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Ultrasonographic cervical evaluation: A tool to select ewes for non-surgical embryo recovery.

Lucas Machado FigueiraNadja Gomes AlvesJoanna Maria Gonçalves Souza-FabjanGabriel Brun VerganiMaria Emilia Franco OliveiraRenato Ribeiro de LimaJeferson Ferreira da Fonseca
Published in: Reproduction in domestic animals = Zuchthygiene (2020)
This study assessed the cervical ultrasonography mapping as a tool to select donor ewes for non-surgical embryo recovery (NSER). Lacaune ewes had their cervix evaluated by ultrasonography 12 hr after induced oestrus onset (Trial 1, n = 24) or 30 min before NSER (Trial 2, n = 17). Cervical rings were longitudinally evaluated and classified by their degree of misalignment on ultrasonography (DMUS) into: DMUS-1-cervix rectilinear, DMUS-2-intermediate and DMUS-3-highly asymmetrical. For predicting cervical transposing, only DMUS-1 and DMUS-2 were considered suitable. Similar ranking was attributed to degree of misalignment on the cervical map (DMCM 1-3), established immediately before NSER, which was performed at days 6 to 7 after oestrus. In Trial 1, cervical retraction for NSER was not possible only in three ewes classified as DMUS-3 (3/14, 21.4%). No difference (p > .05) was observed in the cervical transposing rates between ewes with different DMUS (ranged from 80% to 100%). In Trial 2, DMUS-1 and DMUS-2 reached 100% of transposing, and the only DMUS-3 ewe has not been transposed. In Trial 1, the prediction performance for successful cervical transposing showed low sensitivity (45%) and no specificity due to a high incidence of false negatives (52%). However, in Trial 2, sensitivity and specificity were both 100%. The DMCM and DMUS were uncorrelated, probably due to cervical stretching required to perform NSER. In conclusion, cervical ultrasound assessment immediately before NSER was more efficient to predict the cervical transposing than at induced oestrus, allowing the classification and selection of ewes eligible for NSER.
Keyphrases
  • study protocol
  • clinical trial
  • phase iii
  • phase ii
  • machine learning
  • risk factors
  • computed tomography
  • deep learning
  • oxidative stress
  • pregnant women
  • contrast enhanced
  • mass spectrometry
  • optical coherence tomography