Surface-enhanced Raman scattering (SERS) spectroscopy for prostate cancer diagnosis: A review

Cancer Cell Line Classification Using Raman Spectroscopy of Cancer-Derived Exosomes and Machine Learning

Liquid biopsies are an emerging, noninvasive tool for cancer diagnostics, utilizing biological fluids for molecular profiling. Nevertheless, the current methods often lack the sensitivity and specificity necessary for early detection and real-time monitoring. This work explores an advanced approach to improving liquid biopsy techniques through machine learning analysis of the Raman spectra measured to classify distinct exosome solutions by their cancer origin. This was accomplished by conducting principal component analysis (PCA) of the Raman spectra of exosomes from three cancer cell lines (COLO205, A375, and LNCaP) to extract chemically significant features. This reduced set of features was then utilized to train a linear discriminant analysis (LDA) classifier to predict the source of the exosomes. Furthermore, we investigated differences in the lipid composition in these exosomes by their spectra. This spectral similarity analysis revealed differences in lipid profiles between the different cancer cell lines as well as identified the predominant lipids across all exosomes. Our PCA-LDA framework achieved 93.3% overall accuracy and F1 scores of 98.2%, 91.1%, and 91.0% for COLO205, A375, and LNCaP, respectively. Our results from spectral similarity analysis were also shown to support previous findings of lipid dynamics due to cancer pathology and pertaining to exosome function and structure. These findings underscore the benefits of enhancing Raman spectroscopy analysis with machine learning, laying the groundwork for the development of early noninvasive cancer diagnostics and personalized treatment strategies. This work potentially establishes the foundation for refining the classification model and optimizing exosome extraction and detection from clinical samples for clinical translation.

Highly Accurate and Robust Early Stage Detection of Cholangiocarcinoma Using Near-Lossless SERS Signal Processing with Machine Learning and 2D CNN for Point-of-care Mobile Application

INTRODUCTION

Cholangiocarcinoma (CCA), a malignancy of the bile ducts, presents a significant health burden with a notably high prevalence in Northeast Thailand, where its incidence ratio is 85 per 100,000 population per year. The prognosis for CCA patients remains poor, particularly for proximal tumors, with a dismal 5-year survival rate of just 10%. The challenge in managing CCA is exacerbated by its typically late detection, contributing to a high mortality rate. Current screening methods, such as ultrasound, are insufficient, as many CCA patients do not exhibit prior symptoms or detectable liver fluke (Opisthorchis viverrini : OV) infections, underscoring the urgent need for alternative early detection methods.

METHODS

In this study, we introduce a novel approach utilizing surface-enhanced Raman spectroscopy (SERS) combined with near-lossless signal compression via discrete wavelet transform (DWT) together with 2D CNN for the first time. Hamster serums of different stages were collected as the data set. DWT was employed for feature extraction, enabling the capture of the entire SERS spectrum, unlike traditional methods like PCA and LDA, which focus only on specific peaks. These features were used to train a 2D convolutional neural network (2D CNN), which is particularly robust against translation, rotation, and scaling, thus effectively addressing the SERS peak shifting issues. We validated our approach using gold-standard histology, and notably, our method could detect CCA at an early stage. The ability to identify CCA at the early stage significantly improves the chances of successful intervention and patient outcomes.

RESULTS AND CONCLUSION

Our results demonstrate that our method, combining SERS with extremely compact wavelet feature extraction and 2D CNN, outperformed other approaches (PCA + SVM, PCA + 1D CNN, PCA + 2D CNN, LDA + SVM, and DWT + 1D CNN), achieving performance of 95.1% accuracy, 95.08% sensitivity, 98.4% specificity, and an area under the curve (AUC) of 95%. The trained model was further deployed on a server and mobile application interface, paving the way for future field experiments in rural areas and home-use potential point-of-care services.

Imaging of prostate micro-architecture using three-dimensional wide-field optical coherence tomography

Prostate cancer is a global health issue that requires new diagnostic methods to provide accurate and precise visualization of prostate tissue on the micro-scale. Such methods have the potential to improve nerve-sparing surgery and to provide image guidance during prostate biopsy. In this feasibility study, we assess the potential of en face three-dimensional wide-field optical coherence tomography (OCT), covering a volumetric imaging field-of-view up to 46 × 46 × 1 mm3, to visualize micro-architecture in 18 freshly excised human prostate specimens. In each case, validation of contrast in OCT images is provided by co-registered wide-field histology images. Using this co-registration, we demonstrate that OCT can distinguish between healthy and cancerous glands at different stages, as well as visualize micro-architecture in the prostate, such as epineurium and perineurium in nerves and the tunica intima and tunica media in blood vessels.

Advances in Surface-Enhanced Raman Spectroscopy for Urinary Metabolite Analysis: Exploiting Noble Metal Nanohybrids

This review examines recent advances in surface-enhanced Raman spectroscopy (SERS) for urinary metabolite analysis, focusing on the development and application of noble metal nanohybrids. We explore the diverse range of hybrid materials, including carbon-based, metal-organic-framework (MOF), silicon-based, semiconductor, and polymer-based systems, which have significantly improved SERS performance for detecting key urinary biomarkers. The principles underlying SERS enhancement in these nanohybrids are discussed, elucidating both electromagnetic and chemical enhancement mechanisms. We analyze various fabrication methods that enable precise control over nanostructure morphology, composition, and surface chemistry. The review critically evaluates the analytical performance of different hybrid systems for detecting specific urinary metabolites, considering factors such as sensitivity, selectivity, and stability. We address the analytical challenges associated with SERS-based urinary metabolite analysis, including sample preparation, matrix effects, and data interpretation. Innovative solutions, such as the integration of SERS with microfluidic devices and the application of machine learning algorithms for spectral analysis, are highlighted. The potential of these advanced SERS platforms for point-of-care diagnostics and personalized medicine is discussed, along with future perspectives on wearable SERS sensors and multi-modal analysis techniques. This comprehensive overview provides insights into the current state and future directions of SERS technology for urinary metabolite detection, emphasizing its potential to revolutionize non-invasive health monitoring and disease diagnosis.

Non-enzymatic electrochemical detection of sarcosine in serum of prostate cancer patients by CoNiWBO/rGO nanocomposite

Selective and sensitive sarcosine detection is crucial due to its recent endorsement as a prostate cancer (PCa) biomarker in clinical diagnosis. The reduced graphene oxide-cobalt nickel tungsten boron oxides (CoNiWBO/rGO) nanocomposite is developed as a non-enzymatic electrochemical sensor for sarcosine detection in PCa patients' serum. CoNiWBO/rGO is synthesized by the chemical reduction method via a one-pot reduction method followed by calcination at 500 °C under a nitrogen environment for 2 h and characterized by UV-Vis, XRD, TGA, and SEM. CoNiWBO/rGO is then deposited on a glassy carbon electrode, and sarcosine sensing parameters are optimized, including concentration and pH. This non-enzymatic sensor is employed to directly determine sarcosine in serum samples. Differential pulse voltammetry (DPV) and linear sweep voltammetry (LSV) are employed to monitor the electrochemical behavior where sarcosine binding leads to oxidation. Chronoamperometric studies show the stability of the developed sensor. The results demonstrate a wide linear range from 0.1 to 50 µM and low limits of detection, i.e., 0.04 µM and 0.07 µM using DPV and LSV respectivel. Moreover, the calculated recovery of sarcosine in human serum of prostate cancer patients is 78-96%. The developed electrochemical sensor for sarcosine detection can have potential applications in clinical diagnosis.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Diagnostic application in streptozotocin-induced diabetic retinopathy rats: A study based on Raman spectroscopy and machine learning

Vision impairment caused by diabetic retinopathy (DR) is often irreversible, making early-stage diagnosis imperative. Raman spectroscopy emerges as a powerful tool, capable of providing molecular fingerprints of tissues. This study employs RS to detect ex vivo retinal tissue from diabetic rats at various stages of the disease. Transmission electron microscopy was utilized to reveal the ultrastructural changes in retinal tissue. Following spectral preprocessing of the acquired data, the random forest and orthogonal partial least squares-discriminant analysis algorithms were employed for spectral data analysis. The entirety of Raman spectra and all annotated bands accurately and distinctly differentiate all animal groups, and can identify significant molecules from the spectral data. Bands at 524, 1335, 543, and 435 cm-1 were found to be associated with the preproliferative phase of DR. Bands at 1045 and 1335 cm-1 were found to be associated with early stages of DR.

Magnetic Titanium Dioxide Nanocomposites as a Recyclable SERRS Substrate for the Ultrasensitive Detection of Histidine

A highly sensitive, selective and recyclable histidine detection method based on magnetic Fe3O4@mTiO2 (M-TiO2) nanocomposites with SERRS was developed. Mesoporous M-TiO2 nanoparticles were functionalized with 4-aminothiophenol and then coupled with histidine through an azo coupling reaction in 5 min, producing the corresponding azo compound. The strong and specific SERRS response of the azo product allowed for ultrasensitive and selective detection for histidine with an M-TiO2 device loaded with Ag NPs due to the molecular resonance effect and plasmonic effect of Ag NPs under a 532 nm excitation laser. The sensitivity was further enhanced with the magnetic enrichment of M-TiO2. The limit of detection (LOD) was as low as 8.00 × 10-12 mol/L. The M-TiO2 demonstrated applicability towards histidine determination in human urine without any sample pretreatment. Additionally, the M-TiO2 device can be recycled for 3 cycles with the photodegradation of the azo product under UV irradiation due to TiO2-assisted and plasmon-enhanced photocatalysis. In summary, a multifunctional and recyclable M-TiO2 device was synthesized based on azo coupling and SERRS spectroscopy for ultra-sensitive and specific histidine sensing. In addition, the proposed system demonstrated the potential for the multiplex determination of toxic compounds in the fields of food safety, industrial production and environmental protection, which benefit from the fingerprint property and universality of SERRS.

Multiplexed Opto-Microfluidic Biosensing: Advanced Platform for Prostate Cancer Detection

Cancer stands as a prominent global cause of mortality, necessitating early detection to augment survival rates and alleviate economic burdens on healthcare systems. In particular, prostate cancer (PCa), impacting 1.41 million men globally in 2020, accentuates the demand for sensitive and cost-effective detection methods beyond traditional prostate-specific antigen (PSA) testing. While clinical techniques exhibit limitations, biosensors emerge as compact, user-friendly alternatives to traditional laboratory approaches. However, existing biosensors predominantly concentrate on PSA detection, prompting the necessity for advancing toward multiplex sensing platforms. This study introduces a compact opto-microfluidic sensor featuring a substrate of gold nanospikes, fabricated via electrodeposition, for enhanced sensitivity. Embedded within a microfluidic chip, this nanomaterial enables the precise and concurrent measurement of PSA, alongside two complementary PCa biomarkers, matrix metalloproteinase-2 (MMP-2) and anti-α-methylacyl-CoA racemase (anti-AMACR) in diluted human plasma, offering a comprehensive approach to PSA analysis. Taking advantage of the localized surface plasmon resonance principle, this biosensor offers robustness and sensitivity in real sample analysis without the need for labeling agents. With the limit of detection at 0.22, 0.37, and 0.18 ng/mL for PSA, MMP-2, and anti-AMACR, respectively, this biosensing platform holds promise for point-of-care analysis, underscoring its potential impact on medical diagnostics.

Microfluidic bead-based biosensor: Ultrasensitive ctDNA detection based on duplex-functional split-DNAzyme and dendritic enzyme-free signal amplification

Circulating tumor DNA (ctDNA) is a crucial cancer biomarker for early or noninvasive monitoring, which is essential for developing ultrasensitive and selective assays in cancer diagnosis and treatment. Herein, a cascade signal amplification of duplex-functional split-DNAzyme and dendritic probes was proposed for ultrasensitive and specific detection of nasopharyngeal carcinoma-associated Epstein-Barr virus (EBV) DNA on microfluidic microbead array chips. With the assistance of Pb2+, the duplex-functional split-DNAzyme recognizes EBV DNA and then rapidly cleaves the substrate strand. Subsequently, the released target could be recycled, and its exposed capture probe, triggered the dendritic enzyme-free signal amplification. As the enhanced mass transfer capability, target recycling, and dendritic DNA structure signal amplification inherent to microfluidic bead arrays were integrated, it achieved an excellent detection limit of 0.36 fM and a wide linear range of 1 fM∼103 fM. Further, it was applied to content detect simulated samples of EBV DNA, recovery ranged from 97.2 % to 108.1 %, and relative standard deviation (RSD) from 3.3 % to 5.9 %, exhibiting satisfactory recovery results. The developed microfluidic biosensor was a high-sensitivity and anti-interference system for ctDNA analysis, with minimal reagent volumes (microlitres) required. Thus, it is a promising platform for ctDNA at the lowest level at their earliest incidence.

Computational analysis of anti-cancer drug hydroxyurea adsorption on nanocages of gold, silver and copper: SERS and DFT assessment

The use of nanostructures in targeted drug delivery is effective in decreasing anticancer drug toxicity. Here, we discuss the theoretically predicted adsorption and interaction behavior of hydroxyurea [HU] with nano metal cages (nmC). HU interact the nmC through the N4 in primary amine with energies of -29.776, -30.684 and -22.105 kcal/mol for Au, Ag and Cu cage, respectively. As a result of reactivity studies, HU complexes with nmC (Au/Ag/Cu) are becoming more electrophilic and this gives the nmC system their bioactivity. It is suggested that nanocage is going to change the FMO's energy levels by means of absorption, so that it is used in drug administration. DOS and MEP were accomplished to gain additional understandings into the reactivity of proposed complexes. Method for improving the Raman signal of biomolecules is surface enhanced Raman scattering (SERS), which uses nanosized metal substrates. Chemical enhancement is evidenced by Mulliken charge distributions of all systems for detection and chemical compositions and exerts a significant role in determining them. In HU complexes containing nmC (Au/Ag/Cu), electron density was detected via ELF and LOL calculations. Based on the results of a non-covalent interaction (NCI) analysis, Van der Waals/hydrogen bonds/repulsive steric - interactions have been found. The title compound will also be analyzed in order to determine its bioactivity and drug likeness parameters, as a result, we will able to create a molecule with a highly favorable pharmacological profile and use the docking method to determine the values of the interaction energies for drug delivery. This study suggests that adsorption of drugs on nanocage surface occurs physically and functionalizing the nanocage has increased adsorption energy.

Nasopharyngeal carcinoma cell screening based on the electroporation-SERS spectroscopy

Nasopharyngeal carcinoma (NPC) is a malignant tumor in head and neck. Early diagnosis can effectively improve the survival rate of patients. Nasopharyngeal exfoliative cytology, as a convenient and noninvasive auxiliary diagnostic method, is suitable for the population screening of NPC, but its diagnostic sensitivity is low. In this study, an electroporation-based SERS technique was proposed to detect and screen the clinical nasopharyngeal exfoliated cell samples. Firstly, nasopharyngeal swabs was used to collected the nasopharyngeal exfoliated cell samples from NPC patients (n = 54) and healthy volunteers (n = 60). Then, gold nanoparticles, as the Raman scattering enhancing substrates, were rapidly introduced into cells by electroporation technique for surface-enhanced Raman scattering (SERS) detection. Finally, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples. Raman peak assignments combined with spectral differences reflected the biochemical changes associated with NPC, including nucleic acid, amino acid and carbohydrates. Based on the PCA-LDA approach, the sensitivity, specificity and accuracy of 98.15 %, 96.67 % and 97.37 %, respectively, were achieved for screening NPC. This study offers valuable assistance for noninvasive NPC auxiliary diagnosis, and has grate potential in expanding the application of the SERS technique in clinical cell sample testing.

Aransay A, Martin JE, Sasselli IR, Carracedo A, Liz-Marzán LM. SERS analysis of cancer cell-secreted purines reveals a unique paracrine crosstalk in MTAP-deficient tumors

The tumor microenvironment (TME) is a dynamic pseudoorgan that shapes the development and progression of cancers. It is a complex ecosystem shaped by interactions between tumor and stromal cells. Although the traditional focus has been on the paracrine communication mediated by protein messengers, recent attention has turned to the metabolic secretome in tumors. Metabolic enzymes, together with exchanged substrates and products, have emerged as potential biomarkers and therapeutic targets. However, traditional techniques for profiling secreted metabolites in complex cellular contexts are limited. Surface-enhanced Raman scattering (SERS) has emerged as a promising alternative due to its nontargeted nature and simplicity of operation. Although SERS has demonstrated its potential for detecting metabolites in biological settings, its application in deciphering metabolic interactions within multicellular systems like the TME remains underexplored. In this study, we introduce a SERS-based strategy to investigate the secreted purine metabolites of tumor cells lacking methylthioadenosine phosphorylase (MTAP), a common genetic event associated with poor prognosis in various cancers. Our SERS analysis reveals that MTAP-deficient cancer cells selectively produce methylthioadenosine (MTA), which is taken up and metabolized by fibroblasts. Fibroblasts exposed to MTA exhibit: i) molecular reprogramming compatible with cancer aggressiveness, ii) a significant production of purine derivatives that could be readily recycled by cancer cells, and iii) the capacity to secrete purine derivatives that induce macrophage polarization. Our study supports the potential of SERS for cancer metabolism research and reveals an unprecedented paracrine crosstalk that explains TME reprogramming in MTAP-deleted cancers.

Flexible, high-performance and facile PVA/cellulose/Ag SERS chips for in-situ and rapid detection of thiram pesticide in apple juice

Flexible surface-enhanced Raman scattering (SERS) sensors have gained significant attention for their practical applications in detecting chemical and biological molecules. However, the fabrication of flexible SERS chips is often complex and requires advanced techniques. In this study, we present a simple and rapid method to design a flexible SERS chip based on polyvinyl alcohol (PVA), cellulose, and silver nanoparticles (AgNPs) using mechanical stirring and drying methods. Benefiting from the abundant hydroxide groups on cellulose, AgNPs easily adhere and distribute evenly on the cellulose surface. The combination of PVA and cellulose forms a bendable film-like SERS chip. This chip allows convenient immersion in liquid analyte samples. We demonstrate its effectiveness by using it to detect the thiram pesticide in apple juice using the "dip and dry" method, achieving an outstanding detection limit of 1.01 × 10-8 M. The Raman signals on the SERS chips exhibit high repeatability and reproducibility, with relative standard deviation values below 10%. These findings show that the flexible PVA/cellulose/Ag SERS chips is a strong candidate for real-world analysis.

Molecular imprinting-based ratiometric fluorescence sensors for environmental and food analysis

Environmental protection and food safety are closely related to the healthy development of human society; there is an urgent need for relevant analytical methods to determine environmental pollutants and harmful substances in food. Molecular imprinting-based ratiometric fluorescence (MI-RFL) sensors, constructed by combining molecular imprinting recognition and ratiometric fluorescence detection, possess remarkable advantages such as high selectivity, anti-interference ability, high sensitivity, non-destruction and convenience, and have attracted increasing interest in the field of analytical determination. Herein, recent advances in MI-RFL sensors for environmental and food analysis are reviewed, aiming at new construction strategies and representative determination applications. Firstly, fluorescence sources and possible sensing principles are briefly outlined. Secondly, new imprinting techniques and dual/ternary-emission fluorescence types that improve sensing performances are highlighted. Thirdly, typical analytical applications of MI-RFL sensors in environmental and food samples are summarized. Lastly, the challenges and perspectives of the MI-RFL sensors are proposed, focusing on improving sensitivity/visualization and extending applications.

Nucleic acid aptamer-based biosensors and their application in thrombin analysis

Thrombin is a multifunctional serine protease that plays an important role in coagulation and anticoagulation processes. Aptamers have been widely applied in biosensors due to their high specificity, low cost and good biocompatibility. This review summarizes recent advances in thrombin quantification using aptamer-based biosensors. The primary focus is optical sensors and electrochemical sensors, along with their applications in thrombin analysis and disease diagnosis.

Emerging integrated SERS-microfluidic devices for analysis of cancer-derived small extracellular vesicles

Cancer-derived small extracellular vesicles (sEVs) are specific subgroups of lipid bilayer vesicles secreted from cancer cells to the extracellular environment. They carry distinct biomolecules (e.g., proteins, lipids and nucleic acids) from their parent cancer cells. Therefore, the analysis of cancer-derived sEVs can provide valuable information for cancer diagnosis. However, the use of cancer-derived sEVs in clinics is still limited due to their small size, low amounts in circulating fluids, and heterogeneous molecular features, making their isolation and analysis challenging. Recently, microfluidic technology has gained great attention for its ability to isolate sEVs in minimal volume. In addition, microfluidics allows the isolation and detection of sEVs to be integrated into a single device, offering new opportunities for clinical application. Among various detection techniques, surface-enhanced Raman scattering (SERS) has emerged as a promising candidate for integrating with microfluidic devices due to its ultra-sensitivity, stability, rapid readout, and multiplexing capability. In this tutorial review, we start with the design of microfluidics devices for isolation of sEVs and introduce the key factors to be considered for the design, and then discuss the integration of SERS and microfluidic devices by providing descriptive examples of the currently developed platforms. Lastly, we discuss the current limitations and provide our insights for utilising integrated SERS-microfluidics to isolate and analyse cancer-derived sEVs in clinical settings.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Surface-Enhanced Raman Analysis of Uric Acid and Hypoxanthine Analysis in Fractionated Bodily Fluids

In recent years, the disease burden of hyperuricemia has been increasing, especially in high-income countries and the economically developing world with a Western lifestyle. Abnormal levels of uric acid and hypoxanthine are associated with many diseases, and therefore, to demonstrate improved methods of uric acid and hypoxanthine detection, three different bodily fluids were analysed using surface-enhanced Raman spectroscopy (SERS) and high-performance liquid chromatography (HPLC). Gold nanostar suspensions were mixed with series dilutions of uric acid and hypoxanthine, 3 kDa centrifugally filtered human blood serum, urine and saliva. The results show that gold nanostars enable the quantitative detection of the concentration of uric acid and hypoxanthine in the range 5-50 μg/mL and 50-250 ng/mL, respectively. The peak areas of HPLC and maximum peak intensity of SERS have strongly correlated, notably with the peaks of uric acid and hypoxanthine at 1000 and 640 cm^-1, respectively. The r² is 0.975 and 0.959 for uric acid and hypoxanthine, respectively. Each of the three body fluids has a number of spectral features in common with uric acid and hypoxanthine. The large overlap of the spectral bands of the SERS of uric acid against three body fluids at spectra peaks were at 442, 712, 802, 1000, 1086, 1206, 1343, 1436 and 1560 cm^-1. The features at 560, 640, 803, 1206, 1290 and 1620 cm^-1 from hypoxanthine were common to serum, saliva and urine. There is no statistical difference between HPLC and SERS for determination of the concentration of uric acid and hypoxanthine (p > 0.05). For clinical applications, 3 kDa centrifugal filtration followed by SERS can be used for uric acid and hypoxanthine screening is, which can be used to reveal the subtle abnormalities enhancing the great potential of vibrational spectroscopy as an analytical tool. Our work supports the hypnosis that it is possible to obtain the specific concentration of uric acid and hypoxanthine by comparing the SER signals of serum, saliva and urine. In the future, the analysis of other biofluids can be employed to detect biomarkers for the diagnosis of systemic pathologies.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Chromatographic methods for rapid aflatoxin B1 analysis in food: a review

Aflatoxin B1 (AFB1) is a mycotoxin and is the most carcinogenic of all known chemicals. In view of the AFB1 characteristics of widespread distribution, serious pollution, great harm to humans, and animals and difficult to remove, it is urgent to develop a convenient and sensitive detection method. Moreover, chromatographic test strips (CTSs) are a rapid detection technology that combines labeling technology with chromatography technology. CTSs have been widely used in the fields of environmental monitoring, medical diagnosis, and food safety analysis in recent years. Different from other immune assays, they have the advantages of short measuring time, low cost, high efficiency and no need for professionals to operate. In addition, the introduction of nanomaterials has laid a good foundation for the detection of high sensitivity, high specificity and high efficiency via CTSs. Herein, we tend to comprehensively introduce the applications of chromatographic methods in AFB1 detection and pay attention to the signal detection modes based on nanomaterials in antibody-based immunochromatographic strips (ICSs), such as colorimetric, fluorescent, chemiluminescent, and Raman scattering sensing. Some typical examples are also listed in this review. In the end, we make a summary and put forward prospects for the development of CTSs.

SERS and EC-SERS detection of local anesthetic procaine using Pd loaded highly reduced graphene oxide nanocomposite substrate

The localized surface plasmon resonance (LSPR) excitations are critical towards achieving sizable spectral enhancements of the Raman scattered light. Herein, the synthesis of palladium-based highly reduced graphene oxide (Pd-HRG) with LSPR properties as an effective surface-enhanced Raman scattering (SERS) substrate and its utility in the highly sensitive detection of procaine are reported. The concentration detection of procaine samples was optimized by applying a set of pre-concentration parameters. The Pd-HRG nanocomposite showed a remarkable LSPR response with a Raman enhancement factor of 8.7 × 10². The Pd-HRG is employed to modify fluorine doped tin oxide electrode (Pd-HRG/FTO), resulted with an enhancement factor of 7.5 × 10⁴ corresponding to the EC-SERS technique. The electronic and surface properties of synthesized Pd-HRG and functionalized FTO electrode were evaluated using Raman, infrared, EIS, XRD, FESEM and EDX techniques. Quantum chemical calculations were carried out to elaborate on the nature of interaction of procaine molecules with a nanostructured surface model. Pd-HRG, with an efficient and cost-effective fabrication, can be considered as a promising EC-SERS substrate for the detection of organic therapeutic drugs.