Evidence map›Paper›PMID 38732061›Full record

ArticleInternational journal of molecular sciences2024

Unraveling the Significance of Nanog in the Generation of Embryonic Stem-like Cells from Spermatogonia Stem Cells: A Combined In Silico Analysis and In Vitro Experimental Approach.

Nima Ghasemi, Hossein Azizi, Thomas Skutella

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Nima GhasemiFaculty of Biotechnology, Amol University of Special Modern Technologies, Amol 49767, Iran.ORCID 0009-0002-4202-0571
Hossein AziziFaculty of Biotechnology, Amol University of Special Modern Technologies, Amol 49767, Iran.ORCID 0000-0001-8246-595X
Thomas SkutellaInstitute for Anatomy and Cell Biology, Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 307, 69120 Heidelberg, Germany.ORCID 0000-0003-4813-1213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Embryonic stem-like cells (ES-like cells) are promising for medical research and clinical applications. Traditional methods involve "Yamanaka" transcription (OSKM) to derive these cells from somatic cells in vitro. Recently, a novel approach has emerged, obtaining ES-like cells from spermatogonia stem cells (SSCs) in a time-related process without adding artificial additives to cell cultures, like transcription factors or small molecules such as pten or p53 inhibitors. This study aims to investigate the role of the Nanog in the conversion of SSCs to pluripotent stem cells through both in silico analysis and in vitro experiments. We used bioinformatic methods and microarray data to find significant genes connected to this derivation path, to construct PPI networks, using enrichment analysis, and to construct miRNA-lncRNA networks, as well as in vitro experiments, immunostaining, and Fluidigm qPCR analysis to connect the dots of Nanog significance. We concluded that

Indexed as

Nanog Homeobox ProteinAnimalsCell DifferentiationComputational BiologyComputer SimulationEmbryonic Stem CellsGene Expression ProfilingGene Regulatory NetworksHumansMaleMiceMicroRNAsPluripotent Stem CellsRNA, Long NoncodingSpermatogoniaMicroRNAsNanog Homeobox ProteinNanog protein, mouseRNA, Long NoncodingES-like cellsimmunostainingNanogpluripotencyPPI networksspermatogonia stem cells

Identifiers

PMID38732061
PMCPMC11084519

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.