Objective To report and analyze the renal function improvement in a case with ad-vanced bilateral renal cell carcinoma after targeted therapy. Methods The patient was a 60-year-old man who complained of lower back pain for 1 month. Ultrasound and CT scan detected bilateral renal masses, left lesion was 11.0 cm×9.4 cm×8.5 cm, and the right one was 3.5 cm×4.3 cm×4.1 cm. X-ray examination showed metastatic lesions in liver and lower right lung. GFR was 20.39 ml/min of left kidney, 25.40 ml/min of right kidney. The renal biopsy confirmed renal clear cell carcinoma. Sorafenib was administrated 400 mg twice or once daily for 12 weeks. Results After the targeted therapy, the decreased bilateral kidney tumor sizes were identified by CT scan. There was liquid nec-rosis in the tumor, and no new metastatic lesion detected. The kidney function was improved as well. The total GFR increased to 71.38 ml/min. Left kidney GFR increased to 31.57 ml/min, right kidney GFR increased to 39.81 ml/min, respectively. Conclusion Targeted therapy could improve renal function in advanced renal cell carcinoma cases by controlling tumor development.

Objective To investigate the relationship between the polymorphism of human multidrug resistance 1 gene(MDR1)polymorphism and early MMF pharmacokinetics.Methods Twenty-eight Chinese primary renal transplant recipients were emrolled.On day 14 post-transplant,patients took the MMF orally on fast.Whole blood samples(2 ml)were obtained at the following time points:predose(G0)and 0.5,1,1.5,2,4,6,8,10 and 12 h(C0.5,C1,C1.5,G2,C4,C6,C8,C10,C12,respectively)postdose during the dosing interval.The MPA plasrna concentration was assayed by high performance liquid chromatography (HPLC).Pharmacokinetie parameters were determined by WINNOLIN 3.1.Three major single nucleotide polymorphisrrls(SNP),C1236 T,G2677 T/A,C3435 T of MDR1 were analyzed by PCR-RFLP.Pharmacokinetie parameters of MPA were compared between different MDR1 genotype and haplotype groups.Ailele frenqueneis were also compared in high(MPA area under concentratation-time curve 0～12 h,frequencies of 1236 TT,2677 TT/AA,3435 TT in three major MDR1 SNP positions,exons 12,21 and 26,were 0.368,0.184 and 0.211,respectively.MPA AUC was significantly higher in 1236 TT group than in 1236 CC/CT group(65.36±11.51 vs 53.33±13.77,P=0.032).On C1236 T SNP,TT genotype frequency showed significant difference between MPA high and low exposure groups(66.7%vs 15.4%,P=0.013,OR=2.526).T allele frequency was marginally higher in MPA high exposure group than that in low exposure group(83.3%vs 53.3%,P=0.072).Conclusion TT genotype on 1236 of MDR1 indicates a risk of early high exposure to MPA in Chinese renal transplant patients given by oral MMF,

Objective:To investigate the prevalence and risk factors of tessellation fundus (TF) among Tianjin Medical University students with different refractive statuses.Methods:A cross-sectional study. From September to December 2019, 346 students from Tianjin Medical University were randomly selected and underwent slit-lamp examination, non-cycloplegic auto-refraction, subjective refraction, best-corrected visual acuity, ocular biometric measurement, and non-dilation fundus photography. The differences in the prevalence of TF in basic characteristics and ocular biometric parameters were compared. Based on the equivalent spherical (SE), refractive status was divided into the non-myopia group (SE>-0.50 D) and the myopia group (SE≤-0.50 D). The myopia group was further divided into mild myopia group (-3.00 D26 mm group. The logistic regression was used to analyze the risk factors affecting TF. Trend tests were performed for each risk factor and TF.Results:Of the 346 subjects, 324 (93.6%, 324/346) were myopia, of whom 73 (21.1%, 73/346), 167 (48.3%, 167/346), and 84 (24.3%, 84/346) were mild myopia, moderate myopia, and high myopia, respectively; 22 (6.4%, 22/346) were non-myopia. There were 294 (85.0%, 294/346) students with TF in the macula, including 9 (40.91%, 9/22), 58 (79.45%, 58/73), 145 (86.83%, 145/167), and 82 (97.62%, 82/84) in non-myopia, low myopia, moderate myopia, and high myopia group, respectively; 52 (15.0%, 52/346) students were without TF in the macula. There were statistically significant gender differences ( χ2=4.47), SE ( t=6.29), AL ( t=-8.29), anterior chamber depth ( Z=-2.62), lens thickness ( Z=-2.23), and average corneal radius ( Z=-3.58) between students with and without TF in the macula ( P<0.05). Spherical equivalent and axial length were independent risk factors for TF and its severity ( P≤0.001). With an increasing degree of myopia, and increasing axial length, the risk of TF increased ( P for trend<0.001). Conclusions:The prevalence of TF is 85.0% among Tianjin Medical University students. TF is detected in the fundus of no myopia, mild myopia, moderate myopia and high myopia. The degree of myopia is higher, the AL is longer, the possibility of TF is higher.

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.