| Autor: |
Aswathi Cheredath, Shubhashree Uppangala, Asha C. S, Ameya Jijo, Vani Lakshmi R, Pratap Kumar, David Joseph, Nagana Gowda G.A, Guruprasad Kalthur, Satish Kumar Adiga |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Reproductive sciences (Thousand Oaks, Calif.). |
| ISSN: |
1933-7205 |
| Popis: |
This study investigated whether combining metabolomic and embryologic data with machine learning (ML) models improve the prediction of embryo implantation potential. In this prospective cohort study, infertile couples (n=56) undergoing day-5 single blastocyst transfer between February 2019 and August 2021 were included. After day-5 single blastocyst transfer, spent culture medium (SCM) was subjected to metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Derived metabolite levels and embryologic parameters between successfully implanted and failed groups were incorporated into ML models to explore their predictive potential regarding embryo implantation. The SCM of blastocysts that resulted in successful embryo implantation had significantly lower pyruvate (pp |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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