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Mendeley readers
Attention Score in Context
| Title |
IHC Profiler: An Open Source Plugin for the Quantitative Evaluation and Automated Scoring of Immunohistochemistry Images of Human Tissue Samples
|
|---|---|
| Published in |
PLOS ONE, May 2014
|
| DOI | 10.1371/journal.pone.0096801 |
| Pubmed ID | |
| Authors |
Frency Varghese, Amirali B. Bukhari, Renu Malhotra, Abhijit De |
| Abstract |
In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in worldwide patient stratification potentially benefitting various multinational clinical trial initiatives. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 50% |
| Germany | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 649 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | <1% |
| Uruguay | 1 | <1% |
| South Africa | 1 | <1% |
| Singapore | 1 | <1% |
| Taiwan | 1 | <1% |
| Denmark | 1 | <1% |
| Japan | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 640 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 133 | 20% |
| Student > Master | 92 | 14% |
| Researcher | 84 | 13% |
| Student > Bachelor | 59 | 9% |
| Student > Doctoral Student | 44 | 7% |
| Other | 105 | 16% |
| Unknown | 132 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 147 | 23% |
| Agricultural and Biological Sciences | 97 | 15% |
| Biochemistry, Genetics and Molecular Biology | 94 | 14% |
| Engineering | 31 | 5% |
| Neuroscience | 29 | 4% |
| Other | 86 | 13% |
| Unknown | 165 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 May 2020.
All research outputs
#12,705,732
of 22,755,127 outputs
Outputs from PLOS ONE
#98,424
of 194,177 outputs
Outputs of similar age
#104,616
of 227,400 outputs
Outputs of similar age from PLOS ONE
#2,158
of 4,737 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,177 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 48th percentile – i.e., 48% 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 227,400 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 4,737 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.