Genetic and biochemical sperm integrity is essential to ensure the reproductive competence. However, spermatogenesis involves physiological changes that could endanger sperm integrity. DNA protamination and apoptosis have been studied extensively. Furthermore, elevated rates of aneuploidy and DNA injury correlate with reproductive failures. Consequently, this study applied the conventional spermiogram method in combination with molecular tests to assess genetic integrity in ejaculate from normozoospermic patients with implantation failure by retrospectively analysing aneuploidy (chromosomes 18, X, Y), DNA fragmentation, externalization of phosphatidylserine and mitochondrial membrane potential status before and after magnetic activated cell sorting (MACS). Aneuploid, apoptotic and DNA-injured spermatozoa decreased significantly after MACS. A positive correlation was detected between reduction of aneuploidy and decreased DNA damage, but no correlation was determined with apoptotic markers. The interactions between apoptotic markers, DNA integrity and aneuploidy, and the effect of MACS on these parameters, remain unknown. In conclusion, use of MACS reduced aneuploidy, DNA fragmentation and apoptosis. A postulated mechanism relating aneuploidy and DNA injury is discussed; on the contrary, cell death markers could not be related. An 'apoptotic-like' route could explain this situation.
Purpose Fluorescence in situ hybridization (FISH) in spermatozoa provides an estimate of the frequency of chromosomal abnormalities, but there is not a clinical consensus on how to statistically analyze sperm FISH results. We therefore propose a statistical approach to establish sperm aneuploidy thresholds in a fertile population. Methods We have determined the distribution and variation of the frequency of nullisomy, disomy, and diploidy for a set of 13 chromosomes (
STUDY QUESTION Can artificial intelligence (AI) algorithms developed to assist embryologists in evaluating embryo morphokinetics be enriched with multi-centric clinical data to better predict clinical pregnancy outcome? SUMMARY ANSWER Training algorithms on multi-centric clinical data significantly increased AUC compared to algorithms that only analyzed the time-lapse system (TLS) videos. WHAT IS KNOWN ALREADY Several AI-based algorithms have been developed to predict pregnancy, most of them based only on analysis of the time-lapse recording of embryo development. It remains unclear, however, whether considering numerous clinical features can improve the predictive performances of time-lapse based embryo evaluation. STUDY DESIGN, SIZE, DURATION A dataset of 9986 embryos (95.60% known clinical pregnancy outcome, 32.47% frozen transfers) from 5226 patients from 14 European fertility centers (in two countries) recorded with three different TLS was used to train and validate the algorithms. A total of 31 clinical factors were collected. A separate test set (447 videos) was used to compare performances between embryologists and the algorithm. PARTICIPANTS/MATERIALS, SETTING, METHODS Clinical pregnancy (defined as a pregnancy leading to a fetal heartbeat) outcome was first predicted using a 3D convolutional neural network that analyzed videos of the embryonic development up to 2 or 3 days of development (33% of the database) or up to 5 or 6 days of development (67% of the database). The output video score was then fed as input alongside clinical features to a gradient boosting algorithm that generated a second score corresponding to the hybrid model. AUC was computed across 7-fold of the validation dataset for both models. These predictions were compared to those of 13 senior embryologists made on the test dataset. MAIN RESULTS AND THE ROLE OF CHANCE The average AUC of the hybrid model across all 7-fold was significantly higher than that of the video model (0.727 versus 0.684, respectively, P = 0.015; Wilcoxon test). A SHapley Additive exPlanations (SHAP) analysis of the hybrid model showed that the six first most important features to predict pregnancy were morphokinetics of the embryo (video score), oocyte age, total gonadotrophin dose intake, number of embryos generated, number of oocytes retrieved, and endometrium thickness. The hybrid model was shown to be superior to embryologists with respect to different metrics, including the balanced accuracy (P ≤ 0.003; Wilcoxon test). The likelihood of pregnancy was linearly linked to the hybrid score, with increasing odds ratio (maximum P-value = 0.001), demonstrating the ranking capacity of the model. Training individual hybrid models did not improve predictive performance. A clinic hold-out experiment was conducted and resulted in AUCs ranging between 0.63 and 0.73. Performance of the hybrid model did not vary between TLS or between subgroups of embryos transferred at different days of embryonic development. The hybrid model did fare better for patients older than 35 years (P < 0.001; Mann–Whitney test), and for fresh transfers (P < 0.001; Mann–Whitney test). LIMITATIONS, REASONS FOR CAUTION Participant centers were located in two countries, thus limiting the generalization of our conclusion to wider subpopulations of patients. Not all clinical features were available for all embryos, thus limiting the performances of the hybrid model in some instances. WIDER IMPLICATIONS OF THE FINDINGS Our study suggests that considering clinical data improves pregnancy predictive performances and that there is no need to retrain algorithms at the clinic level unless they follow strikingly different practices. This study characterizes a versatile AI algorithm with similar performance on different time-lapse microscopes and on embryos transferred at different development stages. It can also help with patients of different ages and protocols used but with varying performances, presumably because the task of predicting fetal heartbeat becomes more or less hard depending on the clinical context. This AI model can be made widely available and can help embryologists in a wide range of clinical scenarios to standardize their practices. STUDY FUNDING/COMPETING INTEREST(S) Funding for the study was provided by ImVitro with grant funding received in part from BPIFrance (Bourse French Tech Emergence (DOS0106572/00), Paris Innovation Amorçage (DOS0132841/00), and Aide au Développement DeepTech (DOS0152872/00)). A.B.-C. is a co-owner of, and holds stocks in, ImVitro SAS. A.B.-C. and F.D.M. hold a patent for ‘Devices and processes for machine learning prediction of in vitro fertilization’ (EP20305914.2). A.D., N.D., M.M.F., and F.D.M. are or have been employees of ImVitro and have been granted stock options. X.P.-V. has been paid as a consultant to ImVitro and has been granted stocks options of ImVitro. L.C.-D. and C.G.-S. have undertaken paid consultancy for ImVitro SAS. The remaining authors have no conflicts to declare. TRIAL REGISTRATION NUMBER N/A.
Novel next-generation sequencing procedures have rapidly emerged into the preimplantation genetic screening framework. This work presents the design and validation of a new low-coverage whole-genome sequencing assay for aneuploidy detection in single blastomeres and trophectodermal samples from preimplantation embryos. The validation ensures analytical sensitivity, specificity, robustness, precision, limit of detection, resolution, and reproducibility. Specific parameters to measure the performance are defined, and the results are compared with a standardized array-based method to stablish the concordance. From the single cell genomics point of view, the main novelties are the length of reads of the libraries (150 nucleotides) together with a paired-end strategy and the design of an original algorithm and copy number viewer. A total of 129 samples were included in six experimental runs using a MiSeq Illumina platform. Samples included: single amniocytes, single blastomeres (cleavage-stage embryos), trophectoderm samples (blastocyst), and diluted DNA. Sensitivity and specificity were calculated per chromosome yielding 96% and 99%, respectively. The percentage of concordant samples was 98.2% and all of the aneuploid samples were confirmed. In conclusion, the validation yields highly reliable and reproducible results, representing an accurate and cost-effective strategy for the routine detection of aneuploidy in human embryos.
No abstract
Study question What is the SARS-CoV-2 positivity rate following the Spanish Fertility Society (SEF)/Association for the Study of Reproductive Biology (ASEBIR) screening recommendations? Summary answer The SARS-CoV-2 positivity rate in the centers following the SEF/ASEBIR screening recommendations was 0.316% after the first survey and 0.364% after the second one What is known already Due to the Sars-Cov-2 pandemic, all the Medical Assisted Reproduction (MAR) centers in Spain had to interrupt their activity most of the time during the first pandemic wave. On April 27th activity was restarted, and SEF and ASEBIR jointly elaborated a guide describing their SARS-CoV-2 screening recommendations for MAR centers. This document aims to achieve a safe environment for patients and staff. It includes the possibility of screening patients through a targeted clinical interview and the use of reverse-transcriptase polymerase chain reaction (RT-PCR). The aim of this study is to quantify the SARS-CoV-2 positivity rate based on these recommendations. Study design, size, duration National multicenter cross-sectional study. Information was gathered from centers using an anonymous survey asking for aggregated data about the number of positive cases among screened patients, sent twice. The first survey covered the period April 27th - June 30th. Second survey covered July 1st - August 31st. Response rates among centres were 9% (29/319) and 6% (20/319), respectively. This study includes 2,695 and 4,068 screenings performed in the first and the second survey, respectively. Participants/materials, setting, methods The SEF/ASEBIR recommendations describe two screening strategies. Strategy (a) consists in a targeted clinical interview (TCI) evaluating clinical symptoms and exposure risk, first before starting the cycle, and before egg-retrieval, intrauterine insemination (IUI), and/or embryo transfer (ET). Suspicious cases could be confirmed by further RT-PCR testing. Strategy (b) consists in conducting the same first TCI, and a systematic RT-PCR testing before the medical procedure in all patients. All patients in both strategies have a TCI. Main results and the role of chance In the 1st survey, 1,177 screenings and 919 RT-PCR (78.07%) were performed before the egg-retrieval. One patient with a negative TCI and positive RT-PCR was detected, and the cycle was cancelled. 1,518 screenings and 1,161 RT-PCRs (76.48%) were performed before the ET/IUI. Two patients with a positive TCI were detected, one did not perform a RT-PCR, while the other resulted in a positive RT-PCR. Both cycles were cancelled. Besides, 5 patients with negative TCI performed a RT-PCR with a positive result; all 5 were cancelled. Overall, the SARS-CoV-2 positivity rate was 8/2533 (0.316%), of which 7/2533 (0.276%) were identified by RT-PCR testing. The 2nd survey included 1,376 screenings and 1,009 RT-PCR (73.32%) performed before the egg-retrieval. Four patients with negative TCI and further positive RT-PCR were detected, and their cycle was cancelled. 2,692 screenings and 2,134 RT-PCR (79.27%) were performed before ET/IUI. Two patients had a positive TCI, one with a negative, the other with a positive RT-PCR testing; both cycles were cancelled. Besides, 8 patients with negative TCI, but positive RT-PCR testing, were detected and their cycles cancelled. Overall, the SARS-CoV-2 positivity rate was 14/3846 (0.364%), of which 13/3846 (0.338%) after positive RT-PCR testing. Limitations, reasons for caution The criteria for performing the RT-PCR testing were not the same in all MAR Centres or even in the same centre at different times. Due to the low response rate of the study, we should not extend these results to all the MAR Centres in Spain. Wider implications of the findings The results of the surveys suggest that the SEF/ASEBIR recommendations could be a good screening strategy for SARS-Cov-2 at MAR Centres. Further survey collected at different times of the pandemic are warranted, including new strategies for screening as antigen tests or vaccination status. Trial registration number Not applicable
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