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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using cheminformatics to predict cross reactivity of “designer drugs” to their currently available immunoassays
|
|---|---|
| Published in |
Journal of Cheminformatics, May 2014
|
| DOI | 10.1186/1758-2946-6-22 |
| Pubmed ID | |
| Authors |
Matthew D Krasowski, Sean Ekins |
| Abstract |
A challenge for drug of abuse testing is presented by 'designer drugs', compounds typically discovered by modifications of existing clinical drug classes such as amphetamines and cannabinoids. Drug of abuse screening immunoassays directed at amphetamine or methamphetamine only detect a small subset of designer amphetamine-like drugs, and those immunoassays designed for tetrahydrocannabinol metabolites generally do not cross-react with synthetic cannabinoids lacking the classic cannabinoid chemical backbone. This suggests complexity in understanding how to detect and identify whether a patient has taken a molecule of one class or another, impacting clinical care. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 2 | 40% |
| Germany | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 7% |
| Germany | 1 | 2% |
| Unknown | 37 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 27% |
| Student > Ph. D. Student | 6 | 15% |
| Student > Bachelor | 4 | 10% |
| Other | 3 | 7% |
| Student > Postgraduate | 2 | 5% |
| Other | 7 | 17% |
| Unknown | 8 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 10 | 24% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 22% |
| Agricultural and Biological Sciences | 3 | 7% |
| Computer Science | 3 | 7% |
| Social Sciences | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 10 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 21 October 2021.
All research outputs
#2,218,272
of 22,756,196 outputs
Outputs from Journal of Cheminformatics
#215
of 828 outputs
Outputs of similar age
#23,682
of 227,074 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 22 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has gotten more attention than average, scoring higher than 74% 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 227,074 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.