Objective Few studies are reported on the construction of a finite element model of human complex knee joint using multimodality CT and MRI images.In this study, we developed a finite element model of the knee joint for total knee arthroplasty (TKA) using matched and fused CT and MRI data, hoping to provide a useful tool for the simulation study of knee joint biomechanics of TKA.Methods The CT and MRI image data about an intact knee of a 26-year-old male volunteer were imported into the Mimics software for the establishment of 3D models of bony and soft-tissue structures.A complete knee model was developed following the registration and fusion of the constructed 3D models based on the external landmarks.After the simulated implantation of TKA components, a finite element model of the TKA knee was constructed with the Hypermesh software.Then the finite element model was analyzed following the definition of its material behavior, boundary conditions and loading.Results The finite element model of the TKA knee, which was composed of bones, ligaments, components, polyethylene insert and bone cement, was developed from CT-MRI image registration and fusion and maintained its important spatial relationship among different structures in the TKA knee.The results obtained from the finite element analysis showed the characteristics of stress distribution in the TKA knee.Conclusion The finite element model of the knee joint for TKA can be established by matching and fusing CT and MRI image data, which can be employed as a useful tool for the study of knee joint biomechanics of TKA.

Objective:To investigate the predictive value of differential distribution of peripheral lymphocyte subsets before and after the first 131I treatment on the therapeutic response to 131I treatment in patients with papillary thyroid cancer (PTC). Methods:A retrospective study was conducted on 46 PTC patients (16 males, 30 females, age 20-77 years) who underwent total thyroidectomy and received 131I treatment between January 2021 and August 2021 in First Hospital of Shanxi Medical University. Peripheral blood lymphocyte subsets (T, B, CD4 + T, CD8 + T, natural killer (NK), helper T (Th)1, Th2, Th17, and regulatory T (Treg) cells) were measured 1-2 d before and 30 d after 131I treatment. Based on serological and imaging evidence, therapeutic response at 6-12 months post- 131I therapy was categorized as either excellent response (ER) or non-excellent response (NER). Differences of preablative stimulated thyroglobulin (psTg) and clinical baseline characteristics between two groups were assessed by using independent-sample t test, paired t test, or Mann-Whitney U test. Predictive value of lymphocyte subsets before and after 131I treatment for therapeutic response was assessed through logistic regression analysis, ROC curve analysis, and decision curve analysis (DCA). Results:In ER group ( n=33) and NER group ( n=13), most lymphocyte subsets showed different degrees of reduction 30 d after 131I treatment compared to before 131I treatment, such as T, B, CD4 + T and Th1 cells in ER group, as well as T, B, CD4 + T, Th1, Th2, Th17, and Treg cells in NER group ( t values: 2.41-9.57, all P<0.05). Before 131I treatment, NER group had significantly higher levels of psTg, Th2, Th17, and Treg cells compared to the ER group ( t values: from -3.32 to -2.48, U=29.00, all P<0.05). After 131I treatment, most of lymphocyte subsets in NER group (T, B, CD4 + T, CD8 + T, Th1 and Treg cells) showed higher trend than those in ER group but without statistical significances ( t values: from -1.12 to -0.06, all P>0.05). Th2 cells before 131I treatment (odds ratio ( OR)=25.00, 95% CI: 1.36-459.10, P=0.030) was identified as a risk factor for NER. ROC curve analysis indicated that AUCs of psTg and Th2 cells for predicting therapeutic response were 0.932 and 0.790, respectively, which was 0.958 for the combined psTg and Th2 cells. DCA showed that within the threshold probability range of 10%-60%, the curves for psTg, Th2 cells, and the combined psTg and Th2 cells were all higher than the extreme curve, suggesting good effect. Conclusions:Most lymphocyte subsets decrease to varying degrees, and NER group shows a significant decrease 30 d after 131I treatment. Th2 cells may be a risk factor for poor response to 131I treatment, providing a certain value in predicting the therapeutic response to 131I treatment.