Evidence map›Paper›PMID 38895443›Full record

ArticlebioRxiv : the preprint server for biology2024

Reversion to sensitivity explains limited transmission of resistance in a hospital pathogen.

Kevin C Tracy, Jordan McKaig, Clare Kinnear, Jess Millar, Aaron A King, Andrew F Read, Robert J Woods

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kevin C TracyDepartment of Computational Medicine and Bioinformatics, University of Michigan.
Jordan McKaigProgram in Biology, University of Michigan.
Clare KinnearDepartment of Internal Medicine, Division of Infectious Disease, University of Michigan.ORCID 0000-0002-4126-7540
Jess MillarDepartment of Computational Medicine and Bioinformatics, University of Michigan.
Aaron A KingDepartment of Ecology and Evolutionary Biology, University of Michigan.
Andrew F ReadDepartment of Biology, Pennsylvania State University.ORCID 0000-0001-7604-7903
Robert J WoodsDepartment of Internal Medicine, Division of Infectious Disease, University of Michigan.ORCID 0000-0002-0679-9998

Funding

Dynamical modeling of hospital transmission and antibiotic resistance evolution in a multidrug resistant nosocomial pathogenR01AI143852 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WOODS, ROBERT J · 2019 to 2023
$2.9M
Population Dynamics of Rotavirus: A Combined Theoretical, Bioinformatic and Laboratory Based ApproachK08AI119182 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WOODS, ROBERT J · 2016 to 2020
$960k
NIAID NIH HHS K08 AI119182NIAID NIH HHS R01 AI143852
6 · The paper itself

Abstract

Bacterial pathogens that are successful in hospital environments must survive times of intense antibiotic exposure and times of no antibiotic exposure. When these organisms are closely associated with human hosts, they must also transmit from one patient to another for the resistance to spread. The resulting evolutionary dynamics have, in some settings, led to rising levels of resistance in hospitals. Here, we focus on an important but understudied aspect of this dynamic: the loss of resistance when the resistant organisms evolve in environments where the antibiotic pressure is removed. Based on prior data, we hypothesize that resistance arising in the context of strong selection may carry a high cost and revert to sensitivity quickly once the selective pressure is removed. Conversely, resistant isolates that persist through times of no antibiotic pressure should carry a lower cost and revert less quickly. To test this hypothesis, we utilize a genetically diverse set of patient-derived, daptomycin-resistant

Identifiers

PMID38895443
PMCPMC11185621

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.