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Deep learning models to map osteocyte networks from confocal microscopy can successfully distinguish between young and aged bone

Overview of attention for article published in PLoS Computational Biology, January 2026
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32 Mendeley
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Article details
Title
Deep learning models to map osteocyte networks from confocal microscopy can successfully distinguish between young and aged bone
Published in
PLoS Computational Biology, January 2026
DOI 10.1371/journal.pcbi.1013914
Pubmed ID
Authors

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Unspecified 7 22%
Professor 3 9%
Student > Doctoral Student 2 6%
Student > Ph. D. Student 2 6%
Researcher 2 6%
Other 3 9%
Unknown 13 41%
Readers by discipline
Readers by discipline Count As %
Unspecified 7 22%
Biochemistry, Genetics and Molecular Biology 2 6%
Computer Science 2 6%
Engineering 2 6%
Nursing and Health Professions 1 3%
Other 4 13%
Unknown 14 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 January 2026.
All research outputs
#27,753,408
of 34,394,092 outputs
Outputs from PLoS Computational Biology
#9,134
of 10,413 outputs
Outputs of similar age
#244,806
of 376,020 outputs
Outputs of similar age from PLoS Computational Biology
#73
of 103 outputs
Altmetric has tracked 34,394,092 research outputs across all sources so far. This one is in the 16th percentile – i.e., 16% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,413 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.8. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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 376,020 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.