Specific discrimination of pathogenic bacteria causing septic arthritis using Raman spectroscopy: In-vitro study

In this study we performed preliminary experiments using Raman spectroscopy as an evolving technology in biofluid and microbial characterization, to explore its potential for rapid diagnosis of pathogenic bacteria in an in-vitro synovial fluid infection model. Normal human synovial fluids samples were collected from patients undergoing knee surgery and the three most common pathogenic bacteria introduced in-vitro into the samples. The bacterial growth was systematically monitored using a Raman spectroscopy. Multivariate regression analysis of acquired spectra showed bacterial characteristic Raman bands related to bacterial cell membranes and DNA structures to increase continuously as the incubation period was increased. Spectra signature recorded from cultured synovial fluid samples showed a significant loss in synovial quality and protein morphology over time compared to control samples. In this study, Raman spectroscopy shows promise for rapid pathogenic bacteria identification in synovial fluid. Marker peaks distinguished inoculated bacteria, while chemical changes reveal infection dynamics.

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.

A magnetic field augmented ultra-thin layer chromatography coupled surface enhanced Raman spectroscopy separation of hemozoin from bacterial mixture

Malaria is considered as one the most widespread disease with highest possibility of co-infection at all levels of the disease prognosis. Rapid detection and discrimination of malaria from other co-infections remains a challenge. Hemozoin is a metabolic biproduct of malaraia possessing paramagnetic property due to presence of iron at its centre. Here, we report a label free, rapid and highly sensitive magnetic field based ultra-thin layer chromatography (UTLC) coupled with surface enhanced Raman spectroscopy (SERS) technique for detection and separation of hemozoin from a bacterial mixture. Highly optimized silver nanorods chip fabricated using glancing angle deposition (GLAD) is explored for the UTLC-SERS separation. These chips possessing channel like characteristic and high surface to the volume ratio serve as excellent UTLC plates. The magnetic nature of hemozoin has been exploited for its separation from the mixture of P. aeruginosa (Gram-negative) and S. aureus (Gram-positive) by allocating a 0.6 T magnet over the UTLC flow setup. The solvent front migrated approximately to a distance of 13 mm from the sample point due to the magnetic environment. Spatially resolved SERS data was collected along the mobile phase and separation of mixture was confirmed. Further, staining of hemozoin, P. aeruginosa and S. aureus was done using methylene blue, acridine orange and rhodamine 6 G respectively. The separation was confirmed for the stained analytes. The present developed method provides plate height as low as 18 µm and hemozoin detection limit as <10 parasites/mL. Therefore, we establish a highly specific and sensitive technique capable of separating small amounts of bioanalytes, aiding in the removal of co-infections from the disease at a very early stage of infection.

Developing New Diagnostic Tools Based on SERS Analysis of Filtered Salivary Samples for Oral Cancer Detection

Cancer still represents one of the biggest challenges in current medical practice. Among different types of cancer, oral cancer has a huge impact on patients due to its great visibility, which is more likely to create social stigma and increased anxiety. New early diagnose methods are still needed to improve treatment efficiency and patients' life quality. Raman/SERS (Surface Enhanced Raman Spectroscopy) spectroscopy has a unique and powerful potential for detecting specific molecules that can become priceless biomarkers in different pathologies, such as oral cancer. In this study, a batch of saliva samples obtained from a group of 17 patients with oro-maxillofacial pathologies compared with saliva samples from 18 healthy donors using the aforementioned methods were evaluated. At the same time, opiorphin, potassium thiocyanate and uric acid were evaluated as potential specific biomarkers for oro-maxillofacial pathologies using multivariate analysis. A careful examination of SERS spectra collected on saliva samples showed that the spectra are dominated by the vibrational bands of opiorphin, potassium thiocyanate and uric acid. Given the fact that all these small molecules are found in very small amounts, we filtrated all the samples to get rid of large molecules and to improve our analysis. By using solid plasmonic substrates, we were able to gain information about molecular concentration and geometry of interaction. On the other hand, the multivariate analysis of the salivary spectra contributed to developing a new detection method for oral cancer.

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review

Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.

Fabrication of optoplasmonic particles through electroless deposition and the application in SERS-based screening of nodule-involved lung cancer

In this work, a core-satellite optoplasmonic particle containing a silica microsphere covered with gold nanoparticles (AuNPs) was developed through wet chemistry synthesis in aqueous phase. The electroless deposition and galvanic replacement were employed to anchor AuNPs onto silica sphere surface. The escalated as well as expanded electric field enhancement within the dielectric-metallic interface was analyzed through finite difference time domain (FDTD) simulation. The numerical models and the surface-enhancement Raman spectroscopy (SERS) measurements over blood serum both support that the equatorial plane is the preferred collecting plane for improved signal intensity and stability. The nanocomposite emerged lower relative standard deviation (RSD) in repetitive measurement compared to AuNPs. In practice, this hybrid structure was applied for lung cancer diagnosis based on serum SERS spectra analysis of the patients diagnosed with nodules. The prediction with the aid of principal component analysis (PCA) and support-vector machine (SVM) was attempted for the classification of healthy, benign and relatively malignant sample groups. The accuracy of distinguish benign samples from malignant ones reaches over 90%. These advantages make the structure a promising SERS substrate for the early screening of cancer based on the non-invasive biological samples.

Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm^-1, 724 cm^-1, 1094 cm^-1 and 1393 cm^-1 bands are associated with the degree of kidney function inhibition; and the 1001 cm^-1 band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study

Simple Summary

In recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.

Abstract

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning

Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.

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

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