Evidence map›Paper›PMID 23685955›Full record

ReviewActa pharmacologica Sinica2013

Disease modeling and drug screening for neurological diseases using human induced pluripotent stem cells.

Xiao-hong Xu, Zhong Zhong

Abstract readReview
In one paragraph

Review in Acta pharmacologica Sinica, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
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  6. Article
  7. Article
  8. Nontraditional systems in aging research: an update.Cellular and molecular life sciences : CMLS · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Stem cells technology: a powerful tool behind new brain treatments.Drug delivery and translational research · 2018
    Review
  15. Review
  16. Review
  17. Review
  18. Review
  19. Review
  20. 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

2 authors.

Xiao-hong XuDepartment of Histology and Embryology, Harbin Medical University, Harbin 150081, China.
Zhong Zhong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the general decline of pharmaceutical research productivity, there are concerns that many components of the drug discovery process need to be redesigned and optimized. For example, the human immortalized cell lines or animal primary cells commonly used in traditional drug screening may not faithfully recapitulate the pathological mechanisms of human diseases, leading to biases in assays, targets, or compounds that do not effectively address disease mechanisms. Recent advances in stem cell research, especially in the development of induced pluripotent stem cell (iPSC) technology, provide a new paradigm for drug screening by permitting the use of human cells with the same genetic makeup as the patients without the typical quantity constraints associated with patient primary cells. In this article, we will review the progress made to date on cellular disease models using human stem cells, with a focus on patient-specific iPSCs for neurological diseases. We will discuss the key challenges and the factors that associated with the success of using stem cell models for drug discovery through examples from monogenic diseases, diseases with various known genetic components, and complex diseases caused by a combination of genetic, environmental and other factors.

Indexed as

Drug DesignAnimalsDisease Models, AnimalDrug DiscoveryHumansInduced Pluripotent Stem CellsModels, BiologicalNervous System DiseasesStem Cell Research

Identifiers

PMID23685955
PMCPMC3674515

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.