ObjectiveTo understand the genetics associations between low-density lipoprotein receptor-related protein 5 (LRP5) gene polymorphisms and susceptibility of osteoporosis in patients with chronic obstructive pulmonary disease (COPD).MethodsThree hundred and seventy-nine patients with acute exacerbation of COPD were divided into groups of non osteoporosis and osteoporosis. Genomic DNA was extracted from all patients. UCSC genome browser and Haploview 4.2 software were used to screen tag single nucleotide polymorphisms (tagSNP) of LRP5 gene. The tagSNP was genotyped by Sequenom MassARRAY SNP detection method. Logistic regression were used to analysis the odds ratio (OR) values and confidence intervals (CI) of each SNP in different genetic models to assess the association between single nucleotide polymorphisms in LRP5 gene and osteoporosis in COPD patients.ResultsEight tagSNPs of LRP5 gene (rs312016 T/C, rs312017 C/T, rs312018 A/G, rs3736228 C/T, rs901823 T/C, rs589963 G/A, rs638051 A/G, rs671494 C/A) were selected for association analysis. Patients of rs901823 carrying C/C genotype had a higher risk of osteoporosis than those carrying T/T and C/T genotypes in COPD patients (in recessive mode, C/C vs. T/T+C/T, OR=9.42, 95%CI=2.01–44.29), P=0.000 431 8).ConclusionsThere is a significant association between rs901823 of LRP5 gene and osteoporosis in patients with COPD. Further studies are needed to discover the mechanism of LRP5 gene polymorphism in the pathogenesis of osteoporosis in COPD patients.
Objective To investigate the risk factors of infection after radiofrequency ablation in patients with liver metastases after choledochojejunostomy. Methods The clinical data of patients with liver metastases treated by radiofrequency ablation in our hospital from January 2010 to April 2022 were collected retrospectively and analyzed by univariate and multivariate logistic regression analysis. Results A total of 57 patients were included in the study, and the total number of postoperative infections was 19 (33.33%). Univariate logistic regression analysis showed that the tumor location, maximum tumor diameter, number of tumors, ablation times, and ablation duration were related to the occurrence of infection after radiofrequency ablation (P<0.01). The results of multivariate logistic regression analysis showed that the tumor location [OR=6.45, 95%CI (1.11, 37.35), P=0.037] and ablation duration [OR=1.49, 95%CI (1.16, 1.91), P=0.002] were independent risk factors for infection after radiofrequency ablation in patients with choledocho-jejunostomy. Conclusions For patients with metastatic liver cancer with a history of choledochojejunostomy, the tumor location and the duration of ablation are closely related to postoperative infection. We should strengthen the indivi-dualized management of such patients during and after operation should be strengthened to promote disease recovery.
ObjectiveTo establish a model for predicting microvascular invasion (MVI) of hepatocellular carcinoma based on magnetic resonance imaging (MRI) radiomics features.MethodsThe clinical and pathological datas of 190 patients with hepatocellular carcinoma who received surgical treatment in our hospital from September 2017 to May 2020 were prospectively collected. The patients were randomly divided into training group (n=158) and test group (n=32) with a ratio of 5∶1. Gadoxetate disodium (Gd-EOB-DTPA) -enhanced MR images of arterial phase and hepatobiliary phase were used to select radiomics features through the region of interest (ROI). The ROI included the tumor lesions and the area dilating to 2 cm from the margin of the tumor. Based on a machine learning algorithm logistic, a radiomics model for predicting MVI of hepatocellular carcinoma was established in the training group, and the model was evaluated in the test group.ResultsSeven radiomics features were obtained. The area under the receiver operating characteristic curve (AUC) of the training group and the test group were 0.830 [95%CI (0.669, 0.811)] and 0.734 [95%CI (0.600, 0.936)], respectively.ConclusionThe model based on MRI radiomics features seems to be a promising approach for predicting the microvascular invasion of hepatocellular carcinoma, which is of clinical significance for the management of hepatocellular carcinoma treatment.