体外受精-胚胎移植中新鲜睾丸精子与解冻后睾丸精子助孕结局比较

目的比较单精子卵泡浆内显微注射受精助孕(intracytoplasmic sperminjection,ICSI)中,使用新鲜睾丸精子与解冻后睾丸精子两组的助孕结局。方法回顾性分析2002年1月至2008年8月在本所接受ICSI助孕的272个周期,其中117个周期使用新鲜睾丸精子,为A组;155个周期使用解冻后睾丸精子,为B组。结果(1)A、B两组受精率为77.1%vs 78.9%、优质胚胎率:20.7%vs 17.5%,两组比较差异无统计学意义。(2)妊娠及出生结局分析,A、B组临床妊娠率、着床率、流产率分别为48.7%vs 52.3%、26.9%vs 32.3%、8.8%vs 14.8%。足月活产率,低体重儿发生率分别为63.2%vs 55.6%、20.3%vs29.5%,两组比较差异均无统计学意义。结论体外受精-胚胎移植中采用解冻睾丸精子其助孕结局与新鲜睾丸精子相同,解冻后睾丸精子的使用可以最大限度减少反复睾丸活检对睾丸组织的损伤,增加睾丸组织的利用率。

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.