Nikaido, Y. | Search bioRxiv

bioRxiv2026

Correlation-resolved sum-frequency generation microscopy of biological tissue with bright twin beams

Sum-frequency generation (SFG) driven by bright twin beams generated through high-gain parametric down-conversion comprises a coherent component arising from correlated mode pairs and an incoherent component arising from interactions between uncorrelated modes. Modal correlations can enhance coherent SFG in anisotropic nonlinear media, for example by extending the effective interaction length. Whether such correlations can likewise benefit image formation in heterogeneous biological tissue remains unknown. Here we address this question by performing SFG imaging of biological tissue using bright twin beams as the excitation source. Spectral decomposition of the SFG signal separates its coherent and incoherent components, allowing us to reconstruct co-registered images and examine how twin-beam correlations influence image formation. In mouse skeletal muscle, the coherent-component image revealed sarcomere periodicity with a Bragg-spot-to-background ratio of approximately 310, compared with 7.5 in the incoherent-component image. In fresh human dermis, the coherent-component image showed greater Fourier power than the incoherent-component image in the 0.30-0.50 \mu m^{-1} band and a greater fractional autocovariance decay at short spatial lags. These results demonstrate that twin-beam correlations can enhance contrast and detectability at selected spatial frequencies in heterogeneous tissue. Correlation-resolved SFG microscopy thus offers a route for exploiting bright quantum-light sources in nonlinear optical bioimaging.

biophysics↗

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↗