The passive restraining function of the hip capsule plays a critical role in maintaining joint stability after total hip arthroplasty (THA). However, restoring this function remains challenging due to the complex biomechanics of the capsule and the lack of efficient quantitative tools for implant parameter adjustment. This study developed a rapidly calibratable numerical parametric hip capsule model to characterize passive capsular mechanics and to quantitatively evaluate implant geometric and positioning parameters. A nonlinear spring-based model was constructed by dividing the capsule into 12 sectors with 25 parameters and calibrated using experimentally measured torque–rotation data from cadaveric hips. Model accuracy was validated through comparison with finite element (FE) simulations and experimental measurements across multiple motions. The calibrated framework was applied to nine hip specimens to assess the influence of femoral head size, neck length increment, and femoral stem anteversion on capsular restraint after THA. The model achieved efficient calibration (0.74 h per specimen; < 1 s per iteration) and demonstrated strong agreement with FE simulations, with an overall torque root-mean-square error (RMSE) of 0.21 ± 0.02 Nm. Rotational RMSEs relative to experiments ranged from 5.2° to 9.4°. Parametric analyses indicated that femoral head size alone was insufficient to restore capsular restraint, whereas coordinated adjustment of neck length and stem anteversion significantly improved stability and reduced rotational hypermobility. This study presents a computationally efficient and accurate hip capsule modeling framework that enables rapid calibration and quantitative evaluation of capsular mechanics, providing a practical tool to support personalized THA planning and postoperative stability assessment.
Cardiac tissue engineering still faces significant challenges in restoring electrical and mechanical function after myocardial infarction. Conventional biomaterials often lack the necessary conductivity and topographical guidance. This review examines the potential of MXene-based nanomaterials as emerging candidates in advancing cardiac patch technology. As two-dimensional transition metal carbides/nitrides, MXenes offer exceptional electrical conductivity, mechanical flexibility, and tunable surface chemistry that enable electromechanical integration with myocardial tissue. We analyze how MXene-mediated platforms enhance cardiomyocyte alignment, maturation, and synchronized contraction through integrated topographical cues and electrical coupling. Beyond their conductive properties, MXenes exhibit multifunctional capabilities including antioxidant, anti-inflammatory, and antibacterial effects that create a regenerative microenvironment. Compared to existing conductive biomaterials, MXenes demonstrate distinct properties that may facilitate restoration of electrical conduction across infarcted regions while providing mechanical support. This review identifies key translational challenges and outlines future directions for developing intelligent cardiac patches, emphasizing the convergence of MXene technology with personalized medicine approaches. The insights presented here position MXenes as promising biomaterials requiring further investigation that bridge critical gaps in cardiac tissue engineering and offer solutions for myocardial regeneration.
Finite element (FE) models are widely used to study traumatic brain injury, but most are based on single-subject scans or generic atlases that may not represent specific cohorts, introducing variability in biomechanical outputs. This study presents a reproducible framework for generating a cohort-specific MRI-derived template brain and constructing a representative FE head model. A cohort-specific template was created from T1-weighted and diffusion-weighted MRI scans of collision sport athletes (n = 78). An FE head model was then generated from this template, with material properties mapped using MRI-derived microstructural information. The anatomical accuracy of the template was evaluated using deformation-based morphometry against standard brain atlases. The FE model was validated against cadaveric head impact experiments. Subject-specific FE models were also developed to test the pipeline’s applicability and model performance. Registration of an external non-collision sport controls (n = 20) to the template showed reduced deformation compared with registration to the MNI152 and an existing adolescent atlas, indicating improved cohort representation. Validation against cadaveric data demonstrated small to moderate correlations between simulated and experimental nodal displacements. The subject-specific model showed significantly different strain distributions from isotropic and fiber dispersion-informed models at both impact intensities; the two template models did not differ significantly. The proposed framework enables reproducible construction of cohort-specific FE brain models using open-access tools. Results highlight the importance of subject- and cohort-specific modeling for accurately characterizing brain biomechanics. This approach improves the anatomical and microstructural realism of FE models and supports more precise mapping of mechanical strain to clinical outcomes in TBI.
Quantification of walking function, including joint motions, ground reactions, and joint loads, outside the lab without the use of force plates is a growing research area. Because only joint motions can currently be measured outside the lab, researchers are utilizing tracking optimizations of walking to estimate associated ground reactions and inverse dynamic joint loads. However, foot-ground contact models used in such optimizations have been generic rather than personalized, which may limit the accuracy of estimated ground reactions and joint loads. This study compares the predictive capabilities of generic versus personalized deformable foot-ground contact models. Generic and personalized deformable foot-ground contact models were evaluated in calibration and tracking optimizations performed using experimental walking data collected from three subjects in varying states of health. Foot-only calibration optimizations evaluated how well both models could reproduce experimental ground reaction and foot motion data while tracking both types of data simultaneously, while whole-body tracking optimizations evaluated how well both models could reproduce experimental ground reactions, joint motion, and joint load data while tracking only experimental joint motion data and achieving dynamic consistency. For all three subjects and both types of optimizations, personalized foot-ground contact models reproduced experimental ground reaction, joint motion, and joint load data more accurately than did generic foot-ground contact models. Personalized foot-ground contact models can improve the accuracy with which ground reactions and joint loads can be estimated via tracking optimizations of walking using only experimental motion data as inputs. Personalized models are easy to calibrate using freely available software tools and should improve the accuracy of predictive simulations of walking as well.
Accommodative Intraocular Lenses (AIOLs) have long been thought to be a promising solution for restoring visual accommodation after cataract surgery, but their performance critically depends on both the geometric design and the selected materials. The present study aims to compare how haptic design and material combination affect the simulated paraxial power response and mechanical behavior of selected dual-optic AIOL configurations under prescribed radial compression. Twelve dual-optic AIOL finite element models were constructed by combining three different haptic designs (non-folded, mono-folded, and multi-folded) with four different combinations of materials. A prescribed radial inward displacement was applied to each model to analyze the deformation, reaction force, and stress distributions. The resulting deformations were then used for modeling optical ray tracing to evaluate optical power changes. Comparative analyses were conducted to assess the influence of haptic design and material parameters on the paraxial power response and mechanical performance of the AIOLs. Both the haptic design and optic materials affect the optomechanical behavior of the AIOL. At a common radial displacement, multi-folded and mono-folded haptics yield greater power change than non-folded haptics. The multi-folded design yields greater power change per unit force than the mono-folded design. Material combinations also influence refractive power changes. High stresses were concentrated at the peripheral region of the anterior optic for mono- and multi-folded designs and at the junction between posterior optic and haptic for the non-folded design. Under the same applied radial displacement, the multi-folded haptic design provides greater power change per unit force than the mono-folded and non-folded designs. The simulated paraxial power response and biomechanical behavior of the dual-optic AIOL are jointly determined by haptic design and material selection, which should be considered in future AIOL development.
