Development of a 3D Bioartificial Shoulder Joint Implant Mimetic of Periprosthetic Joint Infection

Post-surgical infections of the shoulder joint involving Cutibacterium acnes are difficult to diagnose and manage. Despite the devastating clinical complications and costly healthcare burden of joint infections, the scarcity of joint infection models was identified as an unmet need by the 2019 International Consensus on Orthopedic Infections. In this study, we have developed a novel 3D shoulder joint implant mimetic (S-JIM) that includes a surgical metal surface and supports a co-culture of C. acnes and patient-derived shoulder capsule fibroblasts. Our findings indicate the S-JIM can generate a near anaerobic interior environment that allows for C. acnes proliferation and elicit fibroblast cell lysis responses that are consistent with clinical reports of tissue necrosis. Using the S-JIM, we provided proof-of-concept for the use of mass spectrometry in real-time detection of C. acnes joint infections during surgery. The S-JIM is the first in vitro cell culture-based biomimetic of periprosthetic joint infection that provides a preclinical method for the rapid and reliable testing of novel anti-PJI interventions.

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef.