↓ Skip to main content

Developing Renal Allograft Surveillance Strategies – Urinary Biomarkers of Cellular Rejection

Overview of attention for article published in Canadian Journal of Kidney Health and Disease, August 2015
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
31 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Developing Renal Allograft Surveillance Strategies – Urinary Biomarkers of Cellular Rejection
Published in
Canadian Journal of Kidney Health and Disease, August 2015
DOI 10.1186/s40697-015-0061-x
Pubmed ID
Authors

Patricia Hirt-Minkowski, Sacha A De Serres, Julie Ho

Abstract

Developing tailored immunosuppression regimens requires sensitive, non-invasive tools for serial post-transplant surveillance as the current clinical standards with serum creatinine and proteinuria are ineffective at detecting subclinical rejection. The purpose of this review is: (i) to illustrate the rationale for allograft immune monitoring, (ii) to discuss key steps to bring a biomarker from bench-to-bedside, and (iii) to present an overview of promising biomarkers for cellular rejection. PubMed. Recent multicentre prospective observational cohort studies have significantly advanced biomarker development by allowing for the adequately powered evaluation of multiple biomarkers capable of detecting allograft rejection. These studies demonstrate that urinary CXCR3 chemokines (i.e. CXCL9 and CXCL10) are amongst the most promising for detecting subclinical inflammation; increasing up to 30 days prior to biopsy-proven acute rejection; decreasing in response to anti-rejection therapy; and having prognostic significance for the subsequent development of allograft dysfunction. Urinary CXCR3 chemokines are measured by simple and cost-effective ELISA methodology, which can readily be implemented in clinical labs. Many biomarker studies are performed in highly selected patient groups and lack surveillance biopsies to accurately classify healthy transplants. Few validation studies have been done in unselected, consecutive patient populations to characterize population-based diagnostic performance. Based on these data, prospective interventional trials should be undertaken to determine if chemokine-based post-transplant monitoring strategies can improve long-term renal allograft outcomes. This last step will be necessary to move novel biomarkers from the bench-to-bedside.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 16%
Student > Doctoral Student 5 16%
Researcher 4 13%
Professor > Associate Professor 4 13%
Student > Postgraduate 2 6%
Other 6 19%
Unknown 5 16%
Readers by discipline Count As %
Medicine and Dentistry 13 42%
Biochemistry, Genetics and Molecular Biology 3 10%
Immunology and Microbiology 3 10%
Computer Science 2 6%
Psychology 2 6%
Other 2 6%
Unknown 6 19%

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 25 April 2019.
All research outputs
#11,663,945
of 14,719,300 outputs
Outputs from Canadian Journal of Kidney Health and Disease
#245
of 270 outputs
Outputs of similar age
#162,243
of 237,127 outputs
Outputs of similar age from Canadian Journal of Kidney Health and Disease
#18
of 19 outputs
Altmetric has tracked 14,719,300 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 270 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one is in the 6th percentile – i.e., 6% 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 237,127 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.