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Clinical endometritis with Trueperella pyogenes reduces reproductive performance and milk production in dairy cows.

Renan Braga PaianoJeannine BonillaAndrea Micke MorenoPietro Sampaio Baruselli
Published in: Reproduction in domestic animals = Zuchthygiene (2021)
The aim of this study was to identify the impact of Trueperella pyogenes in cows with clinical endometritis (CE) on reproductive performance and milk production in affected cows. In total, 230 lactating Holstein dairy cows from six commercial dairy herds were sampled once between 28 and 33 days post-partum. Cows included in the present study did not receive antibiotic or anti-inflammatory treatments prior to the experimental period. Clinical endometritis (CE) was characterized as cow with vaginal mucus score = 3 (>50% of purulent vaginal discharge) and >18% polymorphonuclear neutrophilic leukocyte (PMNL). The body condition scores (BCS) and milk production were evaluated at the time of enrolment. The identification of isolated bacteria was carried out through the analysis of MALDI-TOF MS (matrix-assisted laser desorption ionization time-of-flight mass spectrometry). According to uterine health, three groups of dairy cows were formed: healthy control cows without T. pyogenes (n = 147), CE cows with T. pyogenes (n = 22) and CE cows without T. pyogenes (n = 61). CE cows with T. pyogenes had lower BCS, milk production and conception at first AI (p < .01) than CE cows without T. pyogenes and control cows. Furthermore, CE cows with T. pyogenes had higher (p < .01) service per pregnancy and had greater (p < .01) days to get pregnant than CE cows without T. pyogenes and control cows. This study demonstrates that CE cows with T. pyogenes had impaired reproductive performance and milk production when compared to cows without CE and CE cows without T. pyogenes. This information can contribute to a strategic treatment in cows affected by clinical endometritis, favouring the rational use of antibiotics on dairy farms.
Keyphrases
  • dairy cows
  • healthcare
  • public health
  • machine learning
  • mass spectrometry
  • risk assessment
  • preterm birth
  • quantum dots
  • health information