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Dynamic modeling of folliculogenesis signaling pathways in the presence of miRNAs expression

Overview of attention for article published in Journal of Ovarian Research, December 2017
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Title
Dynamic modeling of folliculogenesis signaling pathways in the presence of miRNAs expression
Published in
Journal of Ovarian Research, December 2017
DOI 10.1186/s13048-017-0371-y
Pubmed ID
Authors

Abolfazl Bahrami, Seyed Reza Miraie-Ashtiani, Mostafa Sadeghi, Ali Najafi, Reza Ranjbar

Abstract

TEK signaling plays a very important role in folliculogenesis. It activates Ras/ERK/MYC, PI3K/AKT/mTORC1 and ovarian steroidogenesis activation pathways. These are the main pathways for cell growth, differentiation, migration, adhesion, proliferation, survival and protein synthesis. TEK signaling on each of the two important pathways where levels of pERK, pMYC, pAkt, pMCL1 and pEIF4EBP1 are increased in dominant follicles and pMYC is decreased in dominant follicles. Over activation of ERK and MYC which are the main cell growth and proliferation and over activation of Akt, MCl1, mTORC1 and EIF4EBP1 which are the main cell survival and protein synthesis factors act as promoting factors for folliculogenesis. In case of over expression of hsa-miR-30d-3p and hsa-miR-451a, MYC activity level is considerably increased in subordinate follicles. Our simulation results show that in the presence of has-miR-548v and bta-miR-22-3p, downstream factors of pathways are inhibited. Our work offers insight into the design of natural biological procedures and makes predictions that can guide further experimental studies on folliculogenesis pathways. Moreover, it defines a simple signal processing unit that may be useful for engineering synthetic biology and genes circuits to carry out cell-based computation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Student > Master 3 15%
Student > Ph. D. Student 3 15%
Lecturer 2 10%
Professor > Associate Professor 2 10%
Other 3 15%
Unknown 2 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 40%
Medicine and Dentistry 6 30%
Environmental Science 1 5%
Agricultural and Biological Sciences 1 5%
Neuroscience 1 5%
Other 0 0%
Unknown 3 15%
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 24 July 2018.
All research outputs
#20,527,576
of 23,096,849 outputs
Outputs from Journal of Ovarian Research
#441
of 604 outputs
Outputs of similar age
#376,398
of 440,714 outputs
Outputs of similar age from Journal of Ovarian Research
#6
of 7 outputs
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So far Altmetric has tracked 604 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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