Explicit Finite Element Models Accurately Predict Subject-Specific and Velocity-Dependent Kinetics of Sideways Fall Impact

The majority of hip fractures in the elderly are the result of a fall from standing or from a lower height. Current injury models focus mostly on femur strength while neglecting subject-specific loading. This article presents an injury modeling strategy for hip fractures related to sideways falls that takes subject-specific impact loading into account. Finite element models (FEMs) of the human body were used to predict the experienced load and the femoral strength in a single model. We validated these models for their predicted peak force, effective pelvic stiffness, and fracture status against matching ex vivo sideways fall impacts (n = 11) with a trochanter velocity of 3.1 m/s. Furthermore, they were compared to sideways impacts of volunteers with lower impact velocities that were previously conducted by other groups. Good agreement was found between the ex vivo experiments and the FEMs with respect to peak force (root mean square error [RMSE] = 10.7%, R² = 0.85) and effective pelvic stiffness (R² = 0.92, RMSE = 12.9%). The FEMs were predictive of the fracture status for 10 out of 11 specimens. Compared to the volunteer experiments from low height, the FEMs overestimated the peak force by 25% for low BMI subjects and 8% for high BMI subjects. The effective pelvic stiffness values that were derived from the FEMs were comparable to those derived from impacts with volunteers. The force attenuation from the impact surface to the femur ranged between 27% and 54% and was highly dependent on soft tissue thickness (R² = 0.86). The energy balance in the FEMS showed that at the time of peak force 79% to 93% of the total energy is either kinetic or was transformed to soft tissue deformation. The presented FEMs allow for direct discrimination between fracture and nonfracture outcome for sideways falls and bridge the gap between impact testing with volunteers and impact conditions representative of real life falls. © 2019 American Society for Bone and Mineral Research.     

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot–savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm³. With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.     

Binding mode of conformations and structure-based pharmacophore development for farnesyltransferase inhibitors

Farnesyltransferase (FTase) is one of the prenyltransferase family enzymes that catalyse the transfer of 15-membered isoprenoid (farnesyl) moiety to the cysteine of CAAX motif-containing proteins including Rho and Ras family of G proteins. Inhibitors of FTase act as drugs for cancer, malaria, progeria and other diseases. In the present investigation, we have developed two structure-based pharmacophore models from protein–ligand complex (3E33 and 3E37) obtained from the protein data bank. Molecular dynamics (MD) simulations were performed on the complexes, and different conformers of the same complex were generated. These conformers were undergone protein–ligand interaction fingerprint (PLIF) analysis, and the fingerprint bits have been used for structure-based pharmacophore model development. The PLIF results showed that Lys164, Tyr166, TrpB106 and TyrB361 are the major interacting residues in both the complexes. The RMSD and RMSF analyses on the MD-simulated systems showed that the absence of FPP in the complex 3E37 has significant effect in the conformational changes of the ligands. During this conformational change, some interactions between the protein and the ligands are lost, but regained after some simulations (after 2 ns). The structure-based pharmacophore models showed that the hydrophobic and acceptor contours are predominantly present in the models. The pharmacophore models were validated using reference compounds, which significantly identified as HITs with smaller RMSD values. The developed structure-based pharmacophore models are significant, and the methodology used in this study is novel from the existing methods (the original X-ray crystallographic coordination of the ligands is used for the model building). In our study, along with the original coordination of the ligand, different conformers of the same complex (protein–ligand) are used. It concluded that the developed methodology is significant for the virtual screening of novel molecules on different targets.