Evaluate the effectiveness of Pregnancy-Associated Glycoprotein and Progesterone in predicting the gestational status in Goats

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DOI:

https://doi.org/10.54174/utjagr.v12i1.252

Abstract

         The objective of this study was to evaluate the PAG and P4 profiles in pregnant and non-pregnant does using Enzyme Immunoassay (EIA) and enzyme-linked immunosorbent assay (ELISA), respectively. Additionally, the study aimed to compare the sensitivity (Se), specificity (Sp), and accuracy (Acc) of pregnancy diagnosis using PAG and P4 detection in serum samples. Twenty does were synchronized using P4 sponge+eCG for 12 days, followed by breeding after estrus. Blood samples were collected at different experimental periods (22, 30, 40, and 60 days post-mating) and analyzed using EIA and ELISA. The lambing and Ultrasonographic examination on day 60 were used as a golden standard for pregnancy evaluation. The results demonstrated that both PAG and P4 mean concentrations significantly increased in pregnant does compared to non-pregnant does in all experimental periods. When comparing the efficiency of each method for pregnancy prediction, the PAG assay method showed a sensitivity (Se) similar to P4 (85%) at day 22 post-mating, but reached 100% at day 30 for PAG and day 40 for P4. Additionally, the accuracy (Acc) of the PAG analysis method was higher than P4 at day 22 (80% vs. 75%) and day 30 (95% vs. 85%), and reached 100% on day 40. In contrast, the Acc of the P4 assay for pregnancy diagnosis was 95% at days 40 and 60. The specificity (Sp) was lower in both methods, but the diagnosis using the PAG assay was better than P4 on all days, reaching its optimal value on day 40, while reach to 83% for P4 assay method on day 40 and 60 PM. The present study concluded that both PAG and P4 analyses were specific and reliable methods for pregnancy determination in does starting from day 22 onward, but the PAG assay was more accurate than the P4 assay.

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2023-06-24

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Evaluate the effectiveness of Pregnancy-Associated Glycoprotein and Progesterone in predicting the gestational status in Goats. (2023). University of Thi-Qar Journal of Agricultural Research, 12(1), 201-210. https://doi.org/10.54174/utjagr.v12i1.252