Evidence map›Paper›PMID 28548056›Full record

ReviewBiomedicines2013

Decoding the Pluripotency Network: The Emergence of New Transcription Factors.

Kai Chuen Lee, Wing Ki Wong, Bo Feng

Abstract readReview
In one paragraph

Review in Biomedicines, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

  1. Article
  2. Canalizing kernel for cell fate determination.Briefings in bioinformatics · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. 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.

Kai Chuen LeeKey Laboratory for Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Room 105A, 1/F, Lo Kwee-Seong Integrated Biomedical Sciences Building, Area 39, Shatin, N.T., Hong Kong, China. bolkcad@gmail.com.
Wing Ki WongKey Laboratory for Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Room 105A, 1/F, Lo Kwee-Seong Integrated Biomedical Sciences Building, Area 39, Shatin, N.T., Hong Kong, China. winisama@gmail.com.
Bo FengKey Laboratory for Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Room 105A, 1/F, Lo Kwee-Seong Integrated Biomedical Sciences Building, Area 39, Shatin, N.T., Hong Kong, China. fengbo@cuhk.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the successful isolation of mouse and human embryonic stem cells (ESCs) in the past decades, massive investigations have been conducted to dissect the pluripotency network that governs the ability of these cells to differentiate into all cell types. Beside the core Oct4-Sox2-Nanog circuitry, accumulating regulators, including transcription factors, epigenetic modifiers, microRNA and signaling molecules have also been found to play important roles in preserving pluripotency. Among the various regulations that orchestrate the cellular pluripotency program, transcriptional regulation is situated in the central position and appears to be dominant over other regulatory controls. In this review, we would like to summarize the recent advancements in the accumulating findings of new transcription factors that play a critical role in controlling both pluripotency network and ESC identity.

Indexed as

embryonic stem cellpluripotencytranscriptional networktranscription factor

Identifiers

PMID28548056
PMCPMC5423462

What Socratic 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 Socratic graph.