Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane
Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane
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DOI：
10.1016/j.bbrc.2021.07.015
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发表时间：
2021-07-10
影响因子：
3.1
通讯作者：
Takahashi, Masashi
中科院分区：
文献类型：
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作者：
Kunii, Hiroki;Kubo, Tomoaki;Takahashi, Masashi
为了提高奶牛的繁殖性能，需要一种早期准确的妊娠诊断方法。在这里，我们开发了一种简单的妊娠检测方法，使用阴道粘膜（VMM），应用逆转录环介导的等温扩增（RT-LAMP）和机器学习。奶牛在第0天进行人工授精（AI），然后在第17-18天进行VMM采集，并在第30天通过超声诊断妊娠。通过对VMM样本的RNA测序，选择了三个妊娠标志物的候选基因（ISG 15和IFIT 1：上调，MUC 16：下调）。利用这些基因进行RT-LAMP，计算反应的上升时间（RUT），反应中第一次吸光度超过0.05。接下来，我们确定了截断值，并计算了每种标志物评估的准确性、灵敏度、特异性、阳性预测值（PPV）和阴性预测值（NPV）。IFIT 1的灵敏度为92.5%，但特异性为77.5%，这表明很难消除假阳性。然后，我们开发了一个机器学习模型，用每个标记组合的RUT训练来预测怀孕。用IFIT 1和MUC 16组合的RUT创建的模型显示出高特异性（86.7%）和灵敏度（93.3%），其高于单独的IFIT 1。总之，使用VMM与RT-LAMP和机器学习算法可以用于在第一次发情返回之前的早期妊娠检测。(c)2021年由Elsevier Inc.出版
An early and accurate pregnancy diagnosis method is required to improve the reproductive performance of cows. Here we developed an easy pregnancy detection method using vaginal mucosal membrane (VMM) with application of Reverse Transcription-Loop-mediated Isothermal Amplification (RT-LAMP) and machine learning. Cows underwent artificial insemination (AI) on day 0, followed by VMMcollection on day 17-18, and pregnancy diagnosis by ultrasonography on day 30. By RNA sequencing of VMM samples, three candidate genes for pregnancy markers (ISG15 and IFIT1: up-regulated, MUC16: down-regulated) were selected. Using these genes, we performed RT-LAMP and calculated the rise-up time (RUT), the first-time absorbance exceeded 0.05 in the reaction. We next determined the cutoff value and calculated accuracy, sensitivity, specificity, positive prediction value (PPV), and negative prediction value (NPV) for each marker evaluation. The IFIT1 scored the best performance at 92.5% sensitivity, but specificity was 77.5%, suggesting that it is difficult to eliminate false positives. We then developed a machine learning model trained with RUT of each marker combination to predict pregnancy. The model created with the RUT of IFIT1 and MUC16 combination showed high specificity (86.7%) and sensitivity (93.3%), which were higher compared to IFIT1 alone. In conclusion, using VMM with RT-LAMP and machine learning algorithm can be used for early pregnancy detection before the return of first estrus. (c) 2021 Published by Elsevier Inc.