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Cited by 417

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging

Open access
Baltekin, Özden, Boucharin, Alexis, Tano, Eva, Andersson, Dan I., Elf, Johan·2017·Proceedings of the National Academy of Sciences

Abstract

Significance: Antibiotic resistance is a global threat to human health. The problem is aggravated by unnecessary and incorrect use of broad spectrum antibiotics. One way to provide correct treatment and slow down the development of antibiotic resistance is to assay the susceptibility profile of the infecting bacteria before treatment is initiated and let this information guide the choice of antibiotic. Here, we present an antibiotic susceptibility test that is sufficiently fast to be used at the point of care. We show that it is possible to determine if a urinary tract infection is caused by resistant bacteria within 30 min of loading a urine sample, even if the bacterial concentration in the urine is very low.

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Metadata

Authors
Baltekin, Ö., Boucharin, A., Tano, E., Andersson, D. I., & Elf, J.
Venue
Proceedings of the National Academy of Sciences
Published
2017-08-08
Volume
114
Issue
34
Pages
9170-9175
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2744332922
Cited by
417
References
21
FWCI
24.11
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. https://app.cochat.ai/papers/10.1073/pnas.1708558114
The paper
Baltekin, Ö., Boucharin, A., Tano, E., Andersson, D. I., & Elf, J. (2017). Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1708558114

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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.