| 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 |
| amplification(LAMP) |
| 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/86714 |
| 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)
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