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Effect of exogenous progesterone administration on luteal sensitivity to PGF during the early development of the corpus luteum in mares and cows.

A Garcia-MuñozX Valldecabres-TorresJ R NewcombeJ Cuervo-ArangoEmpar García-Roselló
Published in: Reproduction in domestic animals = Zuchthygiene (2017)
The objective of this study was to determine the effect of exogenous progesterone administration at ovulation and during the early development of the CL, on its future sensitivity to a single administration of PGF2a in mares and cows. Horse Retrospective reproductive data from an equine clinic in the UK during three breeding seasons were used. Mares were divided into: control group, cycles with single ovulations; double ovulation group cycles with asynchronous double ovulations; and PRID group: cycles with single ovulations and treatment with intravaginal progesterone device (CIDR) immediately after the ovulation. All mares were treated with d-cloprostenol (PGF) at either: (i) 88 hr; (ii) 96 hr; (iii) 104 hr; or (iv) 112 hr after the last ovulation. Cattle A total of nine non-lactating Holstein cows were used. All cows were administered PGF14 d apart and allocated to one of two groups control group GnRH was administered 56 hr after the second PGF administration. CIDR group CIDR was inserted at the same time of GnRH administration. All cows were administered PGF at 120 hr post-ovulation. The complete luteolysis rate of mares with double ovulation (66.7%) and those treated with exogenous progesterone (68.4%) was significantly higher than the rate of mares with single ovulation (35.6%) at 104 hr. In the cow, however, the treatment with CIDR did not increase the luteolytic response in cows treated at 120 hr post-ovulation. In conclusion, the degree of complete luteolysis can be influenced by increasing the concentration of progesterone during the early luteal development in mares.
Keyphrases
  • polycystic ovary syndrome
  • insulin resistance
  • estrogen receptor
  • primary care
  • dairy cows
  • combination therapy
  • deep learning
  • replacement therapy