The majority of periprosthetic joint infections occur shortly after primary joint replacement (<3 months) and require the removal of all implant components for the treatment period (~4 months). A clinically relevant animal model of periprosthetic infection should, therefore, establish an infection with implant components in place. Here, we describe a joint replacement model in the rat with ultrahigh molecular weight polyethylene (UHMWPE) and titanium components inoculated at the time of surgery by methicillin-sensitive Staphylococcus aureus (S. aureus), which is one of the main causative microorganisms of periprosthetic joint infections. We monitored the animals for 4 weeks by measuring gait, weight-bearing symmetry, von Frey testing, and micro-CT as our primary endpoint analyses. We also assessed the infection ex vivo using colony counts on the implant surfaces and histology of the surrounding tissues. The results confirmed the presence of a local infection for 4 weeks with osteolysis, loosening of the implants, and clinical infection indicators such as redness, swelling, and increased temperature. The utility of specific gait analysis parameters, especially temporal symmetry, hindlimb duty factor imbalance, and phase dispersion was identified in this model for assessing the longitudinal progression of the infection, and these metrics correlated with weight-bearing asymmetry. We propose to use this model to study the efficacy of using different local delivery regimens of antimicrobials on addressing periprosthetic joint infections. Statement of clinical significance: We have established a preclinical joint surgery model, in which postoperative recovery can be monitored over a multi-week course by assessing gait, weight-bearing, and allodynia. This model can be used to study the efficacy of different combinations of implant materials and medication regimens.
In recent years, there has been tremendous growth in both the number and diversity of wearable sensors, including sensors for monitoring mental state. Understanding physiological metrics such as fatigue and stress is an important aspect of human factors research, yet collecting and analyzing these measures can be resource intensive. This paper explores the usability and applicability of four off-the-shelf wearable sensors (Emotiv Epoc, Melon Headband, Spire Stone, and Muse™ Headband) for applied healthcare research. We perform a heuristic usability evaluation of the four sensors and discuss the extent to which each device can be applied to human factors healthcare research in clinical settings.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.