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  • 标题:Redox state as assessed using the measurement of human non-mercaptalbumin in embryo culture media is associated with successful embryo development in human in vitro fertilization
  • 本地全文:下载
  • 作者:Yoshika Kusumoto ; Masaya Sato ; Hitoshi Ikeda
  • 期刊名称:Journal of Clinical Biochemistry and Nutrition
  • 印刷版ISSN:0912-0009
  • 电子版ISSN:1880-5086
  • 出版年度:2022
  • 卷号:71
  • 期号:1
  • 页码:69-72
  • DOI:10.3164/jcbn.21-114
  • 语种:English
  • 出版社:The Society for Free Radical Research Japan
  • 摘要:The role of oxidative stress in the pathogenesis of various diseases has been attracting attention. We speculated as to whether the redox state of treatment solutions used for various diseases may play a role in treatment success. In the current study, we focused on the human embryo culture medium used for in vitro fertilization (IVF). A total of 173 oocytes from a total of 91 patients treated with IVF were enrolled. The redox state was assessed by measuring the levels of human non-mercaptalbumin (HNA). We analyzed factors related to blastocyst formation on day 5 or 6 after insemination. We also developed a random forest (RF) model for the prediction of blastocyst formation. The variable importance in the predictive model was assessed using the mean decrease in the Gini impurity. Blastocyst formation was observed in 41.04% (71/173) of the oocytes and was associated with a lower %HNA in the culture medium, a younger patient age, and the fertilization method (standard IVF or intracytoplasmic sperm injection). The RF model developed using these factors and 70% of the samples (training set, n  = 121) was validated in the remaining testing set ( n  = 52) and produced an area under the curve of 0.761, where the %HNA in the culture medium was the most important variable for predicting blastocyst formation. In conclusion, lower levels of oxidative stress in embryo culture media were associated with the success of IVF treatment. The redox state of treatment solutions should be considered to support treatment success.
  • 关键词:human non-mercaptalbumin;assisted reproductive technology;blastocyst;random forest;machine learning
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