Evidence map›Paper›PMID 38386693›Full record

ArticlePLoS computational biology2024

Age-dependent ventilator-induced lung injury: Mathematical modeling, experimental data, and statistical analysis.

Quintessa Hay, Christopher Grubb, Sarah Minucci, Michael S Valentine, Jennifer Van Mullekom, Rebecca L Heise, Angela M Reynolds

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
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 at 2 institutions in 1 country.

Quintessa HayDepartment of Mathematics & Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Christopher GrubbDepartment of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
Sarah MinucciDepartment of Mathematics & Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Michael S ValentineDepartment of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Jennifer Van MullekomDepartment of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
Rebecca L HeiseDepartment of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Angela M ReynoldsDepartment of Mathematics & Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia, United States of America.ORCID 0000-0002-8494-3166
Virginia Commonwealth University · USVirginia Tech · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A variety of pulmonary insults can prompt the need for life-saving mechanical ventilation; however, misuse, prolonged use, or an excessive inflammatory response, can result in ventilator-induced lung injury. Past research has observed an increased instance of respiratory distress in older patients and differences in the inflammatory response. To address this, we performed high pressure ventilation on young (2-3 months) and old (20-25 months) mice for 2 hours and collected data for macrophage phenotypes and lung tissue integrity. Large differences in macrophage activation at baseline and airspace enlargement after ventilation were observed in the old mice. The experimental data was used to determine plausible trajectories for a mathematical model of the inflammatory response to lung injury which includes variables for the innate inflammatory cells and mediators, epithelial cells in varying states, and repair mediators. Classification methods were used to identify influential parameters separating the parameter sets associated with the young or old data and separating the response to ventilation, which was measured by changes in the epithelial state variables. Classification methods ranked parameters involved in repair and damage to the epithelial cells and those associated with classically activated macrophages to be influential. Sensitivity results were used to determine candidate in-silico interventions and these interventions were most impact for transients associated with the old data, specifically those with poorer lung health prior to ventilation. Model results identified dynamics involved in M1 macrophages as a focus for further research, potentially driving the age-dependent differences in all macrophage phenotypes. The model also supported the pro-inflammatory response as a potential indicator of age-dependent differences in response to ventilation. This mathematical model can serve as a baseline model for incorporating other pulmonary injuries.

Indexed as

LungVentilator-Induced Lung InjuryAgedAnimalsHumansMacrophagesMiceModels, TheoreticalRespiration, Artificial

Identifiers

PMID38386693
PMCPMC10914268
OpenAlexW4392083264

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.