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

Abreu, Catarina M.; Thomas, Victoria; Knaggs, Paul; Bunkheila, Adnan; Cruz, Andrea; Teixeira, Sofia Rodrigues; Alpuim, P.; Francis, Lewis; Gebril, Amr; Ibrahim, Ahmed M. S.; Margarit, Lavinia; González, Deyarina; Freitas, P. P.; Conlan, R. Steven; Pinto, Inês Mendes

doi:10.1016/j.bios.2020.112144

Cited by 10 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…During culture, embryos take up pyruvate, glucose, and amino acids and produce different substances such as lactate, ammonia, enzymes, and hormones. Molecular biomarker detection in embryo-conditioned media may reflect physiological differences between embryos with morphologically similar characteristics (68).…”

Section: Measuring Metabolic Endpointsmentioning

confidence: 99%

“…A number of target biomarkers have been investigated and related to embryo development, such as human chorionic gonadotropin (hCG) (79), Apolipoprotein A1 (80), human leukocyte antigen-G (81,82), granulocyte-macrophage colony-stimulating factor (83), pregnancy-specific beta-1 glycoprotein (84), alpha interferone 2 (85), and platelet activating factor (86). In a recent work, using electrochemical impedance spectroscopy, hCGb, IL-8, and tumor necrosis factor a (TNFa) were detected from culture media of single human embryos: nonviable embryos produced higher levels of IL-8 and TNFa; interestingly, hCGb levels were able to discriminate between morphologically identical viable embryos (68). Another recent study used a bead-based digital microfluidic system to simultaneously measure IL-1b and TNFa in a single 520-nL droplet of culture medium in %40 minutes (87).…”

Section: Measuring Metabolic Endpointsmentioning

confidence: 99%

See 1 more Smart Citation

Are we approaching automated assisted reproductive technology? Embryo culture, metabolomics, and cryopreservation

Casciani¹,

Galliano

Franasiak

et al. 2021

F&S Reviews

View full text Add to dashboard Cite

For decades, assisted reproductive technology (ART) procedures have been performed manually thanks to the meticulous work of skilled embryologists. Recently, new technologies have been developed with three main scopes: improving embryo culture conditions; making diagnostic evaluations more consistent and reliable; and allowing ART procedures to become progressively less subjective and operatordependent. This review aimed to answer the following questions: is automation likely to be successfully incorporated into the in vitro fertilization laboratory and clinical ART in the future? If so, would such automation result in improved outcomes in ART?An electronic search of PubMed was performed to identify articles in English language that addressed automation in ART. Studies were classified in decreasing categories: randomized controlled trials; prospective controlled trials; prospective noncontrolled trials; retrospective studies; and experimental studies. Research and development data from investigators were included.There are a number of separate platforms that have been developed until now to address different parts of the ART process: gradual change of embryo culture medium; noninvasive embryo monitoring with time-lapse or metabolome analysis; and automated vitrification. It is conceivable that future automation enhancements in the in vitro fertilization laboratory may improve consistency and throughput and reduce the risk of human error associated with the performance of repetitive tasks. Nevertheless, a need remains for a platform able to integrate all separate technologies, capable of successfully interconnecting them in a way that assures continued chain of custody for the gametes and embryos. Moreover, controlled trials will be fundamental to demonstrate the usefulness of automation in ART. (Fertil Steril Rev Ò 2021;2:251-64. Ó2021 by American Society for Reproductive Medicine.

show abstract

Section: Measuring Metabolic Endpointsmentioning

confidence: 99%

Section: Measuring Metabolic Endpointsmentioning

confidence: 99%

Are we approaching automated assisted reproductive technology? Embryo culture, metabolomics, and cryopreservation

Casciani¹,

Galliano

Franasiak

et al. 2021

F&S Reviews

View full text Add to dashboard Cite

show abstract

“…Abreu и соавт. (2020) [31] был проведен сравнительный анализ концентраций β-ХГЧ, ФНО-α и ИЛ-8 в культуральной среде эмбрионов 5-х суток различного ка чества. В культуральных средах эмбрионов низкого качества (не достигших стадии бластоцисты к 5-м суткам культивирования, имевших повышенную фрагментацию, много ядерность с неравномерным размером клеток) концент рация β-ХГЧ была ниже в сравнении с таковой у эмбрионов хорошего и отличного качества.…”

Section: репродуктивная генетикаunclassified

Modern Methods for Assessment of the Implantation Potential of Embryos in Assisted Reproductive Programs

Savostina¹,

Perminova²,

Timofeeva³

et al. 2021

View full text Add to dashboard Cite

Objective of the Review: To analyse the modern methods for assessment of the implantation potential of embryos in assisted reproductive programs. Key Points. We present the study results for selection of a most optimal embryo for transfer, using visual assessment of embryo quality, preimplantation genetic aneuploidy testing, analysis of metabolomic, proteomic, transcriptomic profiles of culture media and embryo blastocele. We have paid special attention to assessment of small non-coding RNA (sncRNA) in embryo culture medium. Conclusion. Due to the high sensitivity, objectivity and biomarker resistance to degradation, the most promising non-invasive method to assess the implantation potential of an embryo is analysis of the sncRNA profile in embryo culture media. Keywords: aneuploidy, pre-implantation genetic testing, small non-coding RNAs, proteomic analysis, metabolomic analysis.

show abstract

“…[ 3 ] Nevertheless, in the past several years, there has been a decrease in the live birth rates from IVF leading to poorer patient satisfaction and increased costs. [ 4 ] While the causes behind this phenomenon are likely multifactorial (including advanced age), the impact of the process of conventional embryo evaluation must be acknowledged.…”

Section: Introductionmentioning

confidence: 99%

“…This is done through either conventional microscopic analysis or time-lapse imaging systems. [ 4 ] Although the most common scale used for embryo grading is the Gardner scale, which takes into account morphology, rates of cleavage, as well as nutrient uptake and utilization, there is no true universal system that is used. [ 5 ] Thus, the traditional method of embryo grading has been regarded to be quite subjective and time-consuming with limited inter-observer and intra-observer agreement among embryologists.…”

Section: Introductionmentioning

confidence: 99%

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

Geller

Collazo

Pai

et al. 2021

Journal of Human Reproductive Sciences

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 10 publications

References 19 publications

Are we approaching automated assisted reproductive technology? Embryo culture, metabolomics, and cryopreservation

Are we approaching automated assisted reproductive technology? Embryo culture, metabolomics, and cryopreservation

Modern Methods for Assessment of the Implantation Potential of Embryos in Assisted Reproductive Programs

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

Contact Info

Product

Resources

About