Evidence map›Paper›PMID 36094219›Full record

ArticleMicrobiology spectrum2022

Validated Preclinical Mouse Model for Therapeutic Testing against Multidrug-Resistant Pseudomonas aeruginosa Strains.

Jonathan M Warawa, Xiaoxian Duan, Charles D Anderson, Julie B Sotsky, Daniel E Cramer, Tia L Pfeffer, Haixun Guo, Scott Adcock, Alexander J Lepak, David R Andes and 5 more

Open access · goldAbstract read
In one paragraph

Article in Microbiology spectrum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 45% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

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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

15 authors at 3 institutions in 1 country.

Jonathan M WarawaCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Xiaoxian DuanCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Charles D AndersonCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Julie B SotskyCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Daniel E CramerCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Tia L PfefferCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Haixun GuoCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Scott AdcockCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Alexander J LepakDepartment of Medicine, University of Wisconsin, Madison, Wisconsin, USA.
David R AndesDepartment of Medicine, University of Wisconsin, Madison, Wisconsin, USA.ORCID 0000-0002-7927-9950
Stacey A SloneDr. Bing Zhang Department of Statistics, University of Kentuckygrid.266539.d, Lexington, Kentucky, USA.
Arnold J StrombergDr. Bing Zhang Department of Statistics, University of Kentuckygrid.266539.d, Lexington, Kentucky, USA.
Jon D GabbardCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
William E SeversonCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.
Matthew B LawrenzCenter for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisvillegrid.266623.5, Louisville, Kentucky, USA.ORCID 0000-0003-1505-5429
University of Louisville · USUniversity of Kentucky · USUniversity of Wisconsin–Madison · US

Funding

HHS | U.S. Food and Drug Administration (FDA) HHSF223201810171C
6 · The paper itself

Abstract

The rise in infections caused by antibiotic-resistant bacteria is outpacing the development of new antibiotics. The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are a group of clinically important bacteria that have developed resistance to multiple antibiotics and are commonly referred to as multidrug resistant (MDR). The medical and research communities have recognized that, without new antimicrobials, infections by MDR bacteria will soon become a leading cause of morbidity and death. Therefore, there is an ever-growing need to expedite the development of novel antimicrobials to combat these infections. Toward this end, we set out to refine an existing mouse model of pulmonary Pseudomonas aeruginosa infection to generate a robust preclinical tool that can be used to rapidly and accurately predict novel antimicrobial efficacy. This refinement was achieved by characterizing the virulence of a panel of genetically diverse MDR P. aeruginosa strains in this model, by both 50% lethal dose (LD

Indexed as

AmikacinPseudomonas aeruginosaAnimalsAnti-Bacterial AgentsAztreonamBacteriaDrug Resistance, Multiple, BacterialDrugs, InvestigationalHumansMiceMicrobial Sensitivity TestsAmikacinAnti-Bacterial AgentsAztreonamDrugs, Investigationalantibiotic testingmouse modelspharmacokineticspreclinical modelsPseudomonas aeruginosa

Identifiers

PMID36094219
PMCPMC9603883
OpenAlexW4295279868

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.