Assisted reproductive technologies (ART) rely on the functional integrity of spermatozoa, which can be affected by in vitro handling and preparation procedures. HyperSperm is a novel sperm treatment medium developed to enhance sperm function and improve clinical outcomes. This study aimed to evaluate the efficacy and safety of HyperSperm in human semen samples from patients undergoing fertility treatment. A paired analysis was performed on 135 clinical semen samples, each divided into two equal fractions processed using either standard conditions or the HyperSperm protocol. Sperm motility and kinematic parameters were measured with computer-assisted analysis. Safety assessments included sperm viability at baseline and after 24 hours, DNA fragmentation using a fluorescence-based assay, and acrosomal integrity under spontaneous and progesterone-stimulated conditions. Comparisons between paired samples were analyzed using the Wilcoxon matched-pairs signed-rank test. HyperSperm significantly enhanced sperm kinematic parameters, including curvilinear velocity and amplitude of lateral head displacement, resulting in higher levels of hyperactivated motility. Stratification by semen quality demonstrated that samples with reduced motility showed the greatest functional improvement. HyperSperm did not affect sperm viability or DNA integrity, even after 24 hours of incubation, and preserved acrosomal structure and responsiveness to progesterone. The clinical trial was registered at ClinicalTrials.gov (NCT06742437, December 12th 2024). HyperSperm improves critical sperm functional parameters without compromising cellular viability, DNA stability, or acrosomal integrity. These findings support the safe and effective use of HyperSperm to optimize sperm performance and potentially improve outcomes in assisted reproduction.
Can an explainable machine learning–assisted approach uncover data and prediction trends to select the most transparent model for live birth in autologous ICSI cycles? A retrospective multicenter cohort study (January 2011–December 2023) included 8,066 patients. Only single autologous ICSI cycles using fresh oocytes and donor or patient sperm (fresh or frozen) were included. A total of 47 pre-treatment and in-cycle variables from electronic medical records were evaluated as potential predictors. Five machine learning (ML) algorithms were applied to select the most predictive variables of live birth in a completed cycle. Using an explainability approach based on SHAP values, the decision-making processes of the algorithms were explored. A data-driven approach allowed to optimize five ML models for maximum performance, achieving a mean AUC of 88.9 ± 0.5
Can a machine learning (ML) model predict embryo aneuploidy and live births using morphokinetic meta-variables and clinical data? The novel ML model LIFE Predict demonstrated robust predictive performance (AUC = 0.824), discerning between embryos leading to live birth (LB) or carrying aneuploidies. Time-lapse imaging and artificial intelligence have shown promise for improving embryo selection, yet their predictive accuracy remains limited, with AUC values not surpassing 0.75. Deep learning models tend to offer lower transparency and explainability than morphokinetic-based machine learning approaches could potentially provide. However, the use of interpretable variables that aggregate morphokinetic features in predictive models for ploidy and live birth outcomes remains underexplored. This is a multicentre, retrospective case-control study that analysed data from 882 blastocysts obtained from nine different fertility clinics. Embryos were cultured under hypoxic conditions, using different media (G-TL, Vitrolife, Göteborg, Sweden; SAGE 1-Step, Cooper Surgical, Trumbull, USA; CSCM-C, Fujifilm Irvine Scientific, Santa Ana, USA) and time-lapse incubators (Embryoscope, Vitrolife, Göteborg, Sweden; Geri, Genea Biomedx, Sydney, Australia). Data collection included ICSI cycles from 2017 to 2024. A total of 882 embryos (487LB, 395 diagnosed as aneuploid by PGT-A trophectoderm biopsies) were used for model training and testing (V-fold and leave-one-out cross-validation). The predictive features consisted of clinical data, morphokinetic variables, and two meta-variables derived from morphokinetics. Model performance included AUC, accuracy, sensitivity, and specificity. Odds ratios (OR) and 95% confidence intervals (CI, p < 0.05) were used to assess the association between aneuploidy outcomes and both meta-variables and model output (LIFE Predict Score). Significant differences in morphokinetic features were observed between embryos that resulted in live births and those reported as aneuploids. Two novel meta-variables were strongly associated with the embryo outcomes. The first meta-variable, representing the sum of morphokinetic features with values outside the reference range (using LB as the benchmark), showed a notable association with embryo outcomes (OR = 4.36, 95% CI:2.30–6.25). The second meta-variable, denoting the sum of morphokinetic features with Mean Absolute Error (MAE) values above a specific cut-off (based on the MAE of LB) between the morphokinetic values recorded by embryologists and those calculated by a regression machine learning model designed for each event, also obtained significant results (OR = 2.43, 95% CI:1.61–3.72). The predictive model achieved the following metrics: AUC=0.824, accuracy=0.750, sensitivity=0.814, specificity=0.662. The numerical output of the predictive model was converted into the LIFE Predict Score by scaling it from 0 to 10. Higher accuracy was found with the score values at the lower and higher ends. Specifically, scores below 3 contained 84% of aneuploid embryos (OR = 13.3, 95% CI:3.01-19.8). In contrast, this percentage decreased to 22.8% for ratings between 7 and 9 (OR = 0.315, 95% IC:0.197-0.458) and further dropped to 9.7% for ratings exceeding 9 (OR = 0.105, 95% CI: 0.006-0.212). As a retrospective study, the findings require prospective validation (in process) across broader populations and clinical settings to confirm generalizability. This ML model offers an interpretable, data-driven approach to enhance embryo selection and optimize IVF outcomes, highlighting the importance of morphokinetic annotations and clinical data integration. The use of meta-variables allows for a broader analysis, identifying variables that fall outside the reference range and enhancing explainability. No
STUDY QUESTION:Does the diagnosis of mosaicism affect ploidy rates across different providers offering preimplantation genetic testing for aneuploidies (PGT-A)? SUMMARY ANSWER:Our analysis of 36 395 blastocyst biopsies across eight genetic testing laboratories revealed that euploidy rates were significantly higher in providers reporting low rates of mosaicism. WHAT IS KNOWN ALREADY:Diagnoses consistent with chromosomal mosaicism have emerged as a third category of possible embryo ploidy outcomes following PGT-A. However, in the era of mosaicism, embryo selection has become increasingly complex. Biological, technical, analytical, and clinical complexities in interpreting such results have led to substantial variability in mosaicism rates across PGT-A providers and clinics. Critically, it remains unknown whether these differences impact the number of euploid embryos available for transfer. Ultimately, this may significantly affect clinical outcomes, with important implications for PGT-A patients. STUDY DESIGN, SIZE, DURATION:In this international, multicenter cohort study, we reviewed 36 395 consecutive PGT-A results, obtained from 10 035 patients across 11 867 treatment cycles, conducted between October 2015 and October 2021. A total of 17 IVF centers, across eight PGT-A providers, five countries and three continents participated in the study. All blastocysts were tested using trophectoderm biopsy and next-generation sequencing. Both autologous and donation cycles were assessed. Cycles using preimplantation genetic testing for structural rearrangements were excluded from the analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS:The PGT-A providers were randomly categorized (A to H). Providers B, C, D, E, F, G, and H all reported mosaicism, whereas Provider A reported embryos as either euploid or aneuploid. Ploidy rates were analyzed using multilevel mixed linear regression. Analyses were adjusted for maternal age, paternal age, oocyte source, number of embryos biopsied, day of biopsy, and PGT-A provider, as appropriate. We compared associations between genetic testing providers and PGT-A outcomes, including the number of chromosomally normal (euploid) embryos determined to be suitable for transfer. MAIN RESULTS AND THE ROLE OF CHANCE:The mean maternal age (±SD) across all providers was 36.2 (±5.2). Our findings reveal a strong association between PGT-A provider and the diagnosis of euploidy and mosaicism. Amongst the seven providers that reported mosaicism, the rates varied from 3.1% to 25.0%. After adjusting for confounders, we observed a significant difference in the likelihood of diagnosing mosaicism across providers (P < 0.001), ranging from 6.5% (95% CI: 5.2-7.4%) for Provider B to 35.6% (95% CI: 32.6-38.7%) for Provider E. Notably, adjusted euploidy rates were highest for providers that reported the lowest rates of mosaicism (Provider B: euploidy, 55.7% (95% CI: 54.1-57.4%), mosaicism, 6.5% (95% CI: 5.2-7.4%); Provider H: euploidy, 44.5% (95% CI: 43.6-45.4%), mosaicism, 9.9% (95% CI: 9.2-10.6%)); and Provider D: euploidy, 43.8% (95% CI: 39.2-48.4%), mosaicism, 11.0% (95% CI: 7.5-14.5%)). Moreover, the overall chance of having at least one euploid blastocyst available for transfer was significantly higher when mosaicism was not reported, when we compared Provider A to all other providers (OR = 1.30, 95% CI: 1.13-1.50). Differences in diagnosing and interpreting mosaic results across PGT-A laboratories raise further concerns regarding the accuracy and relevance of mosaicism predictions. While we confirmed equivalent clinical outcomes following the transfer of mosaic and euploid blastocysts, we found that a significant proportion of mosaic embryos are not used for IVF treatment. LIMITATIONS, REASONS FOR CAUTION:Due to the retrospective nature of the study, associations can be ascertained, however, causality cannot be established. Certain parameters such as blastocyst grade were not available in the dataset. Furthermore, certain platform-related and clinic-specific factors may not be readily quantifiable or explicitly captured in our dataset. As such, a full elucidation of all potential confounders accounting for variability may not be possible. WIDER IMPLICATIONS OF THE FINDINGS:Our findings highlight the strong need for standardization and quality assurance in the industry. The decision not to transfer mosaic embryos may ultimately reduce the chance of success of a PGT-A cycle by limiting the pool of available embryos. Until we can be certain that mosaic diagnoses accurately reflect biological variability, reporting mosaicism warrants utmost caution. A prudent approach is imperative, as it may determine the difference between success or failure for some patients. STUDY FUNDING/COMPETING INTEREST(S):This work was supported by the Torres Quevedo Grant, awarded to M.P. (PTQ2019-010494) by the Spanish State Research Agency, Ministry of Science and Innovation, Spain. M.P., L.B., A.R.L., A.L.R.d.C.L., N.P.P., M.P., D.S., F.A., A.P., B.M., L.D., F.V.M., D.S., M.R., E.P.d.l.B., A.R., and R.V. have no competing interests to declare. B.L., R.M., and J.A.O. are full time employees of IB Biotech, the genetics company of the Instituto Bernabeu group, which performs preimplantation genetic testing. M.G. is a full time employee of Novagen, the genetics company of Cegyr, which performs preimplantation genetic testing. TRIAL REGISTRATION NUMBER:N/A.
Abstract Study question How does maternal age impact the molecular composition of oocytes during their final meiotic progression? Summary answer Maternal age significantly correlates with proteomic changes during oocyte maturation, particularly in proteostasis and meiosis related proteins, with minimal impact on transcriptome and DNA-methylation patterns. What is known already Oocyte quality declines with maternal age, resulting in diminished developmental competence and higher aneuploidy rates. However, the molecular mechanisms behind this decline in oocyte quality remain elusive. Transcriptomic studies have revealed few differentially expressed genes in advanced maternal age (AMA) oocytes, suggesting that age-related changes in oocyte quality result from a complex interplay of molecular factors, rather than a single cause. However, comprehensive methylation and proteomic data are still lacking. We employed advanced -omics to assess the effect of maternal age on the molecular signature of human oocytes, focusing on DNA-methylation, transcriptome and proteome changes during the final meiotic progression. Study design, size, duration This study included a total of 112 oocytes obtained from young (<35 years, n = 35) and AMA women (>37 years, n = 55), who were recruited in the study from October 2021 to October 2023. Both germinal vesicle (GV, n = 68) and metaphase II (MII, n = 44) oocytes were analysed. Additionally, 19 immature oocytes (GV and metaphase I) from 9 young women were used for validation. Participants/materials, setting, methods Parallel single-cell bisulfite and RNA sequencing was applied to 44 oocytes (26 GV-Young, 6 MII-Young, 8 GV-AMA, 4 MII-AMA) and single-cell proteomics to 68 oocytes (18 GV-Young, 18 MII-Young, 18 GV-AMA, 14 MII-AMA). Additionally, 10 GV and 9 MI oocytes were treated with the proteasome inhibitor MG-312 (0 µM, 10 µM) for 6 hours, followed by rescue in vitro maturation (rIVM) for 36 hours. Chromosomal distribution was assessed by immunocytochemistry. Main results and the role of chance Our analysis revealed no significant changes in DNA methylation patterns in either GV nor MII oocytes associated with AMA. Also, very limited changes were detected in the transcriptome, with transcript levels of only 5 genes in GV oocytes and 7 genes in MII oocytes detected as being significantly changed. In contrast, proteomic analysis, particularly in GV oocytes, revealed significant age-related changes, notably in proteins participating in the proteostasis network (signalosome complex, UCHL1) including chaperones (TRiC-complex, HSP7C, STIP1), and in the cell cycle, including signal transduction factors (1433E, integrins) and cytoskeleton regulators (DYL2, CAPZB, ARHGG) (Rs ≤ |0.5|, p ≤ 0.05). The proteasome complex, which plays a crucial role both in meiosis and the proteostasis network, was found to decline with age; changes were evident in several subunits of the complex (e.g., PRS8, PRS6A, and PRS10; Rs ≤ -0.56, p ≤ 0.05). Compared to controls, treatment of immature oocytes with the proteasome inhibitor MG-132 resulted in either maturation failure or chromosome mislocalization in metaphase plate, further validating the essential role of the proteasome during oocyte maturation. In MII oocytes, 7 proteins showed alterations with age, including the oocyte-specific marker DDX4, which significantly declined in abundance (Rs =-0.6, p ≤ 0.05). Limitations, reasons for caution Unlike GVs that were collected fresh, MII oocytes underwent vitrification and warming before being included in the study due to clinical protocols. These procedures may have unknown effects on the transcriptome and proteome. Wider implications of the findings Our findings suggest that age primarily affects oocyte quality at the post-transcriptional level, potentially through meiosis dysregulation and proteostasis disruption. We also demonstrate the proteasome's vital role in oocyte maturation, suggesting that targeting the proteasome complex may improve oocyte quality in AMA women. Trial registration number Not applicable
