Machine learning-assisted serum SERS strategy for rapid and non-invasive screening of early cystic echinococcosis.

Early and accurate diagnosis of cystic echinococcosis (CE) with existing technologies is still challenging. Herein, we proposed a novel strategy based on the combination of label-free serum surface-enhanced Raman scattering (SERS) spectroscopy and machine learning for rapid and non-invasive diagnosis of early-stage CE. Specifically, by establishing early- and middle-stage mouse models, the corresponding CE-infected and normal control serum samples were collected, and silver nanoparticles (AgNPs) were utilized as the substrate to obtain SERS spectra. The early- and middle-stage discriminant models were developed using a support vector machine, with diagnostic accuracies of 91.7% and 95.7%, respectively. Furthermore, by analyzing the serum SERS spectra, some biomarkers that may be related to early CE were found, including purine metabolites and protein-related amide bands, which was consistent with other biochemical studies. Thus, our findings indicate that label-free serum SERS analysis is a potential early-stage CE detection method that is promising for clinical translation.

Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines.

Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.

Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning.

While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy and machine learning for the rapid and accurate identification of patients with cholecystitis. Significant differences were observed between the fluorescence spectral intensities of the serum of cholecystitis patients (n = 74) serum and those of healthy subjects (n = 71) at 455, 480, 485, 515, 625 and 690 nm. The ratios of characteristic fluorescence spectral peak intensities were first calculated, and principal component analysis (PCA)-linear discriminant analysis (LDA) and PCA-support vector machine (SVM) classification models were then constructed using the ratios as variables. Compared with the PCA-LDA model, the PCA-SVM model displayed better diagnostic performance in differentiating cholecystitis patients from healthy subjects, with an overall accuracy of 96.55%. This exploratory study showed that serum fluorescence spectroscopy combined with the PCA-SVM algorithm has significant potential for the development of a rapid cholecystitis screening method.

Rapid discrimination of Brucellosis in sheep using serum Fourier transform infrared spectroscopy combined with PCA-LDA algorithm.

Brucellosis in sheep is an infectious disease caused by Brucella melitensis in sheep. The current conventional serological methods for screening Brucella-infected sheep have the disadvantage of time consuming and low accuracy, so a simple, rapid and highly accurate screening method is needed. The aim of this study was to evaluate the feasibility of diagnosing Brucella-infected sheep by serum samples based on the Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR spectroscopy of serum from Brucella-infected sheep (n = 102) and healthy sheep (n = 125) revealed abnormal protein and lipid metabolism in serum from Brucella-infected sheep compared to healthy sheep. Principal component analysis-Linear discriminant analysis (PCA-LDA) method was used to differentiate the FTIR spectra of serum from Brucella-infected sheep and healthy sheep in the protein band (3700-3090 cm-1) and lipid band (3000-2800 cm-1), and its overall diagnostic accuracy was 100% (sensitivity 100%, specificity 100%). In conclusion, our results suggest that serum FTIR spectroscopy combined with PCA-LDA algorithm has great potential for brucellosis in sheep screening.

Compared between support vector machine (SVM) and deep belief network (DBN) for multi-classification of Raman spectroscopy for cervical diseases.

In this study, a minimally invasive test method for cervical cancer in vitro was proposed by comparing Raman spectroscopy with support vector machine (SVM) model and deep belief network (DBN) model. The serum Raman spectra of cervical cancer, hysteromyoma, and healthy people were collected. After data processing, SVM classification model and DBN classification model were built respectively. The experimental results show that when the DBN network algorithm is used, the sample test set can be divided accurately and the result of cross-validation is ideal. Compared with the traditional SVM algorithm, this method firstly screened the effective feature matrix from the data, and then classified the data. With high efficiency and accuracy, based on 445 samples collected, this method improved the accuracy by 13.93%±2.47% compared with the SVM method, and provided a new direction and idea for the in vitro diagnosis of cervical diseases.

Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier-transform infrared spectroscopy combined with machine learning algorithms.

Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data. Among all the bands, 1500-1700 cm-1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.

Urine fluorescence spectroscopy combined with machine learning for screening of hepatocellular carcinoma and liver cirrhosis.

In this paper, we investigated the possibility of using urine fluorescence spectroscopy and machine learning method to identify hepatocellular carcinoma (HCC) and liver cirrhosis from healthy people. Urine fluorescence spectra of HCC (n = 62), liver cirrhosis (n = 65) and normal people (n = 60) were recorded at 405 nm excitation using a Fluorescent scan multimode reader. The normalized fluorescence spectra revealed endogenous metabolites differences associated with the disease, mainly the abnormal metabolism of porphyrin derivatives and bilirubin in the urine of patients with HCC and liver cirrhosis compared to normal people. The Support vector machine (SVM) algorithm was used to differentiate the urine fluorescence spectra of the HCC, liver cirrhosis and normal groups, and its overall diagnostic accuracy was 83.42%, the sensitivity for HCC and liver cirrhosis were 93.55% and 73.85%, and the specificity for HCC and liver cirrhosis were 88.00% and 89.34%, respectively. This exploratory work shown that the combination of urine fluorescence spectroscopy and SVM algorithm has great potential for the noninvasive screening of HCC and liver cirrhosis.

Rapid discrimination of hepatic echinococcosis patients' serum using vibrational spectroscopy combined with support vector machines.

Echinococcosis is a severe zoonotic parasitic disease, and it is continuing to be a significant public health issue. The course of the disease is usually slow, and patients often remain asymptomatic for years. There is no standardized and widely accepted treatment, so early and accurate diagnosis is essential. Herein, this study utilized vibrational spectroscopic techniques, namely Raman and Fourier Transform Infrared (FTIR) spectroscopy, to quickly and accurately distinguish hepatic echinococcosis (HE) patients' serum from the healthy group. Serum samples were collected from HE patients as well as healthy control subjects, and then the Raman and FTIR spectra of the two groups were recorded. After a series of pre-processing, support vector machines (SVMs) were then used to establish the classification models for the two spectral data sets. The performance of each diagnostic model was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation methods, respectively. For the distinction between HE and healthy groups, these two spectroscopic techniques had achieved satisfactory classification results, and the diagnostic capabilities of the Raman technique were comparable to that of the FTIR method. The results demonstrate that vibrational spectroscopy has great potential in the rapid and accurate detection of HE and is expected to make up for the shortcomings of the existing clinical diagnosis methods.

Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine.

In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.

Urine surface-enhanced Raman spectroscopy combined with SVM algorithm for rapid diagnosis of liver cirrhosis and hepatocellular carcinoma.

In this paper, we investigated the feasibility of using urine for surface-enhanced Raman spectroscopy (SERS) for the rapid screening of patients with liver cirrhosis and hepatocellular carcinoma (HCC). The SERS spectra were recorded from the urine of 49 liver cirrhosis, 55 HCC, and 50 healthy volunteers using a Raman spectrometer. The normalized mean Raman spectra showed the difference of specific biomolecules associated with the illnesses, and the metabolism of specific nucleic acids and amino acids is abnormal in patients with liver cirrhosis and HCC. Based on the SVM algorithm, the urine SERS method could identify liver cirrhosis (sensitivity 88.9%, specificity 83.3%, and accuracy 85.9%) and HCC (sensitivity 85.5%, specificity 84.0%, and accuracy 84.8%). It has a higher diagnostic sensitivity for HCC than serum Alpha fetoprotein (AFP). This exploratory study showed that the urine SERS spectra combined with the SVM algorithm has indicated great potential in the noninvasive identification of liver cirrhosis and HCC.

Raman spectroscopic study of cervical precancerous lesions and cervical cancer.

Early detection of cervical lesions, accurate diagnosis of cervical lesions, and timely and effective therapy can effectively avoid the occurrence of cervical cancer or improve the survival rate of patients. In this paper, the spectra of tissue sections of cervical inflammation (n = 60), CIN (cervical intraepithelial neoplasia) I (n = 30), CIN II (n = 30), CIN III (n = 30), cervical squamous cell carcinoma (n = 30), and cervical adenocarcinoma (n = 30) were collected by a confocal Raman micro-spectrometer (LabRAM HR Evolution, Horiba France SAS, Villeneuve d'Ascq, France). The Raman spectra of six kinds of cervical tissues were analyzed, the dominant Raman peaks of different kinds of tissues were summarized, and the differences in chemical composition between the six tissue samples were compared. An independent sample t test (p ≤ 0.05) was used to analyze the difference of average relative intensity of Raman spectra of six types of cervical tissues. The difference of relative intensity of Raman spectra of six kinds of tissues can reflect the difference of biochemical components in six kinds of tissues and the characteristic of biochemical components in different kinds of tissues. The classification models of cervical inflammation, CIN I, CIN II, CIN III, cervical squamous cell carcinoma, and cervical adenocarcinoma were established by using a support vector machine (SVM) algorithm. Six types of cervical tissues were classified and identified with an overall diagnostic accuracy of 85.7%. This study laid a foundation for the application of Raman spectroscopy in the clinical diagnosis of cervical precancerous lesions and cervical cancer.

Label-free detection of echinococcosis and liver cirrhosis based on serum Raman spectroscopy combined with multivariate analysis.

In this paper, we investigated the feasibility of using serum Raman spectroscopy and multivariate analysis method to discriminate echinococcosis and liver cirrhosis from healthy volunteers. Raman spectra of serum samples from echinococcosis, liver cirrhosis, and healthy volunteers were recorded under 532 nm excitation. The normalized mean Raman spectra revealed specific biomolecular differences associated with the disease, mainly manifested as the contents of ß carotene in the serum of patients with echinococcosis and liver cirrhosis were lower than those of healthy people. Furthermore, principal components analysis (PCA), combined with linear discriminant analysis (LDA), was adopted to distinguish patients with echinococcosis, liver cirrhosis, and healthy volunteers. The overall diagnostic accuracy based on the PCA-LDA algorithm was 87.7 %. The diagnostic sensitivities to healthy volunteers, patients with echinococcosis, and liver cirrhosis were 92.5 %, 81.5 %, and 89.1 %, and the specificities were 93.2 %, 96.1 %, and 92.4 %, respectively. This exploratory work demonstrated that serum Raman spectroscopy technology combined with PCA-LDA diagnostic algorithm has great potential for the non-invasive identification of echinococcosis and liver cirrhosis.

Triport periareolar thoracoscopic surgery versus right minithoracotomy for repairing atrial septal defect in adults.

OBJECTIVES: Our goal was to investigate the safety and feasibility of triport periareolar thoracoscopic surgery (TPTS) and its advantages in repairing adult atrial septal defect. METHODS: Between January 2017 and January 2020, a total of 121 consecutive adult patients underwent atrial septal defect closure in our institution. Of these, 30 patients had TPTS and 31 patients had a right minithoracotomy (RMT). Operational data and clinical outcomes were compared between the 2 groups. RESULTS: The total operation time, cardiopulmonary bypass time and aortic cross-clamp time in the TPTS group were slightly longer than those in the RMT group, but there were no differences between the 2 groups. Compared with the RMT group, the TPTS group showed a decrease in the volume of chest drainage in 24 h (98.6 ± 191.2 vs 222.6 ± 217.2 ml; P = 0.032) and a shorter postoperative hospital stay (6.5 ± 1.5 vs 8.0 ± 3.7 days; P = 0.042). The numeric rating scale on postoperative day 7 was significantly less in the TPTS group than in the RMT group (2.82 ± 1.14 vs 3.56 ± 1.42; P = 0.034). The patient satisfaction scale for the cosmetic results in the TPTS group was significantly higher than in the RMT group (4.68 ± 0.55 vs 4.22 ± 0.76; P = 0.012). No differences were found in postoperative complications. No in-hospital death or major adverse events occurred in the 2 groups. CONCLUSIONS: TPTS is safe and feasible for the closure of adult atrial septal defect. Compared with RMT, it has been associated with less pain and better cosmetic outcomes.

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

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 ß 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.

Application of Raman spectroscopy in the detection of hepatitis B virus infection.

OBJECTIVE: Detection of hepatitis B virus (HBV) using Raman spectroscopy. METHODS: Raman spectroscopy was used to examine the serum samples of 500 patients with HBV and 500 non-HBV persons. First, the adaptive iterative weighted penalty least squares method (airPLS) was used to deduct the fluorescence background in Raman spectra. Then, a principal component analysis (PCA) was used to extract the processed Raman spectra, and a support vector machine (SVM) was used for modeling and prediction. The particle swarm optimization (PSO) algorithm was selected to optimize the parameters of the SVM instead of a traditional grid search. Finally, 600 serum samples were detected by Raman spectroscopy, and the results wereverified using a double-blind method. RESULTS: In the Raman spectra, the non-HBV human Raman peaks at 509, 957, 1002, 1153, 1260, 1512, 1648 and 2305 cm-1 were different from those of patients with HBV. The reported accuracy, sensitivity and specificity of the HBV serum model established using airPLS-PCA-PSO-SVM was 93.1%, 100% and 88%, respectively. The two groups were verified by a double-blind method. In the first group sensitivity was 87%, specificity was 92%, and the KAPPA value was 0.79; in the second group sensitivity was 80%, specificity was 79%, and the KAPPA value was 0.59. CONCLUSION: This preliminary study shows that serum Raman spectroscopy combined with the airPLS-PCA-PSO-SVM model can be used for hepatitis B virus detection.

Rapid and non-invasive screening of high renin hypertension using Raman spectroscopy and different classification algorithms.

This study presents a rapid and non-invasive method to screen high renin hypertension using serum Raman spectroscopy combined with different classification algorithms. The serum samples taken from 24 high renin hypertension patients and 22 non-high renin hypertension samples were measured in this experiment. Tentative assignments of the Raman peaks in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was first used for feature extraction and reduced the dimension of high-dimension spectral data. Then, support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (KNN) algorithms were employed to establish the discriminant diagnostic models. The accuracies of 93.5%, 93.5% and 89.1% were obtained from PCA-SVM, PCA-LDA and PCA-KNN models, respectively. The results from our study demonstrate that the serum Raman spectroscopy technique combined with multivariate statistical methods have great potential for the screening of high renin hypertension. This technique could be used to develop a portable, rapid, and non-invasive device for screening high renin hypertension.

Bilateral superior cervical ganglionectomy attenuates the progression of ß-aminopropionitrile-induced aortic dissection in rats.

AIMS: Aortic dissection (AD) represents one of the most common aortic emergencies with high incidence of morbidity and mortality. Clinical studies have shown that the increased excitability of the sympathetic nerve may be associated with the formation of AD. In this study, we examined the effects of bilateral superior cervical sympathectomy (SCGx) on the progression of ß-aminopropionitrile (BAPN)-induced AD in rats. MAIN METHODS: Sprague-Dawley rats were randomly divided into three groups, including BAPN, BAPN+SCGx and control groups. For terminal measurements, the mean arterial pressure (MAP) and heart rate (HR) were monitored and the basal sympathetic nerve activity (SNA) was assessed through recording the variation in arterial pressure in response to hexamethonium application. Pathological changes in the aortic wall were observed by histological staining. Matrix metalloproteinase-2 (MMP-2) and MMP-9 concentrations within the aortic wall were analyzed by western blot. KEY FINDINGS: The results show that BAPN administration could elevate SNA and cause the formation of AD in rats with a high incidence (67.7%), while SCGx treatment inhibited the elevation of SNA and significantly reduced the incidence (20%). SCGx may suppress the formation of BAPN-induced AD via restraining the rise of HR and reducing the MMP-9 concentration in aortic wall. SIGNIFICANCE: These results indicate that surgical techniques such as sympathetic nerve block may be a potentially useful therapy for the prevention of AD.

Cardiac autotransplantation for repair of left ventricular rupture after mitral valve replacement.

Left ventricular rupture is an infrequent but potentially fatal complication of mitral valve replacement. Here, we report a case of large posterior mid-ventricular rupture following mitral valve replacement, which was successfully treated by a sandwich style repair and autotransplantation.

Three Circulating LncRNA Predict Early Progress of Esophageal Squamous Cell Carcinoma.

BACKGROUND/AIMS: Previous studies revealed that circulating (either from plasma or serum) long non-coding RNA may predict the occurrence or prognosis of multiple human malignant tumors. In this study, we mainly explored whether circulating lncRNAs can be utilized as biomarkers predicting the development of human esophageal squamous cell carcinoma (ESCC). METHODS: LncRNA microarray was applied to screen the potential biomarkers for ESCC. Each group contained three individual plasma samples. A multi-stage validation and risk score formula detection were used for validation. RESULTS: Eleven dysregulated lncRNAs were obtained after Venny analysis. Further validation in a larger cohort including 205 ESCC patients, 82 patients suffering from esophagus dysplasia and 210 healthy controls confirmed that increased Linc00152, CFLAR-AS1 and POU3F3 might be potential biomarkers for predicting the early progress with an area under curve (AUC) of 0.698, 0.651 and 0.584, respectively. The merged AUC of the three factors and merged with CEA was 0.765 and 0.955, respectively. We also revealed that circulating levels of three lncRNAs were associated with poor post-surgery prognosis of ESCC patients. CONCLUSIONS: The three circulating lncRNAs might serve as potential biomarkers for predicting the early occurrence of ESCC.

High expression of lncRNA PVT1 promotes invasion by inducing epithelial-to-mesenchymal transition in esophageal cancer.

The long non-coding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1) has been identified as an oncogene in numerous diseases, and aberrant lncRNA PVT1 expression has been associated with the development of cancer. However, the underlying mechanism by which lncRNA PVT1 affects cell invasion in esophageal cancer has been not demonstrated. In the current study, the expression of lncRNA PVT1 was found to be increased in esophageal cancer specimens (n=77) by reverse transcription-quantitative polymerase chain reaction, and was correlated with tumor stage (P=0.009) and metastasis (P<0.001). In vitro, by using transwell assay, upregulation of lncRNA PVT1 promoted the invasion of TE-1 esophageal cancer cells; while downregulation of lncRNA PVT1 inhibited Eca-109 cell invasion. In addition, western blot analysis indicated that upregulation of lncRNA PVT1 may induce epithelial-to-mesenchymal transition (EMT) by regulating the expression levels of EMT markers (E-cadherin, N-cadherin and vimentin). In conclusion, lncRNA PVT1 is able to regulate the invasion of esophageal cancer cells by inducing EMT.