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Attention Score in Context
Chapter title |
Characterization of Extracellular Vesicles by Size-Exclusion High-Performance Liquid Chromatography (HPLC)
|
---|---|
Chapter number | 15 |
Book title |
Extracellular Vesicles
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-7253-1_15 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7251-7, 978-1-4939-7253-1
|
Authors |
Tao Huang, Jiang He, Huang, Tao, He, Jiang |
Abstract |
Extracellular vesicles (EVs) have recently attracted substantial attention due to the potential diagnostic and therapeutic relevance. Although a variety of techniques have been used to isolate and analyze EVs, it is still far away from satisfaction. Size-exclusion chromatography (SEC), which separates subjects by size, has been widely applied in protein purification and analysis. The purpose of this chapter is to show the applications of size-exclusion high-performance liquid chromatography (HPLC) as methods for EV characterization of impurities or contaminants of small size, and thus for quality assay for the purity of the samples of EVs. |
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 % |
---|---|---|
United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 54 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 13 | 24% |
Student > Bachelor | 9 | 17% |
Student > Master | 5 | 9% |
Researcher | 5 | 9% |
Student > Doctoral Student | 2 | 4% |
Other | 6 | 11% |
Unknown | 14 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 15 | 28% |
Agricultural and Biological Sciences | 5 | 9% |
Chemistry | 5 | 9% |
Medicine and Dentistry | 4 | 7% |
Engineering | 3 | 6% |
Other | 6 | 11% |
Unknown | 16 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 23 August 2017.
All research outputs
#17,913,495
of 22,999,744 outputs
Outputs from Methods in molecular biology
#7,275
of 13,151 outputs
Outputs of similar age
#294,377
of 421,208 outputs
Outputs of similar age from Methods in molecular biology
#641
of 1,074 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,151 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 421,208 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.