| Title |
Modeling neurological diseases using iPSC-derived neural cells
|
|---|---|
| Published in |
Cell and Tissue Research, October 2017
|
| DOI | 10.1007/s00441-017-2713-x |
| Pubmed ID | |
| Authors |
Li Li, Jianfei Chao, Yanhong Shi |
| Abstract |
Developing efficient models for neurological diseases enables us to uncover disease mechanisms and develop therapeutic strategies to treat them. Discovery of reprogramming somatic cells to induced pluripotent stem cells (iPSCs) has revolutionized the way of modeling human diseases, especially neurological diseases. Currently almost all types of neural cells, including but not limited to neural stem cells, neurons, astrocytes, oligodendrocytes and microglia, can be derived from iPSCs following developmental principles. These iPSC-derived neural cells provide valuable tools for studying neurological disease mechanisms, developing potential therapies, and deepening our understanding of the nervous system. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 197 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 197 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 15% |
| Student > Bachelor | 28 | 14% |
| Student > Master | 26 | 13% |
| Researcher | 20 | 10% |
| Student > Doctoral Student | 11 | 6% |
| Other | 21 | 11% |
| Unknown | 61 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 41 | 21% |
| Biochemistry, Genetics and Molecular Biology | 37 | 19% |
| Agricultural and Biological Sciences | 20 | 10% |
| Medicine and Dentistry | 10 | 5% |
| Engineering | 7 | 4% |
| Other | 19 | 10% |
| Unknown | 63 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 27 June 2023.
All research outputs
#6,576,591
of 23,989,432 outputs
Outputs from Cell and Tissue Research
#404
of 2,290 outputs
Outputs of similar age
#103,805
of 332,030 outputs
Outputs of similar age from Cell and Tissue Research
#8
of 47 outputs
Altmetric has tracked 23,989,432 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 2,290 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 81% 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 332,030 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.