Evidence map›Paper›PMID 26010234›Full record

ArticlePloS one2015

SEGEL: A Web Server for Visualization of Smoking Effects on Human Lung Gene Expression.

Yan Xu, Brian Hu, Sammy S Alnajm, Yin Lu, Yangxin Huang, Diane Allen-Gipson, Feng Cheng

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. 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
0.2field-weighted citation impact, top 44% 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 2 countries.

Yan XuDepartment of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, Florida, United States of America.
Brian HuDepartment of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, Florida, United States of America.
Sammy S AlnajmDepartment of Biology, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany.
Yin LuDepartment of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, Florida, United States of America.
Yangxin HuangDepartment of Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, Florida, United States of America.
Diane Allen-GipsonDepartment of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, Florida, United States of America; Division of Allergy and Clinical Immunology, College of Medicine, University of South Florida, Tampa, Florida, United States of America.
Feng ChengDepartment of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, Florida, United States of America; Department of Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, Florida, United States of America.
University of South Florida · USUniversity of Cologne · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cigarette smoking is a major cause of death worldwide resulting in over six million deaths per year. Cigarette smoke contains complex mixtures of chemicals that are harmful to nearly all organs of the human body, especially the lungs. Cigarette smoking is considered the major risk factor for many lung diseases, particularly chronic obstructive pulmonary diseases (COPD) and lung cancer. However, the underlying molecular mechanisms of smoking-induced lung injury associated with these lung diseases still remain largely unknown. Expression microarray techniques have been widely applied to detect the effects of smoking on gene expression in different human cells in the lungs. These projects have provided a lot of useful information for researchers to understand the potential molecular mechanism(s) of smoke-induced pathogenesis. However, a user-friendly web server that would allow scientists to fast query these data sets and compare the smoking effects on gene expression across different cells had not yet been established. For that reason, we have integrated eight public expression microarray data sets from trachea epithelial cells, large airway epithelial cells, small airway epithelial cells, and alveolar macrophage into an online web server called SEGEL (Smoking Effects on Gene Expression of Lung). Users can query gene expression patterns across these cells from smokers and nonsmokers by gene symbols, and find the effects of smoking on the gene expression of lungs from this web server. Sex difference in response to smoking is also shown. The relationship between the gene expression and cigarette smoking consumption were calculated and are shown in the server. The current version of SEGEL web server contains 42,400 annotated gene probe sets represented on the Affymetrix Human Genome U133 Plus 2.0 platform. SEGEL will be an invaluable resource for researchers interested in the effects of smoking on gene expression in the lungs. The server also provides useful information for drug development against smoking-related diseases. The SEGEL web server is available online at http://www.chengfeng.info/smoking_database.html.

Indexed as

Databases, FactualGene Expression RegulationHumansLungLung NeoplasmsPulmonary Disease, Chronic ObstructiveSex FactorsSmokingSoftwareUser-Computer InterfaceWeb Browser

Identifiers

PMID26010234
PMCPMC4444269
OpenAlexW1921440873

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.