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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
LC–MS-Based Lipidomics and Automated Identification of Lipids Using the LipidBlast In-Silico MS/MS Library
|
|---|---|
| Chapter number | 14 |
| Book title |
Lipidomics
|
| Published in |
Methods in molecular biology, June 2017
|
| DOI | 10.1007/978-1-4939-6996-8_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6995-1, 978-1-4939-6996-8
|
| Authors |
Tomas Cajka, Oliver Fiehn, Cajka, Tomas, Fiehn, Oliver |
| Editors |
Sanjoy K. Bhattacharya |
| Abstract |
This protocol describes the analysis, specifically the identification, of blood plasma lipids. Plasma lipids are extracted using methyl tert-butyl ether (MTBE), methanol, and water followed by separation and data acquisition of isolated lipids using reversed-phase liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry (RPLC-QTOFMS) operated in MS/MS mode. For lipid identification, acquired MS/MS spectra are converted to the mascot generic format (MGF) followed by library search using the in-silico MS/MS library LipidBlast. Using this approach, lipid classes, carbon-chain lengths, and degree of unsaturation of fatty-acid components are annotated. |
X Demographics
The data shown below were collected from the profile of 1 X user 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 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 2% |
| Unknown | 56 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 30% |
| Researcher | 8 | 14% |
| Student > Bachelor | 7 | 12% |
| Student > Doctoral Student | 5 | 9% |
| Lecturer | 4 | 7% |
| Other | 8 | 14% |
| Unknown | 8 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 14 | 25% |
| Biochemistry, Genetics and Molecular Biology | 13 | 23% |
| Agricultural and Biological Sciences | 8 | 14% |
| Medicine and Dentistry | 4 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 5% |
| Other | 2 | 4% |
| Unknown | 13 | 23% |
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 27 March 2018.
All research outputs
#20,431,953
of 22,985,065 outputs
Outputs from Methods in molecular biology
#9,929
of 13,149 outputs
Outputs of similar age
#274,996
of 315,315 outputs
Outputs of similar age from Methods in molecular biology
#210
of 270 outputs
Altmetric has tracked 22,985,065 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 13,149 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% 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 315,315 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 270 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.