Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles.

PURPOSE: To investigate the role of standardized preimplantation genetic testing for aneuploidy (PGT-A) using artificial intelligence (AI) in patients undergoing single thawed euploid embryo transfer (STEET) cycles. METHODS: Retrospective cohort study at a single, large university-based fertility center with patients undergoing in vitro fertilization (IVF) utilizing PGT-A from February 2015 to April 2020. Controls included embryos tested using subjective NGS. The first experimental group included embryos analyzed by NGS utilizing AI and machine learning (PGTaiSM Technology Platform, AI 1.0). The second group included embryos analyzed by AI 1.0 and SNP analysis (PGTai2.0, AI 2.0). Primary outcomes included rates of euploidy, aneuploidy and simple mosaicism. Secondary outcomes included rates of implantation (IR), clinical pregnancy (CPR), biochemical pregnancy (BPR), spontaneous abortion (SABR) and ongoing pregnancy and/or live birth (OP/LBR). RESULTS: A total of 24,908 embryos were analyzed, and classification rates using AI platforms were compared to subjective NGS. Overall, those tested via AI 1.0 showed a significantly increased euploidy rate (36.6% vs. 28.9%), decreased simple mosaicism rate (11.3% vs. 14.0%) and decreased aneuploidy rate (52.1% vs. 57.0%). Overall, those tested via AI 2.0 showed a significantly increased euploidy rate (35.0% vs. 28.9%) and decreased simple mosaicism rate (10.1% vs. 14.0%). Aneuploidy rate was insignificantly decreased when comparing AI 2.0 to NGS (54.8% vs. 57.0%). A total of 1,174 euploid embryos were transferred. The OP/LBR was significantly higher in the AI 2.0 group (70.3% vs. 61.7%). The BPR was significantly lower in the AI 2.0 group (4.6% vs. 11.8%). CONCLUSION: Standardized PGT-A via AI significantly increases euploidy classification rates and OP/LBR, and decreases BPR when compared to standard NGS.

Expression of Ezrin and Estrogen Receptors During Cervical Carcinogenesis.

RATIONALE: Development of cervical squamous carcinoma (CXCA) is accompanied by changes in estrogen receptors (ERs, ERα and ERß) and ezrin expression; however, reports have been conflicting. Using histologically documented staging of cervical biopsies, we determined ezrin and ER relationships during CXCA development. METHODS: Immunoreactive (ir) ezrin, ir-ERα, and ir-ERß were studied in normal epithelium, carcinoma in situ/cervical intraepithelial neoplasia (CIN) 1 to 3, and local invasion or metastatic CXCA. Results were compared using H scoring. Cultures of Caski metastatic CXCA cells were treated with estradiol and/or tamoxifen and studied for ER-driven ir-ezrin and the morphologic response. RESULTS: Koilocytosis was present and indicated viral presence. The ezrin H score increased from CIN1 to CIN3, reaching significant differences from normal by CIN3 ( P = .004) and 2× normal in metastatic CXCA. Estrogen receptor α and ERß H scores fell, reaching significance by CIN3 (ERα, P = .0001; ERß, P = .024). During estradiol treatment, ezrin in Caski cells increased and localized to the periphery, in ruffles and processes. The selective ER modulator tamoxifen blocked the estradiol-induced changes. CONCLUSIONS: During cervical carcinogenesis, the usual relationship between estrogen and ezrin induction is abridged. This is consistent with the effects of human papilloma virus viral proteins such as E6 and E7 that upregulate SIX1, a protein that induces ezrin. Cervical carcinogenesis is progressive but arrests at the preinvasive stage for varying lengths of time. These studies suggest that changes in ezrin may be associated with the development of the invasive phenotype and penetration of the basement membrane. They also raise the possibility that inhibiting ezrin expression could be a target for the prevention of invasive CXCA.

A single-cell assay for telomere DNA content shows increasing telomere length heterogeneity, as well as increasing mean telomere length in human spermatozoa with advancing age.

PURPOSE: The effect of age on telomere length heterogeneity in men has not been studied previously. Our aims were to determine the relationship between variation in sperm telomere length (STL), men's age, and semen parameters in spermatozoa from men undergoing in vitro fertilization (IVF) treatment. METHODS: To perform this prospective cross-sectional pilot study, telomere length was estimated in 200 individual spermatozoa from men undergoing IVF treatment at the NYU Fertility Center. A novel single-cell telomere content assay (SCT-pqPCR) measured telomere length in individual spermatozoa. RESULTS: Telomere length among individual spermatozoa within an ejaculate varies markedly and increases with age. Older men not only have longer STL but also have more variable STL compared to younger men. STL from samples with normal semen parameters was significantly longer than that from samples with abnormal parameters, but STL did not differ between spermatozoa with normal versus abnormal morphology. CONCLUSION: The marked increase in STL heterogeneity as men age is consistent with a role for ALT during spermatogenesis. No data have yet reported the effect of age on STL heterogeneity. Based on these results, future studies should expand this modest sample size to search for molecular evidence of ALT in human testes during spermatogenesis.

Assessing morphokinetic parameters via time lapse microscopy (TLM) to predict euploidy: are aneuploidy risk classification models universal?

PURPOSE: To determine if Aneuploidy Risk Classification Models are predictive of euploidy/aneuploidy amongst IVF facilities. METHODS: We retrospectively applied key time lapse imaging events of embryos (Campbell et al.[5, 6]) to stratify embryos into 3 groups: low, medium and high risk of aneuploidy. The actual ploidy results (from array comparative genomic hybridization) were compared with expectations [5, 6]. Sources of variability in morphokinetic parameters were determined using Analysis of Variance (ANOVA). RESULTS: The model failed to segregate euploid embryos from aneuploid embryos cultured at our facility. Further analysis indicated that the variability of embryos among patients was too great to allow selection of euploid embryos based on simple morphokinetic thresholds. Clinical selection of embryos based on morphokinetics alone is unlikely to identify euploid embryos accurately for transfer or yield higher rates of live delivery. CONCLUSIONS: The use of non-invasive morphokinetics is unlikely to discriminate aneuploid from euploid embryos. Further, it does not approach the accuracy of preimplantation genetic screening with array comparative genomic hybridization.