Holmes, David R

ABSTRACT Periprosthetic joint infection (PJI) is a devastating complication of total joint arthroplasty that can be challenging to diagnose and treat. Neutrophils are a prominent feature in PJI, with neutrophil extracellular traps (NETs) known to be present; little is known, however, about the physiology of PJI-associated NETs. Here, the proteomes of PJI and non-infectious arthroplasty failure (NIAF), analyzed using liquid chromatography tandem mass-spectrometry (LC-MS/MS) and proximity extension assay (PEA)-based profiling of fluids derived from sonication of arthroplasty components removed from patients who had undergone revision arthroplasty, were compared, with a focus on NET formation. In addition, in vitro effects of synovial fluid components on Staphylococcus aureus- induced NET formation were characterized. LC-MS/MS (PJI n = 85, NIAF n = 26) and PEA (PJI n = 95, NIAF n = 39) showed NET formation and neutrophil degranulation to be associated with PJI, with the complement cascade and platelet activation pathways identified as potential contributors. In vitro studies showed delayed NET formation in artificial synovial fluid compared to standard cell culture media conventionally used to evaluate NETs. Together, these results demonstrate the presence of NETs in PJI, identify potential pathways involved in PJI-associated NETosis, and point to an influence of synovial components on NET formation during PJI. IMPORTANCE Periprosthetic joint infection (PJI) is a major complication of joint replacement, placing a significant burden on patients and the healthcare system. Our research reveals that neutrophils and their antimicrobial “traps” are dominant features of the immune response at the infection joint site. Importantly, we found that the unique environment of the joint delays the formation of these traps, potentially contributing to the pathogenesis of PJI. By establishing a new experimental platform tailored for the infected joint, this work provides a foundational platform for probing these mechanisms and supports the development of improved diagnostic and therapeutic strategies.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.