Proteomic identification of cryostress in epididymal spermatozoa

Background： Cryopreservation of epididymal spermatozoa is important in cases in which it is not possible to collect semen using normal methods, as the sudden death of an animal or a catastrophic injury. However, the freezing and thawing processes cause stress to spermatozoa, including cold shock, osmotic damage, and ice crystal formation,thereby reducing sperm quality. We assessed the motility（%）, motion kinematics, capacitation status, and viability of spermatozoa using computer-assisted sperm analysis and Hoechst 33258/chlortetracycline fluorescence staining.Moreover, we identified proteins associated with cryostress using a proteomic approach and performed western blotting to validate two-dimensional electrophoresis（2-DE） results using two commercial antibodies.Results： Cryopreservation reduced viability（%）, motility（%）, straight-line velocity（VSL）, average path velocity（VAP）, amplitude of lateral head displacement（ALH）, and capacitated spermatozoa, whereas straightness（STR）and the acrosome reaction increased after cryopreservation（P 3 fold, P ＆lt; 0.05） before and after cryopreservation. The proteins differentially expressed following cryopreservation are putatively related to several signaling pathways, including the ephrinR-actin pathway, the ROS metabolism pathway, actin cytoskeleton assembly, actin cytoskeleton regulation,and the guanylate cyclase pathway.Conclusion： The results of the current study provide information on epididymal sperm proteome dynamics and possible protein markers of cryo-stress during cryopreservation. This information will further the basic understanding of cryopreservation and aid future studies aiming to identify the mechanism of cryostress responses.

Machine learning-based prediction of pregnancy outcomes in couples with non-obstructive azoospermia using micro-TESE for ICSI: a retrospective cohort study

Objective:To develop a clinically applicable tool for predicting clinical pregnancy,providing individualized patient counseling,and helping couples with non-obstructive azoospermia(NOA)decide whether to use fresh or cryopreserved spermatozoa for oocyte insemination before microdissection testicular sperm extraction(mTESE).Methods:A total of 240 couples with NOA who underwent mTESE-ICSI were divided into two groups based on the type of spermatozoa used for intracytoplasmic sperm injection(ICSI):the fresh and cryopreserved groups.After evaluating several machine learning algorithms,logistic regression was selected.Using LASSO regression and 10-fold cross-validation,the factors associated with clinical pregnancy were analyzed.Results:The area under the curves(AUCs)for the fresh and cryopreserved groups in the Logistic Regression-based prediction model were 0.977 and 0.759,respectively.Compared with various modeling algorithms,Logistic Regression outperformed machine learning in both groups,with an AUC of 0.945 for the fresh group and 0.788 for the cryopreserved group.Conclusion:The model accurately predicted clinical pregnancies in NOA couples.