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Bacterial detection and identification from human synovial fluids on an integrated microfluidic system

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Abstract

Periprosthetic joint infections (PJIs) are among the most severe complications emerging from prosthetic joint replacement surgeries. In order to possess a rapid means of diagnosing PJIs, an integrated microfluidic system was developed herein for detecting and identifying bacteria in human synovial fluid (HSF). The entire molecular diagnostic process, including (1) sample treatment, (2) bacterial isolation, (3) bacterial lysis, (4) nucleic acid amplification (via polymerase chain reaction (PCR)), and (5) optical detection, could be automated on a single chip. First, N-acetyl-L-cysteine was used to decrease the viscosity of HSF samples and consequently enhance bacterial isolation with vancomycin-coated nano-magnetic beads. Then, a universal 16S ribosomal ribonucleic acid PCR primer set and four species-specific primer sets were used for PCR-based detection and identification of four common bacteria previously associated with PJIs, including Staphylococcus aureus, methicillin-resistant S. aureus, Escherichia coli, and Acinetobacter baumannii. With this approach, the limit of detection was as low as 100 colony forming units (CFUs) per milliliter (or 20 CFUs per reaction), which is suitable for clinical diagnostics and for making informed decisions regarding post-operative antibiotic administration. More importantly, bacterial detection and identification data could be acquired within 90 minutes, representing a significant improvement over traditional culture-based methods (3–7 days). The developed microfluidic system may therefore serve as a promising tool for rapid diagnosis of PJIs.

Graphical abstract: Bacterial detection and identification from human synovial fluids on an integrated microfluidic system

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Publication details

The article was received on 12 Sep 2018, accepted on 09 Nov 2018 and first published on 16 Nov 2018


Article type: Paper
DOI: 10.1039/C8AN01764F
Citation: Analyst, 2019, Advance Article
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    Bacterial detection and identification from human synovial fluids on an integrated microfluidic system

    T. Liu, S. Cheng, H. You, M. S. Lee and G. Lee, Analyst, 2019, Advance Article , DOI: 10.1039/C8AN01764F

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