发情周期
受精
繁殖
怀孕
唾液
人工授精
动物科学
生物
男科
内分泌学
内科学
医学
遗传学
作者
Nitin Bhaskar Chavan,A. Kumaresan,Shivani Chhillar,Samiksha Nayak,M. Arul Prakash,Sreela Lathika,Rubina Kumari Baithalu,Suneel Kumar Onteru,A. Manimaran,Shiv Prasad Kimothi
标识
DOI:10.1017/s0022029922000887
摘要
Abstract The present study assessed if salivary crystallization pattern (ferning pattern formed as a result of the higher levels of salt content in the dried sample) could be used for estrus detection and for diagnosis of pregnancy/non-pregnancy in dairy cows. Saliva and blood samples were collected from non-pregnant cycling cows (Sahiwal breed; n = 20) on alternate days from the day of estrus till next estrus. Then, all the cows were inseminated and saliva and blood sampling were continued further for a period of 22 d post-insemination. Pregnancy diagnosis was carried out on day 45 post-insemination and eight cows were found to be pregnant. The salivary crystallization pattern and estradiol:progesterone ratio during estrous cycle and during pregnancy were compared among these cows. Six types of salivary crystallization patterns were discerned; distinct patterns such as branch-like, fern-like, fir-like and combinations of these. Fern-like pattern was observed in all the cows on the day of estrus (first measurement day) and furthermore, all of the cows that subsequently became pregnant had fern-like salivary crystallization pattern at the time of insemination. Saliva of all the pregnant cows showed branch-fir type of crystallization pattern on day 16 post-breeding while only 50% of non-pregnant cows showed this pattern on day 16 of estrous cycle. The appearance of fern-like pattern was positively and significantly related to estradiol:progesterone ratio ( r = 0.86; P < 0.001). The findings were validated on a separate group of cycling cows ( n = 32). We can conclude that salivary crystallization pattern might serve as a non-invasive and cost effective and easy-to-use cow-side tool for estrus detection and early pregnancy/non-pregnancy diagnosis in cows upon validation on a larger sample size.
科研通智能强力驱动
Strongly Powered by AbleSci AI