Effect of sperm DNA fragmentation on blastocyst formation rate and subsequent IVF outcome

Background: Male infertility associated with sperm DNA alteration has raised a new issue in assisted reproduction techniques (ARTs).Methods: It was a retrospective analytical study on 250 cases of routine IVF/ICSI performed at Swagat ART Centre from January 2017 to January 2020. We divided the patient according to the sperm DNA fragmentation index (DFI) as normal DFI≤15%, n=95, a moderate DFI≤30%, n=89, and a high DFI group >30%, n=66. Oocytes of each patient were almost equally divided and fertilization method was adopted as half IVF half ICSI or only ICSI in poor quality (oligo, astheno, teratozoospermia or with two or all three defect and compared the fertilization, cleavage, embryo formation, blastocyst formation, pregnancy and early embryo formation rate among these six groups. Results: Fertilization, cleavage, embryo formation, and clinical pregnancy rates were reported as higher in ≤15% DFI group of both IVF and ICSI-ET (87.3±26.2, 77.7±26.1, 68.2±28.8, 50.8 in IVF and 78.3±17.8, 70.3±31.2, 67.2±28.8, 57.6 respectively). Significant differences (p<0.01) are observed among all six groups. Higher abortion rate is observed in high DFI group of both IVF and ICSI.Conclusions: High sperm DFI causes low blastocyst formation and pregnancy outcome. Higher abortion rate observed in high DFI group indicated need of further study.

Does Conventional Morphological Evaluation Still Play a Role in Predicting Blastocyst Formation?

BackgroundAdvanced models including time-lapse imaging and artificial intelligence technologies have been used to predict blastocyst formation. However, the conventional morphological evaluation of embryos is still widely used. The purpose of the present study was to evaluate the predictive power of conventional morphological evaluation regarding blastocyst formation.MethodsRetrospective evaluation of data from 15613 patients receiving blastocyst culture from January 2013 through December 2020 in our institution were reviewed. Generalized estimating equations (GEE) were used to establish the morphology-based model. To estimate whether including more features regarding patient characteristics and cycle parameters improve the predicting power, we also establish models including 27 more features with either LASSO regression or XGbosst. The predicted number of blastocyst were associated with the observed number of the blastocyst and were used to predict the blastocyst transfer cancellation either in fresh or frozen cycles. ResultsBased on early cleavage and routine observed morphological parameters (cell number, fragmentation, and symmetry), the GEE model predicted blastocyst formation with an AUC of 0.779(95%CI: 0.77-0.787) and an accuracy of 74.7%(95%CI: 73.9%-75.5%) in the validation set. LASSO regression model and XGboost model based on the combination of cycle characteristics and embryo morphology yielded similar predicting power with AUCs of 0.78(95%CI: 0.771-0.789) and 0.754(95%CI: 0.745-0.763), respectively. For per-cycle blastocyst yield, the predicted number of blastocysts using morphological parameters alone strongly correlated with observed blastocyst number (r=0.897, P<0.0001) and predicted blastocyst transfer cancel with an AUC of 0.926((95%CI: 0.911-0.94). ConclusionThe data suggested that routine morphology observation remained a feasible tool to support an informed decision regarding the day of transfer. However, models based on the combination of cycle characteristics and embryo morphology do not increase the predicting power significantly.

Iron overload in endometriosis peritoneal fluid induces early embryo ferroptosis mediated by HMOX1

Endometriosis is one of the most common disorders that causes infertility in women. Iron is overloaded in endometriosis peritoneal fluid (PF), with harmful effects on early embryo development. However, the mechanism by which endometriosis peritoneal fluid affects embryonic development remains unclear. Hence, this study investigated the effect of iron overload on mouse embryos and elucidated the molecular mechanism. Iron overload in endometriosis PF disrupted blastocyst formation, decreased GPX4 expression and induced lipid peroxidation, suggesting that iron overload causes embryotoxicity and induces ferroptosis. Moreover, mitochondrial damage occurs in iron overload-treated embryos, presenting as decreased ATP levels, increased ROS levels and MMP hyperpolarization. The cytotoxicity of iron overload is attenuated by the ferroptosis inhibitor Fer-1. Furthermore, Smart-seq analysis revealed that HMOX1 is upregulated in embryo ferroptosis and that HMOX1 suppresses ferroptosis by maintaining mitochondrial function. This study provides new insight into the mechanism of endometriosis infertility and a potential target for future endometriosis infertility treatment efforts.

DNA methylation and gene expression changes in mouse pre- and post-implantation embryos generated by intracytoplasmic sperm injection with artificial oocyte activation

Background The application of artificial oocyte activation (AOA) after intracytoplasmic sperm injection (ICSI) is successful in mitigating fertilization failure problems in assisted reproductive technology (ART). Nevertheless, there is no relevant study to investigate whether AOA procedures increase developmental risk by disturbing subsequent gene expression at different embryonic development stages. Methods We used a mouse model to explore the influence of AOA treatment on pre- and post-implantation events. Firstly, the developmental potential of embryos with or without AOA treatment were assessed by the rates of fertilization and blastocyst formation. Secondly, transcriptome high-throughput sequencing was performed among the three groups (ICSI, ICSI-AOA and dICSI-AOA groups). The hierarchical clustering and Principal Component Analysis (PCA) analysis were used. Subsequently, Igf2r/Airn methylation analysis were detected using methylation-specific PCR sequencing following bisulfite treatment. Finally, birth rate and birth weight were examined following mouse embryo transfer. Results The rates of fertilization and blastocyst formation were significantly lower in oocyte activation-deficient sperm injection group (dICSI group) when compared with the ICSI group (30.8 % vs. 84.4 %, 10.0 % vs. 41.5 %). There were 133 differentially expressed genes (DEGs) between the ICSI-AOA group and ICSI group, and 266 DEGs between the dICSI-AOA group and ICSI group. In addition, the imprinted gene, Igf2r is up regulated in AOA treatment group compared to control group. The Igf2r/Airn imprinted expression model demonstrates that AOA treatment stimulates maternal allele-specific mehtylation spreads at differentially methylated region 2, followed by the initiation of paternal imprinted Airn long non-coding (lnc) RNA, resulting in the up regulated expression of Igf2r. Furthermore, the birth weight of newborn mice originating from AOA group was significantly lower compared to that of ICSI group. The pups born following AOA treatment did not show any other abnormalities during early development. All offspring mated successfully with fertile controls. Conclusions AOA treatment affects imprinted gene Igf2r expression and mehtylation states in mouse pre- and post-implantation embryo, which is regulated by the imprinted Airn. Nevertheless, no significant differences were found in post-natal growth of the pups in the present study. It is hoped that this study could provide valuable insights of AOA technology in assisted reproduction biology.

The Development of Nomograms to Predict Blastulation Rate Following Cycles of In Vitro Fertilization in Patients With Tubal Factor Infertility, Polycystic Ovary Syndrome, or Endometriosis

It is well known that the transfer of embryos at the blastocyst stage is superior to the transfer of embryos at the cleavage stage in many respects. However, the rate of blastocyst formation remains low in clinical practice. To reduce the possibility of wasting embryos and to accurately predict the possibility of blastocyst formation, we constructed a nomogram based on range of clinical characteristics to predict blastocyst formation rates in patients with different types of infertility. We divided patients into three groups based on female etiology: a tubal factor group, a polycystic ovary syndrome group, and an endometriosis group. Multiple logistic regression was used to analyze the relationship between patient characteristics and blastocyst formation. Each group of patients was divided into a training set and a validation set. The training set was used to construct the nomogram, while the validation set was used to test the performance of the model by using discrimination and calibration. The area under the curve (AUC) for the three groups indicated that the models performed fairly and that calibration was acceptable in each model.

Blastocyst Prediction of Day-3 Cleavage-Stage Embryos Using Machine Learning

Background:Embryo selection plays an important role in the success of in vitro fertilization (IVF). However, morphological embryo assessment has a number of limitations, including the time required, lack of accuracy, and inconsistency. This study determined whether a machine learning-based model could predict blastocyst formation using day-3 embryo images. Methods:Day-3 embryo images from IVF/intracytoplasmic sperm injection (ICSI) cycles performed at My Duc Phu Nhuan Hospital between August 2018 and June 2019 were retrospectively analyzed to inform model development. Day-3 embryo images derived from two-pronuclear (2PN) zygotes with known blastocyst formation data were extracted from the CCM-iBIS time-lapse incubator (Astec, Japan) at 67 hours post ICSI, and labeled as blastocyst/non-blastocyst based on results at 116 hours post ICSI. Images were used as the input dataset to train (85%) and validate (15%) the convolutional neural network (CNN) model, then model accuracy was determined using the training and validation dataset. The performance of 13 experienced embryologists for predicting blastocyst formation based on 100 day-3 embryo images was also evaluated. Results:A total of 1,135 images were allocated into training ([Formula: see text] = 967) and validation ([Formula: see text] = 168) sets, with an even distribution for blastocyst formation outcome. The accuracy of the final model for blastocyst formation was 97.72% in the training dataset and 76.19% in the validation dataset. The final model predicted blastocyst formation from day-3 embryo images in the validation dataset with an area under the curve of 0.75 (95% confidence interval [CI] 0.69–0.81). Embryologists predicted blastocyst formation with the accuracy of 70.07% (95% CI 68.12%–72.03%), sensitivity of 87.04% (95% CI 82.56%–91.52%), and specificity of 30.93% (95% CI 29.35%–32.51%). Conclusions:The CNN-based machine learning model using day-3 embryo images predicted blastocyst formation more accurately than experienced embryologists. The CNN-based model is a potential tool to predict additional IVF outcomes.