Literature DB >> 29172789

Morphometric analysis of human oocytes using time lapse: does it predict embryo developmental outcomes?

Azita Faramarzi1,2, Mohammad Ali Khalili1, Marjan Omidi1.   

Abstract

The aim of this prospective study was to evaluate the relationship between morphometric parameters of metaphase II (MII) oocytes and the morphokinetic behaviour of subsequent embryos derived by intra-cytoplasmic sperm injection (ICSI). The association between oocyte morphometry: (whole oocyte), ooplasm, width of zona pellucida (ZP) and perivitelline space (PVS) and first polar body (PB) with embryo morphokinetic variables, including time of second PB extrusion (tPB2), pronuclei appearance (tPN), pronuclei fading (tPNf), formation of two to eight cells (t2 to t8) and irregular cleavage events [uneven at two cells stage, cell fusion (Fu) and trichomonas mitoses (TM)] were assessed. tPB2, t5 and t8 timings were related to the ooplasm diameter (p = 0.003, r = -0.12; p = 0.001, r = -0.16; p < 0.001 r = -0.36, respectively); otherwise, there were no significant relationships apart from an association between the oocyte morphometry and other morphokinetic parameters, irregular cleavage embryos as well as embryo arrest which approached significance (p > 0.05). Overall, the data showed that morphometric parameters of oocytes did not provide a tool for the prediction of embryo morphokinetic or embryo selection in ICSI cycles. However, ooplasm diameter might be useful as a marker for predicting the timing of embryo cleavage.

Entities:  

Keywords:  Embryo; irregular cleavage; morphokinetic; oocyte morphometry; time lapse

Mesh:

Substances:

Year:  2017        PMID: 29172789     DOI: 10.1080/14647273.2017.1406670

Source DB:  PubMed          Journal:  Hum Fertil (Camb)        ISSN: 1464-7273            Impact factor:   2.767


  4 in total

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2.  Can novel early non-invasive biomarkers of embryo quality be identified with time-lapse imaging to predict live birth?

Authors:  J Barberet; C Bruno; E Valot; C Antunes-Nunes; L Jonval; J Chammas; C Choux; P Ginod; P Sagot; A Soudry-Faure; P Fauque
Journal:  Hum Reprod       Date:  2019-08-01       Impact factor: 6.918

Review 3.  Artificial intelligence in reproductive medicine.

Authors:  Renjie Wang; Wei Pan; Lei Jin; Yuehan Li; Yudi Geng; Chun Gao; Gang Chen; Hui Wang; Ding Ma; Shujie Liao
Journal:  Reproduction       Date:  2019-10       Impact factor: 3.906

Review 4.  Scanning Probe Microscopies: Imaging and Biomechanics in Reproductive Medicine Research.

Authors:  Laura Andolfi; Alice Battistella; Michele Zanetti; Marco Lazzarino; Lorella Pascolo; Federico Romano; Giuseppe Ricci
Journal:  Int J Mol Sci       Date:  2021-04-07       Impact factor: 5.923

  4 in total

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