ReviewCell proliferation2016
Induced pluripotent stem cells and Parkinson's disease: modelling and treatment.
Review in Cell proliferation, 2016. 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
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
9 citing papers in PubMed, 17 citations in OpenAlex.
- Toward Personalized Medicine in Parkinson's Disease: A Scoping Review of Biomarkers, Genetics, and Treatment Stratification.Journal of clinical practice and research · 2025Review
- The Neuroanatomy of Induced Pluripotent Stem Cells: In Vitro Models of Subcortical Nuclei in Neurodegenerative Disorders.Current issues in molecular biology · 2024Review
- Progress in direct reprogramming of dopaminergic cell replacement therapy.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Review
- Mitochondrial genome mutations and neuronal dysfunction of induced pluripotent stem cells derived from patients with Alzheimer's disease.Cell proliferation · 2022Article
- Strategies for the Treatment of Parkinson's Disease: Beyond Dopamine.Frontiers in aging neuroscience · 2020Article
- Cross-species models of attention-deficit/hyperactivity disorder and autism spectrum disorder: lessons from CNTNAP2, ADGRL3, and PARK2.Psychiatric genetics · 2019Review
- One Step Into the Future: New iPSC Tools to Advance Research in Parkinson's Disease and Neurological Disorders.Journal of Parkinson's disease · 2019Review
- Non-human primate models of PD to test novel therapies.Journal of neural transmission (Vienna, Austria : 1996) · 2018Review
- Induced pluripotent stem cells in rat models of Parkinson's disease: A systematic review and meta-analysis.Biomedical reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
Many neurodegenerative disorders, such as Parkinson's disease (PD), are characterized by progressive neuronal loss in different regions of the central nervous system, contributing to brain dysfunction in the relevant patients. Stem cell therapy holds great promise for PD patients, including with foetal ventral mesencephalic cells, human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs). Moreover, stem cells can be used to model neurodegenerative diseases in order to screen potential medication and explore their mechanisms of disease. However, related ethical issues, immunological rejection and lack of canonical grafting protocols limit common clinical use of stem cells. iPSCs, derived from reprogrammed somatic cells, provide new hope for cell replacement therapy. In this review, recent development in stem cell treatment for PD, using hiPSCs, as well as the potential value of hiPSCs in modelling for PD, have been summarized for application of iPSCs technology to clinical translation for PD treatment.
Indexed as
Identifiers
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.