Rapid and non-invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms.

Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.

Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis.

Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically discovered when it is already advanced. In this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.