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Echinococcosis is a zoonotic parasitic disease transmitted by animals and distributed all over the world. There is no standardized and widely accepted treatment method, and early and accurate diagnosis is crucial for the prevention and cure of echinococcosis. Here, we explored the feasibility of using derivative Raman in combination with autofluorescence (AF) to improve the diagnosis performance of echinococcosis. The spectra of serum samples from patients with echinococcosis, as well as healthy volunteers, were recorded at 633 nm excitation. The normalized mean Raman spectra showed that there is a decrease in the relative amounts of β carotene and phenylalanine and an increase in the percentage of tryptophan, tyrosine, and glutamic acid contents in the serum of echinococcosis patients as compared to that of healthy subjects. Then, principal components analysis (PCA), combined with linear discriminant analysis (LDA), were adopted to distinguish echinococcosis patients from healthy volunteers. Based on the area under the ROC curve (AUC) value, the derivative Raman + AF spectral data set achieved the optimal results. The AUC value was improved by 0.08 for derivative Raman + AF (AUC = 0.98), compared to Raman alone. The results demonstrated that the fusion of derivative Raman and AF could effectively improve the performance of the diagnostic model, and this technique has great application potential in the clinical screening of echinococcosis. Combining derivative Raman with autofluorescence can improve the performance of echinococcosis diagnosis model. Unlabelled Image • Early and accurate diagnosis is the key to prevent and treat echinococcosis. • The autofluorescence background contains useful information for diagnosis. • The optimal result was obtained by combining derivative Raman with autofluorescence. • The proposed methods could possibly be used to diagnose other diseases. [ABSTRACT FROM AUTHOR]

In this study, the Raman spectra of serum samples from patients with echinococcosis and healthy controls were recorded by a portable spectrometer under 532 nm excitation. Due to the resonance Raman effect, the two Raman peaks (at 1154 and 1515 cm-1) belonging to β carotene were significantly enhanced, resulting in relatively weak signal values for other peaks. In order to amplify the weak Raman peaks and improve the capability of Raman spectroscopy for the differentiation of healthy and echinococcosis infected serum samples, a logarithmic transformation preprocessing method was used. Furthermore, principal component analysis (PCA), combined with linear discriminant analysis (LDA), were used to distinguish echinococcosis patients from healthy volunteers. The results show that the diagnostic models formed by the log-transformed spectral data sets perform better in the balance of sensitivity and specificity. The accuracy, sensitivity, and specificity of the diagnostic model based on the spectral data set of optimal scale transformation were 98.00%, 98.10%, and 98.00%, respectively, which were slightly superior to the untransformed spectral data set. Our findings suggest that serum log-transformed Raman spectroscopy combined with the PCA-LDA method has great potential for improving the detection of echinococcosis. [ABSTRACT FROM AUTHOR]

In this study, the Raman spectra of serum samples from patients with echinococcosis and healthy controls were recorded by a portable spectrometer under 532 nm excitation. Due to the resonance Raman effect, the two Raman peaks (at 1154 and 1515 cm-1) belonging to β carotene were significantly enhanced, resulting in relatively weak signal values for other peaks. In order to amplify the weak Raman peaks and improve the capability of Raman spectroscopy for the differentiation of healthy and echinococcosis infected serum samples, a logarithmic transformation preprocessing method was used. Furthermore, principal component analysis (PCA), combined with linear discriminant analysis (LDA), were used to distinguish echinococcosis patients from healthy volunteers. The results show that the diagnostic models formed by the log-transformed spectral data sets perform better in the balance of sensitivity and specificity. The accuracy, sensitivity, and specificity of the diagnostic model based on the spectral data set of optimal scale transformation were 98.00%, 98.10%, and 98.00%, respectively, which were slightly superior to the untransformed spectral data set. Our findings suggest that serum log-transformed Raman spectroscopy combined with the PCA-LDA method has great potential for improving the detection of echinococcosis. [ABSTRACT FROM AUTHOR]