[No improvement in live birth rate after luteal phase support by GnRH agonist]. / Absence d'amélioration du taux de naissance vivante après soutien de la phase lutéale par agoniste de la GnRH.

OBJECTIVES: To evaluate the impact of adding a GnRH agonist (GnRH-a) in luteal phase support (LPS) on live birth rates in IVF/ICSI in antagonist protocols. METHODS: In total, 341 IVF/ICSI attempts are analyzed in this retrospective study. Patients were divided into two groups: A f: LPS with progesterone alone (179 attempts) between March 2019 and May 2020; B: LPS with progesterone and an injection of triptorelin (GnRH-a) 0.1mg 6 days after oocyte retrieval (162 attempts) between June 2020 and June 2021. The primary outcome was live birth rate. The secondary outcomes were miscarriage rate, pregnancy rate and ovarian hyperstimulation syndrome rate. RESULTS: The baseline characteristic are identical between the two groups except the infertility duration (longer in the group B). There was no significant difference between the two groups in live birth rate (24.1% versus 21.2%), pregnancy rate (33.3% versus 28.1%), miscarriage rate (4.9% versus 3.4%) and no increase the SHSO rate. The multivariate regression analysis after adjustment for age, ovarian reserve and infertility duration did not reveal a significant difference in live birth rate between the two groups. CONCLUSION: In this study, the results showed no statistically significant association with the single injection of a GnRH-a in addition to progesterone on live birth rate in luteal phase support.

A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems.

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.

In utero delivery of rAAV2/9 induces neuronal expression of the transgene in the brain: towards new models of Parkinson's disease.

Animal models are essential tools for basic pathophysiological research as well as validation of therapeutic strategies for curing human diseases. However, technical difficulties associated with classical transgenesis approaches in rodent species higher than Mus musculus have prevented this long-awaited development. The availability of viral-mediated gene delivery systems in the past few years has stimulated the production of viruses with unique characteristics. For example, the recombinant adeno-associated virus serotype 9 (rAAV2/9) crosses the blood-brain barrier, is capable of transducing developing cells and neurons after intravenous injection and mediates long-term transduction. Whilst post-natal delivery is technically straightforward, in utero delivery bears the potential of achieving gene transduction in neurons at embryonic stages during which the target area is undergoing development. To test this possibility, we injected rAAV2/9 carrying either A53T mutant human α-synuclein or green fluorescent protein, intracerebroventricularly in rats at embryonic day 16.5. We observed neuronal transgene expression in most regions of the brain at 1 and 3 months after birth. This proof-of-concept experiment introduces a new opportunity to model brain diseases in rats.

Reproductive outcome in globozoospermic men: update and prospects.

Infertility affects approximately 15% of couples in reproductive age. Male infertility is estimated to represent about 20% of the etiologies. Among them, a rare type of teratozoospermia known as globozoospermia leads to disappointing pregnancy outcomes. Morphological, physiological and genetic aspects of this severe disorder have been described. We undertook a complete review of the available data on the reproductive outcomes in globozoospermic patients. To this end, a literature review in both English and French, over a 20-year time period using PubMed/Medline, ScienceDirect, and Scopus was performed. A total of 45 publications describing 172 attempts of treatment with assisted reproduction techniques (ICSI or IMSI with or without oocyte activation) were identified. We reviewed 28 deliveries and 34 children. However, for these patients, the fertilization rate after ICSI remained low. The present review suggests that oocyte activation (in particular with calcium ionophore) could improve the pregnancy rate significantly when dealing with globozoospermia. Once the exact pathogenesis of human globozoospermia is clearly identified, it is likely that other treatments such as recombinant phospholipase C zeta (PLC zeta, PLCζ), which seems to be a promising biological tool, would be developed.