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Attention Score in Context
Chapter title |
Lab-on-a-Chip Immunoassay for Prediction of Severe COVID-19 Disease.
|
---|---|
Chapter number | 17 |
Book title |
Multiplex Biomarker Techniques
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Published in |
Methods in molecular biology, January 2022
|
DOI | 10.1007/978-1-0716-2395-4_17 |
Pubmed ID | |
Book ISBNs |
978-1-07-162394-7, 978-1-07-162395-4
|
Authors |
Peter, Harald, Mattig, Emily, Guest, Paul C, Bier, Frank F, Guest, Paul C., Bier, Frank F. |
Abstract |
Most people infected by the SARS-CoV-2 virus which causes COVID-19 disease experience mild or no symptoms. Severe forms of the disease are often marked by a hyper-inflammatory response known as a cytokine storm. Thus, biomarker tests which can identify these patients and place them on the appropriate treatment regime at the earliest possible phase would help to improve outcomes. Here we describe an automated microarray-based immunoassay using the Fraunhofer lab-on-a-chip platform for analysis of C-reactive protein due to its role in the hyper-inflammatory response. |
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 % |
---|---|---|
Unknown | 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 5 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Librarian | 1 | 20% |
Unknown | 4 | 80% |
Readers by discipline | Count | As % |
---|---|---|
Veterinary Science and Veterinary Medicine | 1 | 20% |
Unknown | 4 | 80% |
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 15 July 2022.
All research outputs
#16,342,641
of 24,079,942 outputs
Outputs from Methods in molecular biology
#5,671
of 13,577 outputs
Outputs of similar age
#292,467
of 506,241 outputs
Outputs of similar age from Methods in molecular biology
#296
of 814 outputs
Altmetric has tracked 24,079,942 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,577 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 43rd percentile – i.e., 43% 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 506,241 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 814 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.