Kameni Serge L, Semon Bryan, Chen Li-Dunn, Dlamini Notsile H, Ariunbold Gombojav O, Vance-Kouba Carrie K, Feugang Jean M
Department of Animal and Dairy Sciences, Mississippi State University, Starkville, MS 39759, USA.
Department of Physics and Astronomy, Mississippi State University, Starkville, MS 39759, USA.
Biology (Basel). 2024 Sep 26;13(10):763. doi: 10.3390/biology13100763.
Artificial insemination (AI) plays a critical role in livestock reproduction, with semen quality being essential. In swine, AI primarily uses cool-stored semen adhering to industry standards assessed through routine analysis, yet fertility inconsistencies highlight the need for enhanced semen evaluation. Over 10-day storage at 17 °C, boar semen samples were analyzed for motility, morphology, sperm membrane integrity, apoptosis, and oxidative stress indicators. Additionally, machine learning tools were employed to explore the potential of Raman and near-infrared (NIR) spectroscopy in enhancing semen sample evaluation. Sperm motility and morphology gradually decreased during storage, with distinct groups categorized as "Good" or "Poor" survival semen according to motility on Day 7 of storage. Initially similar on Day 0 of semen collection, "Poor" samples revealed significantly lower total motility (21.69 ± 4.64% vs. 80.19 ± 1.42%), progressive motility (4.74 ± 1.71% vs. 39.73 ± 2.57%), and normal morphology (66.43 ± 2.60% vs. 87.91 ± 1.92%) than their "Good" counterparts by Day 7, using a computer-assisted sperm analyzer. Furthermore, "Poor" samples had higher levels of apoptotic cells, membrane damage, and intracellular reactive oxygen species on Day 0. Conversely, "Good" samples maintained higher total antioxidant capacity. Raman spectroscopy outperformed NIR, providing distinctive spectral profiles aligned with semen biochemical changes and enabling the prediction of semen survival during storage. Overall, the spectral profiles coupled with machine learning tools might assist in enhancing semen evaluation and prognosis.
人工授精（AI）在牲畜繁殖中起着关键作用，精液质量至关重要。在猪的繁殖中，人工授精主要使用符合行业标准的冷藏精液，通过常规分析进行评估，但生育能力的不一致凸显了加强精液评估的必要性。在17°C下储存超过10天，对公猪精液样本进行了活力、形态、精子膜完整性、凋亡和氧化应激指标的分析。此外，还使用了机器学习工具来探索拉曼光谱和近红外（NIR）光谱在加强精液样本评估方面的潜力。储存期间精子活力和形态逐渐下降，根据储存第7天的活力将存活精液分为“良好”或“不佳”两组。在精液采集第0天，“不佳”样本与“良好”样本最初相似，但到第7天，通过计算机辅助精子分析仪检测，“不佳”样本的总活力（21.69±4.64%对80.19±1.42%）、前向运动率（4.74±1.71%对39.73±2.57%）和正常形态（66.43±2.60%对87.91±1.92%）明显低于“良好”样本。此外，“不佳”样本在第0天的凋亡细胞、膜损伤和细胞内活性氧水平较高。相反，“良好”样本保持较高的总抗氧化能力。拉曼光谱的表现优于近红外光谱，提供了与精液生化变化一致的独特光谱特征，并能够预测储存期间精液的存活情况。总体而言，光谱特征与机器学习工具相结合可能有助于加强精液评估和预后判断。
