↓ Skip to main content

Quantifying growing versus non-growing ovarian follicles in the mouse

Overview of attention for article published in Journal of Ovarian Research, January 2017
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
42 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
Quantifying growing versus non-growing ovarian follicles in the mouse
Published in
Journal of Ovarian Research, January 2017
DOI 10.1186/s13048-016-0296-x
Pubmed ID
Authors

Bahar Uslu, Carola Conca Dioguardi, Monique Haynes, De-Qiang Miao, Meltem Kurus, Gloria Hoffman, Joshua Johnson

Abstract

A standard histomorphometric approach has been used for nearly 40 years that identifies atretic (e.g., dying) follicles by counting the number of pyknotic granulosa cells (GC) in the largest follicle cross-section. This method holds that if one pyknotic granulosa nucleus is seen in the largest cross section of a primary follicle, or three pyknotic cells are found in a larger follicle, it should be categorized as atretic. Many studies have used these criteria to estimate the fraction of atretic follicles that result from genetic manipulation or environmental insult. During an analysis of follicle development in a mouse model of Fragile X premutation, we asked whether these 'historical' criteria could correctly identify follicles that were not growing (and could thus confirmed to be dying). Reasoning that the fraction of mitotic GC reveals whether the GC population was increasing at the time of sample fixation, we compared the number of pyknotic nuclei to the number of mitotic figures in follicles within a set of age-matched ovaries. We found that, by itself, pyknotic nuclei quantification resulted in high numbers of false positives (improperly categorized as atretic) and false negatives (improperly categorized intact). For preantral follicles, scoring mitotic and pyknotic GC nuclei allowed rapid, accurate identification of non-growing follicles with 98% accuracy. This method most often required the evaluation of one follicle section, and at most two serial follicle sections to correctly categorize follicle status. For antral follicles, we show that a rapid evaluation of follicle shape reveals which are intact and likely to survive to ovulation. Combined, these improved, non-arbitrary methods will greatly improve our ability to estimate the fractions of growing/intact and non-growing/atretic follicles in mouse ovaries.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 19%
Researcher 7 17%
Student > Master 5 12%
Student > Doctoral Student 4 10%
Student > Bachelor 3 7%
Other 4 10%
Unknown 11 26%
Readers by discipline Count As %
Medicine and Dentistry 9 21%
Biochemistry, Genetics and Molecular Biology 9 21%
Agricultural and Biological Sciences 3 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Environmental Science 1 2%
Other 4 10%
Unknown 15 36%
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 07 April 2017.
All research outputs
#18,540,642
of 22,962,258 outputs
Outputs from Journal of Ovarian Research
#327
of 597 outputs
Outputs of similar age
#311,943
of 421,832 outputs
Outputs of similar age from Journal of Ovarian Research
#4
of 5 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 597 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 26th percentile – i.e., 26% 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 421,832 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.