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Gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) were used as substrates in combination with a low-concentration serum surface-enhanced Raman spectroscopy (SERS) detection scheme to obtain spectral signals from healthy individuals and cervical cancer patients. The best principal component scores were selected by principal component analysis (PCA) combined with linear discriminant analysis (LDA) and support vector machine (SVM) to analyze the spectral differences distinguishing healthy and cervical cancer patients. The accuracy of the two models was 97.9% and 96.9%, respectively. SERS technique based on Au NPs/785 PSi PhCs has great potential to improve the screening of cervical cancer. [ABSTRACT FROM AUTHOR]

This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were measured in this experiment. Specific biomolecular changes in three groups of serum samples could be seen in the tentative assignment of Raman peaks. First, principal component analysis (PCA) was used for extracting key features of spectral data, which reduces the dimension of the multidimensional spectrum. Then, LSTM is used to train the spectral data. Finally, the full connection layer completes the classification of HBV. The diagnostic accuracy of the first LSTM model is 97.32%, and the value of AUC is 0.995. The results from the study demonstrate that the combination of serum Raman spectroscopy technique and LSTM provides an effective technical approach to the screening of hepatitis B. [ABSTRACT FROM AUTHOR]

[Display omitted] • Triple-negative breast cancer and HER2-positive breast cancer are highly malignant, and early diagnosis is helpful to improve the chances of cure. • Serum Raman spectroscopy combined with deep learning algorithms to diagnose triple-negative breast cancer and HER2-positive breast cancer and achieved great diagnostic results. • The proposed method is helpful for early breast cancer diagnosis and can provide a reference for breast cancer patients to make a personalised diagnosis. Breast cancer is common in women, and its number of patients ranks first among female malignant tumors. Breast cancer is highly heterogeneous, and different types of breast cancer have different biological behaviors and prognoses. Therefore, identifying the different types of breast cancer is of great help in formulating individualized treatment plans. Based on serum Raman spectroscopy and deep learning algorithms, we propose a fast and low-cost diagnosis method for screening triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and healthy controls. We collected 75 serum samples in this study, including 23 triple-negative breast cancers, 22 HER2-positive breast cancers, and 30 healthy controls. Using the preprocessed Raman spectra as the input of deep learning, three deep learning models, neural network language model (NNLM), bidirectional long-short-term memory network (BiLSTM), and convolutional neural network (CNN), were established, and the accuracy rates of the three models were 87.78%, 90.37%, and 91.11%, respectively. The experimental results demonstrate the feasibility of serum Raman spectroscopy combined with deep learning algorithms to diagnose breast cancer, which can be used as an effective auxiliary diagnosis method for breast cancer. [ABSTRACT FROM AUTHOR]

In this research, PLSR, GA-SVR and ELM models combined with sheep serum Raman spectroscopy were used for quantitative detection, the results of PLSR determination are better (a). In order to further improve the performance of the model, the SPA is used for band selection (c). The optimized quantitative model result (b) of DOSC-SPA-PLSR-MN performs well. • It is proposed for the first time to use sheep serum Raman spectroscopy combined with a quantitative model to determine the concentration (/enzyme activity) of seven indicators. • It is the first time to deviation standardization of the model evaluation index for many kinds of substances. • The comparison results of SPA band selection show that there is a correlation among the indicators in the serum. • The optimized DOSC-SPA-PLSR-DS quantitative model shows that the average R p 2 of the seven indicators is 0.9834. Hepatic fascioliasis, ketosis of pregnancy, toxemia of pregnancy and other common sheep diseases will directly affect the concentration (/enzymatic activity) of seven indicators, such as cortisol and high-density lipoprotein cholesterol (HDL-C) in sheep serum. Whether the concentrations (/enzymatic activity) of these indicators can be detected quickly will directly affect the prevention of sheep diseases and the targeted adjustment of breeding methods, thereby affecting the economic benefits of sheep breeding. In this research, we established partial least square regression (PLSR), support vector regression based on genetic algorithm optimization (GA-SVR) and extreme learning machine (ELM) models. Due to the large differences in the content of different substances, it is difficult to directly use the RMSE to evaluate the quantitative effect of the model. This study is the first to propose conducting deviation standardization (DS) for the determination results of various substances. To further improve the performance of the model, we use the successive projections algorithm (SPA) to optimize feature extraction and combine it with the better-performing PLSR model for training. The results show that the optimized DOSC-SPA-PLSR-DS quantitative model has better determination results for 101 sheep serum samples. The average RMSE p* of the concentration of the six substances decreased from 0.0408 to 0.0387, the R p 2 increased from 0.9758 to 0.9846, and the running time was reduced from 0.1659 to 0.0008 s. And the determination performance of lipase (LPS) enzymatic activity has also been improved. The results of this research show that sheep serum Raman spectroscopy combined with DOSC-SPA-PLSR-DS optimization can efficiently monitor the concentration (/enzyme activity) of seven indicators in real time and provide a new strategy for future intelligent supervision of animal husbandry. [ABSTRACT FROM AUTHOR]

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]

In this study, serum samples from 199 patients with thyroid dysfunction and 183 people with normal thyroid function were collected by Raman spectroscopy, and the data were dimensions-reduced by PCA. The reduced data were input into a multi-feature fusion convolutional neural network (MCNN), the improved AlexNet, VGGNet, GoogLeNet and ResNet, Support Vector Machine (SVM) and Decision Tree (DT) for classification, and the results of the seven classification algorithms were compared. Their classification accuracy are 94.01 %, 91.91 %, 90.34 %, 93.46 %, 92.42 %, 82.78 % and 80.89 %, respectively. The results of this study indicate that the combination of serum Raman spectra and MCNN has a good diagnostic effect for identifying thyroid dysfunction, and it is feasible to improve the classic deep learning models for Raman spectrum classification. [ABSTRACT FROM AUTHOR]