ReviewMolecular neurobiology2017
MicroRNA-Mediated Reprogramming of Somatic Cells into Neural Stem Cells or Neurons.
Review in Molecular neurobiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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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.
Who cites it
9 citing papers in PubMed.
- Application of neural stem cells in the treatment of intracerebral hemorrhage: a systematic review.Frontiers in pharmacology · 2025Review
- The role of exosomes and exosomal microRNA in diabetic cardiomyopathy.Frontiers in endocrinology · 2023Review
- Therapeutic potential of glial cell line-derived neurotrophic factor and cell reprogramming for hippocampal-related neurological disorders.Neural regeneration research · 2022Review
- Conversion of Human Fibroblasts into Induced Neural Stem Cells by Small Molecules.International journal of molecular sciences · 2022Article
- Circular RNA TTC3 regulates cerebral ischemia-reperfusion injury and neural stem cells by miR-372-3p/TLR4 axis in cerebral infarction.Stem cell research & therapy · 2021Article
- Article
- MicroRNA-Directed Neuronal Reprogramming as a Therapeutic Strategy for Neurological Diseases.Molecular neurobiology · 2018Review
- Current progress in the derivation and therapeutic application of neural stem cells.Cell death & disease · 2017Review
- Reprogramming cell fates by small molecules.Protein & cell · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular reprogramming is a promising strategy to generate neural stem cells (NSCs) or desired subtype-specific neurons for cell-based therapeutic intervention. By far, the intricate cell event like reprogramming of non-neural cells to desired cell types can be achieved by forced expression of lineage-related transcription factors (TFs), nuclear transfer, a defined set of factors, and via non-coding microRNAs (miRNAs), as well as other precisely defined conditions. In addition, scientists have been trying to develop better approaches for reprogramming, either by using distinct combinations of a set of small molecules and certain TFs or delivery of appropriate small molecules and miRNAs. The miRNA-mediated approach is fascinating because of its potential to rapidly generate a variety of therapeutically desired cell types from other cell lineages. Recent studies have made great progress in miRNA-mediated neural reprogramming of somatic cells to various specific neuronal subtypes with more efficiency even though the exact mechanisms remain to be further explored. Based on key roles of miRNAs in neural reprogramming across differentiated cell lineages, it is of vital interest to summarize the recent knowledge regarding the instructive role of miRNAs in direct conversion of somatic cells into neural lineages. This precise review mainly focuses on recent discoveries of miRNAs functions in initiating cell reprogramming and fate specification of the neuronal subtype. Moreover, we discuss most recent findings about some miRNAs' activity in regulating various developmental stages of neurons, which is helpful for understanding the event network between miRNAs and their targets.
Indexed as
Identifiers
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Registered trials
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.