During early embryogenesis, the phenotype reflecting the embryonic genotype emerges only as maternal proteins are replaced by embryonically encoded forms, a process known as the maternal-to-embryonic transition (MET). Little is understood about MET for most proteins. This study investigates how complete deficiency of the murine dihydrolipoamide dehydrogenase gene (Dld), a gene that encodes an enzyme of mitochondrial energy metabolism, affects the phenotype of the early embryo and how the MET of the DLD protein affects the phenotype. Dld-deficient (−/−) embryos were found to develop similarly to wild-type (+/+) or heterozygous (+/−) embryos throughout the preimplantation period. These three genotypic classes also have comparable rates of glucose uptake (4.9–5.0 pmoles/embryo/h) and lactate production (0.97–1.0 pmoles/embryo/h). Dld-deficient embryos at the end of the preimplantation stage have 44% of DLD enzyme present in oocytes, a proportion similar to that found in +/+ or +/− embryos. This study demonstrates that Dld-deficient preimplantation embryos are phenotypically normal, as the MET for the DLD enzyme is only partially complete by the end of the preimplantation period. These findings have implications for phenotype- or enzyme-based approaches to identify mutations in Dld and other genes that encode proteins with similar MET kinetic profiles.
BACKGROUND: Despite decades of experience with in vitro fertilization, there is still significant uncertainty in predicting patient outcomes. Standard operating procedures for clinical decision-making rely on few variables analyzed independently of each other. While the predictive power of a single variable, such as age, is clinically useful, a more sophisticated and personalized approach could improve the quality of patient care.OBJECTIVE: The purpose of this study was to determine the potential of machine learning algorithms to predict successful blastocyst formation. This information will guide the decision to push patients to a day 5 transfer while minimizing the no-transfer rate due to arrested embryo development.MATERIALS AND METHODS: Using our fresh IVF cycle data from 2014-2018 (n ¼ 1777), we trained multiple classification algorithms in R 3.5.1. The training data was a randomly-selected subset of 70% of the total data. Using data that was available on or before day 3 of embryo development, the classifiers were trained using 10-fold cross-validation and tested for performance. Successful blastocyst formation was defined as successful pregnancy or the presence of blastocysts on day 5. The best performer on the pre-specified test data was a gradient boosted machine (GBM). Algorithm performance and variable importance metrics were calculated and the algorithm was simplified to the 10 most predictive variables before being reapplied to the pre-specified test set.RESULTS: The final GBM accurately predicted blastocyst formation with an AUC of 0.922 and an accuracy of 0.858. The variables with the greatest predictive value were the number of good-quality day 3 embryos, number of mature oocytes retrieved, age at retrieval, maximum FSH and estradiol levels, and semen parameters. This algorithm was significantly more accurate at predicting blastocyst formation than our practice's current standard of using >4 good-quality day 3 embryos (95% CI: 0.80-0.90 vs. 0.64-0.71). Of all patients that received day 3 transfers, the algorithm predicted blastocyst formation in 167 patients, of which 148 (89%) formed successful blastocysts, while 19 patients (11%) did not.CONCLUSION: A machine learning approach provides notable discriminatory power for prediction of blastocyst formation in patients undergoing in vitro fertilization. The use of a GBM in our practice will increase the number of patients receiving a day 5 transfer, which in turn should increase both implantation rate and the number of single embryo transfers while decreasing the incidence of multiples. This tool may help providers optimize embryo transfer protocols and can be used to counsel patients to improve pregnancy outcomes.
150 saliva samples were obtained and correlated with number and size of follicles.Weak correlation (r¼ 0.37) was seen in salivary oestradiol to total number of follicles A more promising correlation (r¼ 0.42) was observed when total number of follicles were adjusted to above 14mm.No correlation was detected in progesterone levels to follicular growth. No relationship was observed in salivary oestradiol or progesterone to endometrial thickness.CONCLUSIONS: These preliminary results show promising findings for correlation of saliva oestradiol to follicular growth to be explored in larger studies.The limitations of saliva sample include discoloured saliva samples, which is likely secondary to poor sampling technique or failure to collect according to protocol, all of which can be mitigated with improved training and education.The ease of saliva sampling allows a reduction in intervention and thereby provides a more "patient-friendly" approach to IVF.IMPACT STATEMENT: Current status quo of serial serum monitoring should be challenged as excessive.This alternative method of monitoring of cycle response in place of serum testing, especially during mid-cycle stimulation at day 5 or 6, is worth exploring.These preliminary results call for larger studies and has potential to revolutionise development of home testing kits, especially in the era of a pandemic, thereby reducing clinic footfall and patient safety.References
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