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Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells

Overview of attention for article published in Advanced Science, August 2023
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 5,566)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
22 news outlets
blogs
2 blogs
twitter
456 X users
facebook
11 Facebook pages
wikipedia
1 Wikipedia page
reddit
1 Redditor

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
40 Mendeley
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Title
Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells
Published in
Advanced Science, August 2023
DOI 10.1002/advs.202302146
Pubmed ID
Authors

Jiabao Xu, Tiffany Lodge, Caroline Kingdon, James W. L. Strong, John Maclennan, Eliana Lacerda, Slawomir Kujawski, Pawel Zalewski, Wei E. Huang, Karl J. Morten

Abstract

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, a single-cell Raman platform and artificial intelligence are utilized to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. These results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics are identified. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS and can be extended to other unexplained chronic diseases such as long COVID and post-treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.

X Demographics

X Demographics

The data shown below were collected from the profiles of 456 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 7 18%
Researcher 7 18%
Student > Ph. D. Student 6 15%
Student > Master 3 8%
Student > Doctoral Student 2 5%
Other 3 8%
Unknown 12 30%
Readers by discipline Count As %
Unspecified 7 18%
Biochemistry, Genetics and Molecular Biology 5 13%
Medicine and Dentistry 2 5%
Immunology and Microbiology 2 5%
Engineering 2 5%
Other 7 18%
Unknown 15 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 472. 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 22 May 2024.
All research outputs
#58,767
of 25,964,892 outputs
Outputs from Advanced Science
#13
of 5,566 outputs
Outputs of similar age
#1,238
of 360,049 outputs
Outputs of similar age from Advanced Science
#1
of 392 outputs
Altmetric has tracked 25,964,892 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,566 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.4. This one has done particularly well, scoring higher than 99% 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 360,049 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 392 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.