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Background: Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes. Methods: Multicenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve. Results: The Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232–0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821–0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522–0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM). Conclusions: This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes. [ABSTRACT FROM AUTHOR]

Background: Cell cycle regulation influences the proliferation of granulosa cells and affects many processes related to ovarian folliclular growth and ovulation. Abnormal regulation of the cell cycle can lead to many diseases within the ovary. The aim of this study was to describe the expression profile of genes within granulosa cells, which are related to the formation of the cytoskeleton, organization of cell organelles inside the cell, and regulation of cell division. Established in vitro primary cultures from porcine ovarian follicle granulosa cells were maintained for 48, 96, 144 h and evaluated via microarray expression analysis. Results: Analyzed genes were assigned to 12 gene ontology groups "actin cytoskeleton organization", "actin filament organization", "actin filament—based process", "cell—matrix adhesion", "cell—substrate adhesion", "chromosome segregation", "chromosome separation", "cytoskeleton organization", "DNA integrity checkpoint", "DNA replication initiation", "organelle fision", "organelle organization". Among the genes with significantly changed expression, those whose role in processes within the ovary are selected for consideration. Genes with increased expression include (ITGA11, CNN1, CCl2, TPM2, ACTN1, VCAM-1, COL3A1, GSN, FRMD6, PLK2). Genes with reduced expression inlcude (KIF14, TACC3, ESPL1, CDC45, TTK, CDC20, CDK1, FBXO5, NEK2—NIMA, CCNE2). For the results obtained by microarray expressions, quantitative validation by RT-qPCR was performed. Conclusions: The results indicated expression profile of genes, which can be considered as new molecular markers of cellular processes involved in signaling, cell structure organization. The expression profile of selected genes brings new insight into regulation of physiological processes in porcine follicular granulosa cells during primary in vitro culture. [ABSTRACT FROM AUTHOR]

Background: High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in cells for screening. However, this segmentation is challenging due to variabilities in cell confluency, drug-induced alterations in cell morphology, and gradual degradation of fluorescence with the depth of scanning. Despite advances in algorithms for segmenting nuclei for HCS, robust 3D methods that are insensitive to these conditions are still lacking. Results: We have developed an algorithm which first generates a 3D nuclear mask in the original images. Next, an iterative 3D marker-controlled watershed segmentation is applied to downsized images to segment adjacent nuclei under the mask. In the last step, borders of segmented nuclei are adjusted in the original images based on local nucleus and background intensities. The method was developed using a set of 10 3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei demonstrated that our method, in comparison with 6 reference methods, achieved the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation, was similar to that yielded by all reference approaches. Our method was on average more than twice as fast as the reference method that produced the best results. Additional tests carried out on three stacked 3D images comprising heterogenous nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80. Conclusions: The high-performance metrics yielded by the proposed approach suggest that it can be used to reliably delineate nuclei in 3D images of monolayered and stacked cells exposed to cytotoxic drugs. [ABSTRACT FROM AUTHOR]