Non-invasive testing of human preimplantation embryos in vitro as a way to predict the outcomes of in vitro fertilization programs

Obtaining enough good and excellent quality embryos is one of the key factors for achieving pregnancy using assisted reproductive technologies. This work was aimed at developing a mathematical model for predicting good and excellent quality embryos based on semen microbiota assessment in normozoospermia. The study included 127 men whose semen was used for in vitro fertilization (IVF). Patients were divided into 2 groups depending on the proportion of good-quality blastocyst developed on the 5th day of culturing (good-quality blastocyst development rate, GBDR). The 1st group included 57 patients with GBDR ≥ 40%, the 2nd group included 70 patients with GBDR < 40%. All patients’ semen was assessed at the day of fertilization. Semen parameters were evaluated in accordance with the WHO standards and semen microbiota composition was determined by means of real-time PCR. Discriminant analysis was used for development of the prognostic model. We developed a method for predicting efficiency of the embryological IVF stage in normozoospermia: EGO-Pro-N prognostic index (Embryos of GOod and Excellent quality Prognosis in Normozoospermia). If the EGO-Pro-N value is greater than 0.212, the probability of receiving GBDR ≥ 40% is low. Conversely, if the EGO-Pro-N value is less than or equal to 0.212, the probability is high. Sensitivity and specificity of the method were 71.9% and 70.0% respectively, accuracy was 70.9%. The developed model allows us to predict good and excellent quality embryos based on comprehensive semen microbiota assessment in normozoospermia before IVF.

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Predicting the blastocyst development rate during assisted reproductive technologies based on semen microbiota

Panacheva¹,

Kudryavtseva

Зорников

et al. 2023

BRSMU

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Non-invasive testing of human preimplantation embryos in vitro as a way to predict the outcomes of in vitro fertilization programs

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Predicting the blastocyst development rate during assisted reproductive technologies based on semen microbiota

Predicting the blastocyst development rate during assisted reproductive technologies based on semen microbiota

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