Non-invasive molecular assessment of human embryo development and implantation potential

Relationship of the levels of reactive oxygen species in the fertilization medium with the outcome of in vitro fertilization following brief incubation

Embryo selection in in vitro fertilization-embryo transfer (IVF-ET) mostly relies on morphological assessment using a conventional microscope or the time-lapse monitoring system, which is not comprehensive. Inappropriate levels of reactive oxygen species (ROS) in the fertilization medium may cause damage to gametes, eventually leading to adverse IVF outcomes. The present study aimed to identify the optimal oxidation-reduction level in the fertilization medium for IVF outcomes by measuring the static oxidation-reduction potential (sORP) using a highly accurate and sensitive MiOXSYS system. A total of 136 patients undergoing IVF following brief incubation were divided equally into 4 groups in this prospective cohort study. The sORP value in the fertilization medium was detected using the MiOXSYS system, and its relationship with IVF outcomes was analyzed. The primary outcome was pregnancy outcomes, including live birth rate (LBR), clinical pregnancy rate (CPR), biochemical pregnancy rate (BPR), and implantation rate (IR). The secondary outcome was embryo quality, including fertilization rate (FR), cleavage rate (CR), available embryo rate (AER), and good-quality embryo rate (GQER). Group II (sORP: 228.7-235.3 mV) showed a higher LBR, CPR, BPR, and IR compared with Group III (sORP: 235.4-242.7 mV), presented as follows: LBR (32.0% for Group II vs 3.6% for Group III, P = 0.033), CPR (32.0% for Group II vs 3.6% for Group III, P = 0.033), BPR (36.0% for Group II vs 3.6% for Group III, P = 0.019), and IR (31.3% for Group II vs 2.7% for Group III, P = 0.003). The FR in Groups I and II had lower significant differences compared with that in Groups III and IV (71.7% and 70.3% for Groups I and II vs 83.5% and 80.4% for Groups III and IV, P = 0.000). The GQER in Group I to Group IV was 32.7%, 37.4%, 26.5%, and 33.3%, respectively (P = 0.056). This study indicated that the sORP value in the fertilization medium might be a potential indicator of embryo quality and pregnancy outcome.

Nanotechnologies in Obstetrics and Cancer during Pregnancy: A Narrative Review

Nanotechnology, the art of engineering structures on a molecular level, offers the opportunity to implement new strategies for the diagnosis and management of pregnancy-related disorders. This review aims to summarize the current state of nanotechnology in obstetrics and cancer in pregnancy, focusing on existing and potential applications, and provides insights on safety and future directions. A systematic and comprehensive literature assessment was performed, querying the following databases: PubMed/Medline, Scopus, and Endbase. The databases were searched from their inception to 22 March 2022. Five independent reviewers screened the items and extracted those which were more pertinent within the scope of this review. Although nanotechnology has been on the bench for many years, most of the studies in obstetrics are preclinical. Ongoing research spans from the development of diagnostic tools, including optimized strategies to selectively confine contrast agents in the maternal bloodstream and approaches to improve diagnostics tests to be used in obstetrics, to the synthesis of innovative delivery nanosystems for therapeutic interventions. Using nanotechnology to achieve spatial and temporal control over the delivery of therapeutic agents (e.g., commonly used drugs, more recently defined formulations, or gene therapy-based approaches) offers significant advantages, including the possibility to target specific cells/tissues of interest (e.g., the maternal bloodstream, uterus wall, or fetal compartment). This characteristic of nanotechnology-driven therapy reduces side effects and the amount of therapeutic agent used. However, nanotoxicology appears to be a significant obstacle to adopting these technologies in clinical therapeutic praxis. Further research is needed in order to improve these techniques, as they have tremendous potential to improve the accuracy of the tests applied in clinical praxis. This review showed the increasing interest in nanotechnology applications in obstetrics disorders and pregnancy-related pathologies to improve the diagnostic algorithms, monitor pregnancy-related diseases, and implement new treatment strategies.

Microphysiological stem cell models of the human heart

Models of heart disease and drug responses are increasingly based on human pluripotent stem cells (hPSCs) since their ability to capture human heart (dys-)function is often better than animal models. Simple monolayer cultures of hPSC-derived cardiomyocytes, however, have shortcomings. Some of these can be overcome using more complex, multi cell-type models in 3D. Here we review modalities that address this, describe efforts to tailor readouts and sensors for monitoring tissue- and cell physiology (exogenously and in situ) and discuss perspectives for implementation in industry and academia.

An Artificial Intelligence-Based Algorithm for Predicting Pregnancy Success Using Static Images Captured by Optical Light Microscopy during Intracytoplasmic Sperm Injection

CONTEXT BACKGROUND

Analysis of embryos for in vitro fertilization (IVF) involves manual grading of human embryos through light microscopy. Recent research shows that artificial intelligence techniques applied to time lapse embryo images can successfully ascertain embryo quality. However, laboratories often capture static images and cannot apply this research in a real-world setting. Further, current models do not predict the outcome of pregnancy.

AIMS

To create and assess a convolutional neural network to predict embryo quality using static images from a limited dataset. We considered two classification problems: predicting whether an embryo will lead to a pregnancy or not and predicting the outcome of that pregnancy.

SETTINGS AND DESIGN

We utilized transfer learning techniques using a pretrained Inception V1 network. All models were built using the Tensorflow software package.

METHODS

We utilized a total of 361 randomly sampled static images collected from four South Florida IVF clinics. Data were collected between 2016 and 2019.

STATISTICAL ANALYSIS USED

We utilized deep-learning techniques, including data augmentation to reduce model variance and transfer learning to bolster our limited dataset. We used a standard train/validation/ test dataset split to avoid model overfitting.

RESULTS

Our algorithm achieved 0.657 area under the curve for predicting pregnancy versus nonpregnancy. However, our model was unable to meaningfully predict whether a pregnancy led a to live birth.

CONCLUSIONS

Despite the limited dataset that achieved somewhat of a lower accuracy than conventional embryo selection, this is the first study that has successfully made IVF predictions from static images alone. Future availability of more data may allow for prospective validation and further generalisability of results.

Current Advancements in Noninvasive Profiling of the Embryo Culture Media Secretome

There have been over 8 million babies born through in vitro fertilization (IVF) and this number continues to grow. There is a global trend to perform elective single embryo transfers, avoiding risks associated with multiple pregnancies. It is therefore important to understand where current research of noninvasive testing for embryos stands, and what are the most promising techniques currently used. Furthermore, it is important to identify the potential to translate research and development into clinically applicable methods that ultimately improve live birth and reduce time to pregnancy. The current focus in the field of human reproductive medicine is to develop a more rapid, quantitative, and noninvasive test. Some of the most promising fields of research for noninvasive assays comprise cell-free DNA analysis, microscopy techniques coupled with artificial intelligence (AI) and omics analysis of the spent blastocyst media. High-throughput proteomics and metabolomics technologies are valuable tools for noninvasive embryo analysis. The biggest advantages of such technology are that it can differentiate between the embryos that appear morphologically identical and has the potential to identify the ploidy status noninvasively prior to transfer in a fresh cycle or before vitrification for a later frozen embryo transfer.