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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mining the pre-diagnostic antibody repertoire of TgMMTV-neu mice to identify autoantibodies useful for the early detection of human breast cancer
|
|---|---|
| Published in |
Journal of Translational Medicine, May 2014
|
| DOI | 10.1186/1479-5876-12-121 |
| Pubmed ID | |
| Authors |
Jianning Mao, Jon Ladd, Ekram Gad, Lauren Rastetter, Melissa M Johnson, Edmond Marzbani, Jennifer S Childs, Hailing Lu, Yushe Dang, Elizabeth Broussard, Sasha E Stanton, Sam M Hanash, Mary L Disis |
| Abstract |
The use of autoantibodies for the early detection of breast cancer has generated much interest as antibodies can be readily assayed in serum when antigen levels are low. Ideally, diagnostic autoantibodies would be identified in individuals who harbored pre-invasive disease/high risk lesions leading to malignancy. Prospectively collected human serum samples from these individuals are rare and not often available for biomarker discovery. We questioned whether transgenic animals could be used to identify cancer-associated autoantibodies present at the earliest stages of the malignant transformation of breast cancer. |
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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 7 | 23% |
| Student > Ph. D. Student | 6 | 20% |
| Researcher | 6 | 20% |
| Lecturer | 2 | 7% |
| Student > Doctoral Student | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 5 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 37% |
| Biochemistry, Genetics and Molecular Biology | 6 | 20% |
| Medicine and Dentistry | 3 | 10% |
| Nursing and Health Professions | 2 | 7% |
| Unspecified | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 5 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 14 May 2021.
All research outputs
#2,860,027
of 22,755,127 outputs
Outputs from Journal of Translational Medicine
#462
of 3,977 outputs
Outputs of similar age
#29,946
of 227,074 outputs
Outputs of similar age from Journal of Translational Medicine
#5
of 73 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,977 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done well, scoring higher than 88% 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 86% of its contemporaries.
We're also able to compare this research output to 73 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 93% of its contemporaries.