ReviewFrontiers in neuroscience2020
Prospects of Directly Reprogrammed Adult Human Neurons for Neurodegenerative Disease Modeling and Drug Discovery: iN vs. iPSCs Models.
Review in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in Stem Cell Therapy for Huntington's Disease: A Comprehensive Literature Review.Cells · 2025Pooled it
- Early Reprogramming Intermediates Enable Direct Neuronal Conversion Via NGN2.Journal of molecular neuroscience : MN · 2026Article
- CRISPRi: a way to integrate iPSC-derived neuronal models.Biochemical Society transactions · 2024Review
- Screens in aging-relevant human ALS-motor neurons identify MAP4Ks as therapeutic targets for the disease.Cell death & disease · 2024Article
- N-Acetylglucosamine Kinase-Small Nuclear Ribonucleoprotein Polypeptide N Interaction Promotes Axodendritic Branching in Neurons via Dynein-Mediated Microtubule Transport.International journal of molecular sciences · 2023Article
- Gene Therapy Using Efficient Direct Lineage Reprogramming Technology for Neurological Diseases.Nanomaterials (Basel, Switzerland) · 2023Review
- Human-Induced Pluripotent Stem Cell Technology: Toward the Future of Personalized Psychiatry.Journal of personalized medicine · 2022Review
- In Vitro Models of the Human Blood-Brain Barrier Utilising Human Induced Pluripotent Stem Cells: Opportunities and Challenges.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Role of SNAREs in Neurodegenerative Diseases.Cells · 2021Review
- Single-Cell Genomics: Catalyst for Cell Fate Engineering.Frontiers in bioengineering and biotechnology · 2021Review
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
A reliable disease model is critical to the study of specific disease mechanisms as well as for the discovery and development of new drugs. Despite providing crucial insights into the mechanisms of neurodegenerative diseases, translation of this information to develop therapeutics in clinical trials have been unsuccessful. Reprogramming technology to convert adult somatic cells to induced Pluripotent Stem Cells (iPSCs) or directly reprogramming adult somatic cells to induced Neurons (iN), has allowed for the creation of better models to understand the molecular mechanisms and design of new drugs. In recent times, iPSC technology has been commonly used for modeling neurodegenerative diseases and drug discovery. However, several technological challenges have limited the application of iN. As evidence suggests, iN for the modeling of neurodegenerative disorders is advantageous compared to those derived from iPSCs. In this review, we will compare iPSCs and iN models for neurodegenerative diseases and their potential applications in the future.
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What OpenQuestion holds
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.