Christina Hnida1, Inge Agerholm, Søren Ziebe. 1. The Fertility Clinic, Rigshospitalet, Section 4071, University Hospital of Copenhagen, Blegdamsvej 9, DK-2100 Copenhagen, Denmark.
Abstract
BACKGROUND: Multinuclearity is known to correlate with decreased implantation and pregnancy rates. Thus, a valid detection of nuclear structures especially among otherwise good quality embryos may be of great importance in order to improve clinical outcome. In this study, we have compared traditional manual microscopic analysis with computer-controlled multilevel morphological assessment for analysis of nuclear status in human embryos. METHODS: In total, 84 donated 2- and 4-cell embryos with < or = 20% fragmentation from patients referred for IVF or ICSI treatment were included. Mono- and multinuclearity was recorded using traditional analysis as well as computer-controlled multilevel analysis of each intact embryo. Subsequently, the embryos were separated into individual blastomeres to assess the number of nuclear structures. All nuclear structures were fixed and stained for DNA. RESULTS: There was no significant difference (P = 1.0) between embryonic nuclear status detected by computer-controlled analysis of the intact embryos and of the separated blastomeres. Additionally, 100% of the fixed nuclear structures contained DNA. However, using traditional morphological analysis, significantly more embryos (26%) had incorrect nuclear status detected (P = 0.002). Further, the presence of <10% embryonic fragmentation had no impact on the correct detection of nuclear structures using the multilevel analysis. For embryos with 11-20% fragmentation, 86% of the nuclear structures detected in the separated blastomeres were found in the intact embryos. The mean diameter of nuclear structures was significantly decreased from 22.1 microm in mononucleate 2-cell embryos to 18.7 microm in mononucleate 4-cell embryos (P < 0.001). CONCLUSION: The results of this study indicate that the use of computer-controlled multilevel morphological analysis can improve the detection of nuclear structures in human embryos.
BACKGROUND: Multinuclearity is known to correlate with decreased implantation and pregnancy rates. Thus, a valid detection of nuclear structures especially among otherwise good quality embryos may be of great importance in order to improve clinical outcome. In this study, we have compared traditional manual microscopic analysis with computer-controlled multilevel morphological assessment for analysis of nuclear status in human embryos. METHODS: In total, 84 donated 2- and 4-cell embryos with < or = 20% fragmentation from patients referred for IVF or ICSI treatment were included. Mono- and multinuclearity was recorded using traditional analysis as well as computer-controlled multilevel analysis of each intact embryo. Subsequently, the embryos were separated into individual blastomeres to assess the number of nuclear structures. All nuclear structures were fixed and stained for DNA. RESULTS: There was no significant difference (P = 1.0) between embryonic nuclear status detected by computer-controlled analysis of the intact embryos and of the separated blastomeres. Additionally, 100% of the fixed nuclear structures contained DNA. However, using traditional morphological analysis, significantly more embryos (26%) had incorrect nuclear status detected (P = 0.002). Further, the presence of <10% embryonic fragmentation had no impact on the correct detection of nuclear structures using the multilevel analysis. For embryos with 11-20% fragmentation, 86% of the nuclear structures detected in the separated blastomeres were found in the intact embryos. The mean diameter of nuclear structures was significantly decreased from 22.1 microm in mononucleate 2-cell embryos to 18.7 microm in mononucleate 4-cell embryos (P < 0.001). CONCLUSION: The results of this study indicate that the use of computer-controlled multilevel morphological analysis can improve the detection of nuclear structures in human embryos.
Authors: I E Agerholm; C Hnida; D G Crüger; C Berg; G Bruun-Petersen; S Kølvraa; S Ziebe Journal: J Assist Reprod Genet Date: 2008-02-07 Impact factor: 3.412
Authors: Tine De Coster; Heleen Masset; Olga Tšuiko; Maaike Catteeuw; Yan Zhao; Nicolas Dierckxsens; Ainhoa Larreategui Aparicio; Eftychia Dimitriadou; Sophie Debrock; Karen Peeraer; Marta de Ruijter-Villani; Katrien Smits; Ann Van Soom; Joris Robert Vermeesch Journal: Genome Biol Date: 2022-10-03 Impact factor: 17.906