Application of SERS in In-Vitro Biomedical Detection

Rapid detection and quantitative analysis of thiram in fruits using a shape-adaptable flexible SERS substrate combined with deep learning

Ensuring food safety necessitates rapid identification of pesticide residues on fruits. Herein, we developed a shape-adaptable flexible surface-enhanced Raman scattering (SERS) substrate, combined with a deep learning algorithm, to quickly detect and quantitatively analyze thiram on fruit surfaces. This SERS substrate was fabricated by depositing silver nanoparticles (Ag NPs) onto a thin, corrugated polydimethylsiloxane (PDMS) film. This innovative design improves physical flexibility, ensuring conformal contact with curved surfaces while achieving high sensitivity, reproducibility, and mechanical robustness. The corrugated Ag NPs@PDMS thin film was able to directly detect thiram at concentrations as low as 10-7 M on tomato and blueberry peels, exhibiting consistent SERS activity with small interference from the fruit's shape. Furthermore, we developed a one-dimensional convolutional neural network (1D CNN) model, trained using a dataset of SERS signals from thiram, for quantitative analysis. The developed model achieved high prediction accuracy, with a coefficient of determination (R2) of 0.9905 and a root mean square error (RMSE) of 0.1364. The integration of our flexible SERS substrate, which adapts well to irregular surfaces, with the 1D CNN algorithm for quantitative analysis, holds great potential for rapid thiram detection in fruits.

Metallo-Supramolecular Helicates as Surface-Enhanced Raman Scattering (SERS) Substrates with High Tailorability

The exploration of novel functionalized supramolecular coordination complexes (SCCs) can enable new applications in domains that include purification and sensing. In this study, employing a coordination-driven self-assembly strategy, we designed and prepared a series of benzochalcogenodiazole-based metallohelicates as high-efficiency charge-transfer surface-enhanced Raman scattering (SERS) substrates, expanding the range of applications for these metallohelicates. Through structural modifications, including the substitution of single heteroatoms on ligands, replacement of coordinating metals, and alteration of ligand framework linkages, the Raman performance of these metallohelicates as substrates were systematically optimized. Notably, the SERS enhancement factors (EFs) of the metallohelicate-based SERS substrates were significantly enhanced to levels as high as 1.03×107, which rivals the EFs of noble metals devoid of "hot spots". Additionally, the underlying Raman enhancement mechanisms of these metallohelicates have been investigated through a combination of control experiments and theoretical calculations. This study not only demonstrates the utility of metallohelicates as SERS substrates but also offers insights and materials for the development of high-efficiency new charge-transfer SERS substrates.

Exploring Plasmonic Standalone Surface-Enhanced Raman Scattering Nanoprobes for Multifaceted Applications in Biomedical, Food, and Environmental Fields

Surface-enhanced Raman scattering (SERS) is an innovative spectroscopic technique that amplifies the Raman signals of molecules adsorbed on rough metal surfaces, making it pivotal for single-molecule detection in complex biological and environmental matrices. This review aims to elucidate the design strategies and recent advancements in the application of standalone SERS nanoprobes, with a special focus on quantifiable SERS tags. We conducted a comprehensive analysis of the recent literature, focusing on the development of SERS nanoprobes that employ novel nanostructuring techniques to enhance signal reliability and quantification. Standalone SERS nanoprobes exhibit significant enhancements in sensitivity and specificity due to optimized hot spot generation and improved reporter molecule interactions. Recent innovations include the development of nanogap and core-satellite structures that enhance electromagnetic fields, which are crucial for SERS applications. Standalone SERS nanoprobes, particularly those utilizing indirect detection mechanisms, represent a significant advancement in the field. They hold potential for wide-ranging applications, from disease diagnostics to environmental monitoring, owing to their enhanced sensitivity and ability to operate under complex sample conditions.

Evaluating the accuracy of Raman spectroscopy in differentiating leukemia patients from healthy individuals: A systematic review and meta-analysis

PURPOSE

To assess the accuracy of Raman spectroscopy in distinguishing between patients with leukemia and healthy individuals.

METHOD

PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases were searched for relevant articles published from inception of the respective database to November 1, 2023. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), were calculated along with their corresponding 95 % confidence intervals (CI). A summary comprehensive receiver operating characteristic curve (SROC) was constructed and the area under the curve (AUC) was calculated. The degree of heterogeneity was tested and analyzed.

RESULTS

Fifteen groups of original studies from 13 articles were included. The pooled SEN and SPE were 0.93 (95 % CI, [0.92 -0.93]) and 0.91(95 % CI, [0.90-0.92]), respectively. The DOR was 613.01 (95 %CI, [270.79-1387.75]), and the AUC was 0.99. The Deeks' funnel plot asymmetry test indicated no significant publication bias among the included studies (bias coefficient, 40.80; P = 0.13 < 0.10). The meta-regression analysis findings indicated that the observed heterogeneity could be attributed to variations in sample categories and Raman spectroscopy techniques.

CONCLUSION

We confirmed that Raman spectroscopy has good accuracy in differentiating patients with leukemia from healthy individuals, and may become a means of leukemia screening in clinical practice. In the case of analysis based on live cells using surface-enhanced Raman spectroscopy (SERS) improved diagnostic efficacy was observed.

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

Flexible, foldable and transparent SERS film with high sensitivity and signal homogeneity via silver ion exchange and in-situ reduction

The adhesion between metal plasma and substrate was the key of surface-enhanced Raman scattering (SERS) technology. The preparation of ideal SERS substrate with multiple advantages such as high sensitivity and good signal reproducibility was still the focus of research. A flexible foldable and transparent fluorinated polyimide/silver NPs (FPI@Ag) SERS film was fabricated by the ion exchange and in-situ reduction method in this work. The effects of KOH hydrolysis time, AgNO3 ion exchange time and concentration, the type and concentration of reducing agents on the SERS performance of the FPI@Ag film were systematically discussed. As a result, the hydrolysis time of KOH affected the thickness of the metallic silver layer, the concentration of AgNO3 affected the size and spacing of Ag NPs, and the Raman signal of was remarkably enhanced when borane dimethylamine complex (DMAB) was used as reducing agent. When the detection limit of 4-Aminothiophenol was as low as 1 × 10-11 mol·L-1, the obvious Raman characteristic peak still appeared. The enhancement factor (EF) was up to 9.4 × 107. The linear quantification range was achieved in the range from 10-3-10-11 mol·L-1, R2 = 0.9987. In addition, we also performed multi-cycle bending and torsion test on the FPI@Ag film, and obtained stable Raman signals. The prepared FPI@Ag film can be attached to the surface of uneven samples, which can be used for on-site Raman detection and analysis.

Determination of L-selectin in blood plasma using DNA aptamer-based surface-enhanced Raman spectroscopy assay

In the human body, tumor cell occurrence can be indirectly monitored using the L-selectin concentration in the blood, since selectin ligands are present on the surface of tumor cells, and with tumor progression, a decrease in L-selectin levels can be expected and observed. In this study, we present a selective DNA-based surface-enhanced Raman spectroscopy (SERS) assay for the detection and determination of L-selectin in biological samples. Two calibration curves (linear in the 40-190 ng mL-1 region and exponential in the 40-500 ng mL-1 region) are fitted to the obtained SERS experimental data, i.e., the ratio of I732/I1334 band intensities (LOQ = 46 ng mL-1). Calculated determination coefficients are found to be R2 = 0.997 for the linear region of the calibration curve and R2 = 0.977 for the exponential region. Moreover, we demonstrate very good selectivity of the assay even in the presence of P- and E-selectin in a sample containing L-selectin. With our SERS assay, the L-selectin concentration in biological samples can be estimated directly from the calibration curves.

Noble metal-free SERS: mechanisms and applications

Surface-enhanced Raman scattering (SERS) is a very important tool in vibrational spectroscopy. The coupling of nanomaterials induces local surface plasmon resonance (LSPR), which contributes greatly to SERS. Due to its remarkable sensitivity in trace detection, SERS has gained prominence in the fields of catalysis, biosensors, drug tracking, and optoelectronic devices. SERS activity is believed to be closely related to the LSPR and charge transfer (CT) of the material. Noble metal nanostructures have been commonly used as SERS-active substrates due to their strong local electric fields and relatively mature preparation, application, and enhancement mechanisms. In recent years, SERS research based on semiconductor materials has attracted significant attention because semiconductor materials have advantages such as repeatable preparation, simple pretreatment, stable SERS spectra and superior biocompatibility, stability, and reproducibility. Semiconductor-based SERS has the potential to enrich SERS theory and applications. Thus, the development of semiconductor materials will introduce a new epoch for SERS-based research. In this review, we outline the two main kinds of semiconductor SERS-active substrates: inorganic and organic semiconductor SERS-active substrates. We also provide an overview of the SERS mechanism for different kinds of materials and SERS-based applications.

Preparation of SERS base membrane with cellulose compound dopamine and determination of hypochlorite

Flexible silver substrates were made by in situ reduction of silver nanoparticles in bacterial cellulose membranes using the unique advantage of dopamine. Subsequently, we modified the substrate with 4-mercaptophenol (4-MP), a molecule capable of specifically recognizing ClO-, and its corresponding SERS signal changes with the concentration of hypochlorite, thus allowing the quantitative detection of ClO- content. The method showed a negative linear correlation (R2 = 0.9567) with the SERS intensity at 1077 cm-1 over the concentration range 0.5-100 µM, and the detection limit was 0.15 µM. The RSD of the SERS intensity at 1077 cm-1 under five batches was 4.2%, which proved the good reproducibility of P-BCM-Ag NP-MP. Finally, the P-BCM-Ag NPs were used for the detection of hypochlorite in cell contents, artificial urine, and clinical serum samples, utilizing spike experiments in all three environments. The recoveries were in the range 90-110% indicating the accuracy of the method for the detection of hypochlorite and validating the promising application of this assay for practical detection in intricate biological samples.

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.