Publications by authors named "Xiaoyi Lv"

41 Publications

Rapid identification of papillary thyroid carcinoma and papillary microcarcinoma based on serum Raman spectroscopy combined with machine learning models.

Photodiagnosis Photodyn Ther 2021 Nov 21:102647. Epub 2021 Nov 21.

College of Software, Xinjiang University, Urumqi, 830046, China.; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China.; College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.

Thyroid carcinoma is one kind of cancer with the highest diagnosis rate in the endocrine system, and its main histological subtype is papillary thyroid carcinoma (PTC) accounting for 80% of thyroid malignancies. In recent years, the incidence of thyroid cancer has increased exponentially, and its substantial increase was closely related to the overdiagnosis of papillary microcarcinoma (PMC). Therefore, early and accurate identification of PTC and PMC can prevent patients from being irreversibly damaged. This study aimed to identify PTC and PMC using Raman spectroscopy. We collected serum Raman spectra from 16 patients with PTC and 31 patients with PMC. Firstly, the collected imbalance data were preprocessed using the synthetic minority over-sampling technique (SMOTE). Then, the equalized data were dimensionality reduced by principal component analysis (PCA). Finally, the processed data were fed into the single decision tree (DT) classifier, as well as the random forest (RF) built on the idea of Boosting ensemble and the Adaptive Boosting (Adaboost) model built on the idea of Bagging ensemble for classification. The classification accuracy of the three models in the testing set were 75.38%, 81.54%, and 84.61%, respectively. Compared with the DT classifier, the accuracy of the models introducing the idea of ensemble learning was enhanced by 6.16% and 9.23%, respectively. The best model was the Adaboost. This result demonstrates that serum Raman spectroscopy combined with an ensemble learning algorithm was feasible in rapidly identifying PTC and PMC. At the same time, the method has great potential for application in the field of clinical diagnosis.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102647DOI Listing
November 2021

Non-enzymatic electrochemical detection of glucose using Ni-Cu bimetallic alloy nanoparticles loaded on reduced graphene oxide through a one-step synthesis strategy.

Anal Methods 2021 Nov 15. Epub 2021 Nov 15.

State Key Laboratory of Biobased Material and Green Papermaking, School of Food Science and Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China.

In this work, Ni-Cu bimetallic alloy nanoparticles supported on reduced graphene oxide (Ni-Cu ANPs/RGO) was successfully fabricated through a one-step hydrothermal synthesis method, where simultaneous reduction of graphene oxide, nickel salt and copper salt was performed, and relevant characterization studies were executed. This synthetic method does not require surfactants and high temperature treatment, and is recommended as a green, convenient and effective way to produce composites. The unique two-dimensional architecture of the RGO provides a large specific surface area, contributing to loading more Ni-Cu ANPs, while the uniformly distributed Ni-Cu bimetallic alloy nanoparticles enhance the electrocatalytic performance of glucose oxidation. The non-enzymatic glucose biosensor based on Ni-Cu ANPs/RGO showed a wide linear range (from 0.01 μM to 30 μM), low detection limit (0.005 μM), and excellent sensitivity (1754.72 μA mM cm). More importantly, the high reliability and the excellent selectivity in actual sample detection will broaden its practical application in electrochemical sensing.
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http://dx.doi.org/10.1039/d1ay01357bDOI Listing
November 2021

An ultrasensitive ratiometric immunosensor based on the ratios of conjugated distyrylbenzene derivative nanosheets with AIECL properties and electrochemical signal for CYFRA21-1 detection.

Anal Bioanal Chem 2021 Nov 5. Epub 2021 Nov 5.

State Key Laboratory of Biobased Material and Green Papermaking, School of Food Science and Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, Shandong, China.

Aggregation-induced electrochemiluminescence reagent, a distyrylbenzene derivative with donor-acceptor conjugated nanosheet structure, namely TPAPCN, was used as a trace label and modified on the electrode through the formation of classical sandwich complex of antibody-antigen-antibody in this work. In aggregate state, TPAPCN with twisted structure was limited in nanometer space through intermolecular π - π stacking interactions, which not only restricts the intramolecular motions but also combines a large number of singlet excitons to greatly trigger electrochemiluminescence (ECL). The ECL signal of this system enhanced with more captured cytokeratin 19 fragment 21-1 (CYFRA21-1) on the modified electrode. Three-dimensional graphene/platinum nanoparticles with large specific surface, and excellent electroconductivity and biocompatibility were prepared and acted as excellent carriers for thionine handling (3D-GN/PtNPs/Th), which was employed for improving the loading of antibodies and generating internal electrochemical signal. Consequently, a novel ratiometric sandwich immunosensor for CYFRA21-1 detection was fabricated based on TPAPCN and 3D-GN/PtNPs/Th, that is, a rapid and reliable detection was achieved through the ratio between ECL and electrochemical signals. The prepared sensor performed good linearity in the range of 50 fg/mL to 1 ng/mL with a detection limit as low as 16 fg/mL. Moreover, the detection results revealed well in the analysis of human serum samples, demonstrating a significant application for clinical monitoring and biomolecules detection.
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http://dx.doi.org/10.1007/s00216-021-03764-zDOI Listing
November 2021

Construction of polythiophene-derivative films as a novel electrochemical sensor for highly sensitive detection of nitrite.

Anal Bioanal Chem 2021 Nov 30;413(26):6639-6647. Epub 2021 Sep 30.

State Key Laboratory of Biobased Material and Green Papermaking, School of Food Science and Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, Shandong, China.

Herein, a novel, convenient, and highly selective electrochemical sensor for determination of nitrite based on a polythiophene-derivative film-modified glassy carbon electrode (GCE) was established. In this work, 2,5-di-thiophen-3-yl-thiazolo[5,4-d]thiazole (DTT), a novel thiophene derivative, was synthesized and used to form an original and excellent polymer film (PolyDTTF) on GCE through one-step electropolymerization for the first time. The modified electrodes were characterized by electron microscopy (SEM), Fourier transform infra-red spectroscopy (FT-IR), UV-visible spectra, Raman spectroscopy, and electrochemical technologies, in which the electrochemical sensor based on PolyDTTF was successfully constructed and demonstrated a significant electrocatalytic effect on nitrite. The influence of pH value, electrodeposition scanning times, scanning speed, and potential on the electrochemical behavior of nitrite were investigated in detail. Furthermore, the nitrite sensor exhibits excellent responses proportional to nitrite concentrations (R = 0.9972) over a concentration range of 5.5 × 10 ~ 3.5 × 10 M with a detection limit (LOD) of 2 nM, and has extremely good anti-interference ability for nitrite detection. This proposed sensor can be used to detect nitrite in actual samples, opening the possibility for applications in the food industry and environmental analysis.
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http://dx.doi.org/10.1007/s00216-021-03630-yDOI Listing
November 2021

Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas.

Anal Methods 2021 10 14;13(39):4642-4651. Epub 2021 Oct 14.

College of Software, Xinjiang University, Urumqi 830046, China.

The diffuse growth of glioma cells leads to gliomatosis, which has less cure rate and high mortality. As the severity deepens, the treatment difficulty and mortality of glioma patients gradually increase. Therefore, a rapid and non-invasive diagnostic technique is very important for glioma patients. The target of this study is to classify contract subjects and glioma patients by serum mid-infrared spectroscopy combined with an ensemble learning method. The spectra were normalized and smoothed, and principal component analysis (PCA) was utilized for dimensionality reduction. Particle swarm optimization-support vector machine (PSO-SVM), decision tree (DT), logistic regression (LR) as well as random forest (RF) were used as base classifiers, and AdaBoost integrated learning was introduced. AdaBoost-SVM, AdaBoost-LR, AdaBoost-RF and AdaBoost-DT models were established to discriminate glioma patients. The single classification accuracy of the four models for the test set was 87.14%, 90.00%, 92.00% and 90.86%, respectively. For the purpose of further improving the prediction accuracy, the four models were fused at decision level, and the final classification accuracy of the test set reached 94.29%. Experiments show that serum infrared spectroscopy combined with the ensemble learning method algorithm shows wonderful potential in non-invasive, fast and precise identification of glioma patients, and can also be used for reference in intelligent diagnosis of other diseases.
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http://dx.doi.org/10.1039/d1ay00802aDOI Listing
October 2021

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.

Lasers Med Sci 2021 Jul 9. Epub 2021 Jul 9.

College of Software, Xinjiang University, Urumqi, 830046, China.

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.
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http://dx.doi.org/10.1007/s10103-021-03346-6DOI Listing
July 2021

Serum Raman spectroscopy combined with Deep Neural Network for analysis and rapid screening of hyperthyroidism and hypothyroidism.

Photodiagnosis Photodyn Ther 2021 Sep 6;35:102382. Epub 2021 Jun 6.

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of signal detection and processing, Xinjiang University, Urumqi 830046, Xinjiang, China.

Hyperthyroidism and hypothyroidism may cause a series of clinical complications have a high incidence, and early diagnosis is beneficial to treatment. Based on Raman spectroscopy and deep learning algorithms, we propose a rapid screening method to distinguish serum samples of hyperthyroidism patients, hypothyroidism patients and control subjects. We collected 99 serum samples, including 38 cases from hyperthyroidism patients, 32 cases from hypothyroidism patients and 29 cases from control subjects. By comparing and analyzing the Raman spectra of the three, we found differences in the peak intensity of the spectra, indicating that Raman spectra can be used for the subsequent identification of diseases. After collecting the spectral data, Vancouver Raman algorithm (VRA) was used to remove the fluorescence background of the data, and kernel principal component analysis (KPCA) was used to extract the spectral data features with a cumulative explained variance ratio of 0.9999. Then, five neural network models, the adjusted AlexNet, LSTM-CNN, IndRNNCNN, the adjusted GoogLeNet and the adjusted ResNet, were constructed for classifications. The total accuracy was 91%, 84%, 82%, 75% and 71% respectively. The results of our study show that it is feasible to use Raman spectroscopy combined with deep learning to distinguish hyperthyroidism, hypothyroidism and control subjects. After comparing the models, we found that as the neural network deepens and the complexity of the model increases, the classification effect of Raman spectroscopy gradually deteriorates, and we put forward three conjectures for this.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102382DOI Listing
September 2021

Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis.

Front Genet 2021 17;12:628136. Epub 2021 May 17.

College of Information Science and Engineering, Xinjiang University, Urumqi, China.

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene expression profiles of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to classify BC samples and normal samples. The method proposed in this article triumphs over some of the shortcomings of traditional diagnostic methods and can conduct BC diagnosis more rapidly with high sensitivity and have no radiation. This study first selected the genes most relevant to cancer through weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA). Then it used the protein-protein interaction (PPI) network to screen 23 hub genes. Finally, it used the support vector machine (SVM), decision tree (DT), Bayesian network (BN), artificial neural network (ANN), convolutional neural network CNN-LeNet and CNN-AlexNet to process the expression levels of 23 hub genes. For gene expression profiles, the ANN model has the best performance in the classification of cancer samples. The ten-time average accuracy is 97.36% (±0.34%), the F1 value is 0.8535 (±0.0260), the sensitivity is 98.32% (±0.32%), the specificity is 89.59% (±3.53%) and the AUC is 0.99. In summary, this method effectively classifies cancer samples and normal samples and provides reasonable new ideas for the early diagnosis of cancer in the future.
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http://dx.doi.org/10.3389/fgene.2021.628136DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165442PMC
May 2021

Establishment of a reliable scheme for obtaining highly stable SERS signal of biological serum.

Biosens Bioelectron 2021 Oct 18;189:113315. Epub 2021 May 18.

First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830046, China. Electronic address:

As a rapid and non-destructive biological serum detection method, SERS technology was widely used in the screening and medical diagnosis of various diseases by combining the analysis of serum SERS spectrum and multivariate statistical algorithm. Because of the high complexity of serum components and the variability of SERS spectra, which often resulted in the phenomenon that the SERS spectrum of the same biological serum was significantly different due to the different test conditions. In this experiment, through the dilution treatment of the serum and the systematic test of the serum of all concentration gradients with lasers of wavelength of 785, 633 and 532 nm, the most suitable conditions for detecting the serum were investigated. The experimental results showed that only when the serum is diluted to low concentration (10 ppm), the SERS spectrum with high reproducibility and stability could be obtained, furthermore, the low concentration serum had weak tolerance to laser, and 532 nm laser was not suitable for serum detection. In this paper, a set of test scheme for obtaining highly stable serum SERS spectra was established by using high-performance gold nanoparticles (Au NPs) as the active substrate of SERS. Through comparative analysis of SERS spectrum of serum of normal people and cervical cancer, the reliability of the established low-concentration serum test program was verified, as well as its great potential advantages in disease screening and diagnosis.
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http://dx.doi.org/10.1016/j.bios.2021.113315DOI Listing
October 2021

Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Nov 13;260:119956. Epub 2021 May 13.

College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, Xinjiang, China. Electronic address:

Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.
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http://dx.doi.org/10.1016/j.saa.2021.119956DOI Listing
November 2021

Rapid noninvasive screening of cerebral ischemia and cerebral infarction based on tear Raman spectroscopy combined with multiple machine learning algorithms.

Lasers Med Sci 2021 May 10. Epub 2021 May 10.

College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.

Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future.
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http://dx.doi.org/10.1007/s10103-021-03273-6DOI Listing
May 2021

Recognition of chronic renal failure based on Raman spectroscopy and convolutional neural network.

Photodiagnosis Photodyn Ther 2021 Jun 26;34:102313. Epub 2021 Apr 26.

College of Software, Xinjiang University, Urumqi, 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, Xinjiang, China.

Purpose: Chronic renal failure (CRF) is a disease with a high morbidity rate that can develop into uraemia, resulting in a series of complications, such as dyspnoea, mental disorders, hypertension, and heart failure. CRF may be controlled clinically by drug intervention. Therefore, early diagnosis and control of the disease are of great significance for the treatment and prevention of chronic renal failure. Based on the complexity of CRF diagnosis, this study aims to explore a new rapid and noninvasive diagnostic method.

Methods: In this experiment, the serum Raman spectra of samples from 47 patients with CRF and 53 normal subjects were obtained. In this study, Serum Raman spectra of healthy and CRF patients were identified by a Convolutional Neural Network (CNN) and compared with the results of identified by an Improved AlexNet. In addition, different amplitude of noise were added to the spectral data of the samples to explore the influence of a small random noise on the experimental results.

Results: A CNN and an Improved AlexNet was used to classify the spectra, and the accuracy was 79.44 % and 95.22 % respectively. And the addition of noise did not significantly interfere with the classification accuracy.

Conclusion: The accuracy of CNN of this study can be as high as 95.22 %, which greatly improves its accuracy and reliability, compared to 89.7 % in the previous study. The results of this study show that the combination of serum Raman spectrum and CNN can be used in the diagnosis of CRF, and small random noise will not cause serious interference to the data analysis results.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102313DOI Listing
June 2021

Human serum mid-infrared spectroscopy combined with machine learning algorithms for rapid detection of gliomas.

Photodiagnosis Photodyn Ther 2021 Sep 24;35:102308. Epub 2021 Apr 24.

College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China; College of Software, Xinjiang University, Urumqi, 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China. Electronic address:

Glioma has a low cure rate and a high mortality rate. Therefore, correct diagnosis and treatment are essential for patients. This research aims to use mid-infrared spectroscopy combined with machine learning algorithms to identify patients with glioma. The glioma infrared spectra and the control group serum are smoothed and normalized, then the principal component analysis (PCA) algorithm is used to reduce the data dimensionality, and finally, the particle swarm optimization-support vector machine (PSO-SVM), backpropagation (BP) neural network and decision tree (DT) model are established. The classification accuracy of the three models was 92.00 %, 91.83 %, 87.20 %, and the AUC values were 0.919, 0.945, and 0.866, respectively. The results show that PCA-PSO-SVM has a better classification effect. This study shows that mid-infrared spectroscopy combined with machine learning algorithms has great potential in the application of non-invasive, rapid and accurate identification of glioma patients.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102308DOI Listing
September 2021

Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study.

Anal Bioanal Chem 2021 May 22;413(12):3209-3222. Epub 2021 Mar 22.

Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China.

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. Graphical Abstract.
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http://dx.doi.org/10.1007/s00216-021-03258-yDOI Listing
May 2021

Serum Raman spectroscopy combined with multiple algorithms for diagnosing thyroid dysfunction and chronic renal failure.

Photodiagnosis Photodyn Ther 2021 Jun 1;34:102241. Epub 2021 Mar 1.

Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi 830046, China; School of Software, Xinjiang University, Urumqi, 840046, China.

In this study, 60 samples taken from patients with thyroid dysfunction, 40 samples taken from patients with chronic renal failure (CRF) and 60 samples taken from healthy people were classified. We used partial least squares (PLS) to extract features to reduce the dimension of the spectral data to discriminate among the different samples. The Decision Trees (DT), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN), Back Propagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) algorithms were used to build classification models and compare the results. The PLS-PNN algorithm distinguished between patients with thyroid dysfunction and patients with chronic renal failure with up to a 96.67 % accuracy rate, the PLS-BP algorithm distinguished between patients with chronic renal failure and healthy people with up to a 98.33 % accuracy rate, and the PLS-PNN algorithm and the PLS-DT algorithm distinguished between healthy people and patients with chronic renal failure with up to a 100 % accuracy rate. The results showed that serum Raman spectroscopy can be used in conjunction with classification algorithms to rapidly and accurately diagnose and distinguish between thyroid dysfunction and chronic renal failure.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102241DOI Listing
June 2021

Detection of breast cancer of various clinical stages based on serum FT-IR spectroscopy combined with multiple algorithms.

Photodiagnosis Photodyn Ther 2021 Mar 27;33:102199. Epub 2021 Jan 27.

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China. Electronic address:

Background: Breast cancer screening is time consuming, requires expensive equipment, and has demanding requirements for doctors. Hence, a large number of breast cancer patients may miss screening and early treatment, which greatly threatens their health around the world. Infrared spectroscopy may be able to be used as a screening tool for breast cancer detection. Fourier transform infrared (FT-IR) spectroscopy of serum was combined with traditional machine learning algorithms to achieve an auxiliary diagnosis that could quickly and accurately distinguish patients with different stages of breast cancer, including stage 1 disease, from control subjects without breast cancer.

Materials And Methods: FT-IR spectroscopy were performed on the serum of 114 non-cancer control subjects, 35 patients with stage I, 43 patients with stage II, and 29 patients with stage III & IV breast cancer. Due to the experimental sample imbalance, we used the oversampling to process the four classes of sample. The oversampling selected Synthetic Minority Oversampling Technique (SMOTE). Subsequently, we used the random discarding method in undersampling to do experiments as well. The average FT-IR spectroscopy results for the four groups showed differences in phospholipids, nucleic acids, lipids, and proteins between non-cancer control subjects and breast cancer patients at different stages. Based on these differences, four classification models were used to classify stage I, II, III & IV breast cancer patients and non-cancer control subjects. First, standard normal variate transformation (SNV) was used to preprocess the original data, and then partial least squares (PLS) was used for feature extraction. Finally, the five models were established including extreme learning machine (ELM), k-nearest neighbor (KNN), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM) and grid search-support vector machine (GS-SVM).

Conclusion: In oversampling experiment, the GS-SVM classifier obtained the highest average classification accuracy of 95.45 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 90 %; breast cancer stage II was 84.62 %; and breast cancer stage III & IV was 100 %. In undersampling experiment, the GA-SVM model obtained the highest average classification accuracy of 100 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 100 %; breast cancer stage II was 100 %; and breast cancer stage III & IV was 100 %. The results show that FT-IR spectroscopy combined with powerful classification algorithms has great potential in distinguishing patients with different stages of breast cancer from non-cancer control subjects. In addition, this research provides a reference for future multiclassification studies of cervical cancer, ovarian cancer and other female high-incidence cancers through serum FT-IR spectroscopy.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102199DOI Listing
March 2021

AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features.

Sensors (Basel) 2020 Dec 27;21(1). Epub 2020 Dec 27.

School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 () is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features.
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http://dx.doi.org/10.3390/s21010122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795214PMC
December 2020

Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function.

Photodiagnosis Photodyn Ther 2020 Dec 15;32:101923. Epub 2020 Oct 15.

College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi 830046, China. Electronic address:

Background: To evaluate the Fourier transform infrared spectroscopy (FT-IR) combined with deep learning models to allow for quick diagnosis of abnormal thyroid function.

Materials And Methods: Serum samples of 199 patients with abnormal thyroid function and 183 healthy patients were collected by infrared spectroscopy data and combined with different decibel noise for data expansion. The data were directly imported into three deep models: multilayer perceptron (MLP), a long-short-term memory network (LSTM), and a convolutional neural network (CNN), and 10-fold cross-validation was used to evaluate the performance of the model.

Results: The accuracy rates of the three models using the original data were 91.3 %, 88.6 % and 89.3 %, and the accuracy rates of the three models after data enhancement were 92.7 %, 93.6 % and 95.1 %.

Conclusion: The results of this study indicated that the use of large sample serum infrared spectroscopy data combined with deep learning algorithms is a promising method for the diagnosis of abnormal thyroid function.
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http://dx.doi.org/10.1016/j.pdpdt.2020.101923DOI Listing
December 2020

Rapid detection of seven indexes in sheep serum based on Raman spectroscopy combined with DOSC-SPA-PLSR-DS model.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Mar 28;248:119260. Epub 2020 Nov 28.

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; College of Software, Xinjiang University, Urumqi 830002, China. Electronic address:

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 of the concentration of the six substances decreased from 0.0408 to 0.0387, the R 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.
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http://dx.doi.org/10.1016/j.saa.2020.119260DOI Listing
March 2021

Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms.

Photodiagnosis Photodyn Ther 2021 Mar 16;33:102104. Epub 2020 Nov 16.

Xinjiang Xinmeilvcheng Travel Agency Co., Ltd., China.

Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.
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http://dx.doi.org/10.1016/j.pdpdt.2020.102104DOI Listing
March 2021

Combining derivative Raman with autofluorescence to improve the diagnosis performance of echinococcosis.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Feb 17;247:119083. Epub 2020 Oct 17.

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

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.
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http://dx.doi.org/10.1016/j.saa.2020.119083DOI Listing
February 2021

Discrimination of alcohol dependence based on the convolutional neural network.

PLoS One 2020 27;15(10):e0241268. Epub 2020 Oct 27.

College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China.

In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vector machine (GS-SVM), the convolutional neural network (CNN) and the convolutional neural network combined with long and short-term memory (CNN-LSTM) are used to classify and discriminate between alcohol-dependent patients (AD) and the non-alcohol-dependent control group. The results show that 19 SNPs combined with academic qualifications have the best discrimination effect. In the GS-SVM, the area under the receiver operating characteristic (ROC) curve (AUC) is 0.87, the AUC of CNN-LSTM is 0.88, and the performance of the CNN model is the best, with an AUC of 0.92. This study shows that the CNN model can more accurately discriminate AD than the SVM to treat patients in time.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241268PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591038PMC
December 2020

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks.

PLoS One 2020 24;15(8):e0238149. Epub 2020 Aug 24.

College of Information Science and Engineering, Xinjiang University, Urumqi, China.

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238149PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444812PMC
October 2020

Fully convolutional attention network for biomedical image segmentation.

Artif Intell Med 2020 07 5;107:101899. Epub 2020 Jun 5.

College of Software Engineering, Xin Jiang University, Urumqi 830000, China.

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy. The features of each location are aggregated through the spatial attention module, so that similar features promote each other in space size. At the same time, the channel attention module treats each channel of the feature map as a feature detector and emphasizes the channel dependency between any two channel maps. Finally, we weight the sum of the output features of the two types of attention modules to retain the feature information of the long-range and short-range distances, to further improve the representation of the features and make the biomedical image segmentation more accurate. In particular, we verify that the proposed attention module can seamlessly connect to any end-to-end network with minimal overhead. We perform comprehensive experiments on three public biomedical image segmentation datasets, i.e., the Chest X-ray collection, the Kaggle 2018 data science bowl and the Herlev dataset. The experimental results show that FCANet can improve the segmentation effect of biomedical images. The source code models are available at https://github.com/luhongchun/FCANet.
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http://dx.doi.org/10.1016/j.artmed.2020.101899DOI Listing
July 2020

Rapid, non-invasive screening of keratitis based on Raman spectroscopy combined with multivariate statistical analysis.

Photodiagnosis Photodyn Ther 2020 Sep 24;31:101932. Epub 2020 Jul 24.

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. Electronic address:

This study proposes a multivariate statistical analysis method based on Raman spectroscopy and different dimensionality reduction methods combined with the support vector machine (SVM) algorithm for rapid, non-invasive, high-accuracy classification of keratitis screenings. In this experiment, tear samples from 19 subjects with keratitis and 27 healthy subjects were detected, Raman spectra of the two groups of subjects were compared and analysed, and we found that their spectral intensities were different at 1005 cm and 1155 cm Principal component analysis (PCA) and partial least squares (PLS) were used for feature extraction, which greatly reduced the dimensionality of the high-dimensional spectral data. Then, the above two feature extraction methods were used as input to an SVM to build the discriminant diagnosis model. The average accuracy obtained from the PCA-SVM and PLS-SVM models was 77.86 % and 100 %, respectively. Our results suggest that tear Raman spectroscopy combined with multivariate statistical analysis has great potential in screening for keratitis. We expect this technology to could lead to the development of a portable, non-invasive and highly accurate keratitis screening device.
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http://dx.doi.org/10.1016/j.pdpdt.2020.101932DOI Listing
September 2020

Three-dimensional nanoporous starch-based material for fast and highly efficient removal of heavy metal ions from wastewater.

Int J Biol Macromol 2020 Dec 11;164:415-426. Epub 2020 Jul 11.

State Key Laboratory of Biobased Material and Green Papermaking, School of Food Science and Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, China. Electronic address:

The development of advanced adsorbents with fast adsorption rate, simple preparation, low cost, and high adsorption capacity is one of the most important topics for water purification. Herein, a novel and pollution-free adsorbent, three-dimensional nanoporous starch-based nanomaterial (3D-PSN), was prepared via sacrifice template method and functionalized for the first time in this work. Relevant characterization was performed through XRD, SEM, TGA, zeta potential analysis, FTIR, and XPS to confirm the formation of nanomaterials. Owing to its unique three-dimensional network nanostructure and abundant active sites, this adsorbent displayed outstanding adsorption properties for heavy metal ions removal, as high as 532.28 mg/g for Cd (II), 381.47 mg/g for Hg(II), 354.15 mg/g for Cu(II), 238.39 mg/g for Pb(II), completed within 30 min. In this process, the pseudo-second-order kinetic model appeared more consistent with the adsorption kinetic data, and the adsorption behavior complied with the Langmuir adsorption model. The adsorption mechanism mainly replied on the ion-exchange reaction, as well as chemical complexation formation. This adsorbent has remarkable recyclability, exhibiting strong application prospects for water purification and environmental remediation.
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http://dx.doi.org/10.1016/j.ijbiomac.2020.07.017DOI Listing
December 2020

Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network.

Lasers Med Sci 2020 Oct 13;35(8):1791-1799. Epub 2020 Apr 13.

Department of Laboratory Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi, 830000, China.

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.
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http://dx.doi.org/10.1007/s10103-020-03003-4DOI Listing
October 2020

Microwave method synthesis of magnetic ionic liquid/gold nanoparticles as ultrasensitive SERS substrates for trace clopidol detection.

Anal Bioanal Chem 2020 May 26;412(13):3063-3071. Epub 2020 Mar 26.

Department of Science, China University of Petroleum Beijing, Beijing, 102249, China.

Clopidol is one of the most widely used anti-coccidiosis drugs. Its residues in poultry products and the environment pose a serious threat to human health. In this work, microwave-assisted synthesis of magnetic ionic liquid/gold nanoparticles (MIL-Au NPs) as the SERS substrates were first designed for sensitive and reliable determination of clopidol residue in egg samples. The experiment shows that MIL(1-methyl-3-hexyl imidazole ferric tetrachloride ([Cmim]FeCl)) and microwave play a key role in the dispersion and morphology of Au NPs. Under the optimal conditions, the as-prepared MIL-Au NPs were applied to the SERS detection of clopidol in methanol and egg solution and its detection limits can be as low as to 0.5 μg/kg (equal to 0.5 ppb) in both solutions. The standard curves with regression coefficients of 0.9298 and 0.93496 were constructed in the linear range of 100-1000 ppb and 0.5-50 ppb for clopidol in egg solutions. Moreover, satisfactory recoveries (97.5-103.2%) were obtained for egg samples. The developed SERS method provides a way for quantitation of clopidol and can be applied for the convenient, reliable, and highly sensitive detection of antibiotic residues in food and environment, which has great potential in food safety and biological monitoring. Graphical abstract.
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http://dx.doi.org/10.1007/s00216-020-02588-7DOI Listing
May 2020

Multiclass identification of hepatitis C based on serum Raman spectroscopy.

Photodiagnosis Photodyn Ther 2020 Jun 29;30:101735. Epub 2020 May 29.

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; School of Software, Xinjiang University, Urumqi 830046, China. Electronic address:

Hepatitis C is a chronic infectious disease, and early detection and diagnosis are key to curing it. In this study, human serum Raman spectroscopy combined with a support vector machine (SVM) classification algorithm was used to identify multiple types of hepatitis C. The HCV genome is highly mutated and its nucleic acid sequence diversity is up to 30%, according to the homology of nucleotide sequences, the virus strains were divided into seven genotypes and more than 90 subtypes, there were geographical differences in the distribution of HCV of different genotypes, and hcv-1, 2 and 3 were widely prevalent in the world, the main prevalent HCV genotypes in China include 1b,2a,3a,3b and 6a. Combined with the characteristics of Urumqi, xinjiang, China as a multi-ethnic gathering area and the distribution characteristics of HCV genotypes in Urumqi, xinjiang reported in literature, HCV1, HCV2, HCV3a and HCV3b were selected as groups in this paper (Messina et al., 2015; Chen et al., 2017; Ohno et al., 1997). The serum Raman spectra of 55 healthy people, 55 hepatitis C virus cluster 1 (HCV1) patients, and 55 hepatitis C virus cluster 2 (HCV2) patients were collected. The normalized average Raman spectra of the three groups of serum, the differences in the average spectra between groups were plotted and analyzed. The attributions, similarities and differences in the main characteristic peaks in the three types of serum Raman spectra were described. The SVM (support vector machine) algorithm was combined with the normalized Raman spectral data to identify the three groups of serum with 91.1 % accuracy. Furthermore, serum Raman spectroscopy data from 17 hepatitis C virus genotype 3a (HCV3a) patients, 7 hepatitis C virus genotype 3b (HCV3b) patients, and 6 hepatitis C virus cluster 4 (HCV4) patients were also collected. Because of the small number of serum samples, the HCV3b and HCV4 patient sera were classified into one group to discriminate them from HCV3a patients. A model of HCV3a hepatitis was detected. As with the abovementioned groups of patients, the normalized mean Raman spectra of the HCV3a patients and HCV3b patients + HCV4 patients, the difference between the average spectra of the two groups were plotted and analyzed; the attributions, similarities and differences of the main characteristic peaks from these two groups of serum Raman spectra were described. The SVM algorithm was combined with the normalized Raman spectroscopy data to identify the two groups of patient sera with 90 % identification accuracy. This study shows that serum Raman spectroscopy combined with an SVM algorithm can be used for multiclass identification of hepatitis C.
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http://dx.doi.org/10.1016/j.pdpdt.2020.101735DOI Listing
June 2020

Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM.

Sensors (Basel) 2020 Mar 6;20(5). Epub 2020 Mar 6.

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.
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http://dx.doi.org/10.3390/s20051447DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085772PMC
March 2020
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