On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations

Enhancing the understanding of SARS-CoV-2 protein with structure and detection methods: An integrative review

As the rapid and accurate screening of infectious diseases can provide meaningful information for outbreak prevention and control, as well as owing to the existing limitations of the polymerase chain reaction (PCR), it is imperative to have new and validated detection techniques for SARS-CoV-2. Therefore, the rationale for outlining the techniques used to detect SARS-CoV-2 proteins and performing a comprehensive comparison to serve as a practical benchmark for future identification of similar viral proteins is clear. This review highlights the urgent need to strengthen pandemic preparedness by emphasizing the importance of integrated measures. These include improved tools for pathogen characterization, optimized societal precautions, the establishment of early warning systems, and the deployment of highly sensitive diagnostics for effective surveillance, triage, and resource management. Additionally, with an improved understanding of the virus' protein structure, considerable advances in targeted detection, treatment, and prevention strategies are expected to greatly improve our ability to respond to future outbreaks.

Exploring monkeypox virus proteins and rapid detection techniques

Monkeypox (mpox) is an infectious disease caused by the mpox virus and can potentially lead to fatal outcomes. It resembles infections caused by viruses from other families, challenging identification. The pathogenesis, transmission, and clinical manifestations of mpox and other Orthopoxvirus species are similar due to their closely related genetic material. This review provides a comprehensive discussion of the roles of various proteins, including extracellular enveloped virus (EEV), intracellular mature virus (IMV), and profilin-like proteins of mpox. It also highlights recent diagnostic techniques based on these proteins to detect this infection rapidly.

Vibration assisted electron tunnelling in COVID-19 infection using quantum state diffusion

The spread of the COVID-19 virus has become a global health crisis, and finding effective treatments and preventions is a top priority. The field of quantum biology primarily focuses on energy or charge transfer, with a particular emphasis on photosynthesis. However, there is evidence to suggest that cellular receptors such as olfactory or neural receptors may also use vibration-assisted electron tunnelling to enhance their functions. Quantum tunnelling has also been observed in enzyme activity, which is relevant to the invasion of host cells by the SARS-CoV-2 virus. Additionally, COVID-19 appears to disrupt receptors such as olfactory receptors. These findings suggest that quantum effects could provide new insights into the mechanisms of biological systems and disease, including potential treatments for COVID-19. We have applied the open quantum system approach using Quantum State Diffusion to solve the non-linear stochastic Schrödinger equation (SSE) for COVID-19 virus infection. Our model includes the mechanism when the spike protein of the virus binds with an ACE2 receptor is considered as dimer. These two entities form a system and then coupled with the cell membrane, which is modelled as a set of harmonic oscillators (bath). By simulating the SSE, we find that there is vibration-assisted electron tunnelling happening in certain biological parameters and coupling regimes. Furthermore, our model contributes to the ongoing research to understand the fundamental nature of virus dynamics. It proposes that vibration-assisted electron tunneling could be a molecular phenomenon that augments the lock-and-key process for olfaction. This insight may enhance our understanding of the underlying mechanisms governing virus-receptor interactions and could potentially lead to the development of novel therapeutic strategies.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

Two-Dimensional SERS Sensor Array for Identifying and Visualizing the Gas Spatial Distributions of Two Distinct Odor Sources

The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.

Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy

The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.

Characterization and discrimination of spike protein in SARS-CoV-2 virus-like particles via surface-enhanced Raman spectroscopy

Non-infectious virus-like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS-CoV-2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low-cost, and easy-to-use technique for both characterization and detection of S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was achieved using surface-enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k-nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS-CoV-2 VLPs and HIV-based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.

Antiviral functionalization of cellulose using tannic acid and tannin-rich extracts

Due to seasonally appearing viruses and several outbreaks and present pandemic, we are surrounded by viruses in our everyday life. In order to reduce viral transmission, functionalized surfaces that inactivate viruses are in large demand. Here the endeavor was to functionalize cellulose-based materials with tannic acid (TA) and tannin-rich extracts by using different binding polymers to prevent viral infectivity of both non-enveloped coxsackievirus B3 (CVB3) and enveloped human coronavirus OC43 (HCoV-OC43). Direct antiviral efficacy of TA and spruce bark extract in solution was measured: EC50 for CVB3 was 0.12 and 8.41 μg/ml and for HCoV-OC43, 78.16 and 95.49 μg/ml, respectively. TA also led to an excellent 5.8- to 7-log reduction of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus infectivity. TA functionalized materials reduced infectivity already after 5-min treatment at room temperature. All the tested methods to bind TA showed efficacy on paperboard with 0.1 to 1% (w/v) TA concentrations against CVB3 whereas material hydrophobicity decreased activities. Specific signatures for TA and HCoV-OC43 were discovered by Raman spectroscopy and showed clear co-localization on the material. qPCR study suggested efficient binding of CVB3 to the TA functionalized cellulose whereas HCoV-OC43 was flushed out from the surfaces more readily. In conclusion, the produced TA-materials showed efficient and broadly acting antiviral efficacy. Additionally, the co-localization of TA and HCoV-OC43 and strong binding of CVB3 to the functionalized cellulose demonstrates an interaction with the surfaces. The produced antiviral surfaces thus show promise for future use to increase biosafety and biosecurity by reducing pathogen persistence.

Performance evaluation of SARS-CoV-2 antigen detection in the post-pandemic era: multi-laboratory assessment

OBJECTIVES

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen detection is an indispensable tool for epidemic surveillance in the post-pandemic era. Faced with irregular performance, a comprehensive external quality assessment (EQA) scheme was conducted by the National Center for Clinical Laboratories (NCCL) to evaluate the analytical performance and status of SARS-CoV-2 antigen tests.

METHODS

The EQA panel included ten lyophilized samples containing serial 5-fold dilutions of inactivated SARS-CoV-2-positive supernatants of the Omicron BA.1 and BA.5 strains and negative samples, which were classified into "validating" samples and "educational" samples. Data were analyzed according to qualitative results for each sample.

RESULTS

A total of 339 laboratories in China participated in this EQA scheme, and 378 effective results were collected. All validating samples were correctly reported by 90.56 % (307/339) of the participants and 90.21 % (341/378) of the datasets. The positive percent agreement (PPA) was >99 % for samples with concentrations of 2 × 107 copies/mL but was 92.20 % (697/756) for 4 × 106 copies/mL and 25.26 % (382/1,512) for 8 × 105 copies/mL samples. Colloidal gold was the most frequently used (84.66 %, 320/378) but showed the lowest PPAs (57.11 %, 1,462/2,560) for positive samples compared with fluorescence immunochromatography (90 %, 36/40) and latex chromatography (79.01 %, 335/424). Among 11 assays used in more than 10 clinical laboratories, ACON showed a higher sensitivity than other assays.

CONCLUSIONS

The EQA study can help to validate whether it's necessary to update antigen detection assays for manufacturers and provide participants with information about the performance of assays to take the first step toward routine post-market surveillance.

Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning

Isoelectric focusing (IEF) is a powerful tool for resolving complex protein samples, which generates IEF patterns consisting of multiplex analyte bands. However, the interpretation of IEF patterns requires the careful selection of isoelectric point (pI) markers for profiling the pH gradient and a trivial process of pI labeling, resulting in low IEF efficiency. Here, we for the first time proposed a marker-free IEF method for the efficient and accurate classification of IEF patterns by using a convolutional neural network (CNN) model. To verify our method, we identified 21 meat samples whose IEF patterns comprised different bands of meat hemoglobin, myoglobin, and their oxygen-binding variants but no pI marker. Thanks to the high throughput and short assay time of the microstrip IEF, we efficiently collected 1449 IEF patterns to construct the data set for model training. Despite the absence of pI markers, we experimentally introduced the severe pH gradient drift into 189 IEF patterns in the data set, thereby omitting the need for profiling the pH gradient. To enhance the model robustness, we further employed data augmentation during the model training to mimic pH gradient drift. With the advantages of simple preprocessing, a rapid inference of 50 ms, and a high accuracy of 97.1%, the CNN model outperformed the traditional algorithm for simultaneously identifying meat species and cuts of meat of 105 IEF patterns, suggesting its great potential of being combined with microstrip IEF for large-scale IEF analyses of complicated protein samples.

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

SARS-CoV-2 Virus-like Particles with Plasmonic Au Cores and S1-Spike Protein Coronas

The COVID-19 pandemic has stimulated the scientific world to intensify virus-related studies aimed at the development of quick and safe ways of detecting viruses in the human body, studying the virus-antibody and virus-cell interactions, and designing nanocarriers for targeted antiviral therapies. However, research on dangerous viruses can only be performed in certified laboratories that follow strict safety procedures. Thus, developing deactivated virus constructs or safe-to-use virus-like objects, which imitate real viruses and allow performing virus-related studies in any research laboratory, constitutes an important scientific challenge. Such species, called virus-like particles (VLPs), contain instead of capsids with viral DNA/RNA empty or synthetic cores with real virus proteins attached to them. We have developed a method for the preparation of VLPs imitating the virus responsible for the COVID-19 disease: the SARS-CoV-2. The particles have Au cores surrounded by "coronas" of S1 domains of the virus's spike protein. Importantly, they are safe to use and specifically interact with SARS-CoV-2 antibodies. Moreover, Au cores exhibit localized surface plasmon resonance (LSPR), which makes the synthesized VLPs suitable for biosensing applications. During the studies, the effect allowed us to visualize the interaction between the VLPs and the antibodies and identify the characteristic vibrational signals. What is more, additional functionalization of the particles with a fluorescent label revealed their potential in studying specific virus-related interactions. Notably, the universal character of the developed synthesis method makes it potentially applicable for fabricating VLPs imitating other life-threatening viruses.

Analysis of SARS-CoV-2 spike RBD binding to ACE2 and its inhibition by fungal cohaerin C using surface enhanced Raman spectroscopy

The structure of the SARS-CoV-2 spike RBD and human ACE2 as well as changes in the structure due to binding activities were analysed using surface enhanced Raman spectroscopy. The inhibitor cohaerin C was applied to inhibit the binding between spike RBD and ACE2. Differences and changes in the Raman spectra were determined using deconvolution of the amide bands and principal component analysis. We thus demonstrate a fast and label-free analysis of the protein structures and the differentiation between bound and unbound states. The approach is suitable for sensing and screening and might be relevant to investigate other protein systems as well.

Rapid Detection of the Monkeypox Virus Genome and Antigen Proteins Based on Surface-Enhanced Raman Spectroscopy

The conventional detection methods cannot satisfy the need for early and rapid detection of monkeypox virus (MPXV) infection. This is due to complicated pretreatment, time consumption, and complex operation of the diagnostic tests. Based on surface-enhanced Raman spectroscopy (SERS), this study attempted to capture the characteristic fingerprints of the MPXV genome and multiple antigenic proteins without the need to design specific probes. The minimum detection limit of this method is 100 copies/mL, with good reproducibility and signal-to-noise ratio. Therefore, the relationship between characteristic peak intensity and the protein and nucleic acid concentration can be used to construct a concentration-dependent spectral line with a good linear relationship. Additionally, principal component analysis (PCA) could identify the SERS spectra of four different MPXV proteins in serum. Therefore, this rapid detection method in the current outbreak of monkeypox control and the future response to possible new outbreaks has broad application prospects.

A microfluidic approach for label-free identification of small-sized microplastics in seawater

Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts.

Diagnostics and analysis of SARS-CoV-2: current status, recent advances, challenges and perspectives

The disastrous spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has induced severe public healthcare issues and weakened the global economy significantly. Although SARS-CoV-2 infection is not as fatal as the initial outbreak, many infected victims suffer from long COVID. Therefore, rapid and large-scale testing is critical in managing patients and alleviating its transmission. Herein, we review the recent advances in techniques to detect SARS-CoV-2. The sensing principles are detailed together with their application domains and analytical performances. In addition, the advantages and limits of each method are discussed and analyzed. Besides molecular diagnostics and antigen and antibody tests, we also review neutralizing antibodies and emerging SARS-CoV-2 variants. Further, the characteristics of the mutational locations in the different variants with epidemiological features are summarized. Finally, the challenges and possible strategies are prospected to develop new assays to meet different diagnostic needs. Thus, this comprehensive and systematic review of SARS-CoV-2 detection technologies may provide insightful guidance and direction for developing tools for the diagnosis and analysis of SARS-CoV-2 to support public healthcare and effective long-term pandemic management and control.

Nano-biosensor for SARS-CoV-2/COVID-19 detection: methods, mechanism and interface design

The epidemic of coronavirus disease 2019 (COVID-19) was a huge disaster to human society. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to COVID-19, has resulted in a large number of deaths. Even though the reverse transcription-polymerase chain reaction (RT-PCR) is the most efficient method for the detection of SARS-CoV-2, the disadvantages (such as long detection time, professional operators, expensive instruments, and laboratory equipment) limit its application. In this review, the different kinds of nano-biosensors based on surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), field-effect transistor (FET), fluorescence methods, and electrochemical methods are summarized, starting with a concise description of their sensing mechanism. The different bioprobes (such as ACE2, S protein-antibody, IgG antibody, IgM antibody, and SARS-CoV-2 DNA probes) with different bio-principles are introduced. The key structural components of the biosensors are briefly introduced to give readers an understanding of the principles behind the testing methods. In particular, SARS-CoV-2-related RNA mutation detection and its challenges are also briefly described. We hope that this review will encourage readers with different research backgrounds to design SARS-CoV-2 nano-biosensors with high selectivity and sensitivity.

Detection of live SARS-CoV-2 virus and its variants by specially designed SERS-active substrates and spectroscopic analyses

A method using label-free surface enhanced Raman spectroscopy (SERS) based on substrate design is provided for an early detection and differentiation of spike glycoprotein mutation sites in live SARS-CoV-2 variants. Two SERS-active substrates, Au nanocavities (Au NCs) and Au NPs on porous ZrO₂ (Au NPs/pZrO₂), were used to identify specific peaks of A.3, Alpha, and Delta variants at different concentrations and demonstrated the ability to provide their SERS spectra with detection limits of 0.1–1.0% (or 10⁴⁻⁵ copies/mL). Variant identification can be achieved by cross-examining reference spectra and analyzing the substrate-analyte relationship between the suitability of the analyte upon the hotspot(s) formed at high concentrations and the effective detection distance at low concentrations. Mutation sites on the S1 chain of the spike glycoprotein for each variant may be related and distinguishable. This method does not require sample preprocessing and therefore allows for fast screening, which is of high value for more comprehensive and specific studies to distinguish upcoming variants.

Recent development of surface-enhanced Raman scattering for biosensing

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

Raman spectroscopy for viral diagnostics

Raman spectroscopy offers the potential for fingerprinting biological molecules at ultra-low concentration and therefore has potential for the detection of viruses. Here we review various Raman techniques employed for the investigation of viruses. Different Raman techniques are discussed including conventional Raman spectroscopy, surface-enhanced Raman spectroscopy, Raman tweezer, tip-enhanced Raman Spectroscopy, and coherent anti-Stokes Raman scattering. Surface-enhanced Raman scattering can play an essential role in viral detection by multiplexing nanotechnology, microfluidics, and machine learning for ensuring spectral reproducibility and efficient workflow in sample processing and detection. The application of these techniques to diagnose the SARS-CoV-2 virus is also reviewed.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-023-01059-4.

Label-Free Saliva Test for Rapid Detection of Coronavirus Using Nanosensor-Enabled SERS

The recent COVID-19 pandemic has highlighted the inadequacies of existing diagnostic techniques and the need for rapid and accurate diagnostic systems. Although molecular tests such as RT-PCR are the gold standard, they cannot be employed as point-of-care testing systems. Hence, a rapid, noninvasive diagnostic technique such as Surface-enhanced Raman scattering (SERS) is a promising analytical technique for rapid molecular or viral diagnosis. Here, we have designed a SERS- based test to rapidly diagnose SARS-CoV-2 from saliva. Physical methods synthesized the nanostructured sensor. It significantly increased the detection specificity and sensitivity by ~ten copies/mL of viral RNA (~femtomolar concentration of nucleic acids). Our technique combines the multiplexing capability of SERS with the sensitivity of novel nanostructures to detect whole virus particles and infection-associated antibodies. We have demonstrated the feasibility of the test with saliva samples from individuals who tested positive for SARS-CoV-2 with a specificity of 95%. The SERS-based test provides a promising breakthrough in detecting potential mutations that may come up with time while also preparing the world to deal with other pandemics in the future with rapid response and very accurate results.

Asymmetric parameter enhancement in the split-ring cavity array for virus-like particle sensing

Quantitative detection of virus-like particles under a low concentration is of vital importance for early infection diagnosis and water pollution analysis. In this paper, a novel virus detection method is proposed using indirect polarization parametric imaging method combined with a plasmonic split-ring nanocavity array coated with an Au film and a quantitative algorithm is implemented based on the extended Laplace operator. The attachment of viruses to the split-ring cavity breaks the structural symmetry, and such asymmetry can be enhanced by depositing a thin gold film on the sample, which allows an asymmetrical plasmon mode with a large shift of resonance peak generated under transverse polarization. Correspondingly, the far-field scattering state distribution encoded by the attached virus exhibits a specific asymmetric pattern that is highly correlated to the structural feature of the virus. By utilizing the parametric image sinδ to collect information on the spatial photon state distribution and far-field asymmetry with a sub-wavelength resolution, the appearance of viruses can be detected. To further reduce the background noise and enhance the asymmetric signals, an extended Laplace operator method which divides the detection area into topological units and then calculates the asymmetric parameter is applied, enabling easier determination of virus appearance. Experimental results show that the developed method can provide a detection limit as low as 56 vp/150µL on a large scale, which has great potential in early virus screening and other applications.

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

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

Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering

Breast cancer is the most commonly diagnosed cancer type worldwide. Overexpression of human epidermal growth factor receptor 2 (HER2) is an important subtype of breast cancer and results in an increased risk of recurrence and metastasis in patients. At present, immunohistochemistry (IHC) is used to detect the expression of HER2 in breast cancer tissues as the golden standard. However, IHC has some shortcomings, such as large subjective impact, long time consumption, expensive reagents, etc. In this paper, a combined morphological and spectroscopic diagnostic method based on label-free surface-enhanced Raman scattering (SERS) for HER2 expression in breast cancer is proposed. It can not only quantitively detect HER2 expression in breast cancer tissues by spectroscopic measurements but also give morphological images reflecting the distribution of HER2 in tissues. The results show that the consistency between this method and IHC is 95% and achieves the annotation of tumor regions on tissue sections. This method is time-consuming, quantifiable, intuitive, scalable, and easy to understand. Combined with deep learning approaches, it is expected to promote the development of clinical detection and diagnosis technology for breast cancer and other cancers.

Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis

The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration.

T-Shaped Aptamer-Based LSPR Biosensor Using Ω-Shaped Fiber Optic for Rapid Detection of SARS-CoV-2

SARS-CoV-2, especially the variant strains, is rapidly spreading around the world. Rapid detection methods for the virus are crucial for controlling the COVID-19 epidemic. Herein, a localized surface plasmonic resonance (LSPR) biosensor based on Ω-shaped fiber optic (Ω-FO) was developed for dual assays of SARS-CoV-2 monitoring. Due to its strong ability to control the orientation and density, a new T-shaped aptamer exhibits enhanced binding affinity toward N proteins. After being combined on the fiber optic surface, the T-shaped aptamer sensitively captured N proteins of SARS-CoV-2 for a direct assay. Further, core-shell structured gold/silver nanoparticles functionalized with a T-shaped aptamer (apt-Ag@AuNPs) can amplify the signal of N protein detection for a sandwich assay. The real-time analytical feature of the dual assays endows time-dependent sensitivity enhancement behavior, which provides a guideline to save analytical time. With those characteristics, the LSPR biosensor has been successfully used to rapidly identify 39 healthy volunteers and 39 COVID-19 patients infected with the ancestral or variant SARS-CoV-2. With the help of simple pretreatment, we obtain a true negative rate of 100% and a true positive rate of 92.3% with a short analysis time of 45 min using the direct assay. Further, the LSPR biosensor could also broaden the detection application range to the surface of cold-chain foods using a sandwich assay. Thus, the LSPR biosensor based on Ω-FO was demonstrated to have broad application potential to detect SARS-CoV-2 rapidly.

SARS-CoV-2 proteins monitored by long-range surface plasmon field-enhanced Raman scattering with hybrid bowtie nanoaperture arrays and nanocavities

The identification of biomacromolecules by using surface-enhanced Raman scattering (SERS) remains a challenge because of the near-field effect of traditional substrates. Long-range surface plasmon resonance (LRSPR) is a special type of surface optical phenomenon that provides higher electromagnetic field enhancement and longer penetration depth than conventional surface plasmon resonance. To break the limit of SERS detection distance and obtain a SERS substrate with increased enhancement ability, a bowtie nanoaperture array was sandwiched between two symmetric dielectric environments. Then, an Au mirror was inserted to form a metal-insulator-metal configuration. Finite-difference time-domain simulations revealed that numerous hybrid modes can be provided by this novel configuration (denoted as long-range SERS [LR-SERS] substrate). In particular, the LRSPR mode can be excited and reach the maximum value through the regulation of the polarizations of the incident light and the geometrical parameters of the LR-SERS substrate. The optimized LR-SERS substrate was then applied to detect SARS-CoV-2 spike (S) and nucleocapsid (N) proteins. This substrate displayed ultralow detection limits of ∼9.2 and ∼11.3 pg mL^-1 for the S and N proteins, respectively. Moreover, with the help of principal component analysis and receiver operating characteristic methods, our fabricated sensors exhibited excellent selectivity and hold great potential for the diagnosis of SARS-CoV-2 proteins in real samples.

SARS-CoV-2 severity classification from plasma sample using confocal Raman spectroscopy

The world is on the brink of facing coronavirus's (COVID-19) fourth wave as the mutant forms of viruses are escaping neutralizing antibodies in spite of being vaccinated. As we have already witnessed that it has encumbered our health system, with hospitals swamped with infected patients observed during the viral outbreak. Rapid triage of patients infected with SARS-CoV-2 is required during hospitalization to prioritize and provide the best point of care. Traditional diagnostics techniques such as RT-PCR and clinical parameters such as symptoms, comorbidities, sex and age are not enough to identify the severity of patients. Here, we investigated the potential of confocal Raman microspectroscopy as a powerful tool to generate an expeditious blood-based test for the classification of COVID-19 disease severity using 65 patients plasma samples from cohorts infected with SARS-CoV-2. We designed an easy manageable blood test where we used a small volume (8 μl) of inactivated whole plasma samples from infected patients without any extra solvent usage in plasma processing. Raman spectra of plasma samples were acquired and multivariate exploratory analysis PC-LDA (principal component based linear discriminant analysis) was used to build a model, which segregated the severe from the non-severe COVID-19 group with a sensitivity of 83.87%, specificity of 70.60% and classification efficiency of 76.92%. Among the bands expressed in both the cohorts, the study led to the identification of Raman fingerprint regions corresponding to lipids (1661, 1742), proteins amide I and amide III (1555, 1247), proteins (Phe) (1006, 1034), and nucleic acids (760) to be differentially expressed in severe COVID-19 patient's samples. In summary, the current study exhibits the potential of confocal Raman to generate simple, rapid, and less expensive blood tests to triage the severity of patients infected with SARS-CoV-2.

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review

Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.

Antibiotic Susceptibility Testing with Raman Biosensing

Antibiotics guard us against bacterial infections and are among the most commonly used medicines. The immediate consequence of their large-scale production and prescription is the development of antibiotic resistance. Therefore, rapid detection of antibiotic susceptibility is required for efficient antimicrobial therapy. One of the promising methods for rapid antibiotic susceptibility testing is Raman spectroscopy. Raman spectroscopy combines fast and contactless acquisition of spectra with good selectivity towards bacterial cells. The antibiotic-induced changes in bacterial cell physiology are detected as distinct features in Raman spectra and can be associated with antibiotic susceptibility. Therefore, the Raman-based approach may be beneficial in designing therapy against multidrug-resistant infections. The surface-enhanced Raman spectroscopy (SERS) and resonance Raman spectroscopy (RRS) additionally provide excellent sensitivity. In this review, we present an analysis of the Raman spectroscopy-based optical biosensing approaches aimed at antibiotic susceptibility testing.

Detection of A and B Influenza Viruses by Surface-Enhanced Raman Scattering Spectroscopy and Machine Learning

We demonstrate the possibility of applying surface-enhanced Raman spectroscopy (SERS) combined with machine learning technology to detect and differentiate influenza type A and B viruses in a buffer environment. The SERS spectra of the influenza viruses do not possess specific peaks that allow for their straight classification and detection. Machine learning technologies (particularly, the support vector machine method) enabled the differentiation of samples containing influenza A and B viruses using SERS with an accuracy of 93% at a concentration of 200 μg/mL. The minimum detectable concentration of the virus in the sample using the proposed approach was ~0.05 μg/mL of protein (according to the Lowry protein assay), and the detection accuracy of a sample with this pathogen concentration was 84%.

Integrated System for On-Site Rapid and Safe Screening of COVID-19

Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.

Quantum tunnelling in the context of SARS-CoV-2 infection

The SARS-CoV-2 pandemic has added new urgency to the study of viral mechanisms of infection. But while vaccines offer a measure of protection against this specific outbreak, a new era of pandemics has been predicted. In addition to this, COVID-19 has drawn attention to post-viral syndromes and the healthcare burden they entail. It seems integral that knowledge of viral mechanisms is increased through as wide a research field as possible. To this end we propose that quantum biology might offer essential new insights into the problem, especially with regards to the important first step of virus-host invasion. Research in quantum biology often centres around energy or charge transfer. While this is predominantly in the context of photosynthesis there has also been some suggestion that cellular receptors such as olfactory or neural receptors might employ vibration assisted electron tunnelling to augment the lock-and-key mechanism. Quantum tunnelling has also been observed in enzyme function. Enzymes are implicated in the invasion of host cells by the SARS-CoV-2 virus. Receptors such as olfactory receptors also appear to be disrupted by COVID-19. Building on these observations we investigate the evidence that quantum tunnelling might be important in the context of infection with SARS-CoV-2. We illustrate this with a simple model relating the vibronic mode of, for example, a viral spike protein to the likelihood of charge transfer in an idealised receptor. Our results show a distinct parameter regime in which the vibronic mode of the spike protein enhances electron transfer. With this in mind, novel therapeutics to prevent SARS-CoV-2 transmission could potentially be identified by their vibrational spectra.

Label-free laser spectroscopy for respiratory virus detection: A review

Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real-time, low cost and label-free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

Spectroscopic methods for COVID-19 detection and early diagnosis

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications.

Understanding the Excitation Wavelength Dependence and Thermal Stability of the SARS-CoV-2 Receptor-Binding Domain Using Surface-Enhanced Raman Scattering and Machine Learning

COVID-19 has cost millions of lives worldwide. The constant mutation of SARS-CoV-2 calls for thorough research to facilitate the development of variant surveillance. In this work, we studied the fundamental properties related to the optical identification of the receptor-binding domain (RBD) of SARS-CoV-2 spike protein, a key component of viral infection. The Raman modes of the SARS-CoV-2 RBD were captured by surface-enhanced Raman spectroscopy (SERS) using gold nanoparticles (AuNPs). The observed Raman enhancement strongly depends on the excitation wavelength as a result of the aggregation of AuNPs. The characteristic Raman spectra of RBDs from SARS-CoV-2 and MERS-CoV were analyzed by principal component analysis that reveals the role of secondary structures in the SERS process, which is corroborated with the thermal stability under laser heating. We can easily distinguish the Raman spectra of two RBDs using machine learning algorithms with accuracy, precision, recall, and F1 scores all over 95%. Our work provides an in-depth understanding of the SARS-CoV-2 RBD and paves the way toward rapid analysis and discrimination of complex proteins of infectious viruses and other biomolecules.

High-sensitivity and point-of-care detection of SARS-CoV-2 from nasal and throat swabs by magnetic SERS biosensor

The outbreak of COVID-19 caused by SARS-CoV-2 urges the development of rapidly and accurately diagnostic methods. Here, one high-sensitivity and point-of-care detection method based on magnetic SERS biosensor composed of Fe₃O₄-Au nanocomposite and Au nanoneedles array was developed to detect SARS-CoV-2 directly. Among, the magnetic Fe₃O₄-Au nanocomposite is applied to capture and separate virus from nasal and throat swabs and enhance the Raman signals of SARS-CoV-2. The magnetic SERS biosensor possessed high sensitivity by optimizing the Fe₃O₄-Au nanocomposite. More significantly, the on-site detection of inactivated SARS-CoV-2 virus was achieved based on the magnetic SERS biosensor with ultra-low limit of detection of 100 copies/mL during 15 mins. Furthermore, the contaminated nasal and throat swabs samples were identified by support vector machine, and the diagnostic accuracy of 100% was obtained. The magnetic SERS biosensor combined with support vector machine provides giant potential as the point-of-care detection tool for SARS-CoV-2.

Constructing high-accuracy theoretical Raman spectra of SARS-CoV-2 spike proteins based on a large fragment method

In order to control COVID-19, rapid and accurate detection of the pathogenic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an urgent task. The target spike proteins of SARS-CoV-2 have been detected experimentally via Raman spectroscopy. However, there lacks high-accuracy theoretical Raman spectra of the spike proteins to as a standard reference for the clinic diagnostic purpose. In this paper, we propose a large fragment method to construct the high-precision Raman spectra for the spike proteins. The large fragment method not only reduces the calculation error but also improves the accuracy of the protein Raman spectra by completely calculating the interactions within the large fragment. The Pearson correlation coefficient of theoretical Raman spectra is greater than 0.929 or more. Compared with the experimental spectra, the characteristic patterns are easily visible. This work provides a detection standard for the spike proteins which shall bring a step closer to the fast recognition of SARS-CoV-2 via Raman spectroscopy method.

Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution has been characterized by the emergence of sets of mutations impacting the virus characteristics, such as transmissibility and antigenicity, presumably in response to the changing immune profile of the human population. The presence of mutations in the SARS-CoV-2 virus can potentially impact therapeutic and diagnostic test performances. We design and develop here a unique set of DNA probes i.e., antisense oligonucleotides (ASOs) which can interact with genetic sequences of the virus irrespective of its ongoing mutations. The probes, developed herein, target a specific segment of the nucleocapsid phosphoprotein (N) gene of SARS-CoV-2 with high binding efficiency which do not mutate among the known variants. Further probing into the interaction profile of the ASOs reveals that the ASO-RNA hybridization remains unaltered even for a hypothetical single point mutation at the target RNA site and diminished only in case of the hypothetical double or triple point mutations. The mechanism of interaction among the ASOs and SARS-CoV-2 RNA is then explored with a combination of surface-enhanced Raman scattering (SERS) and machine learning techniques. It has been observed that the technique, described herein, could efficiently discriminate between clinically positive and negative samples with ∼100% sensitivity and ∼90% specificity up to 63 copies/mL of SARS-CoV-2 RNA concentration. Thus, this study establishes N gene targeted ASOs as the fundamental machinery to efficiently detect all the current SARS-CoV-2 variants regardless of their mutations.

Performance of Antigen Detection Tests for SARS-CoV-2: A Systematic Review and Meta-Analysis

Coronavirus disease 2019 (COVID-19) initiated global health care challenges such as the necessity for new diagnostic tests. Diagnosis by real-time PCR remains the gold-standard method, yet economical and technical issues prohibit its use in points of care (POC) or for repetitive tests in populations. A lot of effort has been exerted in developing, using, and validating antigen-based tests (ATs). Since individual studies focus on few methodological aspects of ATs, a comparison of different tests is needed. Herein, we perform a systematic review and meta-analysis of data from articles in PubMed, medRxiv and bioRxiv. The bivariate method for meta-analysis of diagnostic tests pooling sensitivities and specificities was used. Most of the AT types for SARS-CoV-2 were lateral flow immunoassays (LFIA), fluorescence immunoassays (FIA), and chemiluminescence enzyme immunoassays (CLEIA). We identified 235 articles containing data from 220,049 individuals. All ATs using nasopharyngeal samples show better performance than those with throat saliva (72% compared to 40%). Moreover, the rapid methods LFIA and FIA show about 10% lower sensitivity compared to the laboratory-based CLEIA method (72% compared to 82%). In addition, rapid ATs show higher sensitivity in symptomatic patients compared to asymptomatic patients, suggesting that viral load is a crucial parameter for ATs performed in POCs. Finally, all methods perform with very high specificity, reaching around 99%. LFIA tests, though with moderate sensitivity, appear as the most attractive method for use in POCs and for performing seroprevalence studies.

Research on the Comprehensive Evaluation Method for the Automatic Recognition of Raman Spectrum under Multidimensional Constraint

Raman spectrum contains abundant substance information with fingerprint characteristics. However, due to the huge variety of substances and their complex characteristic information, it is difficult to recognize the Raman spectrum accurately. Starting from dimensions like the Raman shift, the relative peak intensity, and the overall hit ratio of characteristic peaks, we extracted and recognized the characteristics in the Raman spectrum and analyzed these characteristics from local and global perspectives and then proposed a comprehensive evaluation method for the recognition of Raman spectrum on the basis of the data fusion of the recognition results under multidimensional constraint. Based on the common spectrum database of the normal Raman and surface-enhanced Raman of thousands of substances, we analyzed the performance of the evaluation method. It shows that even for the identification of spectra from instruments of low technical specifications, the automatic recognition rate of the sample can reach 98% and above, a great improvement compared with that of the common identification algorithms, which proves the effectiveness of the comprehensive evaluation method under multidimensional constraint.

Label-Free Spectroscopic SARS-CoV-2 Detection on Versatile Nanoimprinted Substrates

Widespread testing and isolation of infected patients is a cornerstone of viral outbreak management, as underscored during the ongoing COVID-19 pandemic. Here, we report a large-area and label-free testing platform that combines surface-enhanced Raman spectroscopy and machine learning for the rapid and accurate detection of SARS-CoV-2. Spectroscopic signatures acquired from virus samples on metal-insulator-metal nanostructures, fabricated using nanoimprint lithography and transfer printing, can provide test results within 25 min. Not only can our technique accurately distinguish between different respiratory and nonrespiratory viruses, but it can also detect virus signatures in physiologically relevant matrices such as human saliva without any additional sample preparation. Furthermore, our large area nanopatterning approach allows sensors to be fabricated on flexible surfaces allowing them to be mounted on any surface or used as wearables. We envision that our versatile and portable label-free spectroscopic platform will offer an important tool for virus detection and future outbreak preparedness.

Surface-enhanced Raman scattering-based immunoassay for severe acute respiratory syndrome coronavirus 2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected humans worldwide for over a year now. Although various tests have been developed for the detection of SARS-CoV-2, advanced sensing methods are required for the diagnosis, screening, and surveillance of coronavirus disease 2019 (COVID-19). Here, we report a surface-enhanced Raman scattering (SERS)-based immunoassay involving an antibody pair, SERS-active hollow Au nanoparticles (NPs), and magnetic beads for the detection of SARS-CoV-2. The selected antibody pair against the SARS-CoV-2 antigen, along with the magnetic beads, facilitates the accurate direct detection of the virus. The hollow Au NPs exhibit strong, reproducible SERS signals, allowing sensitive quantitative detection of SARS-CoV-2. This assay had detection limits of 2.56 fg/mL for the SARS-CoV-2 antigen and 3.4 plaque-forming units/mL for the SARS-CoV-2 lysates. Furthermore, it facilitated the identification of SARS-CoV-2 in human nasopharyngeal aspirates and diagnosis of COVID-19 within 30 min using a portable Raman device. Thus, this assay can be potentially used for the diagnosis and prevention of COVID-19.

Microfluidics-Based Biosensing Platforms: Emerging Frontiers in Point-of-Care Testing SARS-CoV-2 and Seroprevalence

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the ongoing COVID-19 (coronavirus disease-2019) outbreak and has unprecedentedly impacted the public health and economic sector. The pandemic has forced researchers to focus on the accurate and early detection of SARS-CoV-2, developing novel diagnostic tests. Among these, microfluidic-based tests stand out for their multiple benefits, such as their portability, low cost, and minimal reagents used. This review discusses the different microfluidic platforms applied in detecting SARS-CoV-2 and seroprevalence, classified into three sections according to the molecules to be detected, i.e., (1) nucleic acid, (2) antigens, and (3) anti-SARS-CoV-2 antibodies. Moreover, commercially available alternatives based on microfluidic platforms are described. Timely and accurate results allow healthcare professionals to perform efficient treatments and make appropriate decisions for infection control; therefore, novel developments that integrate microfluidic technology may provide solutions in the form of massive diagnostics to control the spread of infectious diseases.

Investigation of mammalian cells expressing SARS-CoV-2 proteins by surface-enhanced Raman scattering and multivariate analysis

COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial for controlling its spread through a population. COVID-19 is currently detected by nucleic acid-based tests and serological tests. However, these methods have limitations such as the requirement of high-cost reagents, false negative results and being time consuming. Surface-enhanced Raman scattering (SERS), which is a powerful technique that enhances the Raman signals of molecules using plasmonic nanostructures, can overcome these disadvantages. In this study, we developed a virus-infected cell model and analyzed this model by SERS combined with Principal Component Analysis (PCA). HEK293 cells were transfected with plasmids encoding the nucleocapsid (N), membrane (M) and envelope (E) proteins of SARS-CoV-2 via polyethyleneimine (PEI). Non-plasmid transfected HEK293 cells were used as the control group. Cellular uptake was optimized with green fluorescence protein (GFP) plasmids and evaluated by fluorescence microscopy and flow cytometry. The transfection efficiency was found to be around 60%. The expression of M, N, and E proteins was demonstrated by western blotting. The SERS spectra of the total proteins of transfected cells were obtained using a gold nanoparticle-based SERS substrate. Proteins of the transfected cells have peak positions at 646, 680, 713, 768, 780, 953, 1014, 1046, 1213, 1243, 1424, 2102, and 2124 cm^-1. To reveal spectral differences between plasmid transfected cells and non-transfected control cells, PCA was applied to the spectra. The results demonstrated that SERS coupled with PCA might be a favorable and reliable way to develop a rapid, low-cost, and promising technique for the detection of COVID-19.

Revealing protein binding affinity on metal surfaces: an electrochemical approach

Revealing the binding affinity between viruses and surfaces of environmental matrices is crucial to evaluate the bioactivity of an immobilized virus and accompanying indirect virus-related infection pathways. The understanding for SARS-CoV-2 remaining infective for even days on stainless steel but only hours on copper is still unclear. Electrochemical chronoamperometry, ultrasensitive to interfacial capacitance on surface species, was used to investigate the binding affinity of SARS-CoV-2 on metal surfaces. S_RBD, the surrogate of SARS-CoV-2, shows the highest adsorption capacity on a gold surface, followed by Cu, but lowest on a stainless steel surface. The strong binding of S_RBD on copper is a result of the naturally grown Cu₂O under ambient conditions. Measurement of electrochemical capacitance provides a simple strategy to explore and evaluate the potential risk of an indirect virus-related infection pathway through conductive environmental matrices.

Bioconjugated Nanomaterial for Targeted Diagnosis of SARS-CoV-2

Infectious diseases by pathogenic microorganisms are one of the leading causes of mortality worldwide. Healthcare and socio-economic development have been seriously affected for different civilizations because of bacterial and viral infections. According to the Centers for Disease Control and Prevention (CDC), pandemic in 1918 by the Influenza A virus of the H1N1 subtype was responsible for 50 to 100 million deaths worldwide. Similarly, the Asian flu pandemic in 1957, Hong Kong flu in 1968, and H1N1pdm09 flu pandemic in 2009 were responsible for more than 1 million deaths across the globe each time. As per the World Health Organization (WHO), the current pandemic by coronavirus disease 2019 (COVID-19) due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is responsible for more than 4.8 M death worldwide until now. Since the gold standard polymerase chain reaction (PCR) test is more time-consuming, the health care system cannot test all symptomatic and asymptomatic Covid patients every day, which is extremely important to tackle the outbreak. One of the significant challenges during the current pandemic is developing mass testing tools, which is critical to control the virus spread in the community. Therefore, it is highly desirable to develop advanced material-based approaches that can provide a rapid and accurate diagnosis of COVID-19, which will have the capability to save millions of human lives. Aiming for the targeted diagnosis of deadly virus, researchers have developed nanomaterials with various sizes, shapes, and dimensions. These nanomaterials have been used to identify biomolecules via unique optical, electrical, magnetic, structural, and functional properties, which are lacking in other materials. Despite significant progress, nanomaterial-based diagnosis of biomolecules is still facing several obstacles due to low targeting efficiency and nonspecific interactions. To overcome these problems, the bioconjugated nanoparticle has been designed via surface coating with polyethylene glycol (PEG) and then conjugated with antibodies, DNA, RNA, or peptide aptamers. Therefore, the current Account summarizes an overview of the recent advances in the design of bioconjugated nanomaterial-based approached as effective diagnosis of the SARS-CoV-2 virus and the SARS-CoV-2 viral RNA, antigen, or antibody, with a particular focus on our work and other's work related to this subject. First, we present how to tailor the surface functionalities of nanomaterials to achieve bioconjugated material for targeted diagnosis of the virus. Then we review the very recent advances in the design of antibody/aptamer/peptide conjugated nanostructure, which represent a powerful platform for naked-eye colorimetric detection via plasmonic nanoparticles. We then discuss nanomaterial-based surface-enhanced Raman scattering (SERS) spectroscopy, which has the capability for very low-level fingerprint identification of virus, antigen, and antibody via graphene, plasmonic nanoparticle, and heterostructure material. After that, we summarized about fluorescence and nanoparticle surface energy transfer (NSET)-based on specific identification of SARS-CoV-2 infections via CNT, quantum dots (QDs), and plasmonic nanoparticles. Finally, we highlight the merit and significant challenges of nanostructure-based tools in infectious diseases diagnosis. For the researchers who want to engage in the new development of bioconjugated material for our survival from the current and future pandemics, we hope that this Account will be helpful for generating ideas that are scientifically stimulating and practically challenging.