Surface-enhanced Raman Scattering Spectra Research Articles

SERS-Encoded Nanoprobes Based on Silver-Coated Gold Nanorods for Cell Sorting.

Optically-encoded probes have great potential for applications in the fields of biosensing and imaging. By employing specific encoding methods, these probes enable the detection of multiple target molecules and high-resolution imaging within the same sample. Among the various encoding methods, surface-enhanced Raman scattering (SERS) spectral encoding stands out due to its extremely narrow linewidth. Compared to fluorescence spectral encoding, SERS encoding significantly reduces crosstalk between adjacent peaks, thereby achieving a larger encoding capacity and enabling multi-channel parallel analysis. This article presents the design and construction of two novel sets of SERS-encoded probes based on noble metal core-shell nanostructures. Two different encoding strategies are successfully applied to encode the SERS spectra of the probes: 1D encoding based on the wavenumber of characteristic peaks in the SERS spectrum, and 2D encoding combining both wavenumber and intensity of characteristic peaks in the SERS spectrum. In addition, this study also demonstrates the potential application of 1D encoded probes in cell sorting. These studies verify the feasibility of applying these two encoding methods to SERS core-shell probes and provide new insights into the construction of optically encoded probes.

Label-Free SERS Sensors for Real-Time Monitoring of Tyrosine Phosphorylation.

Dysregulation of receptor tyrosine kinases (RTKs) has been shown to correlate with cancer cell proliferation and drug resistance. Thus, monitoring the activity of RTKs at a chemical level could provide new biomedical insights and methods to assess the drug efficacy. However, direct monitoring of kinase activity is challenging and most commonly relies on in vitro techniques such as Western blotting and ELISAs. Herein, we report the development of a gold nanoparticle-based surface-enhanced Raman scattering (SERS) sensor, which allows the real-time monitoring of tyrosine phosphorylation of a reporter peptide (Axltide) by the Axl enzyme. We demonstrate that our sensor shows strong signal localization, and we are able to detect tyrosine phosphorylation of the reporter peptide through chemical phosphorylation and enzymatically with similar peak changes to those observed in the spontaneous Raman spectra. Through monitoring the SERS spectrum, we can observe changes in phosphorylation in real time.

The applicability of the SERS technique in food contamination testing – The detailed spectroscopic, chemometric, genetic, and comparative analysis of food-borne Cronobacter spp. strains

Microorganisms assigned as Cronobacter are Gram-negative, facultatively anaerobic, bacteria widely distributed in nature, home environments, and hospitals. They can also be detected in foods, milk powder, and powdered infant formula (PIF). Additionally, as an opportunistic pathogen, Cronobacter may cause serious infections, sometimes leading to the death of neonates and infants. Thus, it is essential to test food products for the presence of Cronobacter spp. The currently used standard described in ISO 22964:2017 is a laborious method that could be easily replaced by surface-enhanced Raman scattering (SERS).Here, we demonstrate that SERS allows the identification of food-borne bacteria belonging to Cronobacter spp. based on their SERS spectra. For this purpose, twenty-six Cronobacter strains from different food samples were analyzed. Additionally, it was shown that it is possible to differentiate them from other closely related pathogens such as Salmonella enterica subsp. enterica, Escherichia coli, or Enterobacter spp. The SERS results were supported by principal component analysis (PCA), as well as and sequencing of 16S rRNA, rpoB and fusA genes. Last but not least, it was demonstrated that the cells of Cronobacter sakazakii may be easily separated from PIF using an appropriate filter, microfluidic chip, and dielectrophoresis (DEP) technique.

Surfactant-Free Method to Prevent Gold Nanoparticle Aggregation and Its Surface-Enhanced Raman Scattering Applications.

The introduction of surfactants to stabilize colloidal citrate-reduced gold nanoparticles (prevent aggregation) is usually used in surface-enhanced Raman scattering (SERS) applications. However, the surfactants have many drawbacks for SERS applications, such as increasing the SERS background and blocking surface active sites. Here, we develop a surfactant-free method to stabilize colloidal cit-AuNPs based on alkali regulation, and this method can prevent gold nanoparticle aggregation under different harsh treatments, including ligand modification, centrifugation-based washing/enrichment, and salt addition. The SERS spectra, density functional theory simulation, and ζ potentials of cit-AuNPs indicate that the stability of enhanced cit-AuNPs under alkaline conditions is attributed to both the increased negative charge density (by ∼6 times from pH 7 to 12) and the molecular configuration on the metal surface. Compared with surfactant-based methods, this method can well maintain the inherent optical and interface properties of nanoparticles, avoid the SERS background, and avoid blocking of the surface active site due to the presence of surfactants. This method will enable AuNPs to have a wide range of applications in areas such as highly sensitive SERS sensors.

Mid-level data fusion strategy based on urinary nucleosides SERS spectra and blood CEA levels for enhanced preoperative detection of lymph node metastasis in colorectal cancer

BackgroundPreoperative prediction of lymph node metastasis (LNM) plays a crucial role in the treatment and prognosis of colorectal cancer (CRC). The traditional histopathological examination is invasive and time-consuming, providing pathological features only postoperatively. Preoperative serum carcinoembryonic antigen (CEA) is strongly correlated with postoperative LN status. However, the detection accuracy of LNM based on a single preoperative CEA level is low. Therefore, developing a more powerful and sensitive diagnostic tool would be of great clinical value for improving the accurate preoperative prediction of LNM in CRC patients. ResultsThis study aimed to develop a mid-level fusion approach using urinary nucleosides Raman spectra and blood CEA data to enhance the preoperative discrimination of CRC patients with and without LNM. Surface-enhanced Raman scattering (SERS) spectra of urinary modified nucleosides, isolated by affinity chromatography, were first acquired from 48 patients with LNM and 49 patients without LNM. The principal component analysis (PCA) scores obtained from the SERS spectra were then combined with preoperative blood CEA values to create a fused data array. The discriminant accuracy based on either dataset alone or the fused data was evaluated using three machine learning algorithms: linear discriminant analysis, k-nearest neighbors, and support vector machine. Results showed that the fused data could discriminate between the two groups with an accuracy of up to 91 %, outperforming SERS alone (86 %) and CEA alone (69 %). SignificanceTo our knowledge, this is the first report of mid-level data fusion of urinary nucleosides SERS spectra with blood CEA levels for the preoperative prediction of LNM in CRC. This work demonstrates that the mid-level data fusion strategy aided by SVM algorithm can greatly improve the preoperative prediction accuracy of LNM. This is crucial for therapeutic decision-making and prognostic assessment in CRC.

Utilizing surface-enhanced Raman spectroscopy for the adjunctive diagnosis of osteoporosis

Osteoporosis (OP) is a chronic disease characterized by diminished bone mass and structural deterioration, ultimately leading to compromised bone strength and an increased risk of fractures. Diagnosis primarily relies on medical imaging findings and clinical symptoms. This study aims to explore an adjunctive diagnostic technique for OP based on surface-enhanced Raman scattering (SERS). Serum SERS spectra from the normal, low bone density, and osteoporosis groups were analyzed to discern OP-related expression profiles. This study utilized partial least squares (PLS) and support vector machine (SVM) algorithms to establish an OP diagnostic model. The combination of Raman peak assignments and spectral difference analysis reflected biochemical changes associated with OP, including amino acids, carbohydrates, and collagen. Using the PLS-SVM approach, sensitivity, specificity, and accuracy for screening OP were determined to be 77.78%, 100%, and 88.24%, respectively. This study demonstrates the substantial potential of SERS as an adjunctive diagnostic technology for OP.

Label-Free Exosome Analysis by Surface-Enhanced Raman Scattering Spectroscopy with Laser-Ablated Silver Nanoparticle Substrate.

Early diagnostics of breast cancer is crucial to reduce the risk of cancer metastasis and late relapse. Exosome, which contains distinct information of its origin, can be the target object as a liquid biopsy. However, its low sensitivity and inadequate diagnostic tools interfere with the point-of-care testing (POCT) of the exosome. Recently, Surface-enhanced Raman Scattering (SERS) spectroscopy, which amplifies the Raman scattering, has been proved as a promising tool for exosome detection. However, the fabrication process of SERS probe or substrate is still inefficient and far from large-scale production. This study proposes rapid and label-free detection of breast cancer-derived exosomes by statistical analysis of SERS spectra using silver-nanoparticle-based SERS substrate fabricated by selective laser ablation and melting (SLAM). Employing silver nanowires and optimizing laser process parameters enable rapid and low-energy fabrication of SERS substrate. The functionalities including sensitivity, reproducibility, stability, and renewability are evaluated using rhodamine 6G as a probe molecule. Then, the feasibility of POCT is examined by the statistical analysis of SERS spectra of exosomes from malignant breast cancer cells and non-tumorigenic breast epithelial cells. The presented framework is anticipated to be utilized in other biomedical applications, facilitating cost-effective and large-scale production performance.

Early detection for carbohydrate antigen-19-9 based on surface enhanced Raman spectroscopy aptamer sensor

BackgroundIncipient screening for cervical cancer is crucial for treatment and improving prognosis. In this work, SERS based aptamer sensor (aptasensor) has been developed and applied to high-sensitivity detection of cervical cancer tumor marker (Carbohydrate antigen19-9 (CA19-9)) with a microporous chip, which was used as surface enhanced Raman scattering (SERS) analysis platform. MethodsThe aptamer of CA19-9 was immobilized on the gold nanodumbbell as a capture probe and the gold nanobipyramid containing complementary aptamer chains of the signal molecule Cy5 was modified as a signal probe. SERS aptasensor was constructed by combining signal probes and capture probes. It caused signal molecule Cy5 were sandwiched between the signal probes and capture probes; the constructed SERS aptasensor generated a strong SERS signal of Cy5. Based on the strong specificity of the aptamer, the CA19-9 aptamer binds specifically to CA19-9 when CA19-9 exist in the detection environment. The signal probe was released, resulting in a decrease in the SERS signal of the detection environment. ResultsUnder the optimal detection conditions, the detection limit of the aptasensor was calculated to be 1.16 × 10−3 U/mL and limit of quantification is equal to 3.87 × 10−3 U/mL. In addition, a standard cervical cancer bearing mouse model was constructed and the SERS spectra of the serum of mice at different stages were measured. The CA19-9 content in the serum of mice at different stages was calculated. Compared with the results of enzyme-linked immunosorbent assay (ELISA), relative error (RE) values (2.54 %, −6.093 %, −4.922 %, and 3.04 %) of the CA19-9 concentrations detected by the two methods on the day of 0, 7, 14 and 28. ConclusionThe proposed SERS aptasensor provides a new and reliable platform for early cervical cancer screening, and its low RE value proves that it will be a promising CA19-9 detection method.

A dendrimer-based platform integrating surface-enhanced Raman scattering and class-incremental learning for rapidly detecting four pathogenic bacteria

Rapid monitoring of pathogens is crucial for preventing foodborne diseases, thus making it an urgent need to develop efficient, fast, and simple methods for on-site detection of multiple pathogens. With advances in SERS-based label-free biosensors and machine learning, progress is evident, yet challenges such as complex substrates and limited model interpretability persist. In this work, we reported a novel dendrimer-based platform that integrated surface-enhanced Raman scattering (SERS) with class-incremental learning (CIL) for quick and simultaneous detection of four pathogenic bacteria, namely Escherichia coli, Salmonella Paratyphi B, Pseudomonas aeruginosa, and Staphylococcus aureus. First, the poly(amidoamine) (PAMAM)-based gold nanoassemblies on silicon wafer (PGNAs/Si) was designed as a highly active SERS substrate with an easy fabrication process. Compared with naked gold nanoparticles, both sensitivity and repeatability were improved by introducing a controllable dendritic nanostructure with ultra-small internal nanogaps. The detection limits of four pathogens in water, food, and dietary supplement matrices were all on the order of 10 CFU/mL. Subsequently, to analyze the complex SERS spectra and distinguish samples with different pathogens, CIL models were established using the light gradient boosting machine (LightGBM) algorithm. Notably, the discriminative models demonstrated excellent classification performance with an accuracy of over 93.44 %. Further, the SHapley Additive exPlanations (SHAP) method was utilized to identify the key SERS features in accurately discriminating different pathogens. Together, these results demonstrate that the dendrimer-based platform, by integrating SERS with CIL, can rapidly detect four pathogenic bacteria, underscoring its potential for food safety testing or quality monitoring in medical supplies manufacturing.

Integrating Metal-Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification.

Increasing accumulation of nanoplastics across ecosystems poses a significant threat to both terrestrial and aquatic life. Surface-enhanced Raman scattering (SERS) is an emerging technique used for nanoplastics detection. However, the identification and classification of nanoplastics using SERS faces challenges regarding sensitivity and accuracy as nanoplastics are sparsely dispersed in the environment. Metal-phenolic networks (MPNs) have the potential to rapidly concentrate and separate various types and sizes of nanoplastics. SERS combined with machine learning may improve prediction accuracy. Herein, we report the integration of MPNs-mediated separation with machine learning-aided SERS methods for the accurate classification and high-precision quantification of nanoplastics, which is tailored to include the complete region of characteristic peaks across diverse nanoplastics in contrast to the traditional manual analysis of SERS spectra on a singular characteristic peak. Our customized machine learning system (e.g., outlier detection, classification, quantification) allows for the identification of detectable nanoplastics (accuracy 81.84%), accurate classification (accuracy > 97%), and sensitive quantification of various types of nanoplastics (polystyrene (PS), poly(methyl methacrylate) (PMMA), polyethylene (PE), and poly(lactic acid) (PLA)) down to ultralow concentrations (0.1 ppm) as well as accurate classification (accuracy > 92%) of nanoplastic mixtures at a subppm level. The effectiveness of this approach is substantiated by its ability to discern between different nanoplastic mixtures and detect nanoplastic samples in natural water systems.

Rapid detection of benzoic, sorbic, and dehydroacetic acids in processed foods using surface-enhanced Raman scattering spectroscopy

Benzoic acid (BA), sorbic acid (SA), and dehydroacetic acid (DHA) are among the most common food additives that are classified as antimicrobial preservatives. However, their consumption in excess can lead to damage to the human liver and kidneys. At the present time, no rapid method has been developed for the simultaneous detection of these preservatives in processed foods. In this study, an expedited process based on surface-enhanced Raman scattering (SERS) and solid-phase extraction (SPE) has been developed to determine the content of the preservatives in processed seafood, minced fish, and cheese products. The effect of local heating on SERS spectra at the center of the droplet, which was deposited onto silver nanopillar arrays used as the SERS substrate, was examined. A recirculating flow, manifested as twin vortices merging within the droplet, brought the preservative molecules down to the droplet’s center, resulting in an increase in the SERS signals. Furthermore, the screening efficiency of the detection method for the three preservatives was experimentally evaluated in real samples. The experimentally determined Raman bands of the added BA, SA, and DHA were compared with amounts obtained by the conventional method of high-performance liquid chromatograph (HPLC).

A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions.

Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early and accurate detection of these lesions is crucial for effective treatment and improved patient outcomes. This paper proposed a label-free and highly sensitive classification method for serum of patients with different degrees of gastric lesions by combining surface-enhanced Raman scattering (SERS) and machine learning analysis. Specifically, we prepared Au lotus-shaped (AuLS) nanoarrays substrates using seed-mediated and liquid-liquid interface self-assembly method for measuring SERS spectra of serum, and then the collected spectra were processed by principal component analysis (PCA) - multi-local means based nearest neighbor (MLMNN) model to achieve differentiation. By employing this pattern analysis, AuLS nanoarray substrates can achieve fast, sensitive, and label-free serum spectral detection. The classification accuracy can reach 97.5%, the sensitivity is higher than 96.7%, and the specificity is higher than 95.0%. Moreover, by analyzing the PCs loading plots, the most critical spectral features distinguishing different degrees of gastric lesions were successfully captured. This discovery lays the foundation for combining SERS with machine learning for real-time diagnosis and recognition of gastric lesions.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450–1800 cm−1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Toward Modeling the Complexity of the Chemical Mechanism in SERS.

Surface-enhanced Raman scattering (SERS) provides detailed information about the binding of molecules at interfaces and their interactions with the local environment due to the large enhancement of Raman scattering. This enhancement arises from a combination of the electromagnetic mechanism (EM) and chemical mechanism (CM). While it is commonly accepted that EM gives rise to most of the enhancement, large spectral changes originate from CM. To elucidate the rich information contained in SERS spectra about molecules at interfaces, a comprehensive understanding of the enhancement mechanisms is necessary. In this Perspective, we discuss the current understanding of the enhancement mechanisms and highlight their interplay in complex local environments. We will also discuss emerging areas where the development of computational and theoretical models is needed with specific attention given to how the CM contributes to the spectral changes. Future efforts in modeling should focus on overcoming the challenges presented in this review in order to capture the complexity of CM in SERS.

The Deposition by Raffaello Sanzio: New analytical insights on old cross sections for the characterisation of pictorial palette

In 2020, 500th anniversary of Raffaello Sanzio death, his Deposition (1507), -the altarpiece known also as the Pala Baglioni, today located at the Borghese Gallery in Rome-has been subjected to conservative revision and preventive conservation project. This included in-depth diagnostic campaigns through most modern non-invasive techniques, together with the analysis of old cross sections from the same Pala. These latters, prepared between 1966 and 1972, preserved in ICR laboratory of chemistry and testing materials archive, have been used to deepen the knowledge of Raffaello painting techniques. The use of such cross sections was fundamental to verify the original pictorial film and restoration re-paintings before the conservation intervention in the same years.In this paper, the results of analytical insights on Raffaello pictorial palette are presented. The information is obtained by the analysis of the old ICR stratigraphic sections, through the use of Scanning Electron Microscope with Energy Dispersive X-Ray analysis (SEM-EDX) and micro-Raman (632.8 nm), while Surface Enhanced Raman Scattering (SERS) analysis through colloidal paste has been tested for the identification of organic lake-pigments present in low concentration and for the successful recognition of copper resinate, whose SERS spectrum is here reported for the first time, according to our knowledge. This combined diagnostic approach has made it possible to recognize the pigments employed in the different pictorial layers, such species in traces and those from organic materials, responding to open questions arising previous non-invasive analyses and highlighting further aspects of the illustrious master refined painting technique.

Surface-Enhanced Raman Scattering Nanosensing and Imaging in Neuroscience.

Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro, ex vivo, and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo, and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

Compact SERS detection system enabling automated assay on disc combined with advanced data analysis – A case study of methotrexate

The demand for a compact, all-in-one, sample-to-answer solution for analyzing small molecules within complex matrices is persistent and challenging in analytical chemistry. This manuscript presents a breakthrough in enabling surface-enhanced Raman scattering (SERS) - based analytical assay in a compact detection system, integrating the sample preparation, detection, and data analysis enhanced by machine learning techniques. As a case study, methotrexate (MTX), an anticancer drug, was selected for detection in human serum.The compact tabletop Raman detection unit is equipped with a spectrometer and a motor for the rotation of the incorporated Lab-on-a-Disc unit, which allows simultaneous sample pretreatment of up to eight samples in less than 5 minutes and SERS mapping with a speed of 5 min/analysis. The automated program of the SERS on Disc system simplifies the workflow and enhances the efficiency of analyses.To address the complexity of serum before high sensitivity and specificity detection with SERS, we developed a sample preparation method on a microfluidic disc cartridge that involves precipitation and a nanopillar-assisted separation process, ensuring the isolation of MTX from other analytes. In addition, we implemented a machine learning technique, Partial Least Squares Regression (PLSR), to utilize the fingerprint of SERS spectra for precise and robust quantification of MTX, even in low concentrations. We calculated all figures of merit for the developed method and found that for the PLSR, the accuracy, precision, systematic error, sensitivity, analytical sensitivity, and inverse of analytical sensitivity were 9.41 μM, 9,45 μM, 0.98 μM, 211.64, 1.06 μM−1, and 0.94 μM respectively.

High-Throughput Single-Molecule Surface-Enhanced Raman Spectroscopic Profiling of Single-Amino Acid Substitutions in Peptides by a Gold Plasmonic Nanopore.

Simultaneous detection and structural characterization of protein variants on a single platform are highly desirable but technically challenging. Herein, we present a single-molecule spectral system based on a gold plasmonic nanopore for analyzing two peptides and their single-point mutated variants. The gold plasmonic nanopore enabled the high-throughput acquisition of surface-enhanced Raman scattering (SERS) spectra at the single-molecule level by electrically driving analytes into hot spots. Furthermore, a statistical method based on Boolean operations was developed to extract prominent features from fluctuated single-molecule SERS spectra. The effects of the single-amino acid substitutions on both the intramolecular interactions and the peptide conformations were directly characterized by the nanopore system, and the results agreed with the predictions by AlphaFold2. This study highlights the mutual benefits of spectroscopy and nanopore technology, whereby the gold plasmonic nanopore offers a powerful tool for the structural analysis of single-molecule proteins.

Thiol-polyethylene glycol-folic acid (HS-PEG-FA) induced aggregation of Au@Ag nanoparticles: A SERS and extinction UV–Vis spectroscopy combined study

Plasmonic colloidal nanoparticles (NPs) functionalised with polymers are widely employed in diverse applications, offering advantages demonstrated over non-functionalised NPs such as enhanced colloidal stability or increased biocompatibility. However, functionalisation with polymers does not always increase the stability of the colloidal system. This work explores the intricate relationship between the functionalisation of plasmonic core@shell Au@Ag nanoparticles (NPs) with thiol-polyethylene glycol-folic acid (HS-PEG-FA) polymer chains and the resulting stability and spectral characteristics of Surface-Enhanced Raman Scattering (SERS) nanotags based on these NPs. We demonstrate that varying levels of HS-PEG-FA grafting influence nanotag stability, with a low level of grafting causing aggregation and subsequently affecting the spectral signature of Raman-reporter molecules attached to the surface of the NP. Electrostatic destabilisation is identified as the primary mechanism driving aggregation, impacting the SERS spectrum of Malachite Green isothiocyanate (MGITC) whose spectral shape is different between the aggregated and non-aggregated NPs. The findings provide valuable insights into NPs stability under different conditions, offering essential considerations for the design and optimisation of SERS nanotags in bio-analytical applications, particularly those involving data processing based on spectral shape, such as in multiplex approaches where experimental spectra are decomposed with several reference components.

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.