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Modeling psychiatric disorders using patient stem cell-derived neurons: a way forward

Overview of attention for article published in Genome Medicine, January 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 1,593)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
498 X users
googleplus
1 Google+ user

Citations

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57 Dimensions

Readers on

mendeley
69 Mendeley
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Title
Modeling psychiatric disorders using patient stem cell-derived neurons: a way forward
Published in
Genome Medicine, January 2018
DOI 10.1186/s13073-017-0512-3
Pubmed ID
Authors

Krishna C. Vadodaria, Debha N. Amatya, Maria C. Marchetto, Fred H. Gage

Abstract

Our understanding of the neurobiology of psychiatric disorders remains limited, and biomarker-based clinical management is yet to be developed. Induced pluripotent stem cell (iPSC) technology has revolutionized our capacity to generate patient-derived neurons to model psychiatric disorders. Here, we highlight advantages and caveats of iPSC disease modeling and outline strategies for addressing current challenges.

X Demographics

X Demographics

The data shown below were collected from the profiles of 498 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 17%
Student > Ph. D. Student 10 14%
Researcher 9 13%
Student > Bachelor 8 12%
Student > Postgraduate 3 4%
Other 8 12%
Unknown 19 28%
Readers by discipline Count As %
Neuroscience 18 26%
Biochemistry, Genetics and Molecular Biology 10 14%
Agricultural and Biological Sciences 10 14%
Medicine and Dentistry 4 6%
Nursing and Health Professions 1 1%
Other 5 7%
Unknown 21 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 464. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 May 2018.
All research outputs
#59,088
of 25,529,543 outputs
Outputs from Genome Medicine
#16
of 1,593 outputs
Outputs of similar age
#1,366
of 451,409 outputs
Outputs of similar age from Genome Medicine
#4
of 30 outputs
Altmetric has tracked 25,529,543 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,593 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 451,409 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.