Evidence map›Paper›PMID 27458330›Full record

ArticleBiomedical informatics insights2010

Differential Transcriptional Changes in Mice Exposed to Chemically Distinct Diesel Samples.

Tina Stevens, Susan Hester, M Ian Gilmour

Open access · diamondAbstract read
In one paragraph

Article in Biomedical informatics insights, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
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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

3 authors at 1 institution in 1 country.

Tina StevensEnvironmental Public Health Division and.
Susan HesterIntegrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.
M Ian GilmourEnvironmental Public Health Division and.
Environmental Protection Agency · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemiological studies have linked exposure to ambient particulate matter (PM) with increased asthmatic symptoms. Diesel exhaust particles (DEP) are a predominant source of vehicle derived ambient PM, and experimental studies have demonstrated that they may have adjuvant potential when given with an antigen. We previously compared 3 DEP samples: N-DEP, A-DEP, and C-DEP in a murine ovalbumin (OVA) mucosal sensitization model and reported the adjuvant activity to be: C-DEP ≈ A-DEP > N-DEP. The present study analyzed gene expression changes from the lungs of these mice. Transcription profiling demonstrated that all the DEP samples altered cytokine and toll-like receptor pathways regardless of type, with or without antigen sensitization. Further analysis of DEP exposure with OVA showed that all DEP treatments altered networks involved in immune and inflammatory responses. The A- and C-DEP/OVA treatments induced differential expression of apoptosis pathways in association with stronger adjuvant responses, while expression of cell cycle control and DNA damage pathways were also altered in the C-DEP/OVA treatment. This comprehensive approach using gene expression analysis to examine changes at a pathway level provides detailed information on events occurring in the lung after DEP exposure, and confirms that the most bioactive sample induced many more individual genes and changes in immunoregulatory and homeostatic pathways.

Indexed as

allergydiesel exhaust particlesgenomicsinflammationlungmouse model

Identifiers

PMID27458330
PMCPMC4948654
OpenAlexW1830965125

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.