Aims The aim of this study was to evaluate the accuracy of implant placement when using robotic assistance during total hip arthroplasty (THA). Patients and Methods A total of 20 patients underwent a planned THA using preoperative CT scans and robotic-assisted software. There were nine men and 11 women (n = 20 hips) with a mean age of 60.8 years (sd 6.0). Pelvic and femoral bone models were constructed by segmenting both preoperative and postoperative CT scan images. The preoperative anatomical landmarks using the robotic-assisted system were matched to the postoperative 3D reconstructions of the pelvis. Acetabular and femoral component positions as measured intraoperatively and postoperatively were evaluated and compared. Results The system reported accurate values for reconstruction of the hip when compared to those measured postoperatively using CT. The mean deviation from the executed overall hip length and offset were 1.6 mm (sd 2.9) and 0.5 mm (sd 3.0), respectively. Mean combined anteversion was similar and correlated between intraoperative measurements and postoperative CT measurements (32.5°, sd 5.9° versus 32.2°, sd 6.4°; respectively; R2 = 0.65; p < 0.001). There was a significant correlation between mean intraoperative (40.4°, sd 2.1°) acetabular component inclination and mean measured postoperative inclination (40.12°, sd 3.0°, R2 = 0.62; p < 0.001). There was a significant correlation between mean intraoperative version (23.2°, sd 2.3°), and postoperatively measured version (23.0°, sd 2.4°; R2 = 0.76; p < 0.001). Preoperative and postoperative femoral component anteversion were significantly correlated with one another (R2 = 0.64; p < 0.001). Three patients had CT scan measurements that differed substantially from the intraoperative robotic measurements when evaluating stem anteversion. Conclusion This is the first study to evaluate the success of hip reconstruction overall using robotic-assisted THA. The overall hip reconstruction obtained in the operating theatre using robotic assistance accurately correlated with the postoperative component position assessed independently using CT based 3D modelling. Clinical correlation during surgery should continue to be practiced and compared with observed intraoperative robotic values. Cite this article: Bone Joint J 2018;100-B:1303–9.
β-Glucosidase (EC 3.2.1.21) plays an essential role in biofuel production since it can cleave β-1,4-glycosidic bond to convert cellobiose into fermentable glucose. Based on the structure of Trichoderma reesei β-glucosidase 2 (TrBgl2) we solved, the amino acids in the outer channel of active site were mutated in this study. Mutants P172L and P172L/F250A showed the most enhanced k(cat)/K(m) and k(cat) values by 5.3- and 6.9-fold, respectively, compared to the wild type (WT) toward 4-nitrophenyl-β-D-glucopyranoside (p-NPG) substrate at 40°C. L167W and P172L/F250A mutations resulted in shift of optimal temperature to 50°C, at which WT was almost inactive. However, thin-layer chromatography analysis revealed that mutant L167W had the best synergism with T. reesei cellulases on degrading cellulosic substrates into glucose. This study enhances our understanding on the roles of amino acids in the substrate entrance region away from the active site and provides engineered T. reesei β-glucosidases with better activity and/or thermostability to hydrolyze cellobiose.
The traditional biological assay is very time-consuming, and thus the ability to quickly screen large numbers of compounds against a specific biological target is appealing. To speed up the biological evaluation of compounds, high-throughput screening is widely used in the fields of biomedical, biological information, and drug discovery. The research presented in this study focuses on the use of support vector machines, a machine learning method, various classes of molecular descriptors, and different sampling techniques to overcome overfitting to classify compounds for cytotoxicity with respect to the Jurkat cell line. The cell cytotoxicity data set is imbalanced (a few active compounds and very many inactive compounds), and the ability of the predictive modeling methods is adversely affected in these situations. Commonly imbalanced data sets are overfit with respect to the dominant classified end point; in this study the models routinely overfit toward inactive (noncytotoxic) compounds when the imbalance was substantial. Support vector machine (SVM) models were used to probe the proficiency of different classes of molecular descriptors and oversampling ratios. The SVM models were constructed from 4D-FPs, MOE (1D, 2D, and 21/2D), noNP+MOE, and CATS2D trial descriptors pools and compared to the predictive abilities of CATS2D-based random forest models. Compared to previous results in the literature, the SVM models built from oversampled data sets exhibited better predictive abilities for the training and external test sets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.