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Adjusted method of penis fixation during boar semi-automatic semen collection aiming to reduce bacterial contamination.

Aline Fernanda Lopes PaschoalAna Paula Gonçalves MellagiCristina Vicenti FerrariKarine Ludwig TakeutiGabriela da Silva OliveiraMari Lourdes BernardiRafael da Rosa UlguimFernando Pandolfo Bortolozzo
Published in: Reproduction in domestic animals = Zuchthygiene (2021)
Semen collection has an essential role in the initial bacterial load in boar ejaculates and extended semen. The study aimed to explore the efficacy of an adjusted penis fixation in a semi-automatic collection system on reducing bacterial contamination of ejaculates in two-boar studs with different scenarios. Historically, stud A had low levels of bacterial load in raw semen, while stud B had a high level of contamination. A total of 56 mature boars had their semen collected using two methods of penis fixation: (a) Traditional: The penis was fixed directly with the artificial cervix and transferred to the adjustable clamp; (b) Adjusted: The fixation was performed with one gloved-hand, and after exteriorization, the penis was gripped using the artificial cervix with the other gloved-hand and transferred to the adjustable clamp. The bacterial load (p = .0045) and the occurrence of ejaculates >231 CFU/ml (p = .0101) were reduced in the Adjusted compared to the Traditional method. Bacterial load was reduced when using the Adjusted method in stud B (p = .0011), which showed a greater occurrence of critical factors for bacterial contamination (p ≤ .0034). The Adjusted method reduced the occurrence of ejaculates >231 CFU/ml when the preputial ostium was dirty (p = .016) and the duration of semen collection was >7 min (p = .022) compared to the Traditional method. In conclusion, the Adjusted penis fixation was efficient in reducing bacterial load of ejaculates, mainly in boar stud B, which had high contamination challenges.
Keyphrases
  • risk assessment
  • minimally invasive
  • drinking water
  • health risk
  • human health
  • machine learning
  • deep learning
  • preterm birth
  • heavy metals
  • climate change