精子获能作为野猪生育能力的预测因子

IF 2.7 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular Reproduction and Development Pub Date : 2023-06-12 DOI:10.1002/mrd.23690
Alexandra Keller, Karl Kerns
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引用次数: 0

摘要

预测公猪的生育水平对母猪群具有重要的经济意义。在满足标准精子形态和运动指标后,大约25%的公猪受孕率低于80%。部分由于受精过程中涉及许多因素,一个包含多个相关精子生理因素的多因素模型可能会增加对公猪生育能力的了解。在这里,我们回顾了目前关于猪精子获能作为猪生育能力预测因子的文献。虽然有限,但一些研究已经提供了在化学介质中能够经历精子获能的精液百分比与人工授精领域、生育能力以及蛋白质组学和其他方法之间的相关性。这里总结的工作强调了进一步了解公猪生育能力的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Sperm capacitation as a predictor of boar fertility

Prediction of a boar's fertility level has great economic importance for sow herds. After standard sperm morphology and motility metrics are met, approximately 25% of boars have less than 80% conception rates. Due in part to the many factors involved in the fertilization process, a multifactorial model incorporating multiple relevant sperm physiology factors will likely lead to increased understanding of boar fertility. Here we review the current literature on boar sperm capacitation as a predictor of boar fertility. While limited, several studies have provided correlations between the percentage of sperm in an ejaculate that are capable of undergoing sperm capacitation in a chemically defined media and artificial insemination field fertility as well as proteome and other methods. Work summarized here underscores the need for further understanding of boar fertility.

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来源期刊
CiteScore
5.20
自引率
0.00%
发文量
78
审稿时长
6-12 weeks
期刊介绍: Molecular Reproduction and Development takes an integrated, systems-biology approach to understand the dynamic continuum of cellular, reproductive, and developmental processes. This journal fosters dialogue among diverse disciplines through primary research communications and educational forums, with the philosophy that fundamental findings within the life sciences result from a convergence of disciplines. Increasingly, readers of the Journal need to be informed of diverse, yet integrated, topics impinging on their areas of interest. This requires an expansion in thinking towards non-traditional, interdisciplinary experimental design and data analysis.
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