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Background: This study aimed to evaluate the influences of SARS-CoV-2 infection on semen parameters and investigate the impact of the infection on in vitro fertilization (IVF) outcomes. Methods: This retrospective study enrolled couples undergoing IVF cycles between May 2020 and February 2021 at Tongji Hospital, Wuhan. Baseline characteristics were matched using propensity score matching. Participants were categorized into an unexposed group (SARS-COV-2 negative) and exposed group (SARS-COV-2 positive) based on a history of SARS-CoV-2 infection, and the populations were 148 and 50 after matching, respectively. IVF data were compared between the matched cohorts. Moreover, semen parameters were compared before and after infection among the infected males. The main measures were semen parameters and IVF outcomes, including laboratory and clinical outcomes. Results: Generally, the concentration and motility of sperm did not significantly differ before and after infection. Infected males seemed to have fewer sperm with normal morphology, while all values were above the limits. Notably, the blastocyst formation rate and available blastocyst rate in the exposed group were lower than those in the control group, despite similar mature oocytes rates, normal fertilization rates, cleavage rates, and high-quality embryo rates. Moreover, no significant differences were exhibited between the matched cohorts regarding the implantation rate, biochemical pregnancy rate, clinical pregnancy rate, or early miscarriage rate. Conclusions: The results of this retrospective cohort study suggested that the semen quality and the chance of pregnancy in terms of IVF outcomes were comparable between the males with a history of SARS-CoV-2 infection and controls, although a decreased blastocyst formation rate and available blastocyst rate was observed in the exposed group, which needs to be reinforced by a multicenter long-term investigation with a larger sample size. [ABSTRACT FROM AUTHOR]

Background: To improve the developmental competence of in vitro cultured oocytes, extensive literature focused on maturation rate improvement with different additives in culture medium, while studies investigating the maturation dynamics of oocytes during in vitro maturation (IVM) and the influencing factors on oocyte viability are scarce. Methods: The study involved a retrospective observation by time-lapse monitoring of the IVM process of 157 donated GV oocytes from 59 infertile couples receiving ICSI in 2019, in Tongji Hospital, Wuhan, China. The GV oocytes derived from controlled ovarian hyperstimulation (COH) cycles underwent rescue IVM (R-IVM), and the maturation dynamics, including GVBD time (GV-MI), time from GVBD to maturation (MI-MII), maturation time (GV-MII), and MII arrest duration (MII-ICSI), were recorded by time-lapse monitoring. The matured oocytes were inseminated at different MII arrest points and subsequent embryo developments were assessed. The effects of baseline clinical characteristics, oocyte diameters, and maturation dynamics on the developmental competence of the oocytes were also analyzed. Results: Totally, 157 GV oocytes were collected. GVBD happened in 111 oocytes, with a median GV-MI duration of 3.7 h. The median MI-MII duration was 15.6 h and the median GV-MII duration was 19.5 h. The maturation rate reached 56.7% at 24 h and 66.9% at 48 h, and the clinical factors, including patient age, FSH level, AMH level, ovarian stimulation protocol, and serum estradiol and progesterone levels on hCG trigger day, showed no effects on the 24-h maturation rate. The normal fertilization rate of oocytes resuming meiosis within 8 h and matured within 24 h was significantly higher than that of oocytes resuming meiosis after 8 h and matured after 24 h. Furthermore, among those oocytes matured within 24 h, the high-quality embryo formation rate of oocytes resuming meiosis within 4.5 h and matured within 19 h was significantly higher. All stated time was measured from the start point of IVM. Additionally, for oocytes from patients with serum progesterone levels less than 1 ng/ml on hCG trigger day, the high-quality embryo formation rate was significantly increased. Conclusion: R-IVM technology could increase the available embryos for patients in routine COH cycles, but excessive culture beyond 24 h is not recommended. GV-MI duration of the oocyte, recorded by time-lapse system, and serum progesterone levels of patients on hCG trigger day can significantly affect the developmental potential of the IVM oocytes. [ABSTRACT FROM AUTHOR]

Background: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods: This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes. Results: For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion: Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes. [ABSTRACT FROM AUTHOR]