Characterizing the Native Microbiome Using Next-Generation Sequencing of Bilateral 'Aseptic' Knees.

BACKGROUND: Next generation sequencing (NGS) has proven ability to identify organisms beyond those identified through traditional culture-based techniques in cases of suspected prosthetic joint infection. However, there is concern that some microorganisms identified may represent the natural joint microbiome rather than pathogenic agents. This work sought to evaluate the presence of microorganisms identified with NGS in bilateral native, presumed "aseptic" knees with osteoarthritis. METHODS: There were 40 patients undergoing primary unilateral (30) or bilateral (10) total knee arthroplasty enrolled prospectively. During surgery, samples of fluid and tissue were obtained from operative knees, and joint fluid was obtained from nonoperative knees. Samples were sent for NGS analysis and processed according to manufacturer protocols. Patient age, body mass index, comorbidities, prior history of injections, and grade of arthritis were evaluated for association with positive NGS results. RESULTS: There were 3 of 80 samples (3.8%) that demonstrated positive NGS. There were two of these that had multiple microorganisms identified (1 knee with 4 microorganisms; 1 knee with 2 microorganisms). An additional 2 samples had positive NGS results below the manufacturer's threshold for reporting. The most common organism identified was Cutibacterium acnes, present in 2 of the 3 positive samples. No patient baseline characteristics were associated with positive NGS results. CONCLUSIONS: Some native knee joints with osteoarthritis have positive microorganisms identified with NGS. The presence of microorganisms in the native knee has important implications for better understanding the native joint microbiome as well as utilization of NGS in cases of suspected prosthetic joint infection.

Automated motion artifact correction for dynamic contrast-enhanced fluorescence imaging during open orthopedic surgery.

Indocyanine green (ICG)-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) can objectively assess bone perfusion intraoperatively. However, it is susceptible to motion artifacts due to patient's involuntary respiration during the 4.5-minute DCE-FI data acquisition. An automated motion correction approach based on mutual information (MI) frameby-frame was developed to overcome this problem. In this approach, MIs were calculated between the reference and the adjacent frame translated and the maximal MI corresponded to the optimal translation. The images obtained from eighteen amputation cases were utilized to validate the approach and the results show that this correction can significantly reduce the motion artifacts and can improve the accuracy of bone perfusion assessment.