Evidence map›Paper›PMID 41015422›Full record

ArticleAmerican journal of physiology. Lung cellular and molecular physiology2025

Computer model-based injury prediction and evaluation of lung function in mice with acute and ventilator-induced lung injuries.

Elizabeth Kaye, Alexander Sosa, Katharine D Warner, David J Albers, Peter D Sottile, Bradford J Smith

Abstract read
In one paragraph

Article in American journal of physiology. Lung cellular and molecular physiology, 2025. 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

6 authors.

Elizabeth KayeDepartment of Bioengineering, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, United States.ORCID 0009-0004-5070-2411
Alexander SosaDepartment of Bioengineering, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, United States.
Katharine D WarnerDepartment of Bioengineering, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, United States.
David J AlbersDepartment of Bioengineering, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, United States.
Peter D SottileDivision of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States.
Bradford J SmithDepartment of Bioengineering, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, United States.ORCID 0000-0002-1583-6762

Funding

Predicting and Preventing Ventilator-Induced Lung InjuryR01HL151630 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI SMITH, BRADFORD J · 2021 to 2025
$2.9M
NHLBI NIH HHS R01 HL151630
6 · The paper itself

Abstract

Invasive mechanical ventilation is a lifesaving intervention for patients with acute respiratory distress syndrome (ARDS) but it also causes ventilator-induced lung injury (VILI) that can be challenging to avoid due to interpatient and temporal heterogeneity. Thus, the aim of this study was to characterize and predict experimental VILI using readily available measures of lung function. Initially healthy (CTL) and hydrochloric acid (HCL) lung-injured mice were ventilated for 4 h at positive end-expiratory pressure (PEEP) 1, 3, or 8 cmH

Indexed as

Acute Lung InjuryComputer SimulationLungVentilator-Induced Lung InjuryAnimalsDisease Models, AnimalMaleMiceMice, Inbred C57BLPositive-Pressure RespirationRespiratory Distress Syndromeacute lung injurycomputational modelinglung functionmechanical ventilationventilator-induced lung injury

Identifiers

PMID41015422
PMCPMC12668703

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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.