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Mining SNPs in extracellular vesicular transcriptome of Trypanosoma cruzi: a step closer to early diagnosis of neglected Chagas disease

Overview of attention for article published in PeerJ, November 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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10 X users
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1 Facebook page
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3 Wikipedia pages
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3 Google+ users

Citations

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9 Dimensions

Readers on

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41 Mendeley
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1 CiteULike
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Title
Mining SNPs in extracellular vesicular transcriptome of Trypanosoma cruzi: a step closer to early diagnosis of neglected Chagas disease
Published in
PeerJ, November 2016
DOI 10.7717/peerj.2693
Pubmed ID
Authors

Pallavi Gaur, Anoop Chaturvedi

Abstract

One of the newest and strongest members of intercellular communicators, the Extracellular vesicles (EVs) and their enclosed RNAs; Extracellular RNAs (exRNAs) have been acknowledged as putative biomarkers and therapeutic targets for various diseases. Although a very deep insight has not been possible into the physiology of these vesicles, they are believed to be involved in cell-to-cell communication and host-pathogen interactions. EVs might be significantly helpful in discovering biomarkers for possible target identification as well as prognostics, diagnostics and developing vaccines. In recent studies, highly bioactive EVs have drawn attention of parasitologists for being able to communicate between different cells and having likeliness of reflecting both source and target environments. Next-generation sequencing (NGS) has eased the way to have a deeper insight into these vesicles and their roles in various diseases. This article arises from bioinformatics-based analysis and predictive data mining of transcriptomic (RNA-Seq) data of EVs, derived from different life stages of Trypanosoma cruzi; a causing agent of neglected Chagas disease. Variants (Single Nucleotide Polymorphisms (SNPs)) were mined from Extracellular vesicular transcriptomic data and functionally analyzed using different bioinformatics based approaches. Functional analysis showed the association of these variants with various important factors like Trans-Sialidase (TS), Alpha Tubulin, P-Type H+-ATPase, etc. which, in turn, are associated with disease in different ways. Some of the 'candidate SNPs' were found to be stage-specific, which strengthens the probability of finding stage-specific biomarkers. These results may lead to a better understanding of Chagas disease, and improved knowledge may provide further development of the biomarkers for prognosis, diagnosis and drug development for treating Chagas disease.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 12%
Researcher 5 12%
Student > Doctoral Student 5 12%
Student > Bachelor 4 10%
Professor 3 7%
Other 9 22%
Unknown 10 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 17%
Medicine and Dentistry 5 12%
Pharmacology, Toxicology and Pharmaceutical Science 4 10%
Biochemistry, Genetics and Molecular Biology 4 10%
Immunology and Microbiology 4 10%
Other 6 15%
Unknown 11 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 05 May 2020.
All research outputs
#2,632,757
of 24,909,203 outputs
Outputs from PeerJ
#2,787
of 14,852 outputs
Outputs of similar age
#50,455
of 426,445 outputs
Outputs of similar age from PeerJ
#43
of 270 outputs
Altmetric has tracked 24,909,203 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,852 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.0. This one has done well, scoring higher than 81% 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 426,445 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 88% of its contemporaries.
We're also able to compare this research output to 270 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.