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Toll-like receptor cascade and gene polymorphism in host–pathogen interaction in Lyme disease

Overview of attention for article published in Journal of Inflammation Research, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#12 of 114)
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
15 tweeters
facebook
6 Facebook pages

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
28 Mendeley
Title
Toll-like receptor cascade and gene polymorphism in host–pathogen interaction in Lyme disease
Published in
Journal of Inflammation Research, May 2016
DOI 10.2147/jir.s104790
Pubmed ID
Authors

Alaa Badawi, Shusmita Rahman, Maria Shering, Nicholas Ogden, Robbin Lindsay

Abstract

Lyme disease (LD) risk occurs in North America and Europe where the tick vectors of the causal agent Borrelia burgdorferi sensu lato are found. It is associated with local and systemic manifestations, and has persistent posttreatment health complications in some individuals. The innate immune system likely plays a critical role in both host defense against B. burgdorferi and disease severity. Recognition of B. burgdorferi, activation of the innate immune system, production of proinflammatory cytokines, and modulation of the host adaptive responses are all initiated by Toll-like receptors (TLRs). A number of Borrelia outer-surface proteins (eg, OspA and OspB) are recognized by TLRs. Specifically, TLR1 and TLR2 were identified as the receptors most relevant to LD. Several functional single-nucleotide polymorphisms have been identified in TLR genes, and are associated with varying cytokines types and synthesis levels, altered pathogen recognition, and disruption of the downstream signaling cascade. These single-nucleotide polymorphism-related functional alterations are postulated to be linked to disease development and posttreatment persistent illness. Elucidating the role of TLRs in LD may facilitate a better understanding of disease pathogenesis and can provide an insight into novel therapeutic targets during active disease or postinfection and posttreatment stages.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 25%
Researcher 4 14%
Student > Ph. D. Student 3 11%
Student > Doctoral Student 2 7%
Student > Master 2 7%
Other 4 14%
Unknown 6 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 25%
Agricultural and Biological Sciences 4 14%
Medicine and Dentistry 4 14%
Immunology and Microbiology 3 11%
Philosophy 1 4%
Other 3 11%
Unknown 6 21%

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 25 June 2016.
All research outputs
#1,392,461
of 13,133,141 outputs
Outputs from Journal of Inflammation Research
#12
of 114 outputs
Outputs of similar age
#38,963
of 265,945 outputs
Outputs of similar age from Journal of Inflammation Research
#1
of 9 outputs
Altmetric has tracked 13,133,141 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 114 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one has done well, scoring higher than 89% 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 265,945 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 85% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them