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Integrating imaging-based classification and transcriptomics for quality assessment of human oocytes according to their reproductive efficiency.

Xavier Viñals GonzalezChristopher ThrasivoulouRoy Pascal NajaSrividya SeshadriPaul SerhalSioban Sen Gupta
Published in: Journal of assisted reproduction and genetics (2023)
Imaging properties can be used as a tool to assess differences in the ooplasm and predict laboratory and clinical outcomes. Transcriptomic analysis suggested that oocytes with lower competence may have compromised cell cycle either by non-reparable DNA damage or insufficient ooplasmic maturation. Further development of algorithms based on image parameters is encouraged, with an increased balanced cohort and validated prospectively in multicentric studies.
Keyphrases
  • cell cycle
  • dna damage
  • deep learning
  • machine learning
  • high resolution
  • cell proliferation
  • endothelial cells
  • oxidative stress
  • single cell
  • dna repair
  • induced pluripotent stem cells