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

Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific "metabolic fingerprints" of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are "in-house" protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy

BACKGROUND

The incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers.

OBJECTIVES

To explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC).

METHODS

SERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC).

RESULTS

There are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm-1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively.

CONCLUSIONS

Label-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.

Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis

Bladder cancer is the most common urological malignancy worldwide, and its high recurrence rate leads to poor survival outcomes. The effect of anticancer drug treatment varies significantly depending on individual patients and the extent of drug resistance. In this study, we developed a validation system based on an organ-on-a-chip integrated with artificial intelligence technologies to predict resistance to anticancer drugs in bladder cancer. As a proof-of-concept, we utilized the gemcitabine-resistant bladder cancer cell line T24 with four distinct levels of drug resistance (parental, early, intermediate, and late). These cells were co-cultured with endothelial cells in a 3D microfluidic chip. A dataset comprising 2,674 cell images from the chips was analyzed using a convolutional neural network (CNN) to distinguish the extent of drug resistance among the four cell groups. The CNN achieved 95.2% accuracy upon employing data augmentation and a step decay learning rate with an initial value of 0.001. The average diagnostic sensitivity and specificity were 90.5% and 96.8%, respectively, and all area under the curve (AUC) values were over 0.988. Our proposed method demonstrated excellent performance in accurately identifying the extent of drug resistance, which can assist in the prediction of drug responses and in determining the appropriate treatment for bladder cancer patients.

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

Label-free SERS detection of prostate cancer based on multi-layer perceptron surrogate model method

Prior surface-enhanced Raman spectroscopy (SERS) research has shown that pre-processing is necessary before analysis. Pre-processing also typically serves the dual purposes of removing the auto-fluorescence background and minimizing data volatility. This method allows for a more accurate comparison of spectral traits and relative SERS peak strength. However, because there are so many different kinds of samples, it can take a long time, and there is no assurance that the approach chosen will work well with a particular kind of sample. Therefore, this study employed a deep learning technique called multi-layer perceptron (MLP) to simplify the pre-processing of blood plasma SERS samples in patients with prostate cancer (PC), as well as to enhance the sensitivity and specificity of diagnosis using SERS technology. First of all, significant variations in peak intensity can be observed in the difference spectra, facilitating differentiation between PC and normal groups. Second, the data analysis was carried out in three different stages (raw data, defluorescenced data, and normalized data) using principal component analysis and linear discriminant analysis (PCA-LDA), as well as PCA-multi-layer perceptron (PCA-MLP). Finally, when SERS data was analyzed using PCA-LDA, there were significant differences in classification accuracy across each stage (The classification accuracy of three different stages were 76.90%, 85.60%, 95.20%, respectively). However, when PCA-MLP was utilized for SERS data analysis, the classification accuracy remained consistently high and stable (The classification accuracy of three different stages were 92.00%, 92.40%, 96.70%, respectively). The experimental results of PCA-MLP for classifying specific SERS data indicate that analyzing raw data directly can simplify the experimental process and enhance the efficacy of SERS analysis.

High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis

Renal cell carcinoma (RCC) represents the sixth most frequently diagnosed cancer in men and is asymptomatic, being detected mostly incidentally. The apparition of symptoms correlates with advanced disease, aggressive histology, and poor outcomes. The development of the Surface-Enhanced Raman Scattering (SERS) technique opened the way for investigating and detecting small molecules, especially in biological liquids such as serum or blood plasma, urine, saliva, and tears, and was proposed as a simple technique for the diagnosis of various diseases, including cancer. In this study, we investigated the use of serum label-free SERS combined with two multivariate analysis tests: Principal Component Analysis combined with Linear Discriminate Analysis (PCA-LDA) and Supported Vector Machine (SVM) for the discrimination of 50 RCC cancer patients from 45 apparently healthy donors. In the case of LDA-PCA, we obtained a discrimination accuracy of 100% using 12 principal components and a quadratic discrimination function. The accuracy of discrimination between RCC stages was 88%. In the case of the SVM approach, we obtained a training accuracy of 100%, a validation accuracy of 92% for the discrimination between RCC and controls, and an accuracy of 81% for the discrimination between stages. We also performed standard statistical tests aimed at improving the assignment of the SERS vibration bands, which, according to our data, are mainly due to purinic metabolites (uric acid and hypoxanthine). Moreover, our results using these assignments and Student's t-test suggest that the main differences in the SERS spectra of RCC patients are due to an increase in the uric acid concentration (a conclusion in agreement with recent literature), while the hypoxanthine concentration is not statistically different between the two groups. Our results demonstrate that label-free SERS combined with chemometrics holds great promise for non-invasive and early detection of RCC. However, more studies are needed to validate this approach, especially when combined with other urological diseases.

Rapid and precise detection of cancers via label-free SERS and deep learning

Early, express, and reliable detection of cancer can provide a favorable prognosis and decrease mortality. Tumor biomarkers have been proven to be closely related to tumor occurrence and development. Conventional tumor biomarker detection based on genomic, proteomic, and metabolomic methods is time and equipment-consuming and always needs a specific target marker. Surface-enhanced Raman scattering (SERS), as a non-invasive ultrasensitive and label-free vibrational spectroscopy technique, can detect cancer-related biomedical changes in biofluids. In this paper, 110 serum samples were collected from 30 healthy controls and 80 cancer patients (including 30 bladder cancer (BC), 30 adrenal cancer (AC), and 20 acute myeloid leukemia (AML)). One microliter of blood serum was mixed with 1 μl silver colloid and then was air-dried for SERS measurements. After spectral data augmentation, one-dimensional convolutional neural network (1D-CNN) was proposed for precise and rapid identification of healthy and three different cancers with high accuracy of 98.27%. After gradient-weighted class activation mapping (Grad-CAM) based spectral interpretation, the contributions of SERS peaks corresponding to biochemical substances indicated the most potential biomarkers, i.e., L-tyrosine in bladder cancer; acetoacetate and riboflavin in adrenal cancer and phospholipids, amide-I, and α-Helix in acute myeloid leukemia, which might provide an insight into the mechanism of intelligent diagnosis of different cancers based on label-free serum SERS. The integration of label-free SERS and deep learning has great potential for the rapid, reliable, and non-invasive detection of cancers, which may significantly improve the precise diagnosis in clinical practice.

Combining fiber optical tweezers and Raman spectroscopy for rapid identification of melanoma

Cutaneous melanoma is a skin tumor with a high degree of malignancy and fatality rate, the incidence of which has increased in recent years. Therefore, a rapid and sensitive diagnostic technique of melanoma cells is urgently needed. In this paper, we present a new approach using fiber optical tweezers to manipulate melanoma cells to measure their Raman spectra. Then, combined with Principal Component Analysis and Support Vector Machines (PCA-SVM) classification model, to achieve the classification of common mutant, wild-type and drug-resistant melanoma cells. A total of 150 Raman spectra of 30 cells were collected from mutant, wild-type and drug-resistant melanoma cell lines, and the classification accuracy was 92%, 94%, 97.5%, respectively. These results suggest that the study of tumor cells based on fiber optical tweezers and Raman spectroscopy is a promising method for early and rapid identification and diagnosis of tumor cells.

Trends in Application of SERS Substrates beyond Ag and Au, and Their Role in Bioanalysis

This article compares the applications of traditional gold and silver-based SERS substrates and less conventional (Pd/Pt, Cu, Al, Si-based) SERS substrates, focusing on sensing, biosensing, and clinical analysis. In recent decades plethora of new biosensing and clinical SERS applications have fueled the search for more cost-effective, scalable, and stable substrates since traditional gold and silver-based substrates are quite expensive, prone to corrosion, contamination and non-specific binding, particularly by S-containing compounds. Following that, we briefly described our experimental experience with Si and Al-based SERS substrates and systematically analyzed the literature on SERS on substrate materials such as Pd/Pt, Cu, Al, and Si. We tabulated and discussed figures of merit such as enhancement factor (EF) and limit of detection (LOD) from analytical applications of these substrates. The results of the comparison showed that Pd/Pt substrates are not practical due to their high cost; Cu-based substrates are less stable and produce lower signal enhancement. Si and Al-based substrates showed promising results, particularly in combination with gold and silver nanostructures since they could produce comparable EFs and LODs as conventional substrates. In addition, their stability and relatively low cost make them viable alternatives for gold and silver-based substrates. Finally, this review highlighted and compared the clinical performance of non-traditional SERS substrates and traditional gold and silver SERS substrates. We discovered that if we take the average sensitivity, specificity, and accuracy of clinical SERS assays reported in the literature, those parameters, particularly accuracy (93-94%), are similar for SERS bioassays on AgNP@Al, Si-based, Au-based, and Ag-based substrates. We hope that this review will encourage research into SERS biosensing on aluminum, silicon, and some other substrates. These Al and Si based substrates may respond efficiently to the major challenges to the SERS practical application. For instance, they may be not only less expensive, e.g., Al foil, but also in some cases more selective and sometimes more reproducible, when compared to gold-only or silver-only based SERS substrates. Overall, it may result in a greater diversity of applicable SERS substrates, allowing for better optimization and selection of the SERS substrate for a specific sensing/biosensing or clinical application.

Label-Free Sensing with Metal Nanostructure-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for the detection of small analytes with great potential for medical diagnostic applications. Its high sensitivity and excellent molecular specificity, which stems from the unique fingerprint of molecular species, have been applied toward the detection of different types of cancer. The noninvasive and rapid detection offered by SERS highlights its applicability for point-of-care (PoC) deployment for cancer diagnosis, screening, and staging, as well as for predicting tumor recurrence and treatment monitoring. This review provides an overview of the progress in label-free (direct) SERS-based chemical detection for cancer diagnosis with the main focus on the advances in the design and preparation of SERS substrates on the basis of metal nanoparticle structures formed via bottom-up strategies. It begins by introducing a synopsis of the working principles of SERS, including key chemometric approaches for spectroscopic data analysis. Then it introduces the advances of label-free sensing with SERS in cancer diagnosis using biofluids (blood, urine, saliva, sweat) and breath as the detection media. In the end, an outlook of the advances and challenges in cancer diagnosis via SERS is provided.

Fibre optic Raman spectroscopy for the evaluation of disease state in Duchenne muscular dystrophy: an assessment using the mdx model and human muscle

Introduction/Aims

Raman spectroscopy is an emerging technique for the evaluation of muscle disease. In this study we evaluate the ability of in vivo intramuscular Raman spectroscopy to detect the effects of voluntary running in the mdx model of Duchenne muscular dystrophy (DMD). We also compare mdx data with muscle spectra from human DMD patients.

Methods

Thirty 90‐day‐old mdx mice were randomly allocated to an exercised group (48‐hour access to a running wheel) and an unexercised group (n = 15 per group). In vivo Raman spectra were collected from both gastrocnemius muscles and histopathological assessment subsequently performed. Raman data were analyzed using principal component analysis–fed linear discriminant analysis (PCA‐LDA). Exercised and unexercised mdx muscle spectra were compared with human DMD samples using cosine similarity.

Results

Exercised mice ran an average of 6.5 km over 48 hours, which induced a significant increase in muscle necrosis (P = .03). PCA‐LDA scores were significantly different between the exercised and unexercised groups (P < .0001) and correlated significantly with distance run (P = .01). Raman spectra from exercised mice more closely resembled human spectra than those from unexercised mice.

Discussion

Raman spectroscopy provides a readout of the biochemical alterations in muscle in both the mdx mouse and human DMD muscle.

Cancer Detection and Prediction Using Genetic Algorithms

Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

BACKGROUND

Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine.

METHODS

Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types.

RESULTS

Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy.

CONCLUSION

miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

Vibrational spectroscopy provides the possibility for sensitive and precise detection of chemical changes in biomolecules due to development of cancers. In this work, label-free near-infrared surface enhanced Raman spectroscopy (SERS) was applied for the differentiation between cancerous and normal human bladder tissues via analysis of the extracellular fluid of the tissue. Specific cancer-related SERS marker bands were identified by using a 1064 nm excitation wavelength. The prominent spectral marker band was found to be located near 1052 cm^-1 and was assigned to the C-C, C-O, and C-N stretching vibrations of lactic acid and/or cysteine molecules. The correct identification of 80% of samples is achieved with even limited data set and could be further improved. The further development of such a detection method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during the surgery.

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.

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.

Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM

Crohn's disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, and clinical and pathological examinations are further required to confirm the diagnosis. Hence, the development of a non-invasive, rapid and accurate diagnosis method for CD patients is essential. In this study, urine samples from 95 CD patients (including 58 active CD (aCD) patients and 37 inactive CD (iCD) patients) and 48 healthy controls (HC) were investigated by surface-enhanced Raman spectroscopy (SERS). The statistical analysis of the three groups (i.e., CD/HC, aCD/HC and iCD/HC) was performed on the measured data. Principal component analysis (PCA)-support vector machine (SVM) and PCA-linear discriminant analysis (LDA) were then employed to establish classification models to distinguish between patients and HC. For the average SERS spectra of patients and HC, the Raman peaks belonging to lipids, proteins and nucleic acids were stronger in patients than those in HC. It showed that the classification accuracy of CD/HC based on PCA-SVM was higher than that of PCA-LDA (82.5% vs. 69.9%). And the classification accuracy of aCD/HC based on PCA-SVM was higher than that of iCD/HC (86.8% vs. 76.5%). The classification model we established distinguished between aCD and HC with 86.2% sensitivity and 87.5% specificity. It indicates that the metabolic change of patients could be identified by measuring urine with SERS, and aCD and HC could be distinguished more effectively. Our findings are helpful for clinicians to diagnose CD patients and monitor the progress and recurrence of the disease.

Raman spectroscopy of blood and urine liquid biopsies for ovarian cancer diagnosis: identification of chemotherapy effects

Blood plasma and serum Raman spectroscopy for ovarian cancer diagnosis has been applied in pilot studies, with promising results. Herein, a comparative analysis of these biofluids, with a novel assessment of urine, was conducted by Raman spectroscopy application in a large patient cohort. Spectra were obtained through samples measurements from 116 ovarian cancer patients and 307 controls. Principal component analysis identified significant spectral differences between cancers without previous treatment (n = 71) and following neo-adjuvant chemotherapy (NACT), (n = 45). Application of five classification algorithms achieved up to 73% sensitivity for plasma, high specificities and accuracies for both blood biofluids, and lower performance for urine. A drop in sensitivities for the NACT group in plasma and serum, with an opposite trend in urine, suggest that Raman spectroscopy could identify chemotherapy-related changes. This study confirms that biofluids' Raman spectroscopy can contribute in ovarian cancer's diagnostic work-up and demonstrates its potential in monitoring treatment response.

Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Raman Spectrum-Based Diagnosis Strategy for Bladder Tumor

Raman spectroscopy is an optical technique that can potentially serve as a molecular diagnosis method. This approach is excellent in many aspects for diagnosing bladder tumors, and over the last 20 years, there has been a rapid increase in the number of related studies. However, no review article has covered the wide use of Raman spectroscopy in bladder tumors. A total of 26 original studies have suggested that Raman spectroscopy shows good performance in diagnosing bladder tumors from 4 aspects, including tissue sections, endoscopic methods, cell screening, and biomarkers. However, Raman spectroscopy needs to be modified by combining it with other techniques, and studies based on a large population are still urgently needed to expand its clinical value.

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 β carotene and phenylalanine and an increase in the percentage of tryptophan, tyrosine, and glutamic acid contents in the serum of echinococcosis patients as compared to that of healthy subjects. Then, principal components analysis (PCA), combined with linear discriminant analysis (LDA), were adopted to distinguish echinococcosis patients from healthy volunteers. Based on the area under the ROC curve (AUC) value, the derivative Raman + AF spectral data set achieved the optimal results. The AUC value was improved by 0.08 for derivative Raman + AF (AUC = 0.98), compared to Raman alone. The results demonstrated that the fusion of derivative Raman and AF could effectively improve the performance of the diagnostic model, and this technique has great application potential in the clinical screening of echinococcosis.

Rapid and label-free urine test based on surface-enhanced Raman spectroscopy for the non-invasive detection of colorectal cancer at different stages

The concept of being able to urinate in a cup and screen for colorectal cancer (CRC) is fascinating to the public at large. Here, a simple and label-free urine test based on surface-enhanced Raman spectroscopy (SERS) was employed for CRC detection. Significant spectral differences among normal, stages I-II, and stages III-IV CRC urines were observed. Using discriminant function analysis, the diagnostic sensitivities of 95.8%, 80.9%, and 84.3% for classification of normal, stages I-II, and stages III-IV CRC were achieved in training model, indicating the great promise of urine SERS as a rapid, convenient and noninvasive method for CRC staging detection.

Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum

BACKGROUND

Colorectal cancer (CRC) is an important disease worldwide, accounting for the second highest number of cancer-related deaths and the third highest number of new cancer cases. The blood test is a simple and minimally invasive diagnostic test. However, there is currently no blood test that can accurately diagnose CRC.

AIM

To develop a comprehensive, spontaneous, minimally invasive, label-free, blood-based CRC screening technique based on Raman spectroscopy.

METHODS

We used Raman spectra recorded using 184 serum samples obtained from patients undergoing colonoscopies. Patients with malignant tumor histories as well as those with cancers in organs other than the large intestine were excluded. Consequently, the specific diseases of 184 patients were CRC (12), rectal neuroendocrine tumor (2), colorectal adenoma (68), colorectal hyperplastic polyp (18), and others (84). We used the 1064-nm wavelength laser for excitation. The power of the laser was set to 200 mW.

RESULTS

Use of the recorded Raman spectra as training data allowed the construction of a boosted tree CRC prediction model based on machine learning. Therefore, the generalized R² values for CRC, adenomas, hyperplastic polyps, and neuroendocrine tumors were 0.9982, 0.9630, 0.9962, and 0.9986, respectively.

CONCLUSION

For machine learning using Raman spectral data, a highly accurate CRC prediction model with a high R² value was constructed. We are currently planning studies to demonstrate the accuracy of this model with a large amount of additional data.

Surface-enhanced Raman scattering for the diagnosis of ulcerative colitis: will it change the rules of the game?

Ulcerative colitis (UC) is a relapsing-remitting inflammatory bowel disease that requires numerous costly invasive investigations which lead to physical and psychological patient discomfort. We need a non-invasive technological approach that would significantly improve its diagnosis. Surface-enhanced Raman scattering (SERS) is a growing technique that can provide a molecular diagnostic fingerprint in just a few minutes, without the need for prior sample preparation. The aim of this pilot in vivo study was to prove that multivariate analysis of SER spectra collected on plasma samples could be employed for non-invasive diagnosis of UC. Plasma samples were collected from healthy subjects (n = 35) and patients with UC (n = 28). SERS spectra were acquired using a 785-nm excitation laser line and a solid plasmonic substrate developed in our laboratory using an original procedure described in the literature. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). PCA-LDA differentiated UC samples from those of healthy subjects with a sensitivity of 86%, a specificity of 92%, and an accuracy of 89%, the AUC being 0.96. The PLS-DA analysis resulted in a sensitivity of 89%, a specificity of 94%, an accuracy of 92%, and an AUC value of 0.92. Several spectral bands were associated with UC: 376-420, 440-513, 686-715, 919-939, 1035-1062, 1083-1093, 1120-1132, 1148-1156, 1191-1211, 1234-1262, 1275-1294, 1382-1405, 1511-1526, and 1693-1702 cm^-1. Changes in plasma levels of amino acids, proteins, lipids, and other compounds were noted using SERS in patients with UC. Multivariate analysis of SER spectra collected on a solid plasmonic substrate represents a promising alternative to diagnosing UC, as it is non-invasive, easy to use, and fast.

Diagnostic spectro-cytology revealing differential recognition of cervical cancer lesions by label-free surface enhanced Raman fingerprints and chemometrics

Herein we have stepped-up on a strategic spectroscopic modality by utilizing label free ultrasensitive surface enhanced Raman scattering (SERS) technique to generate a differential spectral fingerprint for the prediction of normal (NRML), high-grade intraepithelial lesion (HSIL) and cervical squamous cell carcinoma (CSCC) from exfoliated cell samples of cervix. Three different approaches i.e. single-cell, cell-pellet and extracted DNA from oncology clinic as confirmed by Pap test and HPV PCR were employed. Gold nanoparticles as the SERS substrate favored the increment of Raman intensity exhibited signature identity for Amide III/Nucleobases and carotenoid/glycogen respectively for establishing the empirical discrimination. Moreover, all the spectral invention was subjected to chemometrics including Support Vector Machine (SVM) which furnished an average diagnostic accuracy of 94%, 74% and 92% of the three grades. Combined SERS read-out and machine learning technique in field trial promises its potential to reduce the incidence in low resource countries.

Solid Plasmonic Substrates for Breast Cancer Detection by Means of SERS Analysis of Blood Plasma

Surface enhanced Raman spectroscopy (SERS) represents a promising technique in providing specific molecular information that could have a major impact in biomedical applications, such as early cancer detection. SERS requires the presence of a suitable plasmonic substrate that can generate enhanced and reproducible diagnostic relevant spectra. In this paper, we propose a new approach for the synthesis of such a substrate, by using concentrated silver nanoparticles purified using the Tangential Flow Filtration method. The capacity of our substrates to generate reproducible and enhanced Raman signals, in a manner that can allow cancer detection by means of Multivariate Analysis (MVA) of Surface Enhanced Raman (SER) spectra, has been tested on blood plasma samples collected from 35 healthy donors and 29 breast cancer patients. All the spectra were analyzed by a combined Principal Component-Linear Discriminant Analysis. Our results facilitated the discrimination between healthy donors and breast cancer patients with 90% sensitivity, 89% specificity and 89% accuracy. This is a direct consequence of substrates’ ability to generate diagnostic relevant spectral information by performing SERS measurements on pristine blood plasma samples. Our results suggest that this type of solid substrate could be employed for the detection of other types of cancer or other diseases by means of MVA-SERS procedure.

Scalable nanolaminated SERS multiwell cell culture assay

This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.

Surface-enhanced Raman spectroscopy of preoperative serum samples predicts Gleason grade group upgrade in biopsy Gleason grade group 1 prostate cancer

PURPOSE

To predict Gleason grade group (GG) upgrade in biopsy Gleason grade group 1 (GG1) prostate cancer (CaP) patients using surface-enhanced Raman spectroscopy (SERS).

MATERIALS AND METHODS

Preoperative serum samples of patients with biopsy GG1 and subsequent radical prostatectomy were analyzed using SERS. The role of clinical variables and distinctive SERS spectra in the prediction of GG upgrade were evaluated. Principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage spectral data and develop diagnostic algorithms.

RESULTS

A total of 342 preoperative serum SERS spectra from 114 patients were obtained. SERS detected a higher level of circulating free nucleic acid bases and a lower level of lipids in patients with GG upgrade to GG3 and higher, presenting as SERS spectral peaks of 728 cm^-1 and 1,655 cm^-1, respectively. Both spectral peaks were independent predictors of GG upgrade and their addition to clinical predictors of PSA and positive core percent significantly improved predictive power of the logistic regression model with area under curve improved from 0.65 to 0.80 (P = 0.0045). Meanwhile, PCA-LDA diagnostic model based on serum SERS spectra showed a high accuracy of 91.2% in predicted groups and remained stable with a sensitivity, specificity, and accuracy of 65%, 97.3%, 86.0%, respectively when validated by leave-one-out cross-validation method.

CONCLUSIONS

By analyzing preoperative serum samples, SERS combined with PCA-LDA model could be a promising tool for prediction of Gleason GG upgrade in biopsy GG1 CaP and assist in treatment decision-making in clinical practice.

A Review of Chinese Raman Spectroscopy Research Over the Past Twenty Years

This paper introduces the major Chinese research groups in the fields of biomedicine, food safety, environmental testing, material research, archaeological and cultural relics, gem identification, forensic science, and other research areas of Raman spectroscopy and combined methods spanning the two decades from 1997 to 2017. Briefly summarized are the research directions and contents of the major Chinese Raman spectroscopy research groups, giving researchers engaged in Raman spectroscopy research a more comprehensive understanding of the state of Chinese Raman spectroscopy research and future development trends to further develop Raman spectroscopy and its applications.

Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting

Label-free surface-enhanced Raman spectroscopy (SERS) can interrogate systems by directly fingerprinting their components' unique physicochemical properties. In complex biological systems however, this can yield highly overlapping spectra that hinder sample identification. Here, we present an artificial-nose inspired SERS fingerprinting approach where spectral data is obtained as a function of sensor surface chemical functionality. Supported by molecular dynamics modeling, we show that mildly selective self-assembled monolayers can influence the strength and configuration in which analytes interact with plasmonic surfaces, diversifying the resulting SERS fingerprints. Since each sensor generates a modulated signature, the implicit value of increasing the dimensionality of datasets is shown using cell lysates for all possible combinations of up to 9 fingerprints. Reliable improvements in mean discriminatory accuracy towards 100% are achieved with each additional surface functionality. This arrayed label-free platform illustrates the wide-ranging potential of high-dimensionality artificial-nose based sensing systems for more reliable assessment of complex biological matrices.

Efficacy of Raman spectroscopy in the diagnosis of bladder cancer: A systematic review and meta-analysis

BACKGROUND

Bladder cancer is one of the severest human malignancies which are hardly detected at an early stage. Raman spectroscopy is reported to maintain a high diagnostic accuracy, sensitivity and specificity in some tumors.

METHODS

We carried out a complete systematic review based on articles from PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, Cochrane Library and CNKI. We identified 2341 spectra with strict criteria in 9 individual studies between 2004 and 2018 in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We summarized the test performance using random effects models.

RESULTS

General pooled diagnostic sensitivity and specificity of RS to kidney cancer were 94% (95% CI 0.93-0.95) and 92% (95% CI 0.90-0.93). The pooled positive LR was 10.00 (95%CI 5.66-17.65) while the negative LR was 0.09 (95%CI 0.06-0.14). The pooled DOR was 139.53 (95% CI 54.60-356.58). The AUC of SROC was 0.9717.

CONCLUSION

Through this meta-analysis, we found a promisingly high sensitivity and specificity of RS in the diagnosis of suspected bladder masses and tumors. Other parameters like positive, negative LR, DOR, and AUC of the SROC curve all helped to illustrate the high efficacy of RS in bladder cancer diagnosis.

Analysis of Human Colon by Raman Spectroscopy and Imaging-Elucidation of Biochemical Changes in Carcinogenesis

Noninvasive Raman imaging of non-fixed and unstained human colon tissues based on vibrational properties of noncancerous and cancerous samples can effectively enable the differentiation between noncancerous and tumor tissues. This work aimed to evaluate the biochemical characteristics of colon cancer and the clinical merits of multivariate Raman image and spectroscopy analysis. Tissue samples were collected during routine surgery. The non-fixed, fresh samples were used to prepare micrometer sections from the tumor mass and the tissue from the safety margins outside of the tumor mass. Adjacent sections were used for typical histological analysis. We have found that the chemical composition identified by Raman spectroscopy of the cancerous and the noncancerous colon samples is sufficiently different to distinguish pathologically changed tissue from noncancerous tissue. We present a detailed analysis of Raman spectra for the human noncancerous and cancerous colon tissue. The multivariate analysis of the intensities of lipids/proteins/carotenoids Raman peaks shows that these classes of compounds can statistically divide analyzed samples into noncancerous and pathological groups, reaffirming that Raman imaging is a powerful technique for the histochemical analysis of human tissues. Raman biomarkers based on ratios for lipids/proteins/carotenoids content were found to be the most useful biomarkers in spectroscopic diagnostics.

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.

Fiber-Optic Raman Spectroscopy with Nature-Inspired Genetic Algorithms Enhances Real-Time in Vivo Detection and Diagnosis of Nasopharyngeal Carcinoma

Raman spectroscopy is an optical vibrational spectroscopic technique capable of probing specific biochemical structures and conformation of tissue and cells in biomedical systems. This work aims to assess the clinical utility of a fiber-optic Raman spectroscopy with nature-inspired genetic algorithms for enhancing in vivo detection and diagnosis of nasopharyngeal carcinoma (NPC) patients. The Raman diagnostic platform is developed based on simultaneous fingerprint (FP) and high-wavenumber (HW) fiber-optic Raman endoscopy associated with genetic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA). A total of 2126 in vivo FP/HW Raman spectra (598 NPC, 1528 normal) acquired from 113 tissue sites of 14 NPC patients and 48 healthy subjects during nasopharyngeal endoscopic examinations. Distinct Raman peaks have been identified (853 cm^-1 - proteins, 1209 cm^-1 - phenylalanine, 1265 cm^-1 - proteins, 1335 cm^-1 - proteins and nucleic acids, 1554 cm^-1 - tryptophan, porphyrin, 2885 cm^-1 - lipids, 2940 cm^-1 - proteins, 3009 cm^-1 - lipids, and 3250 cm^-1 - water) that are related to the significant biochemical changes ( p < 1 × 10^-5) in NPC compared to normal tissue. Raman diagnostic performance is evaluated through the leave-one-object (tissue site)-out cross-validation (LOOCV) method. A statistically significant GA-PLS-LDA model ( p < 1 × 10^-5) on FP/HW Raman yields a CV diagnostic accuracy of 98.23% (111/113), sensitivity of 93.33% (28/30), and specificity of 100% (83/83) for NPC classification. This work demonstrates that the fiber-optic FP/HW Raman diagnostic platform developed has great promise for improving real-time in vivo detection and diagnosis of NPC at the molecular level during clinical nasopharyngeal endoscopy.

Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning

A deep learning network called “residual neural network” (ResNet) was used to decode Raman spectra-encoded suspension arrays (SAs).

Label-free rapid identification of tumor cells and blood cells with silver film SERS substrate

The detection of circulating tumor cells (CTCs) from peripheral blood is considered as great significance for the diagnosis and prognosis of cancer patients. Raman spectroscopy is a highly sensitive optical detection technique that can provide fingerprint molecular identification information. In this paper, the silver film substrate surface-enhanced Raman scattering (SERS) was used to research several tumor cells, immortalized cells, clinical cancer cells isolated from cancer patient's tissue and blood cells. The results display that there is great difference for the nucleic acid characteristic peaks of those cells. The red blood cells have almost none nucleic acid characteristic peak and the SERS signals of white blood cells are only a slight increase. Except for immortalized cells and few tumor cells, the nucleic acid characteristic peaks of some tumor cells have huge enhancement. Nucleic acid characteristic peaks of clinical cancer cells also have greater enhancement. The discriminant model established by the intensity ratio of the nucleic acid characteristic peak 730 cm^-1 to the substrate background peak 900 cm^-1 shows that some tumor cells and clinical sample cells can be separated from white blood cells, but tumor cells with relatively low-DNA index cannot be differentiated from white blood cells. This study demonstrates that thin-film SERS technology can distinguish between blood cells and some types of tumor cells. This study opens up a new possible method for the detection of CTCs with label-free SERS spectra.

Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer

We report an approach in diagnostic imaging based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a recent modality of atomic force microscopy (AFM), subresonance tapping, and machine-leaning analysis. The surface parameters, which are typically used in engineering to describe surfaces, are used to classify cells. The method is applied to the detection of bladder cancer, which is one of the most common human malignancies and the most expensive cancer to treat. The frequent visual examinations of bladder (cytoscopy) required for follow-up are not only uncomfortable for the patient but a serious cost for the health care system. Our method addresses an unmet need in noninvasive and accurate detection of bladder cancer, which may eliminate unnecessary and expensive cystoscopies. The method, which evaluates cells collected from urine, shows 94% diagnostic accuracy when examining five cells per patient's urine sample. It is a statistically significant improvement (P < 0.05) in diagnostic accuracy compared with the currently used clinical standard, cystoscopy, as verified on 43 control and 25 bladder cancer patients.

Label-free detection of nasopharyngeal and liver cancer using surface-enhanced Raman spectroscopy and partial lease squares combined with support vector machine

In this paper, we investigated the feasibility of using surface enhanced Raman spectroscopy (SERS) and multivariate analysis method to discriminate liver cancer and nasopharyngeal cancer from healthy volunteers. SERS measurements were performed on serum protein samples from 104 liver cancer patients, 100 nasopharyngeal cancer patients, and 95 healthy volunteers. Two dimensionality reduction methods, principal component analysis (PCA) and partial least square (PLS) were compared, and the results indicated that the performance of PLS is superior to that of PCA. When the number of components was compressed to 3 by PLS, support vector machine (SVM) with a Gaussian radial basis function (RBF) was employed to classify various cancers simultaneously. Based on the PLS-SVM algorithm, high diagnostic accuracies of 95.09% and 90.67% were achieved from the training set and the unknown testing set, respectively. The results of this exploratory work demonstrate that serum protein SERS technology combined with PLS-SVM diagnostic algorithm has great potential for the noninvasive screening of cancer.

Combination of High-Resolution Optical Coherence Tomography and Raman Spectroscopy for Improved Staging and Grading in Bladder Cancer

We present a combination of optical coherence tomography (OCT) and Raman spectroscopy (RS) for improved diagnosis and discrimination of different stages and grades of bladder cancer ex vivo by linking the complementary information provided by these two techniques. Bladder samples were obtained from biopsies dissected via transurethral resection of the bladder tumor (TURBT). As OCT provides structural information rapidly, it was used as a red-flag technology to scan the bladder wall for suspicious lesions with the ability to discriminate malignant tissue from healthy urothelium. Upon identification of degenerated tissue via OCT, RS was implemented to determine the molecular characteristics via point measurements at suspicious sites. Combining the complementary information of both modalities allows not only for staging, but also for differentiation of low-grade and high-grade cancer based on a multivariate statistical analysis. OCT was able to clearly differentiate between healthy and malignant tissue by tomogram inspection and achieved an accuracy of 71% in the staging of the tumor, from pTa to pT2, through texture analysis followed by k-nearest neighbor classification. RS yielded an accuracy of 93% in discriminating low-grade from high-grade lesions via principal component analysis followed by k-nearest neighbor classification. In this study, we show the potential of a multi-modal approach with OCT for fast pre-screening and staging of cancerous lesions followed by RS for enhanced discrimination of low-grade and high-grade bladder cancer in a non-destructive, label-free and non-invasive way.

Identification and characterization of bladder cancer by low-resolution fiber-optic Raman spectroscopy

Raman spectroscopy has been proved to be a promising diagnostic technique for various cancers detection. A major drawback for its clinical translation is the intrinsic weakness of Raman effects. Highly sensitive equipment and optimal measurement conditions are generally applied to overcome this drawback. However, these equipment are usually bulky, expensive and may also be easily influenced by surrounding environment. In this preliminary work, a low-resolution fiber-optic Raman sensing system is applied to evaluate the diagnostic potential of Raman spectroscopy to identify different bladder pathologies ex vivo. A total number of 262 spectra taken from 32 bladder specimens are included in this study. These spectra are categorized into 3 groups by histopathological analysis, namely normal bladder tissues, low-grade bladder tumors and high-grade bladder tumors. Principal component analysis fed artificial neural network are used to train a classification model for the spectral data with 10-fold cross-validation and an overall prediction accuracy of 93.1% is obtained. The sensitivities and specificities for normal bladder tissues, low-grade bladder tumors and high-grade bladder tumors are 88.5% and 95.1%, 90.3% and 98%, and 97.5% and 96.4%, respectively. These results demonstrate the potential of using a low-resolution fiber-optic Raman system for in vivo bladder cancer diagnosis.

Exploring the margins of SERS in practical domain: An emerging diagnostic modality for modern biomedical applications

Excellent multiplexing capability, molecular specificity, high sensitivity and the potential of resolving complex molecular level biological compositions augmented the diagnostic modality of surface-enhanced Raman scattering (SERS) in biology and medicine. While maintaining all the merits of classical Raman spectroscopy, SERS provides a more sensitive and selective detection and quantification platform. Non-invasive, chemically specific and spatially resolved analysis facilitates the exploration of SERS-based nano probes in diagnostic and theranostic applications with improved clinical outcomes compared to the currently available so called state-of-art technologies. Adequate knowledge on the mechanism and properties of SERS based nano probes are inevitable in utilizing the full potential of this modality for biomedical applications. The safety and efficiency of metal nanoparticles and Raman reporters have to be critically evaluated for the successful translation of SERS in to clinics. In this context, the present review attempts to give a comprehensive overview about the selected medical, biomedical and allied applications of SERS while highlighting recent and relevant outcomes ranging from simple detection platforms to complicated clinical applications.

In-vivo optical imaging in head and neck oncology: basic principles, clinical applications and future directions

Head and neck cancers become a severe threat to human's health nowadays and represent the sixth most common cancer worldwide. Surgery remains the first-line choice for head and neck cancer patients. Limited resectable tissue mass and complicated anatomy structures in the head and neck region put the surgeons in a dilemma between the extensive resection and a better quality of life for the patients. Early diagnosis and treatment of the pre-malignancies, as well as real-time in vivo detection of surgical margins during en bloc resection, could be leveraged to minimize the resection of normal tissues. With the understanding of the head and neck oncology, recent advances in optical hardware and reagents have provided unique opportunities for real-time pre-malignancies and cancer imaging in the clinic or operating room. Optical imaging in the head and neck has been reported using autofluorescence imaging, targeted fluorescence imaging, high-resolution microendoscopy, narrow band imaging and the Raman spectroscopy. In this study, we reviewed the basic theories and clinical applications of optical imaging for the diagnosis and treatment in the field of head and neck oncology with the goal of identifying limitations and facilitating future advancements in the field.

"Optical and Surface Enhanced Raman Scattering properties of Ag modified silicon double nanocone array"

Surface enhanced Raman scattering (SERS) systems with large number of active sites exhibit superior capability in detection of low concentration analytes. In this paper, we present theoretical as well as experimental studies on the optical properties of a unique hybrid nanostructure, Ag NPs decorated silicon double nanocones (Si-DNCs) array, which provide high density of hot spots. The Si-DNC array is fabricated by employing electron beam lithography together with plasma etching process. Multipole analysis of the scattering spectra, based on the multipole expansion theory, confirms that the toroidal dipole moment dominates over other electric and magnetic multipole moments in the Si-DNCs array. This response occurs as a result of generating current densities flowing in opposite directions and consequently generating H-field vortexes inside the nanocones. Moreover, SERS applicability of this type of nanostructure is examined. For this purpose, the Si-DNCs array is decorated with Ag nanoparticles (NPs) by means of electroless deposition method. Simulation results indicate that combination of multiple resonances, including LSPR resonance of Ag NPs, longitudinal standing wave resonance of Ag layer and inter-particle interaction in the gap region, result in a significant SERS enhancement. Our experimental results demonstrate that Si-DNC/Ag NPs array substrate provides excellent reproducibility and ultrahigh sensitivity.

Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL

The surface-enhanced Raman spectroscopy (SERS) of blood serum was investigated to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) in males with a prostate-specific antigen level of 4-10 ng/mL, so as to reduce unnecessary biopsies. A total of 240 SERS spectra from blood serum were acquired from 40 PCa subjects and 40 BPH subjects who had all received prostate biopsies and were given a pathological diagnosis. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms, were used to analyze the spectra data of serum from patients in control (CTR), PCa and BPH groups; results offered a sensitivity of 97.5%, a specificity of 100.0%, a precision of 100.0% and an accuracy of 99.2% for CTR; a sensitivity of 90.0%, a specificity of 97.5%, a precision of 94.7% and an accuracy of 98.3% for BPH; a sensitivity of 95.0%, a specificity of 93.8%, a precision of 88.4% and an accuracy of 94.2% for PCa. Similarly, this technique can significantly differentiate low- and high-risk PCa with an accuracy of 92.3%, a specificity of 95% and a sensitivity of 89.5%. The results suggest that analyzing blood serum using SERS combined with PCA-LDA diagnostic algorithms is a promising clinical tool for PCa diagnosis and assessment.

Cordycepin induces apoptosis in human bladder cancer cells via activation of A3 adenosine receptors

Bladder cancer is a neoplasm originated from bladder epithelial cells. The therapy for bladder cancer is so far not satisfactory. In this study, we examined the effects of Cordyceps militaris hot water extracts containing cordycepin on human bladder cells. Cordyceps militaris hot water extracts containing cordycepin was used to treat human T24 bladder carcinoma cells, and we found that Cordyceps militaris hot water extracts containing cordycepin decreased T24 cell survival in a dose-dependent manner, which was seemingly mediated by activation of A3 adenosine receptor and the subsequent inactivation of Akt pathways, resulting in increases in cleaved Caspase-3 and apoptosis. Overexpression of A3 adenosine receptor in T24 cells mimicked the effects of Cordyceps militaris hot water extracts, while A3 adenosine receptor depletion abolished the effects of Cordyceps militaris hot water extracts containing cordycepin. Together, these data suggest that Cordyceps militaris hot water extracts containing cordycepin may be a promising treatment for bladder cancer via A3 adenosine receptor activation.