Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression.

We investigate the use of different trabecular bone descriptors and advanced machine learning tech niques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determination R2. The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869 ± 0.121, R2: 0.68 ± 0.079), which was significantly better than DXA BMD alone (RMSE: 0.948 ± 0.119, R2: 0.61 ± 0.101) (p < 10-4). For multivariate feature sets, SVR outperformed multiregression (p < 0.05). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.

Topoisomerase I inhibitor evodiamine acts as an antibacterial agent against drug-resistant Klebsiella pneumoniae.

Topoisomerase inhibitors have been developed in a variety of clinical applications. We investigated the inhibitory effect of evodiamine on E. coli topoisomerase I, which may lead to an anti-bacterial effect. Evodiamine inhibits the supercoiled plasmid DNA relaxation that is catalyzed by E. coli topoisomerase I, and computer-aided docking has shown that the Arg161 and Asp551 residues of topoisomerase I interact with evodiamine. We investigated the bactericidal effect of evodiamine against multidrug-resistant Klebsiella pneumoniae. Evodiamine showed a significantly lower minimal inhibitory concentration value (MIC 128 µg/mL) compared with antibiotics (>512 µg/mL) against the clinical isolate of K. pneumoniae. The results suggested that evodiamine is a potential agent against drug-resistant bacteria.