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Staphylococcus aureus Nasal Colonization: An Update on Mechanisms, Epidemiology, Risk Factors, and Subsequent Infections
Open accessAbstract
Up to 30 % of the human population are asymptomatically and permanently colonized with nasal Staphylococcus aureus. To successfully colonize human nares, S. aureus needs to establish solid interactions with human nasal epithelial cells and overcome host defense mechanisms. However, some factors like bacterial interactions in the human nose can influence S. aureus colonization and sometimes prevent colonization. On the other hand, certain host characteristics and environmental factors can predispose to colonization. Nasal colonization can cause opportunistic and sometimes life-threatening infections such as surgical site infections or other infections in non-surgical patients that increase morbidity, mortality as well as healthcare costs.
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“Specifically, S. aureus adheres to cytokeratin 10, a structural protein expressed in nasal epithelial cells, a key mechanism enabling persistent colonization [15].”
From Staphylococcus aureus: A Review of the Pathogenesis and Virulence Mechanisms (2025) · cites this paper as [15]
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“Surgical site infections are significantly more common in nasal carriers, especially among orthopedic surgery patients with prosthetic implants [15].”
From Staphylococcus aureus: A Review of the Pathogenesis and Virulence Mechanisms (2025) · cites this paper as [15]
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Self-citation
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Metadata
- Authors
- Sakr, A., Brégeon, F., Mège, J.-L., Rolain, J.-M., & Blin, O.
- Venue
- Frontiers in Microbiology
- Publisher
- Frontiers Media SA
- Published
- 2018-10-08
- Volume
- 9
- Pages
- 2419-2419
- ISSN
- 1664-302X (ISSN-L)
- OpenAlex
- W2895984304
- Cited by
- 555
- References
- 143
- FWCI
- 28.18
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.