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Mohammad Ibrahim Hossain

Publications and source records attributed to Mohammad Ibrahim Hossain.

2 recordsLinked to original sources

Head Impact Characterization and Cellular Response of a Live-neuron cell-integrated Biomechanical Full-body Surrogate Model

In this study, we develop a novel integrated framework that links the impact response with cellular dynamics using a live-neuron cell-integrated biomechanical full-body surrogate model. The impact event is simulated by allowing the surrogate model to fall from controlled seated release angles of 30-degree, 60-degree, and 90-degree. Three vertically stacked cell-culture Petri dishes, each containing live SH-SY5Y neuroblastoma cells, were placed inside the head of a commercially available surrogate model. The dynamic response of the impact event was evaluated using acceleration measurements from six accelerometers, comprising three sensors mounted on the head surface and three embedded in series with the cell stacks, along with kinematic measurements of the fall and deformation of the head model. In parallel, an OpenSim-based modified musculoskeletal model was used to simulate the fall experiment. We found that variation in contact stiffness produced the largest change in the predicted head acceleration in the simulation. When the cellular response and the measured accelerations are compared, oxidative stress and cell viability showed trends consistent with the regional acceleration and angle of fall. At the 90-degree fall, where median peak linear accelerations ranged from 170-258g, and the maximum headform deformation was approximately 9.4 mm, oxidative stress increased to approximately twice that of the control sample. We also quantified the cellular drift of SH-SY5Y cells, which is focal in nature for the 90-degree impact condition. The corresponding fall scenarios were also simulated in OpenSim and a preliminary calibration relationship was developed to compare the kinematic responses of the physical surrogate and musculoskeletal model. Finally, the framework provides a basis for relating experimental surrogate measurements to human head-neck response during impact.

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A Validated Data-driven Subject and Vehicle Specific Nonlinear Biodynamic Model for Predicting Upper-Body Response in Vehicle Ride

A six-degree-of-freedom (6-DOF) nonlinear lumped-parameter biodynamic model of the seated human upper body is formulated to predict human response under different unknown loading conditions. The model consists of six anatomically partitioned cascade body segments: pelvis, lower torso, central torso, upper torso, neck, and head. The joints are connected through nonlinear viscoelastic joints incorporating strain-stiffening restoring forces consistent with the nonlinear constitutive behavior of biological soft tissue. Fifteen subject-specific mechanical parameters are considered as design variables. The design variables are six joint natural frequencies, six viscous damping ratios, one Rayleigh coefficient, one nonlinear stiffening scale, and one mass scale. The subject-specific design variables are identified from three distinct vehicular loading cases using a hybrid two-phase optimization strategy. Particle Swarm Optimization (PSO) globally searches on a frequency-domain surrogate and seeds a bounded Nelder-Mead refinement for the full nonlinear Ordinary Differential Equation (ODE) response. A rigorous parameter idealization methodology, based on error-weighted geometric mean in log space, was implemented to fuse the multi-case optima into a single subject-representative consensus parameter set. The idealized model retains physical interpretability across loading scenarios and is subsequently used in forward dynamics to predict and validate the subject's experimental response. The framework constitutes a complete subject-specific biodynamic pipeline, from experimental data through optimization, idealization, and forward prediction, suitable for occupational health assessment and protective equipment design across diverse loading scenarios.

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