Biology subjects

Oyamada, Y.

Publications and source records attributed to Oyamada, Y..

3 recordsLinked to original sources

bioRxiv2024

A Deep Learning-Based Evaluation for Microscopic Agglutination Test

The Microscopic Agglutination Test (MAT) is widely recognized as the gold standard for diagnosing zoonosis leptospirosis. However, its reliance on examiners subjective evaluations often leads to inconsistent results. To address this limitation, we propose a deep neural network replicating the agglutination rate estimation in MAT. By leveraging a pre-trained DenseNet121, the network parameters are optimized efficiently during training. We validated our approach using an in-house dataset, and experimental results show that the proposed network achieves accurate agglutination rate estimates. In addition, we employed a standard visualization technique to elucidate the decision-making process, revealing that the network captures image features indicative of leptospire abundance. Overall, our findings suggest that deep learning effectively estimates agglutination rates and that enhancing interpretability supports medical experts in understanding the underlying functionality of deep learning models.

microbiology↗

bioRxiv2023

1,25-dihydroxyvitamin D3 potentiates the innate immune response of peripheral blood mononuclear cells from Japanese Black cattle

1,25-dihydroxyvitamin D3 (1,25(OH)2D3), a bioactive Vitamin D, is known to regulate immune responses in mammals. However, its impact on the innate immune responses of Japanese Black cattle, which are beef cattle endemic to Japan, remains unknown. Thus, in this study, we investigated the effect of 1,25(OH)2D3 on the immune responses of PBMC from Japanese Black cattle. PBMC were cultured with or without 1,25(OH)2D3 for measurement of cell viability, and stimulated with or without 1,25(OH)2D3 and lipopolysaccharide (LPS) for measurement of the gene expressions. As the results, the treatment of 1,25(OH)2D3 increased the cell viability. It also upregulated antibacterial peptides, DEFB10 and LAP with or without LPS stimulation. Moreover, 1,25(OH)2D3 enhanced the inflammatory responses, CXCL8 with LPS stimulation and NOS2 with or without LPS stimulation, while reducing the expression of anti-inflammatory cytokine IL10 with or without LPS stimulation, leading to an inflammatory phenotype. However, in contrast to humans and mice, 1,25(OH)2D3 did not alter the expression of TNF and downregulated TREM1 with LPS treatment. These results suggest that 1,25(OH)2D3 potentiates the innate immune responses of Japanese Black cattle, albeit with different effects and mechanisms as compared to humans and mice.

cell biology↗

bioRxiv2020

A Machine Learning Approach Towards Standardizing Microscopic Agglutination Test for Diagnosis of Leptospirosis

Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospiras, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine aggregation within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira- infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.

microbiology↗