| Autor: |
Keisuke Maeda, Takahiro Ogawa, Tasuku Kayama, Takuya Sasaki, Kazuki Tainaka, Masaaki Murakami, Miki Haseyama |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Bioengineering, Vol 11, Iss 6, p 523 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2306-5354 |
| DOI: |
10.3390/bioengineering11060523 |
| Popis: |
This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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