/mL) in CYP3A5 donor/receptor expression group was longer than in donor/receptor single expression group and donor/receptor non-expression group.Except for the individual time points,there were significant differences between CYP3A5 donor/receptor expression group and donor/receptor non expression group,or between donor/receptor non-expression group and donor/receptor single expression group,or between donor/receptor expression group and donor/receptor single expression group at rest time points (P＜0.05 for all).Conclusion In the CYP3A5 donor/receptor gene expression group,the higher dose was needed to reach the target concentration range than the gene single expression group and the donor/receptor non-expression group.Except for individual time points,there were significant differences in C0/D at rest different time points.Regardless of whether the donor or recipient contained the CYP3A5* 1 allele,C0/D was lower than the non-expressed type of the gene.Considering the polymorphism of the donor/receptor CYP3A5 gene,it was worthful for children with living related liver transplantation to allow the drug concentration to reach the therapeutic window as soon as possible and reduce organ rejection and adverse reactions.

Objective:To propose a model that could improve image quality of cone-beam computed tomography(CBCT),which based on region-discriminative generative adversarial networks(GAN),in radiotherapy for cervical cancer,so as to meet the requirements of self-adaptive radiotherapy for image quality.Methods:We employed a region-discriminative strategy and a generative adversarial networks idea to construct a model of improving CBCT image quality that could focus on local details of the images of radiotherapy for cervical cancer,which discriminator could improve the quality of generating local details of images.This model of image quality was applied to CBCT images of radiotherapy for cervical cancer.And then,the effects of processing image were evaluated through quantitative indicators and visualization.Results:Both texture clarity and contrast were significantly enhanced after CBCT image quality was improved.The signal to noise ratio of peak value of images was increased by 47.2%,and the indicator of similarity of structure was enhanced to>0.838.Compared with other model,both visualization and indicators can appear better efficiency of model.Compared with Unet network and CycleGAN network,the similarities of structure were respectively increased by 11.88% and 19.54%,and the signal to noise ratios were respectively increased by 19.75% and 25.99%.Conclusion:The GAN bases on region-discrimination can significantly improve the quality of generating integral and detailed CBCT image of radiotherapy for cervical cancer,which can provide new technical pathway for image quality of CBCT with low dose,and can play an important role for improving safety and effectiveness of radiotherapy.It has importantly clinical value for formulating and executing radiotherapy plan.

Objective:To develop a machine learning algorithm based on habitat imaging(HI),which can be used in the grading of gliomas by using multimodal magnetic resonance imaging(MRI),so as to construct the model of support vector machine(SVM)and the visualized heterogeneous regions of gliomas.Methods:A total of 335 glioma patients were collected from the 2019 brain tumor segmentation(BraTS)challenge competition of World Health Organization(WHO),which included 259 cases with high-grade gliomas(HGG)and 76 cases with low-grade gliomas(LGG).Subregions were divided based on HI technology.The PyRadiomics open-source package was used to extract the image features of region of interest(ROI),and to screen the features that stronger correlated with the high and low-grade gliomas.An SVM model was used to classify and predict the screened feature data.The heterogeneity of gliomas in images was analyzed through visualized characterization.The efficacy of glioma grading was assessed by using the area under curve(AUC)of the receiver operating characteristic(ROC)curve.Results:The AUC of test set exceeded 90%.The average accuracy of the performance indicators of test set was(92.74±2.88)%,and the average sensitivity was(93.90±2.10)%,and the average specificity was(90.36±4.59)%,and the average F1 score was(95.24±0.66)%when the tumors were divided into six habitat regions.The SVM model could showed important sub-regions in glioma grading in three-dimensional space.Conclusion:The study method based on HI has significant advantages in glioma grading,which can effectively realize visualized heterogeneity of tumor and construct model of the heterogeneity of tumor.