The single-embryo transfer (SET) is the recommended approach to improve the live birth rate and reduce the complications related with multiple pregnancies. However, the physicians generally chose to transfer two embryos when the embryo quality decreased. The effect on the in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) outcomes following the transfer of a poor-quality embryo (PQE) along with a good-quality embryo (GQE) has been explored. However, previous studies were limited by the fresh embryo transfer cycles or the small sample size. Methods: A retrospective cohort study was performed among 26,676 women (the mean age was 31.72 years) undergoing first frozen embryo transfer (FET) from January 2011 to December 2017. Patients were grouped into five subgroups, including SET with one GQE (SET-GQE, 2235 patients for cleavage-stage embryo transfer and 756 patients for blastocyst transfer), SET with one PQE (SET-PQE, 148 patients for cleavage-stage embryo transfer and 362 patients for blastocyst transfer), double-embryo transfer with two GQE (DET-2GQE, 20,461 patients for cleavage-stage embryo transfer and 519 patients for blastocyst transfer), double-embryo transfer (DET) with one GQE plus one PQE (DET-GQE+PQE, 1541 patients for cleavage-stage embryo transfer and 266 patients for blastocyst transfer), and DET with two PQE (DET-2PQE, 228 patients for cleavage-stage embryo transfer and 160 patients for blastocyst transfer). Multivariable logistic regression models were performed after controlling for other potential confounders to estimate the effect of number and quality of transferred embryos on pregnancy outcomes. Result: Although the live birth rate was significantly higher after DET-GQE+PQE compared with SET-GQE for cleavage-stage embryo transfer [574 of 1541 (37.25%) vs. 571 of 2235 (25.55%)], no significant difference was found between DET-GQE+PQE and SET-GQE for blastocyst transfer [143 of 266 (53.76%) vs. 325 of 756 (42.99%)]. However, DET-GQE+PQE also had the highest multiple live births in both cleavage-stage embryo transfer [134 of 1541 (8.70%)] and blastocyst transfer [46 of 266 (17.29%)].
Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semisupervised learning. The weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means.
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