Evidence map›Paper›PMID 31616540›Full record

ReviewWorld journal of stem cells2019

Using induced pluripotent stem cells for modeling Parkinson's disease.

Minjing Ke, Cheong-Meng Chong, Huanxing Su

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in World journal of stem cells, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Stem Cell-Based Modeling Protocol for Parkinson's Disease.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Understanding the Potential of Genome Editing in Parkinson's Disease.International journal of molecular sciences · 2021
    Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Review
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.

Minjing KeState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao 999078, China.
Cheong-Meng ChongState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao 999078, China.
Huanxing SuState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao 999078, China. huanxingsu@umac.mo.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is an age-related neurodegenerative disease caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra. As DA neurons degenerate, PD patients gradually lose their ability of movement. To date no effective therapies are available for the treatment of PD and its pathogenesis remains unknown. Experimental models that appropriately mimic the development of PD are certainly needed for gaining mechanistic insights into PD pathogenesis and identifying new therapeutic targets. Human induced pluripotent stem cells (iPSCs) could provide a promising model for fundamental research and drug screening. In this review, we summarize various iPSCs-based PD models either derived from PD patients through reprogramming technology or established by gene-editing technology, and the promising application of iPSC-based PD models for mechanistic studies and drug testing.

Indexed as

AgingDopaminergic neuronsInduced pluripotent stem cellsParkinson’s diseaseSomatic cell reprogramming

Identifiers

PMID31616540
PMCPMC6789186

What OpenQuestion holds

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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.