Fast and non-invasive serum detection technology based on surface-enhanced Raman spectroscopy and multivariate statistical analysis for liver disease

Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning

Bladder, kidney, and prostate cancers are prevalent urinary cancers, and developing efficient detection methods is of significance for the early diagnosis of them. However, noninvasive and sensitive detection of urinary cancers still challenges traditional techniques. In this study, we developed a SERS-based method to analyze serum samples from patients with urinary cancers. Rapid, label-free, and highly sensitive detection of human sera is achieved by cleaning and aggregating silver nanoparticles. Furthermore, a long short-term memory deep learning algorithm is used to distinguish serum spectra, and the performance of the model is evaluated by comparing the accuracy, sensitivity, specificity, and receiver operating characteristic curves. Taking advantage of SERS and machine learning in sensitivity and data processing, the three urinary cancers are clearly classified. This is the first attempt to exploit the SERS-machine learning strategy to discriminate multiple urinary cancers with clinical serum samples, and our results showed the potential application of this method in the early diagnosis and screening of cancers.

Insights into the molecular basis of gastric mucosa as a first step for using Raman microscopy in paediatrics

Recent advances in endoscopic technology have allowed detailed observation of the gastric mucosa, including Raman microscopy and spectroscopy. To explore the possibilities for future diagnostic use, we discuss the measurements and molecular markers found in this tissue. The Raman spectra of 16 samples of antral mucosa and 16 samples of corpus gastric mucosa obtained from healthy donors were analysed. A stable protocol for measuring reproducible spectra was established. These data suggest that many biomarkers can be used for the rapid analysis of metabolic states and future investigations into the pathogenesis of gastrointestinal diseases.

SERSomes for metabolic phenotyping and prostate cancer diagnosis

Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.

Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease

Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.

Solid-State Au Nanocone Arrays Substrate for Reliable SERS Profiling of Serum for Disease Diagnosis

Surface-enhanced Raman scattering (SERS) is a widely used rapid and noninvasive method for detecting biological substances in serum samples and is commonly employed in disease screening and diagnosis. Solid-state nanoarray SERS substrates used in serum detection may cause spectral instability due to imperfections in the detection method. For the purpose of identifying optimal detection conditions, various dilution levels of the serum were tested in this study. The study found that a complete and stable serum SERS spectrum can be obtained when the serum is diluted by a factor of 50. The study reports the successful preparation of an Au nanocone array (Au NCA) plasmonic substrate with a uniform, controllable microstructure and high activity, achieved through a combination of PS colloidal sphere template-assisted reactive ion etching (RIE) process and magnetron sputtering deposition technology. Based on this substrate, a standard detection scheme was developed to obtain highly stable and repeatable serum SERS spectra. The study verified the reliability of the optimized serum detection scheme by comparing the SERS spectra of serum samples from healthy individuals and gastric cancer patients, and confirmed the potential benefits of the scheme for disease screening and diagnosis.

Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy

The evaluation of the performance levels in athletic horses is of major importance to prevent sports injuries. Raman spectroscopy is an innovative technique that allows for a rapid evaluation of biomolecules in biological fluids. It also permits qualitative and quantitative sample analyses, which lead to the simultaneous determination of the components of the examined biological fluids. On the basis of this, the Raman spectroscopy technique was applied on serum samples collected from five Italian Saddle horses subjected to a standardized obstacle course preceded by a warm-up to evaluate the applicability of this technique for the assessment of central and peripheral fatigue in athletic horses. Blood samples were collected via jugular venipuncture in a vacutainer tube with a clot activator before exercise, immediately after exercise, and 30 min and 1 h after the end of the obstacle course. Observing the obtained Raman spectra, the major changes due to the experimental conditions appeared in the (1300-1360) cm^-1 and (1385-1520) cm^-1 bands. In the (1300-1360) cm^-1 band, lipids and tryptophan were identified; in the (1385-1520) cm^-1 band, leucine, glycine, isoleucine, lactic acid, tripeptide, adenosine, and beta carotene were identified. A significant effect of exercise was recorded on all the sub-bands. In particular, a change immediately after exercise versus before exercise was found. Moreover, the mean lactic concentration was positively correlated with the Raman area of the sub-band assigned to lactic acid. In this context, the application of Raman spectroscopy on blood serum samples represents a useful technique for secondary-structure protein identification to investigate the metabolic changes that occur in athletic horses during physical exercise.

Non-destructive diagnostic testing of cardiac myxoma by serum confocal Raman microspectroscopy combined with multivariate analysis

The symptoms of cardiac myxoma (CM) mainly occur when the tumor is growing, and the diagnosis is determined by clinical presentation. Unfortunately, there is no evidence that specific blood tests are useful in CM diagnosis. Raman spectroscopy (RS) has emerged as a promising auxiliary diagnostic tool because of its ability to simultaneously detect multiple molecular features without labelling. The objective of this study was to identify spectral markers for CM, one of the most common benign cardiac tumors with insidious onset and rapid progression. In this study, a preliminary analysis was conducted based on serum Raman spectra to obtain the spectral differences between CM patients (CM group) and healthy control subjects (normal group). Principal component analysis-linear discriminant analysis (PCA-LDA) was constructed to highlight the differences in the distribution of biochemical components among the groups according to the obtained spectral information. Principal component analysis was combined with a support vector machine model (PCA-SVM) based on three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)) to resolve spectral variations between all study groups. The results showed that CM patients had lower serum levels of phenylalanine and carotenoid than those in the normal group, and increased levels of fatty acids. The resulting Raman data was used in a multivariate analysis to determine the Raman range that could be used for CM diagnosis. Also, the chemical interpretation of the spectral results obtained is further presented in the discussion section based on the multivariate curve resolution-alternating least squares (MCR-ALS) method. These results suggest that RS can be used as an adjunct and promising tool for CM diagnosis, and that vibrations in the fingerprint region can be used as spectral markers for the disease under study.

Multimodal convolutional neural networks based on the Raman spectra of serum and clinical features for the early diagnosis of prostate cancer

We collected surface-enhanced Raman spectroscopy (SERS) data from the serum of 729 patients with prostate cancer or benign prostatic hyperplasia (BPH), corresponding to their pathological results, and built an artificial intelligence-assisted diagnosis model based on a convolutional neural network (CNN). We then evaluated its value in diagnosing prostate cancer and predicting the Gleason score (GS) using a simple cross-validation method. Our CNN model based on the spectral data for prostate cancer diagnosis revealed an accuracy of 85.14 ± 0.39%. After adjusting the model with patient age and prostate specific antigen (PSA), the accuracy of the multimodal CNN was up to 88.55 ± 0.66%. Our multimodal CNN for distinguishing low-GS/high-GS and GS = 3 + 3/GS = 3 + 4 revealed accuracies of 68 ± 0.58% and 77 ± 0.52%, respectively.

Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases

Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.

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.

Raman spectroscopy differ leukemic cells from their healthy counterparts and screen biomarkers in acute leukemia

Precision medicine is important in the treatment of acute leukemia (AL). The target therapies of AL provide an opportunity to reduce the mortality of AL. How AL cells differ from their healthy counterparts is the basis for the development of therapies and the outcome of AL patients. Therefore, a label-free and noninvasive single-cell Raman platform was used to characterize cell molecular profiles and found potential biomarkers from three healthy people and twelve AL patients with more than 90% accuracy. We analyzed myeloblasts, abnormal promyelocytes, monoblasts and B-ALL cells respectively, compared with their healthy counterparts, which could be distinguished by their intrinsic phenotypic Raman spectra using orthogonal partial least squares discriminate analysis (OPLS-DA). Most importantly, we selected statistically significant markers of the four leukemia models. Further analysis of leukemic granulocytes, we found that a combination of the 1003, 1341 and 1579 cm^-1 Raman peaks could discriminate myeloblasts and abnormal promyelocytes from normal granulocytes. The assignments of 1579 cm^-1 gave us a clue to find potential important variables myeloperoxidase related with AL diagnosis. Our study demonstrates the capability of the Raman platform to characterize leukemia cells with non-invasively probing metabolites. The biomarker we identified could be extensible to other blood cells and potentially have a high impact on leukemia therapy.

Rapid discrimination of glycogen particles originated from different eukaryotic organisms

There are many commercially available glycogen particles in the market due to their bioactive functions as food additive, drug carrier and natural moisturizer, etc. It would be beneficial to rapidly determine the origins of commercially-available glycogen particles, which could facilitate the establishment of quality control methodology for glycogen-containing products. With its non-destructive, label-free and low-cost features, surface enhanced Raman spectroscopy (SERS) is an attractive technique with high potential to discriminate chemical compounds in a rapid mode. In this study, we applied the combination of SERS technique and machine leaning algorithms on glycogen analysis, which successfully predicted the origins of glycogen particles from a variety of organisms with convolutional neural network (CNN) algorithm plus attention mechanism having the best computational performance (5-fold cross validation accuracy = 96.97 %). In sum, this is the first study focusing on the discrimination of commercial glycogen particles originated from different organisms, which holds the application potential in quality control of glycogen-containing products.

Surface-enhanced Raman spectroscopic analysis of the centrifugally filtered blood serum samples of the hepatitis C patients

BACKGROUND

Previously Raman spectroscopy technique is a use to analyze non-invasive disease related to body fluids.

OBJECTIVES

For the qualitative and quantitative analysis of HCV serum samples surface-enhanced Raman spectroscopy (SERS) based method is developed.

METHOD

Surface-enhanced Raman spectroscopy (SERS) technique is employed for analysis of filtrate portions of blood serum samples of hepatitis C virus (HCV) infected patients and healthy ones by using 50 kDa centrifugal filter device. The filtrate portions of the serum obtained in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire SERS spectral features of smaller proteins more effectively which are probably associated with Hepatitis C infection. Moreover, SERS spectral features of the filtrates of different level of viral load including low, medium and high viral loads are compared with SERS spectral features of the filtrate portions of healthy/control serum samples. SERS spectral data sets of different samples are further analyzed by using multivariate data analysis techniques such as principal component analysis (PCA) and partial least square regression (PLSR). Some SERS spectral features are solely observed in the filtrate portions of the serum samples of hepatitis C and their intensities are increased as the level of viral load increases and might be used for HCV diagnosis.

RESULTS

PCA was found helpful for differentiation of SERS spectral data sets of filtrate portions of the serum samples of hepatitis C and healthy persons. The PLSR model helped for the quantification of viral loads in the unknown serum samples with 99 % accuracy.

High Uniformity and Enhancement Au@AgNS 3D Substrates for the Diagnosis of Breast Cancer

Breast cancer appears to be one of the leading causes of cancer-related morbidity and mortality for women worldwide. The accurate and rapid diagnosis of breast cancer is hence critical for the treatment and prognosis of patients. With the vibrational fingerprint information and high detection sensitivity, surface-enhanced Raman spectroscopy (SERS) has been extensively applied in biomedicine. Here, an optimized bimetallic nanosphere (Au@Ag NS) 3D substrate was fabricated for the aim of the diagnosis of breast cancer based on the SERS analysis of the extracellular metabolites. The unique stacking mode of 3D Au@Ag NSs provided multiple plasmonic hot spots according to the theoretical calculations of the electromagnetic field distribution. The low relative standard deviation (RSD = 2.7%) and high enhancement factor (EF = 1.42 × 10⁵) proved the uniformity and high sensitivity. More importantly, the normal breast cells and breast cancer cells could be readily distinguished from the corresponding SERS spectra based on the extracellular metabolites. Furthermore, the clear clusters of SERS spectra from MCF-7 and MDA-MB-231 extracellular metabolites in the orthogonal partial least-squares discriminant analysis plot indicate the distinct metabolic fingerprint between breast cancer cells, which imply their potential clinical application in the diagnosis of breast cancer.

Diagnosis and staging of diffuse large B-cell lymphoma using label-free surface-enhanced Raman spectroscopy

Non-invasive diagnosis and staging of diffuse large B-cell lymphoma (DLBCL) were achieved using label-free surface-enhanced Raman spectroscopy (SERS). SERS spectra were measured for serum samples of DLBCL patients at different progressive stages and healthy controls (HCs), using colloidal silver nano-particles (AgNPs) as the substrate. Differences in the spectral intensities of Raman peaks were observed between the DLBCL and HC groups, and a close correlation between the spectral intensities of Raman peaks with the progressive stages of the cancer was obtained, demonstrating the possibility of diagnosis and staging of the disease using the serum SERS spectra. Multivariate analysis methods, including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) classifier, and k-nearest neighbors (kNN) classifier, were used to build the diagnosis and staging models for DLBCL. Leave-one-out cross-validation was used to evaluate the performances of the models. The kNN model achieved the best performances for both diagnosis and staging of DLBCL: for the diagnosis analysis, the accuracy, sensitivity, and specificity were 87.3%, 0.921, and 0.809, respectively; for the staging analysis between the early (Stage I & II) and the late (Stage III & IV) stages, the accuracy was 90.6%, and the sensitivity values for the early and the late stages were 0.947 and 0.800, respectively. The label-free serum SERS in combination with multivariate analysis could serve as a potential technique for non-invasive diagnosis and staging of DLBCL.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

Non-invasive discrimination of multiple myeloma using label-free serum surface-enhanced Raman scattering spectroscopy in combination with multivariate analysis

We report non-invasive discrimination of multiple myeloma (MM) using label-free serum surface-enhanced Raman scattering (SERS) spectroscopy in combination with multivariate analysis. Colloidal silver nano-particles (AgNPs) were used as the SERS substrate. High quality serum SERS spectra were obtained from 53 MM patients and 44 healthy controls (HCs). The SERS spectral differences demonstrated variation of relative concentrations of biomolecules in the serum of MM patients in comparison to HCs. Multivariate analysis methods, including principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM), were used to build discrimination models for MM. Leave-one-out cross-validation (LOOCV) was used to evaluate the performances of the models, in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUC). Using the SVM model, the accuracy for discrimination of MM was achieved as 78.4%, and the corresponding sensitivity, specificity, and AUC values were 0.830, 0.727, and 0.840, respectively. The results show that the serum SERS in combination with multivariate analysis could be a fast, non-invasive, and cost-effective technique for discrimination of MM.

Quantifying Serum Total Lipids and Tryptophan Concentrations by Raman Spectroscopy During Standardized Obstacle Course in Horses

Raman spectroscopy is an inelastic light-scattering phenomenon that provides vibrational spectrum that contains information relative to chemical bonds and symmetry of a specific molecule, allowing the quali-quantitative simultaneous determination of several components in the biological fluids. Raman spectroscopy measurement returns a spectrum over a wavenumber range constituted by several bands representing biomarkers according to investigated biological matrices. In literature, it has been reported that at the frequencies inside the (1,300-1,360) cm^-1 total lipids, and tryptophan have been identified. On the basis of that, the aim of the present study was to compare the serum concentration of total lipids and tryptophan in horses subjected to a standardized obstacle course, in comparison with the data obtained in the (1,300-1,360) cm^-1 band. At this purpose, five clinically healthy and regularly trained Italian Saddle horses aged between 8, and 10 years old performed with the same rider an obstacle course of 350 m/min with twelve 1.30 m high jumps preceded by warm-up. Blood samples were collected by means of jugular venipuncture into a vacutainer tubes with clot activator at rest, after the exercise, and 30 minutes after the end of exercise. A high correlation was observed between the area of total lipids and tryptophan in the (1,300-1,360) cm^-1 band and their serum concentrations in all experimental conditions. Our preliminary results give a hint to study the exact correspondence between the area that identify these parameters in Raman spectrum and their serum concentration in athletic horses.

Dengue models based on machine learning techniques: A systematic literature review

BACKGROUND

Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.

METHODS

Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified.

RESULTS

Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%.

CONCLUSIONS

We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.

Non-invasive SERS serum detection technology combined with multivariate statistical algorithm for simultaneous screening of cervical cancer and breast cancer

Surface-enhanced Raman scattering (SERS), as a rapid, reliable and non-destructive spectral detection technology, has made a series of breakthrough achievements in screening and pre-diagnosis of various cancerous tumors. In this paper, high-performance gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) active SERS substrates were specially designed for serum testing, and realized highly sensitive detection of serum from healthy people, patients with cervical cancer and breast cancer. Based on the SERS spectra of the three groups of serum, the significant differences between the healthy group and cancer group at 1030 cm^-1 and 1051 cm^-1 were analyzed, and the similar but different serum SERS spectra of cervical cancer and breast cancer patients were compared. In addition, the spectral difference detected by SERS technology combined with a multivariate statistical algorithm was used to distinguish three kinds of serum. The serum SERS spectral sensitive bands were extracted by recursive weighted partial least squares (rPLS), and the three classification diagnosis models were established by combining orthogonal partial least squares discriminant analysis (OPLS-DA), linear discriminant analysis (LDA) and principal component analysis support vector machine (PCA-SVM) for synchronous classification and discrimination of the three groups of serum. The diagnostic results showed that the overall screening accuracy of three models were 93.28%, 97.77% and 94.78%, respectively. These above results confirmed that the Au NPs/785 PSi PhCs can realize super-sensitive detection of serum, and the established diagnostic model has great potential for pre-diagnosis and simultaneous screening of cervical cancer and breast cancer.

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

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.

Repeated double cross-validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients

Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metabolites, confirming that bands of uric acid, hypoxanthine, ergothioneine, and glutathione were indeed used by the PCA-LDA algorithm to classify the spectra.

Distinct stratification of normal liver, hepatocellular carcinoma (HCC), and anticancer nanomedicine-treated- tumor tissues by Raman fingerprinting for HCC therapeutic monitoring

Hepatocellular carcinomas (HCCs) are highly vascularized neoplasms with poor prognosis. Nanomedicine possesses great potential to deliver therapeutics and diagnostics. The new aspect of this study is that we have monitored, for the first time, the Raman responses to microtubule targeted vascular disrupting agents (MTVDA), MTVDA encapsulated non-targeted, and targeted cetuximab polymeric nanocomplexes delivery of combinatorial therapeutics in HCC tumor tissues of mice. Biochemical differences majorly demarcated apoptotic lipid bodies, and characteristic amide-I features. HCC tumor and healthy liver tissues could be stratified. Raman spectroscopy served as an excellent, rapid, sensitive and cost-effective approach for anticancer nanomedicine distinct stratification of MTVDA encapsulated targeted cetuximab polymeric nanocomplex combinatorials, a significant potential for HCC therapeutic monitoring.

An antibody-free liver cancer screening approach based on nanoplasmonics biosensing chips via spectrum-based deep learning

The clinical needs of rapidly screening liver cancer in large populations have asked for a facile and low-cost point-of-care testing (POCT) method. We present a nanoplasmonics biosensing chip (NBC) that would empower antibody-free detection with simplified analysis procedures for POCT. The cheaply fabricable NBC consists of multiple silver nanoparticle-decorated ZnO nanorods on cellulose filter paper and would enable one-drop blood tests through surface-enhanced Raman spectroscopy (SERS) detection. In this work, utilizing such an NBC and deep neural network (DNN) modeling, a direct serological detection platform was constructed for automatically identifying liver cancer within minutes. This chip could enhance Raman signals enough to be applied to POCT. A classification DNN model was established by spectrum-based deep learning with 1140 serum SERS spectra in equal proportions from hepatocellular carcinoma (HCC) patients and healthy individuals, achieving an identification accuracy of 91% on an external validation set of 100 spectra (50 HCC versus 50 healthy). The intelligent platform, based on the biosensing chip and DNN, has the potential for clinical applications and generalizable use in quickly screening or detecting other types of cancer.

Surface enhanced Raman spectroscopy of RNA samples extracted from blood of hepatitis C patients for quantification of viral loads

BACKGROUND

Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis.

OBJECTIVES

To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of HCV from blood samples.

METHODS

SERS was employed to characterize the Hepatitis C viral RNA extracted from different blood samples of hepatitis C virus (HCV) infected patients with predetermined viral loads in comparison with total RNA of healthy individuals. The SERS measurements were performed on 27 extracted RNA samples including low viral loads, medium viral loads, high viral loads and healthy/negative viral load samples. For this purpose, silver nanoparticles (Ag NPs) were used as SERS substrates. Furthermore, multivariate data analysis technique, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data.

RESULTS

The SERS spectral features due to biochemical changes in the extracted RNA samples associated with the increasing viral loads were established which could be employed for HCV diagnostic purpose. PCA was found helpful for the differentiation between Raman spectral data of RNA extracted from hepatitis infected and healthy blood samples. PLSR model is established for the determination of viral loads in HCV positive RNA samples with 99 % accuracy.

CONCLUSION

SERS can be employed for qualitative and quantitative analysis of HCV from blood samples.

Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants

Establishing standardized methods for a consistent analysis of spectral data remains a largely underexplored aspect in surface-enhanced Raman spectroscopy (SERS), particularly applied to biological and biomedical research. Here we propose an effective machine learning classification of protein species with closely resembled spectral profiles by a mixed data processing based on principal component analysis (PCA) applied to multipeak fitting on SERS spectra. This strategy simultaneously assures a successful discrimination of proteins and a thorough characterization of the chemostructural differences among them, ultimately opening up new routes for SERS evolution toward sensing applications and diagnostics of interest in life sciences.

Applications of surface-enhanced Raman spectroscopy in detection fields

Surface-enhanced Raman spectroscopy (SERS) is a Raman spectroscopy technique that has been widely used in food safety, environmental monitoring, medical diagnosis and treatment and drug monitoring because of its high selectivity, sensitivity, rapidness, simplicity and specificity in identifying molecular structures. This review introduces the detection mechanism of SERS and summarizes the most recent progress concerning the use of SERS for the detection and characterization of molecules, providing references for the later research of SERS in detection fields.

Deep convolutional neural networks combine Raman spectral signature of serum for prostate cancer bone metastases screening

Prostate cancer most frequently metastasizes to bone, resulting in abnormal bone metabolism and the release of components into the blood stream. Here, we evaluated the capacity of convolutional neural networks (CNNs) to use Raman data for screening of prostate cancer bone metastases. We used label-free surface-enhanced Raman spectroscopy (SERS) to collect 1281 serum Raman spectra from 427 patients with prostate cancer, and then we constructed a CNN based on LetNet-5 to recognize prostate cancer patients with bone metastases. We then used 5-fold cross-validation method to train and test the CNN model and evaluated its actual performance. Our CNN model for bone metastases detection revealed a mean training accuracy of 99.51% ± 0.23%, mean testing accuracy of 81.70% ± 2.83%, mean testing sensitivity of 80.63% ± 5.07%, and mean testing specificity of 82.82% ± 2.94%.

Liquid biopsy for cancer diagnosis using vibrational spectroscopy: systematic review

Background

Vibrational spectroscopy (VS) is a minimally invasive tool for analysing biological material to detect disease. This study aimed to review its application to human blood for cancer diagnosis.

Methods

A systematic review was undertaken using a keyword electronic database search (MEDLINE, Embase, PubMed, TRIP and Cochrane Library), with all original English‐language manuscripts examining the use of vibrational spectral analysis of human blood for cancer detection. Studies involving fewer than 75 patients in the cancer or control group, animal studies, or where the primary analyte was not blood were excluded.

Results

From 1446 results, six studies (published in 2010–2018) examining brain, bladder, oral, breast, oesophageal and hepatic cancer met the criteria for inclusion, with a total population of 2392 (1316 cancer, 1076 control; 1476 men, 916 women). For cancer detection, reported mean sensitivities in each included study ranged from 79·3 to 98 per cent, with specificities of 82·8–95 per cent and accuracies between 81·1 and 97·1 per cent. Heterogeneity in reporting strategies, methods and outcome measures made meta‐analysis inappropriate.

Conclusion

VS shows high potential for cancer diagnosis, but until there is agreement on uniform standard reporting methods and studies with adequate sample size for valid classification models have been performed, its value in clinical practice will remain uncertain.

Differentiation between stages of non-alcoholic fatty liver diseases using surface-enhanced Raman spectroscopy

Non-alcoholic fatty liver disease (NAFLD) is a chronic disorder progressing from an initial benign accumulation of fat (NAFL) towards steatohepatitis (NASH), a degenerative form that can lead to liver cirrhosis and cancer. The development of non-invasive, rapid and accurate method to diagnose NASH is of high clinical relevance. Surface-enhanced Raman spectroscopy (SERS) of plasma was tested as a method to distinguish NAFL from NASH. SERS spectra from plasma of female patients diagnosed with NAFL (n = 32) and NASH (n = 35) were obtained in few seconds, using a portable Raman spectrometer. The sample consisted of 5 μL of biofluid deposited on paper coated with Ag nanoparticles. The spectra show consistent differences between the NAFL and NASH patients, with the uric acid/hypoxanthine band area ratio statistically different (p-value <0.001) between the two groups. The average figures of merit for a diagnostic test based on these ratios, as derived from a repeated 4-fold cross-validation of a logistic regression model, are all between 0.73 and 0.79, with an average area under the curve of 0.81. We conclude that SERS may be a reliable and rapid method to discriminate NAFLD from NASH.

Diagnosis of active tuberculosis and latent tuberculosis infection based on Raman spectroscopy and surface-enhanced Raman spectroscopy

Current tools for screening LTBI are limited due to the long turnaround time required, cross-reactivity of tuberculin skin test to BCG vaccine and the high cost of interferon gamma release assay (IGRA) tests. We evaluated Raman spectroscopy (RS) for serum-protein fingerprinting from 26 active TB (ATB) cases, 20 LTBI cases, 34 early clearance (EC; TB-exposed persons with undetected infection) and 38 healthy controls (HC). RS at 532 nm using candidate peaks provided 92.31% sensitivity and 90.0% to distinguish ATB from LTBI, 84.62% sensitivity and 89.47% specificity to distinguish ATB from HC and 87.10% sensitivity and 85.0% specificity to distinguish LTBI from EC. RS at 532 nm with the random forest model provided 86.84% sensitivity and 65.0% specificity to distinguish LTBI from HC and 94.74% sensitivity and 87.10% specificity to distinguish EC from HC. Using preliminary sample sets (n = 5 for each TB-infection category), surface-enhanced Raman spectroscopy (SERS) showed high potential diagnostic performance, distinguishing very clearly among all TB-infection categories with 100% sensitivity and specificity. With lower cost, shorter turnaround time and performance comparable to that of IGRAs, our study demonstrated RS and SERS to have high potential for ATB and LTBI diagnosis.

High accuracy detection of malignant pleural effusion based on label-free surface-enhanced Raman spectroscopy and multivariate statistical analysis

The present study aims to diagnose malignant pleural effusion (MPE) based on the identification of distinctive Raman spectra bands. The tests on 83 pleural effusion (PE) samples including 32 benign PE (BPE) and 51 MPE were performed based on rapid and label-free surface-enhanced Raman spectroscopy (SERS). The TiO₂ photo-catalyzed Ag NPs were used as SERS substrate. And the SERS spectra of BPE and MPE were compared and diagnosed through orthogonal partial least squares discriminant analysis (OPLS-DA). The diagnosis results showed that the sensitivity and specificity can reach 92.2% and 93.8%, respectively, based on leave-one-out cross validation. And the area under curve values of MPE was 0.985. This study demonstrated an accurate way of combining Raman spectra of PE with OPLS-DA to identify MPE and BPE.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

Label-free liquid biopsy based on blood circulating DNA detection using SERS-based nanotechnology for nasopharyngeal cancer screening

Development of a sensitive, rapid and easy-to-use liquid biopsy method is of imperative clinical value for point-of-care caner diagnostics. Here, a label-free and modification-free nanotechnology based on surface-enhanced Raman spectroscopy (SERS) was employed for DNA analysis. Using the SERS signals of phosphate backbone as internal standard, quantitative detection for nucleobases was achieved even at single base level. The method combined with principal component analysis and linear discriminant analysis was further applied for real blood circulating DNA detection for the first time, and an ideal diagnostic sensitivity of 83.3% and specificity of 82.5% could be obtained for differentiating the nasopharyngeal cancer from the normal group, demonstrating promising potential as an alternative nanotechnology for nasopharyngeal cancer screening based on liquid biopsy.

Rapid identification and antibiotic susceptibility test of pathogens in blood based on magnetic separation and surface-enhanced Raman scattering

An effective surface-enhanced Raman scattering (SERS) method is presented for the rapid identification and drug sensitivity analysis of pathogens in blood. In a first step, polyethyleneimine-modified magnetic microspheres (Fe₃O₄@PEI) were used to enrich bacteria from blood samples. Next, the Fe₃O₄@PEI@bacteria complex was cultured on both ordinary and drug-sensitive plates. Lastly, the SERS spectra of single colonies were acquired in order to identify different pathogens and their resistant strains by comparison with established standardized bacterial SERS spectras and orthogonal partial least squares discriminant analysis (OPLS-DA) method. Staphylococcus aureus, Acinetobacter baumannii, Pseudomonas aeruginosa and their resistant strains were used to evaluate the performance of the SERS method. The results demonstrate that the method can accurately detect and identify all the tested sensitive and drug-resistant strains of bacteria, including 77 clinical blood infection samples. The method provides a way for rapid identification and susceptibility test of pathogens, and has great potential to replace currently used time-consuming methods. Graphical abstract Schematic presentation of a method for the rapid identification and drug sensitivity analysis of pathogens in blood. It is based on a combination of magnetic separation, SERS fingerprint analysis and orthogonal partial least squares discriminant analysis (OPLS-DA).

Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) has recently gained increasing attention for the detection of trace quantities of biomolecules due to its excellent molecular specificity, ultrasensitivity, and quantitative multiplex ability. Specific single or multiple biomarkers in complex biological environments generate strong and distinct SERS spectral signals when they are in the vicinity of optically active nanoparticles (NPs). When multivariate chemometrics are applied to decipher underlying biomarker patterns, SERS provides qualitative and quantitative information on the inherent biochemical composition and properties that may be indicative of healthy or diseased states. Moreover, SERS allows for differentiation among many closely-related causative agents of diseases exhibiting similar symptoms to guide early prescription of appropriate, targeted and individualised therapeutics. This review provides an overview of recent progress made by the application of SERS in the diagnosis of cancers, microbial and respiratory infections. It is envisaged that recent technology development will help realise full benefits of SERS to gain deeper insights into the pathological pathways for various diseases at the molecular level.

Recent Progress on Liquid Biopsy Analysis using Surface-Enhanced Raman Spectroscopy

Traditional tissue biopsy is limited in understanding heterogeneity and dynamic evolution of tumors. Instead, analyzing circulating cancer markers in various body fluids, commonly referred to as "liquid biopsy", has recently attracted remarkable interest for their great potential to be applied in non-invasive early cancer screening, tumor progression monitoring and therapy response assessment. Among the various approaches developed for liquid biopsy analysis, surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most powerful techniques based on its high sensitivity, specificity, tremendous spectral multiplexing capacity for simultaneous target detection, as well as its unique capability for obtaining intrinsic fingerprint spectra of biomolecules. In this review, we will first briefly explain the mechanism of SERS, and then introduce recently reported SERS-based techniques for detection of circulating cancer markers including circulating tumor cells, exosomes, circulating tumor DNAs, microRNAs and cancer-related proteins. Cancer diagnosis based on SERS analysis of bulk body fluids will also be included. In the end, we will summarize the "state of the art" technologies of SERS-based platforms and discuss the challenges of translating them into clinical settings.

Application of surface-enhanced Raman spectroscopy in diagnosis and staging of gastric cancer

AIM

To investigate the value of surface-enhanced Raman spectroscopy (SERS) in the diagnosis of gastric cancer (GC) as well as its feasibility in distinguishing GC of different TNM stages.

METHODS

InVia laser confocal microscope-Raman spectrometer was used to examine the sera of patients with pathologically confirmed GC (63 cases), those with gastric precursor lesions (45 cases), and healthy volunteers (50 cases). One-way ANOVA, Student's t test, principal component analysis (PCA), and linear discriminant analysis (LDA) were used to process and analyze the Raman spectral data, and the receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficiency.

RESULTS

The average SERS spectra of sera differed significantly among GC patients, patients with precancerous lesions, and healthy volunteers. The intensity of Raman spectra located at 725, 1099, 1133, and 1589/cm was significantly stronger in GC patients than in normal controls, while the intensity of Raman spectra at 1004, 1328, 1446, and 1657/cm was significantly stronger in normal persons. A strong enhancement in the intensity of the peak at approximately 815/cm was observed in the spectra of the serum of GC patients. At the Raman shift of 1133, 1446, and 1589/cm, Raman intensity for serum samples was significantly stronger in GC patients with TNM stage Ⅲ/Ⅳ disease than in those with stage Ⅰ/Ⅱ disease, while the Raman intensity at the Raman shift of 1004/cm was significantly stronger in patients with stage Ⅰ/Ⅱ disease. The sensitivity, specificity, and accuracy of SERS combined with multivariate PCA-LDA in diagnosing GC were 96.8% (61/63), 78% (39/50), and 88.5% (100/113), respectively, and the area under the ROC curve was 0.927. The sensitivity, specificity, and accuracy of SERS combined with multivariate PCA-LDA in distinguishing TNM stage Ⅰ/Ⅱ GC and stage Ⅲ/Ⅳ disease were 97.5% (39/40), 73.9% (17/23), and 88.9% (56/63), respectively, and the area under the ROC curve was 0.857.

CONCLUSION

Detection and analysis of sera based on SERS can effectively identify patients with GC, those with gastric precancerous lesions, and healthy volunteers. Furthermore, it can effectively distinguish GC of different stages. SERS is expected to become a new method for early diagnosis, clinical decision guidance, and prognosis evaluation of GC.

Dual dye-loaded Au@Ag coupled to a lateral flow immunoassay for the accurate and sensitive detection of Mycoplasma pneumoniae infection

We present an attractive model of surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-LFIA) for the sensitive and accurate detection of Mycoplasma pneumoniae (MP) infection in human serum. The SERS-LFIA strip uses Au@Ag nanoparticles (Au@Ag NPs) loaded with two layers of Raman dye 5,5′-dithiobis-(2-nitrobenzoic acid) (DTNB) as SERS tags. The advantages of the dual dye-loaded SERS tags (Au/DTNB@Ag/DTNB) are the high sensitivity and the bioconjugation flexibility of the detection antibody. As determined from our SERS-LFIA strip, human IgM was quantified by monitoring the SERS signal on the test line. The limit of detection for human IgM was 0.1 ng mL⁻¹, which was 100 times more sensitive than that by using the colorimetric method. Our assay results for 20 MP-specific IgM positive serum specimens showed 100% accuracy and detection rate, whereas the parallel enzyme-linked immunosorbent assay only showed 85% detection rate. The SERS-LFIA strip also exhibited high specificity and potential clinical applications. Therefore, our SERS-based LFIA strip has strong potential for practical applications in the sensitive and rapid detection of MP.

Schematic illustration of quantitative detection of human IgM using SERS-based lateral flow immunoassay.