Evidence map›Paper›PMID 37819525›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Improving the Predictive Value of Preclinical Mouse Models of Pseudomonas aeruginosa Respiratory Infection to Evaluate Antibiotic Efficacy.

Cristina Cigana, Alice Rossi, Beatriz Alcalá-Franco, Alessandra Bragonzi

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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

4 authors.

Cristina CiganaInfections and Cystic Fibrosis Unit, Division of Immunology, Transplantation and Infectious Disease, IRCSS San Raffaele Scientific Institute, Milan, Italy.
Alice RossiInfections and Cystic Fibrosis Unit, Division of Immunology, Transplantation and Infectious Disease, IRCSS San Raffaele Scientific Institute, Milan, Italy.
Beatriz Alcalá-FrancoInfections and Cystic Fibrosis Unit, Division of Immunology, Transplantation and Infectious Disease, IRCSS San Raffaele Scientific Institute, Milan, Italy.
Alessandra BragonziInfections and Cystic Fibrosis Unit, Division of Immunology, Transplantation and Infectious Disease, IRCSS San Raffaele Scientific Institute, Milan, Italy. bragonzi.alessandra@hsr.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disease-specific animal models and treatment regimens are essential to optimize anti-Pseudomonas drug testing. Mouse models of acute and chronic P. aeruginosa infections provide valuable resources that mimic the initial and progressive bronchopulmonary infection typically observed in a wide range of patients - in hospital settings, with cystic fibrosis or chronic obstructive pulmonary disease. In this chapter, we will explain how mice can be treated using different administration routes in disease-specific models of P. aeruginosa respiratory infection. We will also describe methods used to evaluate multiple endpoints, including profiling of bacterial and host responses. The application of these procedures in disease-specific models is essential to optimize the efficacy of anti-P. aeruginosa treatments and provide an enhanced link between preclinical testing and clinical trials.

Indexed as

Pseudomonas InfectionsRespiratory Tract InfectionsAnimalsAnti-Bacterial AgentsDisease Models, AnimalHumansMiceMicrobial Sensitivity TestsPersistent InfectionPseudomonas aeruginosaAnti-Bacterial AgentsAirway infectionAntibioticsDrug administrationsMurine modelsPseudomonas aeruginosaTreatment regimen

Identifiers

PMID37819525

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.