Effect of meniscus modelling assumptions in a static tibiofemoral finite element model: importance of geometry over material.

Finite element studies of the tibiofemoral joint have increased use in research, with attention often placed on the material models. Few studies assess the effect of meniscus modelling assumptions in image-based models on contact mechanics outcomes. This work aimed to assess the effect of modelling assumptions of the meniscus on knee contact mechanics and meniscus kinematics. A sensitivity analysis was performed using three specimen-specific tibiofemoral models and one generic knee model. The assumptions in representing the meniscus attachment on the tibia (shape of the roots and position of the attachment), the material properties of the meniscus, the shape of the meniscus and the alignment of the joint were evaluated, creating 40 model instances. The values of material parameters for the meniscus and the position of the root attachment had a small influence on the total contact area but not on the meniscus displacement or the force balance between condyles. Using 3D shapes to represent the roots instead of springs had a large influence in meniscus displacement but not in knee contact area. Changes in meniscus shape and in knee alignment had a significantly larger influence on all outcomes of interest, with differences two to six times larger than those due to material properties. The sensitivity study demonstrated the importance of meniscus shape and knee alignment on meniscus kinematics and knee contact mechanics, both being more important than the material properties or the position of the roots. It also showed that differences between knees were large, suggesting that clinical interpretations of modelling studies using single geometries should be avoided.

Prediction of the pathogenicity of antithrombin sequence variations by in silico methods.

Computational prediction tools have been developed to aid in the interpretation of novel sequence variations, but their utility within the diagnostic setting of antithrombin (AT) deficiency has not been evaluated to date. The aim of our study was to test the performance of different bioinformatic tools (Meta-SNP, MutPred, nsSNPAnalyzer, PANTHER, PhD-SNP, PMut, SIFT, SNAP, SNPs&Go, PolyPhen-2, PON-P2, and PredictSNP) in predicting the pathogenicity of AT sequence variations. We analysed all naturally occurring SERPINC1 missense mutations that have been previously characterised to be damaging with regard to the secretion or function of the AT molecule. Additionally, we analysed all reported non-synonymous exonic polymorphisms within SERPINC1 with a population allele frequency >1.0%. The in silico tools had accuracies of 62-96%, sensitivities of 59-98%, and specificities of 33-100% for the prediction of the pathogenicity of AT sequence variations; receiver operating characteristic analysis had area under the curves between 0.54-0.97. When mutations were grouped according to their effect on the phenotype of AT deficiency [type I or type II with a thrombin (IIRS) or heparin (IIHBS) binding defect or pleiotropic effects (IIPE)], we observed the lowest performance characteristics of the tools for mutations causing AT deficiency type IIHBS. Only three tools (MutPred, PhD-SNP, PolyPhen-2) detected mutants causing type IIHBS AT deficiency with high sensitivity (93%), the sensitivities of the other tools ranged between 36% and 79%. This study demonstrates that bioinformatic tools are useful for pathogenicity prediction for AT sequence variations, but they have substantially different performance characteristics, particularly for type IIHBS AT deficiency.