Detection limitations of bacteria in tissue samples Article Swipe
Tim Holm Jakobsen
,
Julius B. Kirkegaard
,
Mads Lichtenberg
,
Lasse Kvich
,
Hans Gottlieb
,
Martin McNally
,
Thomas Bjarnsholt
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1302/2046-3758.146.bjr-2024-0410.r1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1302/2046-3758.146.bjr-2024-0410.r1
Aims Successful identification of bacteria in tissue samples requires careful consideration of multiple factors, including sample type and quality, the type of bacteria being detected, and the sensitivity and specificity of the detection method. Here, we address the issues of detecting a small number of bacteria, often found in biofilms and heterogeneously distributed in a large volume (the surgical site with suspected infection). Specifically, the study seeks to address the difficulties in detecting small numbers of bacteria, and to evaluate the impact of bacterial aggregation on the probability of successful detection. Methods We present simple formulae for the probability of detecting bacteria in different infection scenarios where the number of bacteria and size of bacterial aggregates are incorporated as variables. We define a critical aggregation parameter, above which the probability of sampling bacteria decreases dramatically. Results Our calculations demonstrate that aggregation of bacteria in tissues can strongly impact the probability of detection, where an increase in aggregate size results in a reduced probability of obtaining a positive biopsy. Our calculations underscore the challenges in effectively sampling tissue for diagnostic purposes, particularly in low-grade infections characterized by small bacterial quantities within aggregates. Below the critical aggregation parameter, obtaining five tissue specimens is associated with a high probability of detecting infection, but at a higher aggregation level, increasing the number of specimens is rendered ineffective, resulting in culture-negative diagnoses. Conclusion We hypothesize that the high false-negative rate in diagnosing orthopaedic surgical site infections, such as periprosthetic joint infections, could be partly influenced by the heterogeneous bacterial distribution and the sampling complexities of such populations outlined here. Homogenization of tissue specimens is a technique to enhance the surface area which potentially could increase the detection of heterogeneously distributed bacteria. Cite this article: Bone Joint Res 2025;14(6):560–567.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1302/2046-3758.146.bjr-2024-0410.r1
- https://boneandjoint.org.uk/article/10.1302/2046-3758.146.BJR-2024-0410.R1/pdf
- OA Status
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- Cited By
- 1
- References
- 34
- Related Works
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- OpenAlex ID
- https://openalex.org/W4411439917
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411439917Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1302/2046-3758.146.bjr-2024-0410.r1Digital Object Identifier
- Title
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Detection limitations of bacteria in tissue samplesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-20Full publication date if available
- Authors
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Tim Holm Jakobsen, Julius B. Kirkegaard, Mads Lichtenberg, Lasse Kvich, Hans Gottlieb, Martin McNally, Thomas BjarnsholtList of authors in order
- Landing page
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https://doi.org/10.1302/2046-3758.146.bjr-2024-0410.r1Publisher landing page
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https://boneandjoint.org.uk/article/10.1302/2046-3758.146.BJR-2024-0410.R1/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://boneandjoint.org.uk/article/10.1302/2046-3758.146.BJR-2024-0410.R1/pdfDirect OA link when available
- Concepts
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Bacteria, Sampling (signal processing), Periprosthetic, Biology, Microbiology, Medicine, Computer science, Surgery, Filter (signal processing), Genetics, Arthroplasty, Computer visionTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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