Journal article
Infection inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin
- Abstract:
- Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain's own minimum inhibitory concentration. We used the users' classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 3.3MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-024-69341-3
Authors
+ Wellcome Trust
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- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 110164/Z/15/Z
+ National Institute for Health and Care Research
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- Funder identifier:
- https://ror.org/0187kwz08
- Grant:
- NIHR200915
+ Biotechnology and Biological Sciences Research Council
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- Funder identifier:
- https://ror.org/00cwqg982
- Grant:
- BB/N018656/1
- BB/S008896/1
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 14
- Issue:
- 1
- Article number:
- 19543
- Place of publication:
- England
- Publication date:
- 2024-08-22
- Acceptance date:
- 2024-08-02
- DOI:
- EISSN:
-
2045-2322
- ISSN:
-
2045-2322
- Pmid:
-
39174600
- Language:
-
English
- Pubs id:
-
2023103
- Local pid:
-
pubs:2023103
- Deposit date:
-
2024-09-10
Terms of use
- Copyright holder:
- Farrar et al
- Copyright date:
- 2024
- Rights statement:
- ©2024 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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