Tuberculosis determination using SERS and chemometric methods

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Determination of latent tuberculosis infection from plasma samples via label-free SERS sensors and machine learning

Effective diagnostic tools for screening of latent tuberculosis infection (LTBI) are lacking. We aim to investigate the performance of LTBI diagnostic approaches using label-free surface-enhanced Raman spectroscopy (SERS). We used 1000 plasma samples from Northeast Thailand. Fifty percent of the samples had tested positive in the interferon-gamma release assay (IGRA) and 50 % negative. The SERS investigations were performed on individually prepared protein specimens using the Raman-mapping technique over a 7 × 7 grid area under measurement conditions that took under 10 min to complete. The machine-learning analysis approaches were optimized for the best diagnostic performance. We found that the SERS sensors provide 81 % accuracy according to train-test split analysis and 75 % for LOOCV analysis from all samples, regardless of the batch-to-batch variation of the sample sets and SERS chip. The accuracy increased to 93 % when the logistic regression model was used to analyze the last three batches of samples, following optimization of the sample collection, SERS chips, and database. We demonstrated that SERS analysis with machine learning is a potential diagnostic tool for LTBI screening.

Diagnosis of extra pulmonary tuberculosis: An update on novel diagnostic approaches

Tuberculosis (TB) remains a major global public health problem worldwide. Though Pulmonary TB (PTB) is mostly discussed, one in five cases of TB present are extrapulmonary TB (EPTB) that manifests conspicuous diagnostic and management challenges with respect to the site of infection. The diagnosis of EPTB is often delayed or even missed due to insidious clinical presentation, pauci-bacillary nature of the disease, and lack of laboratory facilities in the resource limited settings. Culture, the classical gold standard for the diagnosis of tuberculosis, suffers from increased technical and logistical constraints in EPTB cases. Other than culture, several other tests are available but their feasibility and effciacy for the detection of EPTB is still the matter of interest. We need more specific and precise test/s for the various forms of EPTB diagnosis which can easily be applied in the routine TB control program is required. A test that can contribute remarkably towards improving EPTB case detection reducing the morbidity and mortality is the utmost requirement. In this review we described the scenario of molecular and other noval methods available for laboratory diagnosis of EPTB, and also discussed the challenges linked with each diagnostic method. This review will make the readers aware of new emerging diagnostic techniques in the field of EPTB diagnosis. They can make an informed decision to choose the appropriate one according to the test availability, their clinical settings and financial considerations.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications

BACKGROUND

The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.

AIM OF REVIEW

Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

Magnetic Fe3O4@COF@Ag SERS substrate combined with machine learning algorithms for detection of three quinolone antibiotics: Ciprofloxacin, norfloxacin and levofloxacin

Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy industry. Currently, the problem of excessive antibiotics in dairy products is very serious. As an ultra-sensitive detection technology, Surface-Enhanced Raman Scattering (SERS) was applied to the detection of quinolone antibiotics in this work. In order to classify and quantify three antibiotics (Ciprofloxacin, Norfloxacin, Levofloxacin) with highly similar molecular structures, a combination of magnetic COF-based SERS substrate and machine learning algorithms (PCA-k-NN, PCA-SVM, PCA-Decision Tree) was used. The classification accuracy of the spectral dataset could reach 100% and the results of LOD calculation were: CIP: 5.61 × 10^-9M, LEV: 1.44 × 10^-8M, NFX: 1.56 × 10^-8M. This provides a new method for the detection of antibiotics in dairy products.

Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSE_label = 6.42 × 10^-2. These results demonstrate the potential of CNN-based ML to advance SERS-based diagnostics.

An efficient and simple SERS approach for trace analysis of tetrahydrocannabinol and cannabinol and multi-cannabinoid detection

Many countries have legalized cannabis and its derived products for multiple purposes. Consequently, it has become necessary to develop a rapid, effective, and reliable tool for detecting delta-9-tetrahydrocannabinol (THC) and cannabinol (CBN), which are important biologically active compounds in cannabis. Herein, we have fabricated SERS chips by using glancing angle deposition and tuned dimensions of silver nanorods (AgNRs) for detecting THC and CBN at low concentrations. Experimental and computational results showed that the AgNR substrate with film thickness (or nanorod length) of 150 nm, corresponding to nanorod diameter of 79 nm and gap between nanorods of 23 nm, can effectively sense trace THC and CBN with good reproducibility and sensitivity. Due to limited spectral studies of the cannabinoids in previous reports, this work also explored towards identifying characteristic Raman lines of THC and CBN. This information is critical to further reliable data analysis and interpretation. Moreover, multianalyte detection of THC and CBN in a mixture was successfully demonstrated by applying an open-source independent component analysis (ICA) model. The overall method is fast, sensitive, and reliable for sensing trace THC and CBN. The SERS chip-based method and spectral results here are useful for a variety of cannabis testing applications, such as product screening and forensic investigation.

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.

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

SERS for antibiotic resistance diagnosis.

SERS-based viral load quantification of hepatitis B virus from PCR products

Hepatitis B is a contagious liver disorder caused by hepatitis B virus and if not treated at an early stage, it becomes chronic and results in liver cirrhosis and hepatocellular carcinoma which can even lead to death. In present study, surface-enhanced Raman spectroscopy (SERS) is employed for the analysis of polymerase chain reaction (PCR) products of DNA extracted from hepatitis B virus (HBV) infected patients in comparison with healthy individuals. SERS spectral features are identified which are solely present in the HBV positive samples and consistently increase in intensities with increase in viral load which can be considered as a SERS spectral marker for HBV infection. For sake of understanding, these various levels of viral loads in this study are classified as low (1-1000 IU), medium (1000-10,000 IU), high (above 10,000 IU) and negative control (>1). In order to explore the efficiency of SERS for discrimination of SERS spectral datasets of different samples of varying viral loads and healthy individuals, principal component analysis (PCA) is applied. PCA is used for comparison of these classes including low, medium and high levels of viral loads with each other and with healthy class. Moreover, partial least square discriminant analysis and partial least square regression analysis are employed for the classification of different levels of viral loads in the HBV positive samples and prediction of viral loads in the unknown samples, respectively. PLS-DA is applied for validity of classification and its sensitivity and specificity was found to be 89% and 98% respectively. PLSR model was constructed for prediction of viral loads on the bases of SERS spectral markers of HBV infection with goodness value of 0.9031 and value of root means square error (RMSE) 0.2923. PLSR model also proved to be valid for prediction of blind sample.

Surface-enhanced Raman spectroscopy for comparison of serum samples of typhoid and tuberculosis patients of different stages

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is a reliable tool for the identification and differentiation of two different human pathological conditions sharing the same symptomology, typhoid and tuberculosis (TB).

OBJECTIVES

To explore the potential of surface-enhanced Raman spectroscopy for differentiation of two different diseases showing the same symptoms and analysis by principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

METHODS

Serum samples of clinically diagnosed typhoid and tuberculosis infected individuals were analyzed and differentiated by SERS using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, the collected serum samples were analyzed under the SERS instrument and unique SERS spectra of typhoid and tuberculosis were compared showing notable spectral differences in protein, lipid and carbohydrates features. Different stages of the diseased class of typhoid (Early acute and late acute stage) and tuberculosis (Pulmonary and extra-pulmonary stage) were compared with each other and with healthy human serum samples, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

RESULTS

SERS Spectral data of typhoid and tuberculosis showed clear differences and were significantly separated using PCA. SERS spectral data of both stages of typhoid and tuberculosis were separated according to 1st principle component. Moreover, by analyzing data using partial least square discriminate analysis, differentiation of two disease classes were considered more valid with a 100% value of sensitivity, specificity and accuracy.

CONCLUSION

SERS can be employed for identification and comparison of two different human pathological conditions sharing same symptomology.

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.

Plasmonic-based platforms for diagnosis of infectious diseases at the point-of-care

Infectious diseases such as HIV-1/AIDS, tuberculosis (TB), hepatitis B (HBV), and malaria still exert a tremendous health burden on the developing world, requiring rapid, simple and inexpensive diagnostics for on-site diagnosis and treatment monitoring. However, traditional diagnostic methods such as nucleic acid tests (NATs) and enzyme linked immunosorbent assays (ELISA) cannot be readily implemented in point-of-care (POC) settings. Recently, plasmonic-based biosensors have emerged, offering an attractive solution to manage infectious diseases in the developing world since they can achieve rapid, real-time and label-free detection of various pathogenic biomarkers. Via the principle of plasmonic-based optical detection, a variety of biosensing technologies such as surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), colorimetric plasmonic assays, and surface enhanced Raman spectroscopy (SERS) have emerged for early diagnosis of HIV-1, TB, HBV and malaria. Similarly, plasmonic-based colorimetric assays have also been developed with the capability of multiplexing and cellphone integration, which is well suited for POC testing in the developing world. Herein, we present a comprehensive review on recent advances in surface chemistry, substrate fabrication, and microfluidic integration for the development of plasmonic-based biosensors, aiming at rapid management of infectious diseases at the POC, and thus improving global health.

Influence of drugs on the prospective diagnostic method for coronary heart disease with urine

The morbidity of coronary heart disease (CHD) with high risks has been rising in recent years. A novel and noninvasive method based on surface-enhanced Raman spectroscopy (SERS) was proposed by Yang et al. (Analyst 143: 2235, 2018) to prospectively diagnose the arterial blockage by detecting platelet-derived growth factor-BB (PDGF-BB) in urine. Clinically, anti-platelet drugs (such as aspirin, statins and clopidogrel) are often used for ordinary CHD patients or patients with percutaneous coronary intervention (PCI). Therefore, whether the previous developed method can be applied to the CHD patients on long-term medication (more than 6 months) or post-PCI patients was investigated here. Firstly, urine samples of 13 CHD patients on long-term medication (aspirin, rosuvastatin, clopidogrel bisulfate) and 13 post-PCI patients were measured by the proposed method. Clinical data of coronary angiography results provided by Xin Hua Hospital and Yangpu District Central Hospital Antu Branch revealed that these 26 patients were with serious arterial blockage, however, characteristic Raman peak at 1509 cm^-1 attributed to PDGF-BB was not observed in the SERS spectra of these 26 patients. In addition, an eight-day follow-up investigation was performed on a CHD patient with PCI three years ago and on long-term medication. It was found that the Raman peak at 1509 cm^-1 could be only observed in the third and fourth day after suspending the drugs. Furthermore, SERS spectra of mixed solutions of PDGF-BB and aspirin, rosuvastatin, mixed solutions of these two drugs and clopidogrel bisulfate were analyzed. The Raman peak at 1509 cm^-1 was not found in all these spectra, it indicated that all the three kinds of drugs could influence on the SERS signal of PDGF-BB. Therefore, the previous developed method is not suitable for CHD patients on long-term medication and post-PCI patients.

Ag Nanorods-Based Surface-Enhanced Raman Scattering: Synthesis, Quantitative Analysis Strategies, and Applications

Surface-Enhanced Raman Scattering (SERS) is a powerful technology that provides abundant chemical fingerprint information with advantages of high sensitivity and time-saving. Advancements in SERS substrates fabrication allow Ag nanorods (AgNRs) possess superior sensitivity, high uniformity, and excellent reproducibility. To further promote AgNRs as a promising SERS substrate candidate to a broader application scope, oxides are integrated with AgNRs by virtue of their unique properties which endow the AgNRs-oxide hybrid with high stability and recyclability. Aside from SERS substrates fabrication, significant developments in quantitative analysis strategies offer enormous approaches to minimize influences resulted from variations of measuring conditions and to provide the reasonable data analysis. In this review, we discuss various fabrication approaches for AgNRs and AgNRs-oxide hybrids to achieve efficient SERS platforms. Then, we introduce three types of strategies which are commonly employed in chemical quantitative analysis to reach a reliable result. Further, we highlight SERS applications including food safety, environment safety, biosensing, and vapor sensing, demonstrating the potential of SERS as a powerful and promising technique. Finally, we conclude with the current challenges and future prospects toward efficient SERS manipulations for broader real-world applications.