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UsingGalleria mellonellato study pathogen dissemination and anti-infective surface coatings

UsingGalleria mellonellato study pathogen dissemination and anti-infective surface coatings
UsingGalleria mellonellato study pathogen dissemination and anti-infective surface coatings
Hospital associated infections and localised hospital outbreaks are a major challenge for infection control teams (ICTs) in hospitals around the world. Advances in artificial intelligence and infection modelling have enabled ICT teams to better predict and trace infection spread. However, it is notoriously difficult to replicate bacterial dissemination or validate prediction models in a laboratory setting due to a lack of effective in vivo models. In this work, we sought to develop Galleria mellonella as a model organism to replicate the dissemination of pathogens in a hospital intensive care unit (ICU). By combining this model organism with 3D printed models of a real hospital ICU, we are able to demonstrate that larvae do disseminate the multidrug resistant pathogen Acinetobacter baumannii within this ICU model and that it is possible to use this model to identify infection hotspots. Importantly, this model can also be used for intervention strategy testing as we also show that bacterial dissemination can be significantly mitigated by the introduction of antimicrobial wall surface coatings. This model provides a robust platform for the testing of antimicrobial surface coatings as well as the study of genetic determinants with a role in pathogen dissemination.
bioRxiv
Maslova, Evgenia
1ca2d250-0169-47c4-ac8e-d1067727fb42
Staber, Ciaram
e42a919a-0da2-4194-8c0b-97bf04118777
McCarthy, Ronan R.
0b2cf2e0-b0ff-4c92-aa04-92d91182d1f2
Maslova, Evgenia
1ca2d250-0169-47c4-ac8e-d1067727fb42
Staber, Ciaram
e42a919a-0da2-4194-8c0b-97bf04118777
McCarthy, Ronan R.
0b2cf2e0-b0ff-4c92-aa04-92d91182d1f2

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Hospital associated infections and localised hospital outbreaks are a major challenge for infection control teams (ICTs) in hospitals around the world. Advances in artificial intelligence and infection modelling have enabled ICT teams to better predict and trace infection spread. However, it is notoriously difficult to replicate bacterial dissemination or validate prediction models in a laboratory setting due to a lack of effective in vivo models. In this work, we sought to develop Galleria mellonella as a model organism to replicate the dissemination of pathogens in a hospital intensive care unit (ICU). By combining this model organism with 3D printed models of a real hospital ICU, we are able to demonstrate that larvae do disseminate the multidrug resistant pathogen Acinetobacter baumannii within this ICU model and that it is possible to use this model to identify infection hotspots. Importantly, this model can also be used for intervention strategy testing as we also show that bacterial dissemination can be significantly mitigated by the introduction of antimicrobial wall surface coatings. This model provides a robust platform for the testing of antimicrobial surface coatings as well as the study of genetic determinants with a role in pathogen dissemination.

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Published date: 11 June 2024

Identifiers

Local EPrints ID: 507057
URI: http://eprints.soton.ac.uk/id/eprint/507057
PURE UUID: d5d84e25-4cca-4394-96f0-15f3d91cb1c0
ORCID for Ronan R. McCarthy: ORCID iD orcid.org/0000-0002-7480-6352

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Date deposited: 26 Nov 2025 17:35
Last modified: 27 Nov 2025 03:15

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Contributors

Author: Evgenia Maslova
Author: Ciaram Staber
Author: Ronan R. McCarthy ORCID iD

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