We created TiO2 nanotubes (TNTs) on the surface of titanium (Ti) implants with subsequential loading with gentamicin and chitosan, acting as a control release agent, by electrophoretic deposition (EPD). We hypothesized femoral implants with TNTs loaded with gentamicin and chitosan would localize antibiotic to the implant and surgical site and prevent PJI in a mouse model. Ti-6Al-4V ELI wires underwent TNT surface modification by two-step anodization. EPD was then used to load gentamicin and chitosan onto the Ti wire with surface TNTs. Control Ti wires contained TNTs with EPD of chitosan only. 12-week-old male C57BL/6J mice underwent received a right femoral intramedullary implant followed by inoculation at the surgical site with 1×103 CFUs of bioluminescent Xen36 Over 14 days assessment following wire implantation and inoculation with Ti femoral implants modified with surface TNTs and coated with gentamicin and chitosan through EPD prevented PJI in all mice through 14 days. In comparison, all control mice demonstrated evidence of PJI over 14 days. Implants with TNTs and EPD of gentamicin were highly effective in this mouse PJI model.
Studies of retrieved total knee replacement (TKR) components demonstrate that Eleven retrieved ultra-high molecular weight polyethylene (UHMWPE) cruciate-retaining tibial liner components from ten separate patients (implantation time = 8.6±5.6 years) had matching gait trials of normal level walking for each knee. Volume loss on retrieved components was calculated using a coordinate measuring machine and autonomous reconstruction.3 Motion analysis of normal level walking gait had been conducted between 1986 and 2005 for various previous studies and stored in a consented Human Mechanics Repository, ranging from pre-operative to long-term post-operative testing. Contact location between the femoral component and the tibial component on the medial and lateral plateaus were calculated throughout stance.4 A previously validated and fine-tuned parametric numerical model was used to calculate TKR contact forces for each gait trial.5 Vertical contact forces and contact paths on the medial and lateral plateaus were input as normal force and sliding distance to a simplified Archard equation for wear with material wear constant = 2.42 × 10−7 mm3/Nm2,6 to compute average wear per gait cycle. Wear rates were calculated using linear regression, and Pearson correlation examined correlations between modeled and measured wear.Introduction
Methods
Aseptic loosening is the leading cause of total knee arthroplasty (TKA) failure in the long term, of which osteolysis from polyethylene wear debris remains a problem that can limit the lifetime of TKA past the second decade. To help speed up design innovations, our goal was to develop a computational framework that could efficiently predict the effect of many sources of variability on TKA wear—including design, surgical, and patient variability. We developed a computational framework for predicting TKA contact mechanics and wear. The framework accepts multiple forms of input data: patient-specific, population-specific, or standardized motions and forces. CAD models are used to create the FEA mesh. An analytical wear model, calibrated from materials testing (wheel-on-flat) experiments, is fully integrated into the FEA process. Isight execution engine runs a design of experiments (DOE) analysis with an outcome variable, such as volumetric wear, to guide statistical model output. We report two DOE applications to test the utility of the computational framework for performing large variable studies in an efficient manner: one to test the sensitivity of TKA wear to the femoral center of rotation, and the second to test the sensitivity of TKA wear to gait input perturbations.Background
Methods
This study explores whether subjects with bicruciate retaining TKRs (BiCR) have more normal knee biomechanics during level walking and stair ascent than subjects with posterior cruciate retaining TKRs (PCR). Due to anterior cruciate ligament (ACL) preservation, we expect BiCR subjects will not show the reduced flexion and altered muscle activation patterns characteristic of persons with TKRs. Motion and electromyography (EMG) data were collected during level walking and stair climbing for 16 BiCR subjects (4/12 m/f, 65±3 years, 30.7±7.0 BMI, 8/8 R/L), 17 PCR subjects (2/15 m/f, 65±7 years, 30.4±5.1 BMI, 7/10 R/L), and 17 elderly healthy control subjects (8/9 m/f, 55±10 years, 25.8±4.0 BMI, 10/7 R/L), using the point cluster marker set. Surface EMG electrodes were placed on the vastus medialis obliquus (VMO), rectus femoris (RF), biceps femoris (BF), and semitendinosus (ST) muscles. EMG data are reported as percent relative voluntary contraction (%RVC), normalized to the average peak EMG signals during level walking. Statistical nonparametric mapping was used for waveform analysis.Introduction
Methods
Bicruciate-retaining (BiCR) total knee replacements (TKRs) were designed to improve implant performance; however, functional advantages during daily activity have yet to be demonstrated. Although level walking is a common way to analyze functionality, it has been shown to be a weak test for identifying gait abnormalities related to ACL pathologies. The goal of this study is to set up a functional motion analysis test that will examine the effects of the ACL in TKR patients by comparing knee kinematics, kinetics, and muscle activation patterns during level and downhill walking for patients with posterior-cruciate retaining (PCR) and BiCR TKRs. Motion and electromyography (EMG) data were collected simultaneously for 12 subjects (4/8 m/f, 64±11 years, 31.3±7.3 BMI, 6/6 right/left) with BiCR TKRs and 15 subjects (6/9 m/f, 67±7 years, 30.5±5.1 BMI, 4/11 right/left) with PCR TKRs during level and downhill walking using the point cluster marker set. Surface electrodes were placed on the vastus medialis obliquus (VMO), rectus femoris (RF), biceps femoris (BF), and semitendinosus (ST) muscles. EMG data are reported as percent relative voluntary contraction (%RVC), normalizing the signal during downhill walking to the mean maximum EMG value during level walking.Introduction
Methods