HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Agriculture / Faculty of Agriculture >
Peer-reviewed Journal Articles, etc >

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

This item is licensed under:Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Files in This Item:
BBRC_569_179.pdf1.24 MBPDFView/Open
Suppl table.docx26.98 kBMicrosoft Word XMLView/Open
Supplementary_data.xlsx12.11 kBMicrosoft Excel XMLView/Open
Please use this identifier to cite or link to this item:

Title: Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane
Authors: Kunii, Hiroki Browse this author
Kubo, Tomoaki Browse this author
Asaoka, Natsuki Browse this author
Balboula, Ahmed Z. Browse this author
Hamaguchi, Yu Browse this author
Shimasaki, Tomoya Browse this author
Bai, Hanako Browse this author →KAKEN DB
Kawahara, Manabu Browse this author →KAKEN DB
Kobayashi, Hisato Browse this author
Ogawa, Hidehiko Browse this author
Takahashi, Masashi Browse this author →KAKEN DB
Keywords: Cow
Early pregnancy detection
Loop-mediated isothermal
Machine learning
Vaginal mucosa
Issue Date: 10-Sep-2021
Publisher: Elsevier
Journal Title: Biochemical and biophysical research communications
Volume: 569
Start Page: 179
End Page: 186
Publisher DOI: 10.1016/j.bbrc.2021.07.015
Abstract: 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.
Rights: ©2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Type: article (author version)
Appears in Collections:農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 高橋 昌志

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 - Hokkaido University