Serum Surface-enhanced Raman Spectroscopy Research Articles

Early differential diagnosis of pancytopenia related diseases based on serum surface-enhanced Raman spectroscopy

Pancytopenia is a common blood disorder defined as the decrease of red blood cells, white blood cells and platelets in the peripheral blood. Its genesis mechanism is typically complex and a variety of diseases have been found to be capable of causing pancytopenia, some of which are featured by their high mortality rates. Early judgement on the cause of pancytopenia can benefit timely and appropriate treatment to improve patient survival significantly. In this study, a serum surface-enhanced Raman spectroscopy (SERS) method was explored for the early differential diagnosis of three pancytopenia related diseases, i.e., aplastic anemia (AA), myelodysplastic syndrome (MDS) and spontaneous remission of pancytopenia (SRP), in which the patients with those pancytopenia related diseases at initial stage exhibited same pancytopenia symptom but cannot be conclusively diagnosed through conventional clinical examinations. The SERS spectral analysis results suggested that certain amino acids, protein substances and nucleic acids are expected to be potential biomarkers for their early differential diagnosis. In addition, a diagnostic model was established based on the joint use of partial least squares analysis and linear discriminant analysis (PLS-LDA), and an overall accuracy of 86.67 % was achieved to differentiate those pancytopenia related diseases, even at the time that confirmed diagnosis cannot be made by routine clinical examinations. Therefore, the proposed method has demonstrated great potential for the early differential diagnosis of pancytopenia related diseases, thus it has significant clinical importance for the timely and rational guidance on subsequent treatment to improve patient survival.

Identification of late-life depression and mild cognitive impairment via serum surface-enhanced Raman spectroscopy and multivariate statistical analysis.

Identification of age-related neuropsychiatric disorders, i.e., late-life depression (LDD) and mild cognitive impairment (MCI) is of imperative clinical value considering the large probability of misdiagnosis and current lack of sensitive, non-invasive and low-cost diagnostic approaches. Here, the serum surface-enhanced Raman spectroscopy (SERS) technique is proposed to identify healthy controls, LDD and MCI patients. Based on SERS peaks analysis, abnormal levels of ascorbic acid, saccharide, cell-free DNA and amino acids in serum are found to be potential biomarkers for identifying LDD and MCI. These biomarkers might be related to oxidative stress, nutritional status, lipid peroxidation and metabolic abnormalities. Moreover, partial least square analysis-linear discriminant analysis (PLS-LDA) is applied to those collected SERS spectra. Finally, the overall identification accuracy is 83.2%, and accuracies are 91.6% and 85.7% for differentiating healthy versus neuropsychiatric disorders and LDD versus MCI, respectively. Thus, the serum SERS combined with multivariate statistical analysis has proved its successful potential for rapid, sensitive and non-invasive identification of healthy, LDD and MCI, which may open new avenues for early diagnosis and timely intervention for age-related neuropsychiatric disorders.

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

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

An auxiliary diagnostic technology and clinical efficacy evaluation in knee osteoarthritis based on serum surface-enhanced Raman spectroscopy

Knee osteoarthritis (KOA), a progressive joint disease, is a leading source of chronic pain and disability, and its diagnosis mainly depends on medical imaging findings and clinical symptoms. This study aimed to explore an auxiliary diagnostic technology and clinical efficacy evaluation in KOA based on surface-enhanced Raman scattering (SERS). Three sequential experiments were performed: 1) preliminary observation of the therapeutic effects of icariin (ICA); 2) using serum SERS spectra obtained from rat models belonging to sham group, KOA group and icariin treatment group, respectively, to analyze the KOA-related expression profiles; 3) employing partial least squares (PLS) and support vector machines (SVM) algorithms to establish KOA diagnosis model. Pathological changes verified the efficacy of icariin in KOA. Raman peak assignment combined with spectral difference analysis reflected the biochemical changes associated with KOA, including amino acid, carbohydrates and collagen. ICA intervention significantly reversed these changes, although full recovery could not be achieved. Based on PLS-SVM approach, the sensitivity, specificity and accuracy of 100%, 98.33% and 98.89%, respectively, were obtained for screening KOA. This work proves that SERS has great potential to be used as an auxiliary diagnostic technology for KOA, and is also helpful for the exploration of novel KOA treatment agent.

Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis.

In this study, we looked at the viability of utilizing serum to differentiate between gallbladder (GB) stones and GB polyps using Surface-enhanced Raman spectroscopy (SERS), which has the potential to be a quick and accurate means of diagnosing benign GB diseases. Rapid and label-free SERS was used to conduct the tests on 148 serum samples, which included those from 51 patients with GB stones, 25 patients with GB polyps and 72 healthy persons. We used an Ag colloid as a Raman spectrum enhancement substrate. In addition, we employed orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component linear discriminant analysis (PCA-LDA) to compare and diagnose the serum SERS spectra of GB stones and GB polyps. The diagnostic results showed that the sensitivity, specificity, and area under curve (AUC) values of the GB stones and GB polyps based on OPLS-DA algorithm reached 90.2%, 97.2%, 0.995 and 92.0%, 100%, 0.995, respectively. This study demonstrated an accurate and rapid means of combining serum SERS spectra with OPLS-DA to identify GB stones and GB polyps.

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Multivariate analysis of serum surface-enhanced Raman spectroscopy of liver cancer patients

Early diagnosis of liver cancer plays a significant role in reducing its high mortality. In this preliminary study, the feasibility of using serum surface-enhanced Raman spectroscopy (SERS) to identify liver cancer was studied. Serum samples were obtained from liver cancer patients and healthy controls. The differences between the SERS spectra of pre-operation and post-operation of liver cancer patients were also analyzed. The general shape and trend of SERS spectra of health control and liver cancer patients were similar. Multivariate analysis, e.g., PLS-SVM, might be useful for the discrimination of serum SERS spectra of pre-operation and post-operation.

Prediction of the postoperative prognosis in patients with non-muscle-invasive bladder cancer based on preoperative serum surface-enhanced Raman spectroscopy

Non-muscle-invasive bladder cancer (NMIBC) is a common urinary tumor and has a high recurrence rate due to improper or inadequate conservative treatment. The early and accurate prediction of its recurrence can be helpful to implement timely and rational treatment. In this study, we explored a preoperative serum surface-enhanced Raman spectroscopy based prognostic protocol to predict the postoperative prognosis for NMIBC patients at the time even before treatment. The biochemical analysis results suggested that biomolecules related to DNA/RNA, protein substances, trehalose and collagen are expected to be potential prognostic markers, which further compared with several routine clinically used immunohistochemistry expressions with prognostic values. In addition, high prognostic accuracies of 87.01% and 89.47% were achieved by using the proposed prognostic models to predict the future postoperative recurrence and recurrent type, respectively. Therefore, we believe that the proposed method has great potential in the early and accurate prediction of postoperative prognosis in patients with NMIBC, which is with important clinical significance to guide the treatment and further improve the recurrence rate and survival time.

Early assessment of chemotherapeutic response in hepatocellular carcinoma based on serum surface-enhanced Raman spectroscopy

In clinical practice, the transcatheter arterial chemoembolization (TACE) has been widely accepted as the first option for non-surgical hepatocellular carcinoma (HCC) treatment. However, patients with HCC often suffer from poor response to TACE therapy. This can be prevented if the chemotherapeutic response can be early and accurately assessed, which is essential to guide timely and rational management. In this study, the serum SERS technique was for the first time investigated as a potential prognostic tool for early assessment of HCC chemotherapeutic response. According to the SERS spectral analysis results, it is newly found that not only the absolute circulating nucleic acids and collagen levels in pre-therapeutic serum but also the changes in circulating nucleic acids and amino acids between pre-therapeutic and post-therapeutic serum are expected to be potential serum markers for HCC prognosis. By further applying chemometrics methods to establish prognostic models, excellent prognostic accuracies were achieved within only 3 days after TACE therapy. Thus, the proposed method is expected to provide guidance on timely and rational management of HCC to improve its survival rate.

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.

Surface-enhanced Raman spectroscopy of serum predicts sensitivity to docetaxel-based chemotherapy in patients with metastatic castration-resistant prostate cancer

Docetaxel-based chemotherapy, as the first-line treatment for metastatic castration-resistant prostate cancer (mCRPC), has succeeded in helping quite a number of patients to improve quality of life and prolong survival time. However, almost half of mCRPC patients are not sensitive to docetaxel chemotherapy initially. This study aimed to establish models to predict sensitivity to docetaxel chemotherapy in patients with mCRPC by using serum surface-enhanced Raman spectroscopy (SERS). A total of 32 mCPRC patients who underwent docetaxel chemotherapy at our center from July 2016 to March 2018 were included in this study. Patients were dichotomized in prostate-specific antigen (PSA) response group ([Formula: see text]) versus PSA failure group ([Formula: see text]) according to the response to docetaxel. In total 64 matched spectra from 32 mCRPC patients were obtained by using SERS of serum at baseline ([Formula: see text]0) and after 1 cycle of docetaxel chemotherapy ([Formula: see text]1). Comparing Raman peaks of serum samples at baseline ([Formula: see text]0) between two groups, significant differences revealed at the peaks of 638, 810, 890 ([Formula: see text]) and 1136[Formula: see text]cm[Formula: see text] ([Formula: see text]). The prediction models of peak 1363[Formula: see text]cm[Formula: see text] and principal component analysis and linear discriminant analysis (PCA–LDA) based on Raman data were established, respectively. The sensitivity and specificity of the prediction models were 71%, 80% and 69%, 78% through the way of leave-one-out cross-validation. According to the results of five-cross-validation, the PCA–LDA model revealed an accuracy of 0.73 and AUC of 0.83.

Identification and assessment of pulmonary Cryptococcus neoformans infection by blood serum surface-enhanced Raman spectroscopy

Cryptococcus neoformans (C. neoformans) is a causative agent for acute pulmonary infection, which can further develop to lethal meningoencephalitis if untreated. The meningoencephalitis infection can be prevented, if timely treatment on pulmonary cryptococcal infection can be implemented based on its early diagnosis and accurate assessment. In this study, blood serum surface-enhanced Raman spectroscopy (SERS) method was investigated on identification and assessment of pulmonary C. neoformans infection. The serum SERS measurements were collected from the mice infected with C. neoformans and the healthy mice, in which the infected mice were further divided into four subgroups according to the duration of infection. Based on those SRES measurements, biochemical differences were analyzed among those different groups to investigate the potential biomarkers for identifying and assessing the pulmonary C. neoformans infection. Furthermore, partial least square (PLS) analysis followed by linear discriminant analysis (LDA) model was employed to identify pulmonary cryptococcal infection and to assess the degrees of infection with the accuracies of 96.7% and 85.3%, respectively. Therefore, our study has demonstrated the great clinical potential of using serum SERS technique for an accurate identification and assessment of pulmonary cryptococcal infection.

Identifying functioning and nonfunctioning adrenal tumors based on blood serum surface-enhanced Raman spectroscopy.

Adrenal tumors are common tumors in urology and they can be further divided into functioning and nonfunctioning tumors according to whether there is uncommon endocrine function. In clinical practice, the early identification and accurate assessment of adrenal tumors are essential for the guidance of subsequent treatment. However, a nonfunctioning adrenal tumor often lacks obvious clinical symptoms, making it difficult to be timely and precisely diagnosed by conventional examinations. Therefore, a rapid and accurate method for identifying the functioning and nonfunctioning adrenal tumors is urgently required to achieve precise treatment of adrenal tumors. In this study, surface-enhanced Raman spectroscopy was investigated as a diagnostic tool to identify the blood serum samples from healthy volunteers as well as the patients with functioning and nonfunctioning adrenal tumors. Based on the SERS peak analysis, abnormal glycolysis, DNA/RNA, and amino acid metabolites were found to be potential biomarkers for identifying patients with adrenal tumors, while metabolites related to disordered protein catabolism and excessive hormone secretion were expected to further differentiate functioning adrenal tumors from nonfunctioning adrenal tumors. In addition, principal component analysis followed by support vector machine (PCA-SVM) was further applied on those serum SERS measurements, and the classification accuracies of 96.8% and 84.5% were achieved for differentiating healthy group versus adrenal tumor group and functioning adrenal tumor group versus nonfunctioning adrenal tumor group, respectively. The results have demonstrated the prodigious potential of precise adrenal tumor diagnosis by using the blood serum surface-enhanced Raman spectroscopy technique.

Identifying benign and malignant thyroid nodules based on blood serum surface-enhanced Raman spectroscopy

The aim of this study is to evaluate the feasibility of using blood serum surface-enhanced Raman spectroscopy (SERS) to identify benign and malignant thyroid nodules. Blood serum samples collected from three different groups including healthy volunteers (n = 22), patients with benign nodules (n = 19) and malignant nodules (n = 22) were measured by SERS. The spectral analysis results demonstrate that biomolecules in serum, such as amino acids, adenine and nucleic acid bases, change differently due to the different progression of nodules. By further combining with partial least square analysis and linear discriminant analysis (PLS-LDA) method, diagnostic accuracies of 93.65% and 82.93%, sensitivities of 92.68% and 81.82% and specificities of 95.45% and 84.21% can be achieved for differentiating healthy versus thyroid nodular groups and benign versus malignant groups, respectively. The above results have suggested that the blood serum SERS technique is helpful for precise diagnosis and timely treatment for patients with thyroid nodules.

Label-free serum detection of Trichinella spiralis using surface-enhanced Raman spectroscopy combined with multivariate analysis

Based on blood serum surface-enhanced Raman spectroscopy (SERS) analysis, this paper proposed a simple and unlabeled non-invasive serum detection for T. spiralis infection. Serum samples were collected and analyzed from 40 rats at 0 days post infection (dpi) (normal rats), 19 uninfected rats, and 16 rats infected with T. spiralis at 28 dpi, using SERS measurements. Multivariate statistical techniques, such as linear discriminant analysis (LDA) and principal components analysis (PCA), were used to analyze and identify the obtained blood serum SERS spectra. The diagnosis algorithms, based on PCA-LDA, achieved a diagnostic sensitivity of 87.5%, a specificity of 94.7%, and an accuracy of 91.4% for separating the samples infected with T. spiralis from the control samples. This exploratory study demonstrated that colloidal Ag NPs-based SERS serum analysis technique combined with PCA-LDA has a great potential in improving the detection of T. spiralis infection and onsite screening.

Identifying non-muscle-invasive and muscle-invasive bladder cancer based on blood serum surface-enhanced Raman spectroscopy.

The assessment of the muscle invasion of bladder cancer typically plays a crucial role in therapeutic decision-making and has significant impacts on the recurrence rate and survival rate. Although histopathology is sufficiently accurate and usually served as the gold standard for bladder cancer diagnosis, it is invasive, time-consuming, and requires intensive sample preparation by a well-trained pathologist to achieve an optimal diagnosis. Therefore, a fast and noninvasive method to accurately identify non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is in demand. In this study, the SERS technique combined with the PLS-LDA method based on a small amount of blood serum samples is employed to distinguish healthy volunteers, NMIBC, and MIBC patients. According to the results, the overall diagnostic accuracy is 93.3%. The diagnostic accuracies are 97.8% and 93.2% for healthy versus bladder cancer groups and NMIBC versus MIBC groups, respectively. Therefore, the proposed method has demonstrated excellent performance on accurately identifying muscle invasion of bladder cancer, which can assist timely diagnosis and proper treatment for bladder cancer patients.

Classifying low-grade and high-grade bladder cancer using label-free serum surface-enhanced Raman spectroscopy and support vector machine

This study aims to classify low-grade and high-grade bladder cancer (BC) patients using serum surface-enhanced Raman scattering (SERS) spectra and support vector machine (SVM) algorithms. Serum SERS spectra are acquired from 88 serum samples with silver nanoparticles as the SERS-active substrate. Diagnostic accuracies of 96.4% and 95.4% are obtained when differentiating the serum SERS spectra of all BC patients versus normal subjects and low-grade versus high-grade BC patients, respectively, with optimal SVM classifier models. This study demonstrates that the serum SERS technique combined with SVM has great potential to noninvasively detect and classify high-grade and low-grade BC patients.

Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases

In this study, surface enhanced Raman spectroscopy (SERS) was used to investigate the spectral characteristics of blood serum for the purpose of diagnosing stomach diseases. SERS spectral data was collected from patients with atrophic gastritis, both pre‐operation and post‐operation gastric cancer, and from healthy individuals. Visual differences in the SERS spectra were observed between the four groups which indicate corresponding biomolecule concentration changes in blood. To further investigate the diagnostic ability of human serum, the spectral data was analyzed with three chemometric processes. These three methods extracted features and classified from the spectral data. Principal component analysis (PCA) was first performed to reduce the dimensionality of the original spectral data. Then, the classification methods support vector machine (SVM), linear discriminant analysis (LDA) and classification and regression tree (CART) were used for the evaluation of diagnostic ability. Accuracies of 96.5%, 88.8% and 87.1% were obtained for PCA‐SVM, PCA‐LDA and PCA‐CART, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

Noninvasive liver diseases detection based on serum surface enhanced Raman spectroscopy and statistical analysis.

In this paper, we investigated the feasibility of using surface enhanced Raman spectroscopy (SERS) of blood serum to discriminate liver cancer and liver cirrhosis patients from normal people. Serum taken from 44 healthy people, 45 liver cancer patients, 42 post-treatment liver cancer patients and 45 liver cirrhosis patients was measured. SERS peaks from these groups were compared and the assignments and biomedical meanings were analyzed and explained. In addition, support vector machine (SVM), partial least square-discriminant analysis (PLS-DA) and artificial neural networks (ANN) was used on the obtained SERS spectra to identify its diagnostic potential for liver diseases. PLS-SVM, PLS-DA and PLS-ANN indicated 91.5%, 89.2% and 90.3% accuracy, respectively. This preliminary study demonstrates that serum SERS can be used for liver cancer screening.

Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms.

This study aims to characterize and classify serum surface-enhanced Raman spectroscopy (SERS) spectra between bladder cancer patients and normal volunteers by genetic algorithms (GAs) combined with linear discriminate analysis (LDA). Two group serum SERS spectra excited with nanoparticles are collected from healthy volunteers (n = 36) and bladder cancer patients (n = 55). Six diagnostic Raman bands in the regions of 481–486, 682–687, 1018–1034, 1313–1323, 1450–1459 and 1582–1587 cm−1 related to proteins, nucleic acids and lipids are picked out with the GAs and LDA. By the diagnostic models built with the identified six Raman bands, the improved diagnostic sensitivity of 90.9% and specificity of 100% were acquired for classifying bladder cancer patients from normal serum SERS spectra. The results are superior to the sensitivity of 74.6% and specificity of 97.2% obtained with principal component analysis by the same serum SERS spectra dataset. Receiver operating characteristic (ROC) curves further confirmed the efficiency of diagnostic algorithm based on GA-LDA technique. This exploratory work demonstrates that the serum SERS associated with GA-LDA technique has enormous potential to characterize and non-invasively detect bladder cancer through peripheral blood.