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

Single-shot, receptor-free, rapid detection and classification of five respiratory viruses by machine learning integrated SERS sensing platform

A machine learning integrated SERS sensing platform is developed for a single-shot, receptor-free, rapid detection and classification of five respiratory viruses including Influenza A, Respiratory Syncytial Virus (RSV), Human Rhinoviruses (Rhino), and two variants of SARS-CoV-2 virus such as Omicron and Delta in clinical nasal and/or nasopharyngeal samples (CNS) in viral transport media (VTM). SERS sensor composed of Ag nano-sculptured thin film (nSTF) is fabricated by glancing angle deposition (GLAD) technique and possess a SERS enhancement factor of the order of 1011. Various machine learning (ML) algorithms like Random Forest Classifier (RF), Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP) are trained on SERS spectra dataset of CNS for classification of the viruses. MLP shows the best performance for the analysis and classification of complex SERS spectra with a 5-fold validation accuracy of 97.61 ± 0.30 %, test accuracy 97.47 %, sensitivity 97 %, precision 97 %, and specificity 99 %. The SERS sensor integrated with ML has a rapid response of 11 minutes, which makes it appropriate for practical implementations in clinical environments, speeding up virus detection and efficient management of respiratory viral infections.

A Comparative Analysis of Optical Biosensors for Rapid Detection of SARS-CoV-2 and Influenza

Airborne pathogens, such as influenza and SARS-CoV-2, pose significant health risks. While traditional diagnostic methods have limitations, optical biosensors offer a promising solution due to their rapid, sensitive, and cost-effective nature. This review focuses on the application of optical biosensors, including colorimetry, surface plasmon resonance, surface-enhanced Raman spectroscopy, and fluorescence techniques, for the detection of influenza and SARS-CoV-2. We discuss the advantages of these techniques, such as their potential for point-of-care testing and early disease detection. By addressing the limitations of existing methods and exploring emerging technologies, optical biosensors can play a crucial role in combating the spread of airborne pathogens. This review provides a comprehensive overview of optical biosensor techniques for the detection of both SARS-CoV-2 and influenza, addressing a significant gap in the literature.

Electro-active evanescent-wave cavity ring-down spectroscopy immunosensor for influenza virus detection

The early and accurate detection of viral pathogens is critical for effective disease management and public health safety. This study introduces an immunosensor that integrates an electro-active evanescent-wave cavity ring-down spectroscopy (EW-CRDS) platform with a sandwich-type bioassay for label-free detection of the influenza A (H5N1) hemagglutinin (HA) protein, achieving a detection limit of 15 ng/mL. The sensor is constructed by functionalizing the EW-CRDS platform within a micro-electrochemical flow cell with a monoclonal antibody specific to the target antigen. Upon antigen binding, a secondary polyclonal antibody conjugated with a redox-active methylene blue dye is captured. This dye undergoes reversible optical signal changes during redox transitions, which are electrochemically modulated and detected with high sensitivity. Unlike conventional approaches, this sensor employs electrochemical modulation to amplify surface-specific optical signals while reducing processing time and minimizing background noise. The results demonstrate the potential of this technology for real-time monitoring and rapid, reliable diagnosis of infectious diseases, offering excellent sensitivity and ease of operation in detecting influenza viruses. This work highlights the promise of the electro-active EW-CRDS platform for antigen detection in clinical settings.

Rapid profiling of carcinogenic types of Helicobacter pylori infection via deep learning analysis of label-free SERS spectra of human serum

WHO classified Helicobacter pylori as a Group I carcinogen for gastric cancer as early as 1994. However, despite the high prevalence of H. pylori infection, only about 3 % of infected individuals eventually develop gastric cancer, with the highly virulent H. pylori strains expressing cytotoxin-associated protein (CagA) and vacuolating cytotoxin (VacA) being critical factors in gastric carcinogenesis. It is well known that H. pylori infection is divided into two types in terms of the presence and absence of CagA and VacA toxins in serum, that is, carcinogenic Type I infection (CagA+/VacA+, CagA+/VacA-, CagA-/VacA+) and non-carcinogenic Type II infection (CagA-/VacA-). Currently, detecting the two carcinogenic toxins in active modes is mainly done by diagnosing their serological antibodies. However, the method is restricted by expensive reagents and intricate procedures. Therefore, establishing a rapid, accurate, and cost-effective way for serological profiling of carcinogenic H. pylori infection holds significant implications for effectively guiding H. pylori eradication and gastric cancer prevention. In this study, we developed a novel method by combining surface-enhanced Raman spectroscopy with the deep learning algorithm convolutional neural network to create a model for distinguishing between serum samples with Type I and Type II H. pylori infections. This method holds the potential to facilitate rapid screening of H. pylori infections with high risks of carcinogenesis at the population level, which can have long-term benefits in reducing gastric cancer incidence when used for guiding the eradication of H. pylori infections.

Establishment of a Raman nanosphere based immunochromatographic system for the combined detection of influenza A and B viruses' antigens on a single T-line

A simple and rapid system based on Raman nanosphere (R-Sphere) immunochromatography was developed in this study for the simultaneous detection of Influenza A, B virus antigens on a single test line (T-line). Two types of R-Sphere with different characteristic Raman spectrum were used as the signal source, which were labeled with monoclonal antibodies against FluA, FluB (tracer antibodies), respectively. A mixture of antibodies containing anti-FluA monoclonal antibody and anti-FluB monoclonal antibody (capture antibody) was sprayed on a single T-line and goat anti-chicken IgY antibody was coated as a C-line, and the antigen solution with known concentration was detected by the strip of lateral flow immunochromatography based on surface-enhanced Raman spectroscopy (SERS). The T-line was scanned with a Raman spectrometer and SERS signals were collected. Simultaneous specific recognition and detection of FluA and FluB were achieved on a single T-line by analyzing the SERS signals. The findings indicated that the test system could identify FluA and FluB in a qualitative manner in just 15 minutes, with a minimum detection threshold of 0.25 ng ml-1, excellent consistency, and specificity. There was no interference with the other four respiratory pathogens, and it exhibited 8 times greater sensitivity compared to the colloidal gold test strip method. The assay system is rapid, sensitive, and does not require repetitive sample pretreatment steps and two viruses can be detected simultaneously on a single T-line by titrating one sample, which improves detection efficiency, and provide a reference for developing multiplexed detection techniques for other respiratory viruses.

Rapid and label-free influenza A virus subtyping using surface-enhanced Raman spectroscopy with incident-wavelength analysis

Early virus identification is a key component of both patient treatment and epidemiological monitoring. In the case of influenza A virus infections, where the detection of subtypes associated with bird flu in humans could lead to a pandemic, rapid subtype-level identification is important. Surface-enhanced Raman spectroscopy coupled with machine learning can be used to rapidly detect and identify viruses in a label-free manner. As there is a range of available excitation wavelengths for performing Raman spectroscopy, we must choose the best one to permit discrimination between highly similar subtypes of a virus. We show that the spectra produced by influenza A subtypes H1N1 and H3N2 exhibit a higher degree of dissimilarity when using 785 nm excitation wavelength in comparison with 532 nm excitation wavelength. Furthermore, the cross-validated area under the curve (AUC) for identification was higher for the 785 nm excitation, reaching 0.95 as compared to 0.86 for 532 nm. Ultimately, this study suggests that exciting with a 785 nm wavelength is better able to differentiate two closely related influenza viruses and likely can extend to other closely related pathogens.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Recent Advances in Molecular and Immunological Diagnostic Platform for Virus Detection: A Review

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing coronavirus disease (COVID-19) outbreak and a rising demand for the development of accurate, timely, and cost-effective diagnostic tests for SARS-CoV-2 as well as other viral infections in general. Currently, traditional virus screening methods such as plate culturing and real-time PCR are considered the gold standard with accurate and sensitive results. However, these methods still require sophisticated equipment, trained personnel, and a long analysis time. Alternatively, with the integration of microfluidic and biosensor technologies, microfluidic-based biosensors offer the ability to perform sample preparation and simultaneous detection of many analyses in one platform. High sensitivity, accuracy, portability, low cost, high throughput, and real-time detection can be achieved using a single platform. This review presents recent advances in microfluidic-based biosensors from many works to demonstrate the advantages of merging the two technologies for sensing viruses. Different platforms for virus detection are classified into two main sections: immunoassays and molecular assays. Moreover, available commercial sensing tests are analyzed.

New and Improved Nanomaterials and Approaches for Optical Bio- and Immunosensors

The current state in the development of biosensors is largely associated with the search for new approaches to simplify measurements and lower detection limits [...].