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Mendeley readers
Attention Score in Context
| Title |
Label-free proteomic methodology for the analysis of human kidney stone matrix composition
|
|---|---|
| Published in |
Proteome Science, February 2016
|
| DOI | 10.1186/s12953-016-0093-x |
| Pubmed ID | |
| Authors |
Frank A. Witzmann, Andrew P. Evan, Fredric L. Coe, Elaine M. Worcester, James E. Lingeman, James C. Williams |
| Abstract |
Kidney stone matrix protein composition is an important yet poorly understood aspect of nephrolithiasis. We hypothesized that this proteome is considerably more complex than previous reports have indicated and that comprehensive proteomic profiling of the kidney stone matrix may demonstrate relevant constitutive differences between stones. We have analyzed the matrices of two unique human calcium oxalate stones (CaOx-Ia and CaOx-Id) using a simple but effective chaotropic reducing solution for extraction/solubilization combined with label-free quantitative mass spectrometry to generate a comprehensive profile of their proteomes, including physicochemical and bioinformatic analysis.`. We identified and quantified 1,059 unique protein database entries in the two human kidney stone samples, revealing a more complex proteome than previously reported. Protein composition reflects a common range of proteins related to immune response, inflammation, injury, and tissue repair, along with a more diverse set of proteins unique to each stone. The use of a simple chaotropic reducing solution and moderate sonication for extraction and solubilization of kidney stone powders combined with label-free quantitative mass spectrometry has yielded the most comprehensive list to date of the proteins that constitute the human kidney stone proteome. |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 5% |
| Unknown | 20 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 3 | 14% |
| Professor | 2 | 10% |
| Student > Postgraduate | 2 | 10% |
| Student > Doctoral Student | 2 | 10% |
| Librarian | 1 | 5% |
| Other | 6 | 29% |
| Unknown | 5 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 38% |
| Agricultural and Biological Sciences | 2 | 10% |
| Unspecified | 1 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 5% |
| Computer Science | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 6 | 29% |
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 02 March 2016.
All research outputs
#20,318,358
of 22,860,626 outputs
Outputs from Proteome Science
#159
of 192 outputs
Outputs of similar age
#251,371
of 297,543 outputs
Outputs of similar age from Proteome Science
#4
of 4 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 192 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.