ArticleScientific reports2021
Comparative meta-omics for identifying pathogens associated with prosthetic joint infection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07256405 (Contribution of Metagenomic Sequencing Using Nanopore Technology to the Management of Post-traumatic Bone and Joint Infections), which is not on this map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Contribution of Metagenomic Sequencing Using Nanopore Technology to the Management of Post-traumatic Bone and Joint Infections
Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Nasal microbiome and the effect of nasal decolonization with a novel povidone-iodine antiseptic solution: a prospective and randomized clinical trial.Scientific reports · 2024Trial
- Managing Bone Infections Beyond Systemic Antibiotics: A Scoping Review.Pathogens (Basel, Switzerland) · 2026Review
- Current Concepts in Periprosthetic Joint Infection: Modern Biomarkers, Microbiome Insights, Molecular Diagnostics, and Emerging Prevention Strategies.Orthopedic research and reviews · 2026Review
- Comparison between metatranscriptomics and viral metagenomics, 16S, and host transcriptomics for comprehensive profiling of the respiratory microbiome and host response.Frontiers in microbiology · 2025Article
- Periprosthetic joint infections: state-of-the-art.Archives of orthopaedic and trauma surgery · 2024Review
- Fostering International Knowledge Sharing and Clinical Excellence: A Partnership and Inaugural Academic Conference.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2024Article
- Application of a metatranscriptomics technology, CSI-Dx, for the detection of pathogens associated with prosthetic joint infections.Scientific reports · 2024Article
- Enhancing Pathogen Detection in Implant-Related Infections through Chemical Antibiofilm Strategies: A Comprehensive Review.Antibiotics (Basel, Switzerland) · 2024Review
- Next Generation Sequencing in orthopaedic infections - Where is the road headed?Journal of clinical orthopaedics and trauma · 2024Article
- Review
- Assessment of a novel continuous cleaning device using metatranscriptomics in diverse hospital environments.Frontiers in medical technology · 2023Article
- Molecular Approach for the Laboratory Diagnosis of Periprosthetic Joint Infections.Microorganisms · 2022Review
- cleanSURFACESFrontiers in cellular and infection microbiology · 2022Article
- Prospective Comparison Between Shotgun Metagenomics and Sanger Sequencing of the 16S rRNA Gene for the Etiological Diagnosis of Infections.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prosthetic joint infections (PJI) are economically and personally costly, and their incidence has been increasing in the United States. Herein, we compared 16S rRNA amplicon sequencing (16S), shotgun metagenomics (MG) and metatranscriptomics (MT) in identifying pathogens causing PJI. Samples were collected from 30 patients, including 10 patients undergoing revision arthroplasty for infection, 10 patients receiving revision for aseptic failure, and 10 patients undergoing primary total joint arthroplasty. Synovial fluid and peripheral blood samples from the patients were obtained at time of surgery. Analysis revealed distinct microbial communities between primary, aseptic, and infected samples using MG, MT, (PERMANOVA p = 0.001), and 16S sequencing (PERMANOVA p < 0.01). MG and MT had higher concordance with culture (83%) compared to 0% concordance of 16S results. Supervised learning methods revealed MT datasets most clearly differentiated infected, primary, and aseptic sample groups. MT data also revealed more antibiotic resistance genes, with improved concordance results compared to MG. These data suggest that a differential and underlying microbial ecology exists within uninfected and infected joints. This study represents the first application of RNA-based sequencing (MT). Further work on larger cohorts will provide opportunities to employ deep learning approaches to improve accuracy, predictive power, and clinical utility.
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