Evidence map›Paper›PMID 37355540›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Assessing Lung Inflammation and Pathology in Preclinical Models of Chronic Obstructive Pulmonary Disease.

Ross Vlahos, Hao Wang, Steven Bozinovski

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
19.5field-weighted citation impact, top 1% 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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Ross VlahosCentre for Respiratory Science and Health, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia. ross.vlahos@rmit.edu.au.
Hao WangCentre for Respiratory Science and Health, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia.
Steven BozinovskiCentre for Respiratory Science and Health, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia.
RMIT University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) is an incurable disease that is a major cause of mortality and morbidity worldwide. Cigarette smoking is a major cause of COPD and triggers progressive airflow limitation, chronic lung inflammation, and irreversible lung damage and decline in lung function. COPD patients often experience various extrapulmonary comorbid diseases, including cardiovascular disease, skeletal muscle wasting, lung cancer, and cognitive decline which markedly impact on disease morbidity, progression, and mortality. People with COPD are also susceptible to respiratory infections which cause exacerbations of the underlying disease (AECOPD). The mechanisms and mediators underlying COPD and its comorbidities are poorly understood and current COPD therapy is relatively ineffective. We and others have used animal modelling systems to explore the mechanisms underlying COPD, AECOPD, and comorbidities of COPD with the goal of identifying novel therapeutic targets. Here we provide a preclinical model and protocols to assess the cellular, molecular, and pathological consequences of cigarette smoke exposure and the development of comorbidities of COPD.

Indexed as

PneumoniaPulmonary Disease, Chronic ObstructiveAnimalsComorbidityDisease Models, AnimalInflammationLungAECOPDCigarette smokeComorbiditiesCOPDEmphysemaLung inflammation

Identifiers

PMID37355540
OpenAlexW4381851516

What OpenQuestion holds

Textmetadata
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.