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      Metabolomics as a tool to identify biomarkers to predict and improve outcomes in reproductive medicine: a systematic review

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          Follicular fluid content and oocyte quality: from single biochemical markers to metabolomics

          The assessment of oocyte quality in human in vitro fertilization (IVF) is getting increasing attention from embryologists. Oocyte selection and the identification of the best oocytes, in fact, would help to limit embryo overproduction and to improve the results of oocyte cryostorage programs. Follicular fluid (FF) is easily available during oocyte pick-up and theorically represents an optimal source on non-invasive biochemical predictors of oocyte quality. Unfortunately, however, the studies aiming to find a good molecular predictor of oocyte quality in FF were not able to identify substances that could be used as reliable markers of oocyte competence to fertilization, embryo development and pregnancy. In the last years, a well definite trend toward passing from the research of single molecular markers to more complex techniques that study all metabolites of FF has been observed. The metabolomic approach is a powerful tool to study biochemical predictors of oocyte quality in FF, but its application in this area is still at the beginning. This review provides an overview of the current knowledge about the biochemical predictors of oocyte quality in FF, describing both the results coming from studies on single biochemical markers and those deriving from the most recent studies of metabolomics
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            Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers.

            To construct a prediction model for live birth after in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and single-embryo transfer (SET) after 2 days of embryo culture.
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              Timing of cell division in human cleavage-stage embryos is linked with blastocyst formation and quality.

              Noninvasive markers of embryo quality are being sought to improve IVF success. The present study aimed to discover possible associations between embryo division kinetics in the cleavage stage, the subsequent ability of human embryos to reach the blastocyst stage and the resulting blastocyst morphology. A retrospective cohort study analysed 834 embryos from 165 oocyte donation couples using a time-lapse monitoring system that allowed the recording of the exact timings for key events related to embryo development. Timing parameters were categorized into four quartiles. The probability of an embryo developing to a blastocyst was linked to a strict chronology of development. To further evaluate the relationships between these morphokinetic parameters and subsequent blastocyst formation, the ensuing blastocyst morphology was compared with a viability score based on a hierarchical classification of the cleavage-stage morphokinetic parameters. It is concluded that the kinetics of early embryo development and the potential for human embryos to develop to the blastocyst stage on day 5 are closely related and that time-lapse-based evaluation of the exact timing of early events in embryo development is a promising tool for the prediction of blastocyst formation and quality according to the proposed model. Copyright © 2012 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

                Author and article information

                Journal
                Human Reproduction Update
                Oxford University Press (OUP)
                1355-4786
                1460-2369
                November 2017
                November 01 2017
                August 08 2017
                November 2017
                November 01 2017
                August 08 2017
                : 23
                : 6
                : 723-736
                Article
                10.1093/humupd/dmx023
                29069503
                a35f6ae3-9686-4d9e-82f8-f6fc53c64361
                © 2017
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