A meta-analysis of expression signatures in glomerular disease
Kidney International, ISSN: 0085-2538, Vol: 84, Issue: 3, Page: 591-599
2013
- 7Citations
- 26Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations7
- Citation Indexes7
- CrossRef6
- Captures26
- Readers26
- 26
Article Description
Glomerular diseases represent major diagnostic and therapeutic challenges with classification of these diseases largely relying on clinical and histological findings. Elucidation of molecular mechanisms of progressive glomerular disease could facilitate quicker development. High-throughput expression profiling reveals all genes and proteins expressed in tissue and cell samples. These methods are very appropriate for glomerular disease as pure glomeruli can be obtained from kidney biopsies. To date, proteome profiling data are only available for normal glomeruli, but more robust transcriptome methods have been applied to many mouse model and a few human glomerular diseases. Here, we have carried out a meta-analysis of currently available glomerular expression data in normal and diseased glomeruli from mice, rats, and humans using a standardized protocol. The results suggest a potential for glomerular transcriptomics in identifying pathogenic pathways, disease monitoring, and the feasibility to use animal models to study human glomerular disease. We also found that currently there are no specific consensus biomarkers or pathways among different disease data sets, indicating there are likely disease-specific mechanisms and expression profiles. Thus, further transcriptomics and proteomics analysis, especially that of dynamic changes in the diseases, may lead to novel diagnostics tools and specific pharmacologic therapies.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0085253815559966; http://dx.doi.org/10.1038/ki.2013.169; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84883466451&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/23783239; https://linkinghub.elsevier.com/retrieve/pii/S0085253815559966; http://www.nature.com/doifinder/10.1038/ki.2013.169; http://www.nature.com/ki/journal/v84/n3/full/ki2013169a.html
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know