↓ Skip to main content

Translational models for vascular cognitive impairment: a review including larger species

Overview of attention for article published in BMC Medicine, January 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

news
1 news outlet
twitter
4 X users
facebook
2 Facebook pages

Citations

dimensions_citation
76 Dimensions

Readers on

mendeley
154 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
Translational models for vascular cognitive impairment: a review including larger species
Published in
BMC Medicine, January 2017
DOI 10.1186/s12916-017-0793-9
Pubmed ID
Authors

Atticus H. Hainsworth, Stuart M. Allan, Johannes Boltze, Catriona Cunningham, Chad Farris, Elizabeth Head, Masafumi Ihara, Jeremy D. Isaacs, Raj N. Kalaria, Saskia A. M. J. Lesnik Oberstein, Mark B. Moss, Björn Nitzsche, Gary A. Rosenberg, Julie W. Rutten, Melita Salkovic-Petrisic, Aron M. Troen

Abstract

Disease models are useful for prospective studies of pathology, identification of molecular and cellular mechanisms, pre-clinical testing of interventions, and validation of clinical biomarkers. Here, we review animal models relevant to vascular cognitive impairment (VCI). A synopsis of each model was initially presented by expert practitioners. Synopses were refined by the authors, and subsequently by the scientific committee of a recent conference (International Conference on Vascular Dementia 2015). Only peer-reviewed sources were cited. We included models that mimic VCI-related brain lesions (white matter hypoperfusion injury, focal ischaemia, cerebral amyloid angiopathy) or reproduce VCI risk factors (old age, hypertension, hyperhomocysteinemia, high-salt/high-fat diet) or reproduce genetic causes of VCI (CADASIL-causing Notch3 mutations). We concluded that (1) translational models may reflect a VCI-relevant pathological process, while not fully replicating a human disease spectrum; (2) rodent models of VCI are limited by paucity of white matter; and (3) further translational models, and improved cognitive testing instruments, are required.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 154 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 18%
Researcher 25 16%
Student > Bachelor 17 11%
Student > Master 13 8%
Other 12 8%
Other 27 18%
Unknown 33 21%
Readers by discipline Count As %
Neuroscience 25 16%
Medicine and Dentistry 18 12%
Agricultural and Biological Sciences 14 9%
Biochemistry, Genetics and Molecular Biology 12 8%
Psychology 9 6%
Other 32 21%
Unknown 44 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 20 November 2017.
All research outputs
#2,671,104
of 22,947,506 outputs
Outputs from BMC Medicine
#1,654
of 3,447 outputs
Outputs of similar age
#57,211
of 419,016 outputs
Outputs of similar age from BMC Medicine
#32
of 61 outputs
Altmetric has tracked 22,947,506 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,447 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one has gotten more attention than average, scoring higher than 51% 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 419,016 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 86% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.