Surface-enhanced Raman scattering method for the identification of methicillin-resistant Staphylococcus aureus using positively charged silver nanoparticles

Rapid and sensitive detection of Staphylococcus aureus using a THz metamaterial biosensor based on aptamer-functionalized Fe3O4@Au nanocomposites

Staphylococcus aureus (S. aureus) poses a serious threat to global public health, necessitating the establishment of rapid and simple tools for its accurate identification. Herein, we developed a terahertz (THz) metamaterial biosensor based on aptamer-functionalized Fe3O4@Au nanocomposites for quantitative S. aureus assays in different clinical samples. Fe3O4@Au@Cys@Apt has the dual advantages of magnetism and a high refractive index in the THz range and was used to rapidly separate and enrich target bacteria in a complex environmental solution. Furthermore, conjugation to the nanocomposites significantly increased the resonance frequency shift of the THz metamaterial after target loading. Our results showed that the shifts in the metamaterial resonance frequency were linearly related to S. aureus concentrations ranging from 1.0 × 103 to 1.0 × 107 CFU/mL, with a detection limit of 4.78 × 102 CFU/mL. The biosensor was further applied to S. aureus detection in spiked human urine and blood with satisfactory recoveries (82.4-109.6%). Our approach also demonstrated strong concordance with traditional plate counting (R2 = 0.99306) while significantly lowering the analysis time from 24 h to <1 h. In conclusion, the proposed biosensor can not only perform culture-free and extraction-free detection of target bacteria but can also be easily extended to the determination of other pathogenic bacteria, rendering it suitable for various bacteria-related disease diagnoses.

A sandwich-structured multifunctional platform based on self-assembled Ti3C2Tx@Au NPs films, antibiotics, and silent region SERS probe for the capture, determination, and drug resistance analysis of Gram-positive bacteria

A multifunctional surface-enhanced Raman scattering (SERS) platform integrating sensitive detection and drug resistance analysis was developed for Gram-positive bacteria. The substrate was based on self-assembled Ti3C2Tx@Au NPs films and capture molecule phytic acid (IP6) to achieve specific capture of Gram-positive bacteria and different bacteria were analyzed by fingerprint signal. It had advantages of good stability and homogeneity (RSD = 8.88%). The detection limit (LOD) was 102 CFU/mL for Staphylococcus aureus and 103 CFU/mL for MRSA, respectively. A sandwich structure was formed on the capture substrate by signal labels prepared by antibiotics (penicillin G and vancomycin) and non-interference SERS probe molecules (4-mercaptobenzonitrile (2223 cm-1) and 2-amino-4-cyanopyridine (2240 cm-1)) to improve sensitivity. The LOD of Au NPs@4-MBN@PG to S. aureus and Au NPs@AMCP@Van to MRSA and S. aureus were all improved to 10 CFU/mL, with a wide dynamic linear range from 108 to 10 CFU/mL (R2 ≥ 0.992). The SERS platform can analyze the drug resistance of drug-resistant bacteria. Au NPs@4-MBN@PG was added to the substrate and captured MRSA to compare the SERS spectra of 4-MBN. The intensity inhomogeneity of 4-MBN at the same concentrations of MRSA and the nonlinearity at the different concentrations of MRSA revealed that MRSA was resistant to PG. Finally, the SERS platform achieved the determination of MRSA in blood. Therefore, this SERS platform has great significance for the determination and analysis of Gram-positive bacteria.

Pharmacological inhibition of endoplasmic reticulum stress mitigates osteoporosis in a mouse model of hindlimb suspension

Hindlimb suspension (HLS) mice exhibit osteoporosis of the hindlimb bones and may be an excellent model to test pharmacological interventions. We investigated the effects of inhibiting endoplasmic reticulum (ER) stress with 4-phenyl butyrate (4-PBA) on the morphology, physicochemical properties, and bone turnover markers of hindlimbs in HLS mice. We randomly divided 21 male C57BL/6J mice into three groups, ground-based controls, untreated HLS group and 4-PBA treated group (HLS+4PBA) (100mg/kg/day, intraperitoneal) for 21 days. We investigated histopathology, micro-CT imaging, Raman spectroscopic analysis, and gene expression. Untreated HLS mice exhibited reduced osteocyte density, multinucleated osteoclast-like cells, adipocyte infiltration, and reduced trabecular striations on micro-CT than the control group. Raman spectroscopy revealed higher levels of ER stress, hydroxyproline, non-collagenous proteins, phenylalanine, tyrosine, and CH2Wag as well as a reduction in proteoglycans and adenine. Furthermore, bone alkaline phosphatase and osteocalcin were downregulated, while Cathepsin K, TRAP, and sclerostin were upregulated. Treatment with 4-PBA partially restored normal bone histology, increased collagen crosslinking, and mineralization, promoted anti-inflammatory markers, and downregulated bone resorption markers. Our findings suggest that mitigating ER stress with 4-PBA could be a therapeutic intervention to offset osteoporosis in conditions mimicking hindlimb suspension.

Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm

FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial species have different molecular compositions, it will then result in unique FTIR spectra for each species and even bacterial strains. Having this important tool, here, we have developed a methodology aimed at refining the analysis and classification of the FTIR absorption spectra obtained from samples of Staphylococcus aureus, with the implementation of machine learning algorithms. In the first stage, the system conforming to four specified species groups, Control, Amoxicillin induced (AMO), Gentamicin induced (GEN), and Erythromycin induced (ERY), was analyzed. Then, in the second stage, five hidden samples were identified and correctly classified as with/without resistance to induced antibiotics. The total analyses were performed in three windows, Carbohydrates, Fatty Acids, and Proteins, of five hundred spectra. The protocol for acquiring the spectral data from the antibiotic-resistant bacteria via FTIR spectroscopy developed by Soares et al. was implemented here due to demonstrating high accuracy and sensitivity. The present study focuses on the prediction of antibiotic-induced samples through the implementation of the hierarchical cluster analysis (HCA), principal component analysis (PCA) algorithm, and calculation of confusion matrices (CMs) applied to the FTIR absorption spectra data. The data analysis process developed here has the main objective of obtaining knowledge about the intrinsic behavior of S. aureus samples within the analysis regions of the FTIR absorption spectra. The results yielded values with 0.7 to 1 accuracy and high values of sensitivity and specificity for the species identification in the CM calculations. Such results provide important information on antibiotic resistance in samples of S. aureus bacteria for potential application in the detection of antibiotic resistance in clinical use.

A cell wall-targeted organic-inorganic hybrid nano-catcher for ultrafast capture and SERS detection of invasive fungi

Due to the extended culture period and various inconveniences in vitro culture, the detection of invasive fungi is rather difficult, leading to high mortality rates of the diseases caused by them. It is, however, crucial for clinical therapy and lowering patient mortality to quickly identify invasive fungus from clinical specimens. A promising non-destructive method for finding fungi is surface-enhanced Raman scattering (SERS), however, its substrate has a low level of selectivity. Clinical sample components can obstruct the target fungi's SERS signal on account of their complexity. Herein, an MNP@PNIPAMAA hybrid organic-inorganic nano-catcher was created by using ultrasonic-initiated polymerization. The caspofungin (CAS), a fungus cell wall-targeting drug, is used in this study. We investigated MNP@PNIPAMAA-CAS as a technique to rapidly extract fungus from complex samples under 3 s. SERS could subsequently be used to instantly identify the fungi that were successfully isolated with an efficacy rate of about 75%. The entire process took just 10 min. This method is an important breakthrough that might be advantageous in terms of the rapid detection of invasive fungi.

Raman spectroscopy combined with deep learning for rapid detection of melanoma at the single cell level

Melanoma is an aggressive and metastatic skin cancer caused by genetic mutations in melanocytes, and its incidence is increasing year by year. Understanding the gene mutation information of melanoma cases is very important for its precise treatment. The current diagnostic methods for melanoma include radiological, pharmacological, histological, cytological and molecular techniques, but the gold standard for diagnosis is still pathological biopsy, which is time consuming and destructive. Raman spectroscopy is a rapid, sensitive and nondestructive detection method. In this study, a total of 20,000 Surface-enhanced Raman scattering (SERS) spectra of melanocytes and melanoma cells were collected using a positively charged gold nanoparticles planar solid SERS substrate, and a classification network system based on convolutional neural networks (CNN) was constructed to achieve the classification of melanocytes and melanoma cells, wild-type and mutant melanoma cells and their drug resistance. Among them, the classification accuracy of melanocytes and melanoma cells was over 98%. Raman spectral differences between melanocytes and melanoma cells were analyzed and compared, and the response of cells to antitumor drugs were also evaluated. The results showed that Raman spectroscopy provided a basis for the medication of melanoma, and SERS spectra combined with CNN classification model realized classification of melanoma, which is of great significance for rapid diagnosis and identification of melanoma.

Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is explored to design a rapid screening method for the characterization and diagnosis of typhoid fever by employing filtrate fractions of blood serum samples obtained by centrifugal filtration with 50 KDa filters.

OBJECTIVES

The purpose of this study, to separate the filtrate portions of blood serum samples in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire the SERS spectral features of smaller proteins more effectively which are probably associated with typhoid disease. Disease caused by Salmonella typhi diagnose more effectively by using surface-enhanced Raman spectroscopy (SERS) and multivariate data analysis tools.

METHODS

SERS was used as a diagnostic tool for typhoid fever by comparison between healthy and diseased samples. For this purpose, all the samples were analyzed by comparing their SERS spectral features. Over the spectral range of 400-1800cm^-1, multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) are applied to diagnose and differentiate different filtrate fractions of blood serum samples of patients of typhoid fever and healthy ones.

RESULTS

By comparing SERS spectra of healthy filtrate with that of filtrate of typhoid sample, the SERS spectral features associated with disease development are identified including PCA is found to be efficient for the qualitative differentiation of all of the samples analyzed. Moreover, PLS-DA successfully identified and classified healthy and typhoid positive blood serum samples with 97 % accuracy, 99 % specificity, 91 % sensitivity and 0.78 area under the receiver operating characteristic (AUROC) curve.

CONCLUSIONS

Surface enhanced Raman spectroscopy using silver nanoparticles SERS substrate, is found to be useful technique for the quick identification and evaluation of filtrate fractions of the blood serum samples of healthy and typhoid samples for disease diagnosis.

Reduced graphene oxide nanosheets decorated with core-shell of Fe3O4-Au nanoparticles for rapid SERS detection and hyperthermia treatment of bacteria

In this study, the core-shell of Fe₃O₄-Au nanoparticles (NPs) were prepared by seeding AuNPs onto Fe₃O₄ NPs modified with poly-ethylenimine (PEI). Later, Fe₃O₄-Au NPs were attached to cationic poly(dimethyldiallylammonium chloride) (PDDA)-modified graphene oxide (GO) nanosheets through in situ self-assembly behaviors, termed as Fe₃O₄-Au@RGO nanocomposites, for surface-enhanced Raman scattering (SERS) detection and hyperthermia treatment of bacteria. The resulting Fe₃O₄-Au@RGO nanocomposites were evaluated systematically by transmission electron microscope, zeta potential, X-ray diffraction, X-ray photoelectron spectroscopy, and vibrating sample magnetometer. It revealed that the core-shell structured Fe₃O₄-Au NPs were dispersed homogeneously on the surface of the GO nanosheets. Furthermore, the rapid SERS detection for small biomolecules and bacteria was conducted by Raman spectroscopy. The results showed that the greatest SERS intensity was fne tuned at the weight ratio of Fe₃O₄-Au/RGO nanosheets was 20/1, displaying the optimal interparticle gap of AuNPs to induce the huge hot-spots effect. The magnetic inductive heating capability of Fe₃O₄-Au@RGO nanocomposites was produced under high frequency magnetic field exposure and can kill high than 90% of the bacteria at 10 min. Hence, the newly developed Fe₃O₄-Au@RGO nanocomposites were demonstrated to be viable for SERS detection of biomolecules and microbes and potential applications for magnetically capturing and hyperthermia treatment of bacteria.

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection

Simple Summary

Blood is considered a rich reservoir of biomarkers for disease diagnosis. Surface-enhanced Raman scattering (SERS) is known for its high sensitivity and has been successfully employed to differentiate blood samples from cancer patients versus healthy individuals. Different from previous reports, this study aims at investigating the reliability of the observed results by varying several parameters influencing the observed spectra. Thus, blood taken from 30 healthy individuals as the control group, 30 patients with different types of cancers, and 15 patients with various types of chronic diseases were used in the study. The results revealed that spectral differences in the cancer group was directly related to the presence of cancer-related biomarkers. Although data were obtained from only small group of patients, the recorded sensitivity and specificity values clearly show the power of the technique to detect cancer.

Abstract

Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

Electrochemical Quantification of Tobramycin Retention in Pseudomonas aeruginosa as Antimicrobial Susceptibility Indicator

The emergence and spread of bacterial resistance to antibiotics has developed into one of the most challenging threats to public health. Antibiotic susceptibility tests (ASTs) for bacterial infections are now essential, because they provide guidance for physicians in the selection of antibiotics, to which bacteria will respond. Most current AST methods require long periods of time, because of bacterial growth and incubation, leading to a prolonged and overuse of broad-spectrum antibiotics. Thus, there is a growing demand for methods and technologies that enable rapid antibiotic susceptibility assessment. Due to advantages related to cost-effectiveness, rapid response time and high sensitivity, electrochemical detection methods are promising analytical tools that can successfully quantify antibiotic uptake and retention in clinically relevant bacterial strains. This study presents the electroanalytical quantification of tobramycin (TOB) retention in susceptible and resistant bacterial strains of Pseudomonas aeruginosa. The electrochemical behavior of TOB was characterized by voltammetry, identifying redox potentials, the current dependence on pH conditions, and the detection limit at unmodified glassy carbon electrodes. The presented methodology was able to distinguish between susceptible and resistant bacterial strains, and is also capable of identifying varying degrees of resistance against TOB. The presented approach detects the immediate interaction of bacteria with an antibiotic, without the need of complex and cost-intense equipment related to genomic testing methods.

Universal Preparation Strategy for Ultradurable Antibacterial Fabrics through Coating an Adhesive Nanosilver Glue

Microbiological protection textile materials played an important role in the battle against the epidemic. However, the traditional active antimicrobial treatment of textiles suffers from narrow textile applicability, low chemical stability, and poor washability. Here, a high-strength adhesive nanosilver glue was synthesized by introducing nontoxic water-soluble polyurethane glue as a protectant. The as-prepared nanosilver glue could adhere firmly to the fiber surfaces by forming a flexible polymer film and could encapsulate nanosilver inside the glue. The as-prepared nanosilver had a torispherical structure with diameter of ~22 nm, zeta potential of −42.7 mV, and good dispersibility in water, and it could be stored for one year. Further studies indicated that the nanosilver glue had wide applicability to the main fabric species, such as cotton and polyester fabric, surgical mask, latex paint, and wood paint. The antimicrobial cotton and polyester fabrics were prepared by a simple impregnation–padding–baking process. The corresponding antimicrobial activity was positively correlated with nanosilver content. The treated fabrics (500 mg/kg) exhibited ultrahigh washing resistance (maintained over 99% antibacterial rates for 100 times of standard washing) and wear resistance (99% antibacterial rates for 8000 times of standard wearing), equivalent breathability to untreated fabric, improved mechanical properties, and good flexibility, demonstrating a potential in cleanable and reusable microbiological protection textiles.

Characterization of Bacteria Using Surface-Enhanced Raman Spectroscopy (SERS): Influence of Microbiological Factors on the SERS Spectra

SERS is currently being explored as a rapid method for identification of bacteria but variation in the experimental procedures has resulted in considerable variation in the spectra reported for a range of bacterial species. Here, we show that mixing bacteria with a conventional citrate-reduced silver colloid (CRSC) and drying the resulting suspension yield highly reproducible spectra. These signals were due to intracellular components released when the structure of the bacteria was disrupted during sample preparation. This reproducibility allowed us to examine the effects of variables that do not arise in SERS of simple solutions but are relevant in studies of bacteria. These included growth phase and biological variation, which occurred when the same bacterial isolates were cultured under nominally identical conditions on different days. It was found that even under optimal standardized conditions the effect of differences in experimental parameters such as growth phase was very large in some bacterial species but insignificant in others. This suggests that it is important to avoid drawing general conclusions about bacterial SERS based on studies using small numbers of samples. Similarly, discrimination between bacterial species was straightforward when a small number of isolates with distinct spectral features were investigated; however, this became more challenging when more bacterial species were included, as this increased the possibility of finding different species of bacteria with similar spectra. These observations are important because they clearly delineate the challenges that will need to be addressed if SERS is to be used for clinical applications.

Nanotechnology Approaches for Rapid Detection and Theranostics of Antimicrobial Resistant Bacterial Infections

As declared by WHO, antimicrobial resistance (AMR) is a high priority issue with a pressing need to develop impactful technologies to curb it. The rampant and inappropriate use of antibiotics due to the lack of adequate and timely diagnosis is a leading cause behind AMR evolution. Unfortunately, populations with poor economic status and those residing in densely populated areas are the most affected ones, frequently leading to emergence of AMR pathogens. Classical approaches for AMR diagnostics like phenotypic methods, biochemical assays, and molecular techniques are cumbersome and resource-intensive and involve a long turnaround time to yield confirmatory results. In contrast, recent emergence of nanotechnology-assisted approaches helps to overcome challenges in classical approaches and offer simpler, more sensitive, faster, and more affordable solutions for AMR diagnostics. Nanomaterial platforms (metallic, quantum-dot, carbon-based, upconversion, etc.), nanoparticle-based rapid point-of-care platforms, nano-biosensors (optical, mechanical, electrochemical), microfluidic-assisted devices, and importantly, nanotheranostic devices for diagnostics with treatment of AMR infections are examples of rapidly growing nanotechnology approaches used for AMR management. This review comprehensively summarizes the past 10 years of research progress on nanotechnology approaches for AMR diagnostics and for estimating antimicrobial susceptibility against commonly used antibiotics. This review also highlights several bottlenecks in nanotechnology approaches that need to be addressed prior to considering their translation to clinics.

Dynamic insights into increasing antibiotic resistance in Staphylococcus aureus by label-free SERS using a portable Raman spectrometer

Rapid and quantitative detection of bacterial antibiotic resistance is of great significance for the prevention and treatment of infections and understanding drug-resistant mechanism. In this study, label-free surface-enhanced Raman spectroscopy (SERS) technology was applied to dynamically explore oxacillin/cefazolin-derived resistance in Staphylococcus aureus using a portable Raman spectrometer. The results showed that S. aureus rapidly responded to oxacillin/cefazolin stimulation and gradually developed different degrees of drug resistance during the 21 days of exposure. The molecular changes that accumulated in the drug-resistant strains were sensitively recorded by SERS in a whole-cell manner. Principal components-linear discriminant analysis correctly distinguished various degrees of drug-resistant strains. The typical Raman peak intensities of I₇₃₄/I₈₆₇ showed a negative and non-linear correlation with the minimum inhibitory concentration (MIC). The correlation coefficient reached above 0.9. The target sites of oxacillin/cefazolin on S. aureus clearly reflected on SERS profiles. The results collected by SERS were further verified by other biological methods including the antibiotic susceptibility test, MIC determination, and PCR results. This study indicates that SERS technology provides a rapid and flexible alternative to current drug susceptibility testing, laying a foundation for qualitative and quantitative evaluation of drug resistance in clinical detection.

Application of Nanomaterials in the Prevention, Detection, and Treatment of Methicillin-Resistant Staphylococcus aureus (MRSA)

Due to differences in geographic surveillance systems, chemical sanitization practices, and antibiotic stewardship (AS) implementation employed during the COVID-19 pandemic, many experts have expressed concerns regarding a future surge in global antimicrobial resistance (AMR). A potential beneficiary of these differences is the Gram-positive bacteria MRSA. MRSA is a bacterial pathogen with a high potential for mutational resistance, allowing it to engage various AMR mechanisms circumventing conventional antibiotic therapies and the host’s immune response. Coupled with a lack of novel FDA-approved antibiotics reaching the clinic, the onus is on researchers to develop alternative treatment tools to mitigate against an increase in pathogenic resistance. Mitigation strategies can take the form of synthetic or biomimetic nanomaterials/vesicles employed in vaccines, rapid diagnostics, antibiotic delivery, and nanotherapeutics. This review seeks to discuss the current potential of the aforementioned nanomaterials in detecting and treating MRSA.

MALDI-TOF MS method for differentiation of methicillin-sensitive and methicillin-resistant Staphylococcus aureus using (E)-Propyl α-cyano-4-Hydroxyl cinnamylate

Differentiating methicillin-sensitive and methicillin-resistant Staphylococcus aureus (MRSA and MSSA) is crucial for clinical diagnosis and anti-microbial treatment. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is an efficient tool for identifying pathogenic microorganisms at the bacterial species level. Here, we found that MRSA and MSSA can be differentiated by MALDI-TOF MS by employing (E)-propylα-cyano-4-hydroxyl cinnamylate (CHCA-C3) as the matrix, which shows great performance for proteins/peptides, especially hydrophobic proteins. The results show that the mass spectra profile of standard MRSA (ATCC 43300) is significantly different from the profiles of standard MSSA strains (ATCC 25923 and 29213) when using CHCA-C3 as the matrix compared to traditional matrix. The mass profiles had great reproducibility and were scarcely influenced by the growth medium. Due to the enhanced discrimination ability of CHCA-C3, we collected the mass spectra of 62 clinical S. aureus strains and selected four representative peaks for principal component analysis, which showed great differentiation. Our results suggest that employing a suitable matrix could enhance the discrimination ability of antibiotic-resistant bacteria by MALDI-TOF MS.

Review of Label-Free Monitoring of Bacteria: From Challenging Practical Applications to Basic Research Perspectives

Novel biosensors already provide a fast way to detect the adhesion of whole bacteria (or parts of them), biofilm formation, and the effect of antibiotics. Moreover, the detection sensitivities of recent sensor technologies are large enough to investigate molecular-scale biological processes. Usually, these measurements can be performed in real time without using labeling. Despite these excellent capabilities summarized in the present work, the application of novel, label-free sensor technologies in basic biological research is still rare; the literature is dominated by heuristic work, mostly monitoring the presence and amount of a given analyte. The aims of this review are (i) to give an overview of the present status of label-free biosensors in bacteria monitoring, and (ii) to summarize potential novel directions with biological relevancies to initiate future development. Optical, mechanical, and electrical sensing technologies are all discussed with their detailed capabilities in bacteria monitoring. In order to review potential future applications of the outlined techniques in bacteria research, we summarize the most important kinetic processes relevant to the adhesion and survival of bacterial cells. These processes are potential targets of kinetic investigations employing modern label-free technologies in order to reveal new fundamental aspects. Resistance to antibacterials and to other antimicrobial agents, the most important biological mechanisms in bacterial adhesion and strategies to control adhesion, as well as bacteria-mammalian host cell interactions are all discussed with key relevancies to the future development and applications of biosensors.

A 3D-ACEK/SERS system for highly efficient and selectable electrokinetic bacteria concentration/detection/ antibiotic-susceptibility-test on whole blood

This study demonstrates a novel multi-functional microfluidic system, designated three dimensional Alternative Current Electrokinetic/Surface Enhanced Raman Scattering (3D-ACEK/SERS), which can concentrate bacteria from whole blood, identify bacterial species, and determine antibiotic susceptibilities of the bacteria rapidly. The system consists of a hybrid electrokinetic mechanism, integrating AC-electroosmosis (AC-EO) and dielectrophoresis (DEP) that allows thousand-fold concentration of bacteria, including S. aureus, Escherichia coli, and Chryseobacterium indologenes, in the center of an electrode with a wide range of working distance (hundreds to thousands of μm), while exclusion of blood cells through negative DEP forces. This microchip employs SERS assay to determine the identity of the concentrated bacteria in approximately 2 min with a limit of detection of 3 CFU/ml, 5 orders of magnitude lower than that using standard centrifugation-purification process. Finally, label-free antibiotic susceptibility testing has been successfully demonstrated on the platform using both antibiotic-sensitive and multidrug-resistant bacterial strains illustrating a potential utility of the system to clinical applications.

Fluorescence detection of Staphylococcus aureus using vancomycin functionalized magnetic beads combined with rolling circle amplification in fruit juice

Staphylococcus aureus is a common foodborne pathogen that can cause a suppurative infection after eating contaminated food. Detection of S. aureus plays an important role in the food industry. In this study, a strategy for the detection of S. aureus using magnetic separation (MS) technology combined with rolling circle amplification (MS-RCA) was proposed. The strategy used antibiotics to capture bacteria and employed RCA products as signal output probes. Vancomycin (Van), as a commonly used antibiotic, can recognize peptidoglycan on the cell wall of Gram-positive bacteria and can effectively identify target bacteria. Therefore, we prepared BSAylated-Van functionalized magnetic beads (Van-MBs) for the pre-enrichment of S. aureus. To ensure the selectivity of this method, we used biotin-pig IgG to bind S. aureus. In addition, to amplify the output signal of the MS-RCA strategy, we introduced streptavidin (SA) and successfully obtained the Van-MBs@S. aureus@biotin-pig IgG@SA@biotin-RCA probe complex and used the biotin-avidin-system (BAS) by combining magnetic separation technology and RCA technology to realize the enrichment and specific detection of S. aureus. Furthermore, by optimizing the experimental conditions such as the magnetic separation time and the amount of Van-MBs, the detection performance of this method was improved. Under the optimal conditions, the detection limit of this method for S. aureus was 3.3 × 10² CFU/mL in fruit juice, and it was less affected by other bacteria.

Positively-charged plasmonic nanostructures for SERS sensing applications

Surface-enhanced Raman (SERS) spectroscopy has been establishing itself as an ultrasensitive analytical technique with a cross-disciplinary range of applications, which scientific growth is triggered by the continuous improvement in the design of advanced plasmonic materials with enhanced multifunctional abilities and tailorable surface chemistry. In this regard, conventional synthetic procedures yield negatively-charged plasmonic materials which can hamper the adhesion of negatively-charged species. To tackle this issue, metallic surfaces have been modified via diverse procedures with a broad array of surface ligands to impart positive charges. Cationic amines have been preferred because of their ability to retain a positive zeta potential even at alkaline pH as well as due to their wide accessibility in terms of structural features and cost. In this review, we will describe and discuss the different approaches for generating positively-charged plasmonic platforms and their applications in SERS sensing.

Integration of ligands equipped with quaternary amines on plasmonic surfaces generates positively-charged nanomaterials suitable for electrostatically binding negatively-charged species paving the way for their application in SERS sensing.

Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis

The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.

Surface-enhanced Raman spectroscopy for analysis of PCR products of viral RNA of hepatitis C patients

In the current study, for a qualitative and quantitative study of Polymerase Chain Reaction (PCR) products of viral RNA of Hepatitis C virus (HCV) infection, surface-enhanced Raman spectroscopy (SERS) methodology has been developed. SERS was used to identify the spectral features associated with the PCR products of viral RNA of Hepatitis C in various samples of HCV-infected patients with predetermined viral loads. The measurements for SERS were performed on 30 samples of PCR products, which included three PCR products of RNA of healthy individuals, six negative controls, and twenty-one HCV positive samples of varying viral loads (VLs) using Silver nanoparticles (Ag NPs) as a SERS substrates. Additionally, on SERS spectral data, the multivariate data analysis methods including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were also carried out which help to illustrate the diagnostic capabilities of this method. The PLSR model is designed to predict HCV viral loads based on biochemical changes observed as SERS spectral features which can be associated directly with HCV RNA. Several SERS characteristic features are observed in the RNA of HCV which are not detected in the spectra of healthy RNA/controls. PCA is found helpful to differentiate the SERS spectral data sets of HCV RNA samples from healthy and negative controls. The PLSR model is found to be 99% accurate in predicting VLs of HCV RNA samples of unknown samples based on SERS spectral changes associated with the Hepatitis C development.

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.

Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains

Tigecycline (TGC) is recognised as last resort of drugs against several antibiotic-resistant bacteria. Bacterial resistance to tigecycline due to presence of plasmid-mediated mobile TGC resistance genes (tet X3/X4) has broken another defense line. Therefore, rapid and reproducible detection of tigecycline-resistant E. coli (TREC) is required. The current study is designed for the identification and differentiation of TREC from tigecycline-sensitive E. coli (TSEC) by employing SERS by using Ag NPs as a SERS substrate. The SERS spectral fingerprints of E. coli strains associated directly or indirectly with the development of resistance against tigecycline have been distinguished by comparing SERS spectral data of TSEC strains with each TREC strain. Moreover, the statistical analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed to check the diagnostic potential of SERS for the differentiation among TREC and TSEC strains. The qualitative identification and differentiation between resistant and sensitive strains and among individual strains have been efficiently done by performing both PCA and HCA. The successful discrimination among TREC and TSEC at the strain level is performed by PLS-DA with 98% area under ROC curve, 100% sensitivity, 98.7% specificity and 100% accuracy.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens

The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.

Relationship between Micromolecules and Quality Changes of Tilapia Fillets after Partial Freezing Treatment with Polyphenols

The study investigated the main characteristic micromolecular changes in tilapia fillets after partial freezing treatment with polyphenols by ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) analysis. A total of 2121 metabolite ion features were identified. The result suggested that procyanidin treatment increased the sweet, salty, and thick peptides' contents and suppressed the formation of bitter peptides. The levels of cis-4-octenedioic acid, 2-amino-heptanoic acid, indoleacrylic acid, and 2-amino-3-methyl-1-butanol in polyphenol treatments were significantly lower compared to those in the control group (P < 0.05), which delayed the formation of micromolecule of acids and alcohols associated with spoilage and inhibited the protein and lipid oxidation and degradation. Polyphenol treatments suppressed the formation of biogenic amines (lower levels of spermidine and 1-naphthylacetylspermine) and reduced fillet quality deterioration. It provided critical novel insights into the understanding of the molecular mechanism for inhibiting the quality deterioration of fillets treated with polyphenols during storage.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

Rapid Methods for Antimicrobial Resistance Diagnostics

Antimicrobial resistance (AMR) is one of the most challenging threats in public health; thus, there is a growing demand for methods and technologies that enable rapid antimicrobial susceptibility testing (AST). The conventional methods and technologies addressing AMR diagnostics and AST employed in clinical microbiology are tedious, with high turnaround times (TAT), and are usually expensive. As a result, empirical antimicrobial therapies are prescribed leading to AMR spread, which in turn causes higher mortality rates and increased healthcare costs. This review describes the developments in current cutting-edge methods and technologies, organized by key enabling research domains, towards fighting the looming AMR menace by employing recent advances in AMR diagnostic tools. First, we summarize the conventional methods addressing AMR detection, surveillance, and AST. Thereafter, we examine more recent non-conventional methods and the advancements in each field, including whole genome sequencing (WGS), matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectrometry, Fourier transform infrared (FTIR) spectroscopy, and microfluidics technology. Following, we provide examples of commercially available diagnostic platforms for AST. Finally, perspectives on the implementation of emerging concepts towards developing paradigm-changing technologies and methodologies for AMR diagnostics are discussed.

Rapid Detection Method for Pathogenic Candida Captured by Magnetic Nanoparticles and Identified Using SERS via AgNPs. Int J Nanomedicine 2021;16:941-950. [PMID: 33603361 PMCID: PMC7884937 DOI: 10.2147/ijn.s285339]

Purpose

Candidemia infection is common in the clinic and has a high mortality rate. Candida albicans, Candida tropicalis, and Candida krusei are very important and common pathogenic species. Candida is difficult to isolate from clinical samples and culture, and immunological detection cannot distinguish these related strains. Furthermore, Candida has a complex cell wall, which causes difficulties in the extraction of DNA for nucleic acid detection. The purpose of this study was to establish a protocol for the direct identification of Candida from serum.

Materials and Methods

We synthesized Fe₃O₄@PEI (where PEI stands for polyethylenimine) magnetic nanoparticles to capture Candida and prepared positively charged silver nanoparticles (AgNPs⁺) as the substrate for surface-enhanced Raman scattering (SERS). Candida was directly identified from serum by SERS detection.

Results

Orthogonal partial least squares discriminant analysis (OPLS-DA) was used as the multivariate analysis tool. Principal component analysis confirmed that this method can clearly distinguish common Candida. After 10-fold cross-validation, the accuracy of training data in this model was 100% and the accuracy of test data was 99.8%, indicating that the model has good classification ability.

Conclusion

The detection could be completed within 40 minutes using Fe₃O₄@PEI and AgNPs⁺ prepared in advance. This is the first time that Fe₃O₄@PEI was used in the detection of Candida by SERS. We report the first rapid method to identify fungi directly from serum without breaking the cell wall to extract DNA from the fungi.

Visual paper-based sensor for the highly sensitive detection of caffeine in food and biological matrix based on CdTe-nano ZnTPyP combined with chemometrics

Caffeine naturally occurs in tea and cocoa, which is also used as an additive in beverages and has pharmacological effects such as refreshing, antidepressant, and digestion promotion, but excessive caffeine can cause harm to the human body. In this work, based on the specific response between nano zinc 5, 10, 15, 20-tetra(4-pyridyl)-21H-23H-porphine (nano ZnTPyP)-CdTe quantum dots (QDs) and caffeine, combined with chemometrics, a visual paper-based sensor was constructed for rapid and on-site detection of caffeine. The fluorescence of QDs can be quenched by nano ZnTPyP. When caffeine is added to the system, it can pull nano ZnTPyP off the surface of the QDs to achieve fluorescence recovery through electrostatic attraction and nitrogen/zinc coordination. The detection range is 5 × 10^-11~3 × 10^-9 mol L^-1, and the detection limit is 1.53 × 10^-11 mol L^-1 (R² = 0.9990) (S/N = 3). The paper-based sensor constructed exhibits good results in real samples, such as tea water, cell culture fluid, newborn bovine serum, and human plasma. Therefore, the sensor is expected to be applied to the rapid instrument-free detection of caffeine in food and biological samples.Graphical abstract.

Rapid and sensitive identification of uropathogenic Escherichia coli using a surface-enhanced-Raman-scattering-based biochip

Rapid, selective and sensitive sensing of bacteria remains challenging. We report on a highly sensitive and reproducible surface-enhanced Raman spectroscopy (SERS)-based sensing approach for the detection of uropathogenic Escherichia coli (E. coli) bacteria in urine. The assay is based on the specific capture of the bacteria followed by interaction with cetyltrimethylammonium bromide (CTAB)-stabilised gold nanorods (Au NRS) as SERS markers. High sensitivity up to 10 CFU mL^-1 is achieved by optimizing the capture interface based on hydrogenated amorphous silicon a-Si:H thin films. The integration of CH₃O-PEG750 onto a-Si:H gives the sensing interface an efficient anti-fouling character, while covalent linkage of antibodies directed against the major type-1 fimbrial pilin FimA of the human pathogen E. coli results in the specific trapping of fimbriated E. coli onto the SERS substrate and their spectral fingerprint identification.

Recent progress and challenges on the bioassay of pathogenic bacteria

The present review (containing 242 references) illustrates the importance and application of optical and electrochemical methods as well as their performance improvement using various methods for the detection of pathogenic bacteria. The application of advanced nanomaterials including hyper branched nanopolymers, carbon-based materials and silver, gold and so on. nanoparticles for biosensing of pathogenic bacteria was also investigated. In addition, a summary of the applications of nanoparticle-based electrochemical biosensors for the identification of pathogenic bacteria has been provided and their advantages, detriments and future development capabilities was argued. Therefore, the main focus in the present review is to investigate the role of nanomaterials in the development of biosensors for the detection of pathogenic bacteria. In addition, type of nanoparticles, analytes, methods of detection and injection, sensitivity, matrix and method of tagging are also argued in detail. As a result, we have collected electrochemical and optical biosensors designed to detect pathogenic bacteria, and argued outstanding features, research opportunities, potential and prospects for their development, according to recently published research articles.

A novel method for identifying and distinguishing Cryptococcus neoformans and Cryptococcus gattii by surface-enhanced Raman scattering using positively charged silver nanoparticles

There are approximately 1 million cryptococcal infections per year among HIV+ individuals, resulting in nearly 625,000 deaths. Cryptococcus neoformans and Cryptococcus gattii are the two most common species that cause human cryptococcosis. These two species of Cryptococcus have differences in pathogenicity, diagnosis, and treatment. Cryptococcal infections are usually difficult to identify because of their slow growth in vitro. In addition, the long detection cycle of Cryptococcus in clinical specimens makes the diagnosis of Cryptococcal infections difficult. Here, we used positively charged silver nanoparticles (AgNPs⁺) as a substrate to distinguish between C. neoformans and C. gattii in clinical specimens directly via surface-enhanced Raman scattering (SERS) and spectral analysis. The AgNPs⁺ self-assembled on the surface of the fungal cell wall via electrostatic aggregation, leading to enhanced SERS signals that were better than the standard substrate negatively charged silver nanoparticles (AgNPs). The SERS spectra could also be used as a sample database in the multivariate analysis via orthogonal partial least-squares discriminant analysis. This novel SERS detection method can clearly distinguish between the two Cryptococcus species using principal component analysis. The accuracy of the training data and test data was 100% after a tenfold crossover validation.

In situ and sensitive monitoring of configuration-switching involved dynamic adsorption by surface plasmon-coupled directional enhanced Raman scattering

Surface adsorption studies play a crucial role in numerous fields from surface catalysis to molecular separation. However, investigation on adsorption mechanisms has been restricted to limited analytes and approaches, which calls for an in situ and sensitive surface analysis technique capable of revealing the mechanisms as well as discriminating different adsorbates and their geometry at different adsorption stages. In this study, we employed surface plasmon-coupled directional enhanced Raman scattering (SPCR), a novel technique developed by coupling surface plasmon-coupled emission with SERS, to study conformation-switching involved dynamic adsorption with background suppression and improved sensitivity (nearly 30-fold). We obtained the isotherms for a conformation-changing Raman model analyte, malachite green. An S-type Langmuir model was fitted from the time-resolved SPCR signals sensitively and without any interference from the bulk solution. The reorientation of the analyte from a predominantly parallel configuration to a perpendicular one was captured by the dramatic increase in the intensity ratios of the adsorption-related peaks to the adsorption-unrelated peak. We believe that this new sensitive and selective SPCR technique will be a promising tool for surface adsorption kinetics analysis.

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.

Highly sensitive detection of Staphylococcus aureus by a THz metamaterial biosensor based on gold nanoparticles and rolling circle amplification

A highly sensitive method for detecting Staphylococcus aureus (S. aureus) is urgently needed to reduce the impact and spread of hospital-acquired infections and food-borne illness. For this purpose, this paper presents a THz metamaterial biosensor based on gold nanoparticles (AuNPs) and rolling circle amplification (RCA). The RCA process amplified the S. aureus DNA fragments and generated copious yields of long single-strand DNA molecules. These molecules were then conjugated with the AuNPs to form complexes that delivered exceptional increases in the refractive indices of the samples, and resulted in corresponding improvements in the THz response of the metamaterial. Under optimal conditions, the shifts in the metamaterial's resonance frequency displayed a linear relationship with concentrations of synthetic S. aureus DNA in the range from 10 fM to 10 pM, with a limit of detection of 2.77 fM. We also tested the practical application of this biosensor in measurements of genomic DNA in clinical bacterial strains, where the sensor showed a detection limit of 0.08 pg μL⁻¹ and a linear range from 0.1 to 5 pg μL⁻¹. It also exhibited reasonable specificity, resisting interference from three other pathogenic bacteria. These findings indicate that the proposed approach offers a cost-effective THz biosensing strategy that can be easily fabricated and conveniently operated to aid the diagnosis of infectious disease and food safety control.

A highly sensitive method for detecting Staphylococcus aureus (S. aureus) is urgently needed to reduce the impact and spread of hospital-acquired infections and food-borne illness.

Streptavidin-exposed magnetic nanoparticles for lectin magnetic separation (LMS) of Staphylococcus aureus prior to three quantification strategies

A lectin magnetic separation (LMS) method for Staphylococcus aureus (S. aureus) was developed with the aim to improve the efficiency of magnetic nanoparticles and to expand the scope of bacterial recognition. Poly(ethylene glycol) (PEG)-mediated magnetic nanoparticles modified with streptavidin (MNP-PEG-SA) were synthesized and then applied to a two-step LMS based on the use of wheat germ agglutinin (WGA). Three specific methods for S. aureus detection (suitable for different requirements including detection time and sensitivity) were designed. The new LMS has improved anchoring efficiency (compared to two-step LMS methods) and requires a reduced number of magnetic particles. The Baird-Parker (B-P) method can detect S. aureus with a detection limit of 3 × 10⁰ CFU·mL^-1 within 15 h; the polymerase chain reaction (PCR) method can be finished within 4 h, with the lowest detection limit (LOD) of 3 × 10² CFU·mL^-1. The LOD of HRP-pig IgG-based colorimetric method is 3 × 10⁵ CFU·mL^-1, and the method only lasts for 2 h. If combined with specific detection methods, it meets different needs for rapid detection of S. aureus. Graphical abstractSchematic representation of lectin magnetic separation (LMS) based on biotin-wheat germ agglutinin (WGA) and poly (ethylene glycol) (PEG)-mediated streptavidin-modified magnetic nanoparticles (MNP-PEG-SA) and three different quantification strategies (including B-P culture assay, PCR assay, and colorimetric assay) for S. aureus.

Rapid identification and antibiotic susceptibility test of pathogens in blood based on magnetic separation and surface-enhanced Raman scattering

An effective surface-enhanced Raman scattering (SERS) method is presented for the rapid identification and drug sensitivity analysis of pathogens in blood. In a first step, polyethyleneimine-modified magnetic microspheres (Fe₃O₄@PEI) were used to enrich bacteria from blood samples. Next, the Fe₃O₄@PEI@bacteria complex was cultured on both ordinary and drug-sensitive plates. Lastly, the SERS spectra of single colonies were acquired in order to identify different pathogens and their resistant strains by comparison with established standardized bacterial SERS spectras and orthogonal partial least squares discriminant analysis (OPLS-DA) method. Staphylococcus aureus, Acinetobacter baumannii, Pseudomonas aeruginosa and their resistant strains were used to evaluate the performance of the SERS method. The results demonstrate that the method can accurately detect and identify all the tested sensitive and drug-resistant strains of bacteria, including 77 clinical blood infection samples. The method provides a way for rapid identification and susceptibility test of pathogens, and has great potential to replace currently used time-consuming methods. Graphical abstract Schematic presentation of a method for the rapid identification and drug sensitivity analysis of pathogens in blood. It is based on a combination of magnetic separation, SERS fingerprint analysis and orthogonal partial least squares discriminant analysis (OPLS-DA).