Evidence map›Paper›PMID 34337001›Full record

ArticleBioMed research international2021

RNA-seq-Based Screening in Coal Dust-Treated Cells Identified

Deyong Ge, Yuhan Shao, Mengjie Wang, Huihui Tao, Min Mu, Xinrong Tao

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2021. 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
0.1field-weighted citation impact, top 56% 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, 2 citations in OpenAlex.

  1. Review
  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

6 authors at 1 institution in 1 country.

Deyong GeKey Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education (Anhui University of Science and Technology), China.ORCID https://orcid.org/0000-0001-7893-8128
Yuhan ShaoSchool of Medicine, Anhui University of Science and Technology, No. 168 Taifeng Road, Huainan, Anhui Province, China.
Mengjie WangSchool of Medicine, Anhui University of Science and Technology, No. 168 Taifeng Road, Huainan, Anhui Province, China.
Huihui TaoKey Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education (Anhui University of Science and Technology), China.ORCID https://orcid.org/0000-0001-6240-4084
Min MuKey Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education (Anhui University of Science and Technology), China.ORCID https://orcid.org/0000-0002-0833-0683
Xinrong TaoKey Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education (Anhui University of Science and Technology), China.ORCID https://orcid.org/0000-0002-2106-6039
Anhui University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is one of the most serious leading cancers with high incidence globally. Identifying molecular markers is key for disease diagnosis and treatment. Coal dust might be important triggering factors in disease development. Here, we first performed RNA-seq-based screening in coal dust treated and nontreated RAW264.7 cell lines.

Indexed as

CoalDustEarly Detection of CancerRNA-SeqAnimalsBiomarkers, TumorCarrier ProteinsCell DeathCell SurvivalEpithelial-Mesenchymal TransitionGene Expression Regulation, NeoplasticHumansLung NeoplasmsMiceRAW 264.7 CellsSurvival AnalysisBiomarkers, TumorCarrier ProteinsCoalDustPHLDB2 protein, human

Identifiers

PMID34337001
PMCPMC8314042
OpenAlexW3186739972

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.