Evidence map›Paper›PMID 35646895›Full record

ArticleFrontiers in cell and developmental biology2022

Dynamic Transcriptome Profiling Reveals LncRNA-Centred Regulatory Networks in the Modulation of Pluripotency.

Shen Wang, Jun Zhang, Yu'an Ding, Haotian Zhang, Xiang Wu, Lingci Huang, Junjie He, Jun Zhou, Xiao-Min Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2022. 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
0.3field-weighted citation impact, top 51% 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, 3 citations in OpenAlex.

  1. Article
  2. Review
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

9 authors at 1 institution in 1 country.

Shen WangSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Jun ZhangSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Yu'an DingSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Haotian ZhangSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Xiang WuSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Lingci HuangSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Junjie HeSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Jun ZhouSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
Xiao-Min LiuSchool of Life Science and Technology, China Pharmaceutical University, Nanjing, China.
China Pharmaceutical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long noncoding RNAs (lncRNAs) have emerged as vital regulators of gene expression during embryonic stem cell (ESC) self-renewal and differentiation. Here, we systemically analyzed the differentially regulated lncRNAs during ESC-derived cardiomyocyte (CM) differentiation. We established a perspicuous profile of lncRNA expression at four critical developmental stages and found that the differentially expressed lncRNAs were grouped into six distinct clusters. The cluster with specific expression in ESC enriches the largest number of lncRNAs. Investigation of lncRNA-protein interaction network revealed that they are not only controlled by classic key transcription factors, but also modulated by epigenetic and epitranscriptomic factors including N

Indexed as

cell fateembryonic stem cellGm2379lncRNAm6A modificaiton

Identifiers

PMID35646895
PMCPMC9130768
OpenAlexW4280490650

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.