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Recent advancements in computational modeling offer opportunities to refine total knee arthroplasty (TKA) design and treatment strategies. This study developed patient-specific simulator external boundary conditions (EBCs) using a PID-controlled lower limb finite element (FE) model. Calibration of the external actuation required to achieve measured patient-specific joint loading and motion was completed for nine patients with telemetric implants during gait, stair descent, and deep knee bend. The study also compared two EBC scenarios: activity-specific hip AP motion and pelvic rotation (that was averaged across all patients for an activity) and patient-specific hip AP motion and pelvic rotation. Including patient-specific data significantly improved reproduction of joint-level loading, reducing root mean squared error between the target and achieved loading by 28.7% and highlighting the importance of detailed patient data in replicating joint kinematics and kinetics. The principal component analysis (PCA) of the EBCs for the patient dataset showed that one component represented 77.8% of the overall variation, while the first three components represented 97.8%. Given the significant loading variability within the patient cohort, this group of patient-specific models can be run individually to provide insight into expected TKA mechanics variability, and the PCA can be utilized to further create reasonable EBCs that expand the variability evaluated. [ABSTRACT FROM AUTHOR]

Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using the remaining 10% of the training set by comparing differences in hip laxity between the original trials and the regression-derived capsules. Root mean square errors (RMSEs) in laxity predictions ranged from 1.8° to 2.3°, depending on the type of laxity used in the training set. The RMSE, when predicting the laxity measured from five cadaveric specimens with total hip arthroplasty, was 4.5°. Model generation time was reduced from days to milliseconds. The results demonstrated the potential of regression-based training to instantaneously generate subject-specific FE models and have implications for integrating subject-specific capsule models into surgical planning software. [ABSTRACT FROM AUTHOR]

Older adults often suffer an accidental fall when navigating challenging surfaces during common locomotor tasks, such as walking and ascending stairs. This study examined the effect of slick and uneven surfaces on lower limb joint work in older and younger adults while walking and ascending stairs. Fifteen young (18–25 years) and 12 older (>65 years) adults had stance phase positive limb and joint work quantified during walking and stair ascent tasks on a normal, slick, and uneven surface, which was then submitted to a two-way mixed model ANOVA for analysis. The stair ascent required greater limb, and hip, knee, and ankle work than walking (all p < 0.001), with participants producing greater hip and knee work during both the walk and stair ascent (both p < 0.001). Surface, but not age, impacted positive limb work. Participants increased limb (p < 0.001), hip (p = 0.010), and knee (p < 0.001) positive work when walking over the challenging surfaces, and increased hip (p = 0.015), knee (p < 0.001), and ankle (p = 0.010) work when ascending stairs with challenging surfaces. Traversing a challenging surface during both walking and stair ascent tasks required greater work production from the large proximal hip and knee musculature, which may increase the likelihood of an accidental fall in older adults. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a pathological degenerative condition of the joints that is widely prevalent worldwide, resulting in significant pain, disability, and impaired quality of life. The diverse etiology and pathogenesis of OA can explain the paucity of viable preventive and disease-modifying strategies to counter it. Advances in genome-editing techniques may improve disease-modifying solutions by addressing inherited predisposing risk factors and the activity of inflammatory modulators. Recent progress on technologies such as CRISPR/Cas9 and cell-based genome-editing therapies targeting the genetic and epigenetic alternations in OA offer promising avenues for early diagnosis and the development of personalized therapies. The purpose of this literature review was to concisely summarize the genome-editing options against chronic degenerative joint conditions such as OA with a focus on the more recently emerging modalities, especially CRISPR/Cas9. Future advancements in novel genome-editing therapies may improve the efficacy of such targeted treatments. [ABSTRACT FROM AUTHOR]