Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane

Kunii, Hiroki; Kubo, Tomoaki; Asaoka, Natsuki; Balboula, Ahmed Z.; Hamaguchi, Yu; Shimasaki, Tomoya; Bai, Hanako; Kawahara, Manabu; Kobayashi, Hisato; Ogawa, Hidehiko; Takahashi, Masashi

Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane

Kunii, Hiroki; Kubo, Tomoaki; Asaoka, Natsuki et al.

Biochemical and biophysical research communications, 2021, 569, 179-186

Permalink : https://hdl.handle.net/2115/86714

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/86714
タイトル	Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane
著者	Kunii, Hiroki ; Kubo, Tomoaki ; Asaoka, Natsuki ; Balboula, Ahmed Z. ; Hamaguchi, Yu ; Shimasaki, Tomoya ; Bai, Hanako ; Kawahara, Manabu ; Kobayashi, Hisato ; Ogawa, Hidehiko ; Takahashi, Masashi
著者名 Kunii, Hiroki
著者名 Kubo, Tomoaki
著者名 Asaoka, Natsuki
著者名 Balboula, Ahmed Z.
著者名 Hamaguchi, Yu
著者名 Shimasaki, Tomoya
e-Rad_Researcher 60775443 著者名 Bai, Hanako
e-Rad_Researcher 70468700 著者名 Kawahara, Manabu
著者名 Kobayashi, Hisato
著者名 Ogawa, Hidehiko
e-Rad_Researcher 10343964 著者名 Takahashi, Masashi
言語	英語
キーワード	Cow
	Early pregnancy detection
	Loop-mediated isothermal
	amplification(LAMP)
	Machine learning
	Vaginal mucosa
発行日	2021-09-10
出版者	Elsevier
収録物名	Biochemical and biophysical research communications
巻(号)	569
ページ	179-186
抄録	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.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
権利情報	©2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
PISSN	0006-291X
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1016/j.bbrc.2021.07.015