Asset Details
Do you wish to reserve the book?
A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep
Do you wish to request the book?
A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep
2024
Overview
The causes of variation in the success of laparoscopic artificial insemination (AI) in sheep are not well understood. As such, this study incorporated the contributions of multiple male and female factors relevant to the success of AI into a comprehensive prediction model for pregnancy success. Data from Merino ewes (
N
= 30 254) including age, uterine tone (1; pale/flaccid-5; turgid/pink), intra-abdominal fat (1; little to no fat present-5; high fat), time of insemination and sire used, were recorded during AI. A subset of semen per sire (
N
= 388) was thawed and assessed for volume, subjective motility, sperm concentration, and morphology. Sperm motility (CASA), viability and acrosome integrity (FITC-PNA/PI), membrane fluidity (M540/Yo-Pro), mitochondrial superoxide production (Mitosox Red/Sytox Green), lipid peroxidation (Bodipy C11), level of intracellular reactive oxygen species (H
2
DCFDA) and DNA fragmentation (Acridine Orange) were also assessed 0, 3 and 6 h post-thaw. Logistic binomial regression revealed sperm concentration (
P
< 0.001), CASA parameters at 0 h (PCA3;
P
= 0.03), viable acrosome intact sperm at 6 h (
P
= 0.02), abnormal morphology (
P
< 0.001), uterine tone (
P
< 0.001) and intra-abdominal fat (
P
= 0.03) of ewes influenced likelihood of pregnancy. Results generated will help standardise the pre-screening and selection of semen and ewes prior to artificial breeding programs, reducing variation in the success of sheep AI.
