SLE diagnosis research based on SERS combined with a multi-modal fusion method.

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Development of a multi-needle fiberoptic Raman spectroscopy technique for simultaneous multi-site deep tissue Raman measurements in the brain.

We report on the development of a multi-needle fiberoptic Raman spectroscopy (MNF-RS) technique for simultaneous multi-site deep Raman measurements in brain tissue. The multi-needle fiberoptic Raman probe is designed and fabricated using a number of 100 µm core diameter, aluminum-coated fibers under a coaxial laser excitation and Raman collection scheme, enabling simultaneous collection of deep tissue Raman spectra from a number of tissue sites. We have also developed a Raman retrieval algorithm based on the transformation matrix of each individual needle fiber probe projected to different pixels of a charge-coupled device (CCD) for recovering the tissue Raman spectra collected by each needle fiber probe, allowing simultaneous multi-channel detection by a single Raman spectrometer. High-quality tissue Raman spectra of different tissue types (e.g., muscle, fat, gray matter, and white matter in porcine brain) can be acquired in both the fingerprint (900-1800 cm-1) and high-wavenumber (2800-3300 cm-1) regions within sub-second times using the MNF-RS technique. We also demonstrate that by advancing the multi-needle fiberoptic Raman probe into deep porcine brain, tissue Raman spectra can be acquired simultaneously from different brain regions (e.g., cortex, thalamus, midbrain, and cerebellum). The significant biochemical differences across different brain tissues can also be distinguished, suggesting the promising potential of the MNF-RS technique for label-free neuroscience study at the molecular level.

Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer.

Measuring, recording and analyzing spectral information of materials as its unique finger print using a ubiquitous smartphone has been desired by scientists and consumers. We demonstrated it as drug classification by chemical components with smartphone Raman spectrometer. The Raman spectrometer is based on the CMOS image sensor of the smartphone with a periodic array of band pass filters, capturing 2D Raman spectral intensity map, newly defined as spectral barcode in this work. Here we show 11 major components of drugs are classified with high accuracy, 99.0%, with the aid of convolutional neural network (CNN). The beneficial of spectral barcodes is that even brand name of drug is distinguishable and major component of unknown drugs can be identified. Combining spectral barcode with information obtained by red, green and blue (RGB) imaging system or applying image recognition techniques, this inherent property based labeling system will facilitate fundamental research and business opportunities.

Intraoperative assessment of resection margins by Raman spectroscopy to guide oral cancer surgery.

Patients with oral cavity cancer are almost always treated with surgery. The goal is to remove the tumor with a margin of more than 5 mm of surrounding healthy tissue. Unfortunately, this is only achieved in about 15% to 26% of cases. Intraoperative assessment of tumor resection margins (IOARM) can dramatically improve surgical results. However, current methods are laborious, subjective, and logistically demanding. This hinders broad adoption of IOARM, to the detriment of patients. Here we present the development and validation of a high-wavenumber Raman spectroscopic technology, for quick and objective intraoperative measurement of resection margins on fresh specimens. It employs a thin fiber-optic needle probe, which is inserted into the tissue, to measure the distance between a resection surface and the tumor. A tissue classification model was developed to discriminate oral cavity squamous cell carcinoma (OCSCC) from healthy oral tissue, with a sensitivity of 0.85 and a specificity of 0.92. The tissue classification model was then used to develop a margin length prediction model, showing a mean difference between margin length predicted by Raman spectroscopy and histopathology of -0.17 mm.

Label-Free Quantification of Microscopic Alignment in Engineered Tissue Scaffolds by Polarized Raman Spectroscopy.

Monitoring of extracellular matrix (ECM) microstructure is essential in studying structure-associated cellular processes, improving cellular function, and for ensuring sufficient mechanical integrity in engineered tissues. This paper describes a novel method to study the microscale alignment of the matrix in engineered tissue scaffolds (ETS) that are usually composed of a variety of biomacromolecules derived by cells. First, a trained loading function was derived from Raman spectra of highly aligned native tissue via principal component analysis (PCA), where prominent changes associated with specific Raman bands (e.g., 1444, 1465, 1605, 1627-1660, and 1665-1689 cm-1) were detected with respect to the polarization angle. These changes were mainly caused by the aligned matrix of many compounds within the tissue relative to the laser polarization, including proteins, lipids, and carbohydrates. Hence this trained function was applied to quantify the alignment within ETS of various matrix components derived by cells. Furthermore, a simple metric called Amplitude Alignment Metric (AAM) was derived to correlate the orientation dependence of polarized Raman spectra of ETS to the degree of matrix alignment. It was found that the AAM was significantly higher in anisotropic ETS than isotropic ones. The PRS method revealed a lower p-value for distinguishing the alignment between these two types of ETS as compared to the microscopic method for detecting fluorescent-labeled protein matrices at a similar microscopic scale. These results indicate that the anisotropy of a complex matrix in engineered tissue can be assessed at the microscopic scale using a PRS-based simple metric, which is superior to the traditional microscopic method. This PRS-based method can serve as a complementary tool for the design and assessment of engineered tissues that mimic the native matrix organizational microstructures.

A clinical Raman spectroscopy imaging system and safety requirements for in situ intraoperative tissue characterization.

Raman spectroscopy imaging is a technique that can be adapted for intraoperative tissue characterization to be used for surgical guidance. Here we present a macroscopic line scanning Raman imaging system that has been modified to ensure suitability for intraoperative use. The imaging system has a field of view of 1 × 1 cm2 and acquires Raman fingerprint images of 40 × 42 pixels, typically in less than 5 minutes. The system is mounted on a mobile cart, it is equiped with a passive support arm and possesses a removable and sterilizable probe muzzle. The results of a proof of concept study are presented in porcine adipose and muscle tissue. Supervised machine learning models (support vector machines and random forests) were trained and they were tested on a holdout dataset consisting of 7 Raman images (10 080 spectra) acquired in different animal tissues. This led to a detection accuracy >96% and prediction confidence maps providing a quantitative detection assessment for tissue border visualization. Further testing was accomplished on a dataset acquired with the imaging probe's contact muzzle and tailored classification models showed robust classifications capabilities with specificity, sensitivity and accuracy all surpassing 95% with a support vector machine classifier. Finally, laser safety, biosafety and sterilization of the system was assest. The safety assessment showed that the system's laser can be operated safetly according to the American National Standards Institute's standard for maximum permissible exposures for eyes and skin. It was further shown that during tissue interrogation, the temperature-history in cumulative equivalent minutes at 43 °C (CEM43 °C) never exceeded a safe threshold of 5 min.

Optimization of Surface-Enhanced Raman Spectroscopy Detection Conditions for Interaction between Gonyautoxin and Its Aptamer.

This study aimed to optimize the detection conditions for surface-enhanced Raman spectroscopy (SERS) of single-stranded DNA (ssDNA) in four different buffers and explore the interaction between gonyautoxin (GTX1/4) and its aptamer, GO18. The influence of the silver colloid solution and MgSO4 concentration (0.01 M) added under four different buffered conditions on DNA SERS detection was studied to determine the optimum detection conditions. We explored the interaction between GTX1/4 and GO18 under the same conditions as those in the systematic evolution of ligands by exponential enrichment technique, using Tris-HCl as the buffer. The characteristic peaks of GO18 and its G-quadruplex were detected in four different buffer solutions. The change in peak intensity at 1656 cm-1 confirmed that the binding site between GTX1/4 and GO18 was in the G-quadruplex plane. The relative intensity of the peak at 1656 cm-1 was selected for the GTX1/4-GO18 complex (I1656/I1099) to plot the ratio of GTX1/4 in the Tris-HCl buffer condition (including 30 µL of silver colloid solution and 2 µL of MgSO4), and a linear relationship was obtained as follows: Y = 0.1867X + 1.2205 (R2 = 0.9239). This study provides a basis for subsequent application of SERS in the detection of ssDNA, as well as the binding of small toxins and aptamers.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling.

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Line-scan Raman scattering image and multivariate analysis for rapid and noninvasive detection of restructured beef.

The mean spectral (MS) features were extracted from Raman scattering images (RSI) of beef samples over the region of interest covering the spectral range of 789-1710cm-1 and the spatial offset range of 0-5 mm (for two sides of the incident laser). The RSI monitored the main change in the protein, amide bands, lipids, and amino acid residues. The classification model performance based on MS features compared the conventional Raman spectral features and confirmed the usefulness of RSI. Finally, the results showed that RSI technology is a reliable tool for rapid and noninvasive detection of restructured beef.

In situ SERS detection of quinolone antibiotic residues in a water environment based on optofluidic in-fiber integrated Ag nanoparticles.

In this paper, we present a microstructured optofluidic in-fiber Raman sensor for the detection of quinolone antibiotic residue in a water environment based on Ag surface-enhanced Raman scattering (SERS) substrate grown on the surface of the suspended core of micro-hollow optical fiber (MHF). Here, MHF has a special structure with a suspended core and a microchannel inside, which can become a natural in-fiber optofluidic device. Meanwhile, the self-assembled Ag SERS substrate can be grown on the suspended core's surface through chemical bonds, forming a microstructured optofluidic device with a Raman enhancement effect. Therefore, it can effectively detect the Raman signal of unlabeled trace quinolone antibiotic residue (ciprofloxacin and norfloxacin) inside the optical fiber. The results show that the ciprofloxacin and norfloxacin detection limits (LOD) are 10-10M and 10-11M, respectively. Compared with the maximum residue limit (3.01×10-7mol/L) stipulated by the European Union, the results are much lower, and an ideal quantitative relationship can be obtained within the detection range. Significantly, this study provides an in-fiber microstructured optofluidic Raman sensor for the label-free detection of quinolone antibiotic residue, which will have good development prospects in the field of antibiotic water pollution environmental detection.

Silk fibroin fibers decorated with urchin-like Au/Ag nanoalloys: a flexible hygroscopic SERS sensor for monitoring of folic acid in human sweat.

Surface-enhanced Raman scattering (SERS) spectroscopy has become a powerful and sensitive analytical tool for the detection and assessment of chemical/biological molecules in special scenarios. Herein we propose a flexible hygroscopic SERS biocompatible sensor based on the silk fibroin fibers (SFF) decorated with urchin-like Au/Ag nanoalloys (NAs). The hybrid SFF-Au/Ag NAs with a stronger absorbance capacity (500∼1100 nm) and excellent hygroscopicity provide a remarkable higher near-infrared (NIR)-SERS activity than that of bare urchin-like Au/Ag NAs. The interesting NIR-SERS sensor enables the limit of detection (LOD) of folic acid (FA) to be achieved at nanomolar (nM, 10-9 M) level, facilitating the ultrasensitive monitoring of FA in human sweat and offering reliable real-time personal health management in the near future.

Low-power swept-source Raman spectroscopy.

'Molecular fingerprinting' with Raman spectroscopy can address important problems-from ensuring our food safety, detecting dangerous substances, to supporting disease diagnosis and management. However, the broad adoption of Raman spectroscopy demands low-cost, portable instruments that are sensitive and use lasers that are safe for human eye and skin. This is currently not possible with existing Raman spectroscopy approaches. Portability has been achieved with dispersive Raman spectrometers, however, fundamental entropic limits to light collection both limits sensitivity and demands high-power lasers and cooled expensive detectors. Here, we demonstrate a swept-source Raman spectrometer that improves light collection efficiency by up to 1000× compared to portable dispersive spectrometers. We demonstrate high detection sensitivity with only 1.5 mW average excitation power and an uncooled amplified silicon photodiode. The low optical power requirement allowed us to utilize miniature chip-scale MEMS-tunable lasers with close to eye-safe optical powers for excitation. We characterize the dynamic range and spectral characteristics of this Raman spectrometer in detail, and use it for fingerprinting of different molecular species consumed everyday including analgesic tablets, nutrients in vegetables, and contaminated alcohol. By moving the complexity of Raman spectroscopy from bulky spectrometers to chip-scale light sources, and by replacing expensive cooled detectors with low-cost uncooled alternatives, this swept-source Raman spectroscopy technique could make molecular fingerprinting more accessible.

Plasmonic Metasurfaces Based on Pyramidal Nanoholes for High-Efficiency SERS Biosensing.

An inverted pyramidal metasurface was designed, fabricated, and studied at the nanoscale level for the development of a label-free pathogen detection on a chip platform that merges nanotechnology and surface-enhanced Raman scattering (SERS). Based on the integration and synergy of these ingredients, a virus immunoassay was proposed as a relevant proof of concept for very sensitive detection of hepatitis A virus, for the first time to our best knowledge, in a very small volume (2 µL), without complex signal amplification, allowing to detect a minimal virus concentration of 13 pg/mL. The proposed work aims to develop a high-flux and high-accuracy surface-enhanced Raman spectroscopy (SERS) nanobiosensor for the detection of pathogens to provide an effective method for early and easy water monitoring, which can be fast and convenient.

3D Printed Microfluidic Device for Magnetic Trapping and SERS Quantitative Evaluation of Environmental and Biomedical Analytes.

Surface-enhanced Raman scattering (SERS) is an ideal technique for environmental and biomedical sensor devices due to not only the highly informative vibrational features but also to its ultrasensitive nature and possibilities toward quantitative assays. Moreover, in these areas, SERS is especially useful as water hinders most of the spectroscopic techniques such as those based on IR absorption. Despite its promising possibilities, most SERS substrates and technological frameworks for SERS detection are still restricted to research laboratories, mainly due to a lack of robust technologies and standardized protocols. We present herein the implementation of Janus magnetic/plasmonic Fe3O4/Au nanostars (JMNSs) as SERS colloidal substrates for the quantitative determination of several analytes. This multifunctional substrate enables the application of an external magnetic field for JMNSs retention at a specific position within a microfluidic channel, leading to additional amplification of the SERS signals. A microfluidic device was devised and 3D printed as a demonstration of cheap and fast production, with the potential for large-scale implementation. As low as 100 µL of sample was sufficient to obtain results in 30 min, and the chip could be reused for several cycles. To show the potential and versatility of the sensing system, JMNSs were exploited with the microfluidic device for the detection of several relevant analytes showing increasing analytical difficulty, including the comparative detection of p-mercaptobenzoic acid and crystal violet and the quantitative detection of the herbicide flumioxazin and the anticancer drug erlotinib in plasma, where calibration curves within diagnostic concentration intervals were obtained.

Optical design for laser tweezers Raman spectroscopy setups for increased sensitivity and flexible spatial detection.

We demonstrate a method to double the collection efficiency in laser tweezers Raman spectroscopy (LTRS) by collecting both the forward-scattered and backscattered light in a single-shot multitrack measurement. Our method can collect signals at different sample volumes, granting both the pinpoint spatial selectivity of confocal Raman spectroscopy and the bulk sensitivity of non-confocal Raman spectroscopy simultaneously. Further, we display that our approach allows for reduced detector integration time and laser power. To show this, we measure the Raman spectra of both polystyrene beads and bacterial spores. For spores, we can trap them at 2.5 mW laser power and acquire a high signal-to-noise ratio power spectrum of the calcium-dipicolinic acid peaks using an integration time of ${2} \times {30}\;{\rm s}$. Thus, our method will enable the monitoring of biological samples sensitive to high intensities for longer times. Additionally, we demonstrate that by a simple modification, we can add polarization sensitivity and retrieve extra biochemical information.

Quantum-enhanced nonlinear microscopy.

The performance of light microscopes is limited by the stochastic nature of light, which exists in discrete packets of energy known as photons. Randomness in the times that photons are detected introduces shot noise, which fundamentally constrains sensitivity, resolution and speed1. Although the long-established solution to this problem is to increase the intensity of the illumination light, this is not always possible when investigating living systems, because bright lasers can severely disturb biological processes2-4. Theory predicts that biological imaging may be improved without increasing light intensity by using quantum photon correlations1,5. Here we experimentally show that quantum correlations allow a signal-to-noise ratio beyond the photodamage limit of conventional microscopy. Our microscope is a coherent Raman microscope that offers subwavelength resolution and incorporates bright quantum correlated illumination. The correlations allow imaging of molecular bonds within a cell with a 35 per cent improved signal-to-noise ratio compared with conventional microscopy, corresponding to a 14 per cent improvement in concentration sensitivity. This enables the observation of biological structures that would not otherwise be resolved. Coherent Raman microscopes allow highly selective biomolecular fingerprinting in unlabelled specimens6,7, but photodamage is a major roadblock for many applications8,9. By showing that the photodamage limit can be overcome, our work will enable order-of-magnitude improvements in the signal-to-noise ratio and the imaging speed.

Non-invasive monitoring of red blood cells during cold storage using handheld Raman spectroscopy.

BACKGROUND: The current best practices allow for the red blood cells (RBCs) to be stored for prolonged periods in blood banks worldwide. However, due to the individual-related variability in donated blood and RBCs continual degradation within transfusion bags, the quality of stored blood varies considerably. There is currently no method for assessing the blood product quality without compromising the sterility of the unit. This study demonstrates the feasibility of monitoring storage lesion of RBCs in situ while maintaining sterility using an optical approach. STUDY DESIGN AND METHODS: A handheld spatially offset Raman spectroscopy (RS) device was employed to non-invasively monitor hemolysis and metabolic changes in 12 red cell concentrate (RCC) units within standard sealed transfusion bags over 7 weeks of cold storage. The donated blood was analyzed in parallel by biochemical (chemical analysis, spectrophotometry, hematology analysis) and RS measurements, which were then correlated through multisource correlation analysis. RESULTS: Raman bands of lactate (857 cm-1 ), glucose (787 cm-1 ), and hemolysis (1003 cm-1 ) were found to correlate strongly with bioanalytical data over the length of storage, with correlation values 0.98 (95% confidence interval [CI]: 0.86-1.00; p = .0001), 0.95 (95% CI: 0.71-0.99; p = .0008) and 0.97 (95% CI: 0.79-1.00; p = .0004) respectively. DISCUSSION: This study demonstrates the potential of collecting information on the clinical quality of blood units without breaching the sterility using Raman technology. This could significantly benefit quality control of RCC units, patient safety and inventory management in blood banks and hospitals.

Competitive immunosensor for sensitive and optical anti-interference detection of imidacloprid by surface-enhanced Raman scattering.

The sensitive detection of pesticides in complex environment is important but still challenging in presence of organic-rich water sample and food matrix. Herein, we reported a nitrile-mediated SERS immunosensor for sensitive and optical anti-interference determination of imidacloprid. Raman tag contained CN bond could provide a sharp characteristic peak in the Raman-silent spectral window (1800 ~ 2800 cm-1), which could resist the optical noises from the fingerprint region (<1800 cm-1). Aucore-Agshell bimetallic nanocuboid (AuNR@Ag) connected with antigen and Raman tag was used as Raman probe, while Fe3O4 magnetic nanoparticle functionalized with anti-imidacloprid antibody was applied as signal enhancer. Owing to the specific recognition ability between antigen and antibody, the competitive system with imidacloprid was formed. Under the optimal condition, the linear relationship was developed in the range of 10-400 nM. Finally, the SERS immunosensor was successfully applied to determine imidacloprid in real samples with recoveries from 96.8% to 100.5%.

Boosting Long-Range Surface-Enhanced Raman Scattering on Plasmonic Nanohole Arrays for Ultrasensitive Detection of MiRNA.

A fundamental challenge, particularly, in surface-enhanced Raman scattering (SERS) analysis is the detection of analytes that are distant from the sensing surface. To tackle this challenge, we herein report a long-range SERS (LR-SERS) substrate supporting an extension of electric field afforded by long-range surface plasmon resonance (LRSPR) excited in symmetrical dielectric environments. The LR-SERS substrate has a sandwich configuration with a triangle-shaped gold nanohole array embedded between two dielectrics with similar refractive indices (i.e., MgF2 and water). The finite-difference time-domain simulation was applied to guide the design of the LR-SERS substrate, which was engineered to have a wavelength-matched LRSPR with 785 nm excitation. The simulations predict that the LR-SERS substrate exhibits great SERS enhancement at distances of more than 10 nm beyond its top surface, and the enhancement factor (EF) has been improved by three orders of magnitude on LR-SERS substrates compared to that on conventional substrates. The experimental results show good agreement with the simulations, an EF of 4.1 × 105 remains available at 22 nm above the LR-SERS substrate surface. The LR-SERS substrate was further applied as a sensing platform to detect microRNA (miRNA) let-7a coupled with a hybridization chain reaction (HCR) strategy. The developed sensor displays a wide linear range from 10 aM to 1 nM and an ultralow detection limit of 8.5 aM, making it the most sensitive among the current detection strategies for miRNAs based on the SERS-HCR combination to the best of our knowledge.

A Review on SERS-Based Detection of Human Virus Infections: Influenza and Coronavirus.

The diagnosis of respiratory viruses of zoonotic origin (RVsZO) such as influenza and coronaviruses in humans is crucial, because their spread and pandemic threat are the highest. Surface-enhanced Raman spectroscopy (SERS) is an analytical technique with promising impact for the point-of-care diagnosis of viruses. It has been applied to a variety of influenza A virus subtypes, such as the H1N1 and the novel coronavirus SARS-CoV-2. In this work, a review of the strategies used for the detection of RVsZO by SERS is presented. In addition, relevant information about the SERS technique, anthropozoonosis, and RVsZO is provided for a better understanding of the theme. The direct identification is based on trapping the viruses within the interstices of plasmonic nanoparticles and recording the SERS signal from gene fragments or membrane proteins. Quantitative mono- and multiplexed assays have been achieved following an indirect format through a SERS-based sandwich immunoassay. Based on this review, the development of multiplex assays that incorporate the detection of RVsZO together with their specific biomarkers and/or secondary disease biomarkers resulting from the infection progress would be desirable. These configurations could be used as a double confirmation or to evaluate the health condition of the patient.