| Title |
An ultrafast insulin formulation enabled by high-throughput screening of engineered polymeric excipients
|
|---|---|
| Published in |
Science Translational Medicine, July 2020
|
| DOI | 10.1126/scitranslmed.aba6676 |
| Pubmed ID | |
| Authors |
Joseph L Mann, Caitlin L Maikawa, Anton A A Smith, Abigail K Grosskopf, Sam W Baker, Gillie A Roth, Catherine M Meis, Emily C Gale, Celine S Liong, Santiago Correa, Doreen Chan, Lyndsay M Stapleton, Anthony C Yu, Ben Muir, Shaun Howard, Almar Postma, Eric A Appel |
X Demographics
The data shown below were collected from the profiles of 90 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 | 24 | 27% |
| United Kingdom | 6 | 7% |
| Saudi Arabia | 5 | 6% |
| India | 5 | 6% |
| Spain | 4 | 4% |
| France | 3 | 3% |
| Canada | 3 | 3% |
| Chile | 2 | 2% |
| Portugal | 2 | 2% |
| Other | 8 | 9% |
| Unknown | 28 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 57 | 63% |
| Scientists | 29 | 32% |
| Practitioners (doctors, other healthcare professionals) | 3 | 3% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 73 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 18% |
| Student > Bachelor | 9 | 12% |
| Researcher | 7 | 10% |
| Other | 6 | 8% |
| Student > Master | 3 | 4% |
| Other | 6 | 8% |
| Unknown | 29 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 16% |
| Chemistry | 8 | 11% |
| Medicine and Dentistry | 6 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 5% |
| Engineering | 3 | 4% |
| Other | 9 | 12% |
| Unknown | 31 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 182. 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 18 August 2022.
All research outputs
#226,425
of 25,847,449 outputs
Outputs from Science Translational Medicine
#672
of 5,130 outputs
Outputs of similar age
#7,370
of 433,888 outputs
Outputs of similar age from Science Translational Medicine
#12
of 106 outputs
Altmetric has tracked 25,847,449 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,130 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 92.9. This one has done well, scoring higher than 86% 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 433,888 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.