A simple and fast spectroscopy-based technique for Covid-19 diagnosis

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

On the feasibility of Vis-NIR spectroscopy and machine learning for real time SARS-CoV-2 detection

The pandemic caused by Covid-19 is still present around the world. Despite advances in combating the disease, such as vaccine development, identifying infected individuals is still essential to optimize the control of human-to-human transmission of the virus. The main technique for detecting the virus is the RT-PCR method, which, despite its high relative cost, has a high accuracy in detecting the coronavirus. Given this, a method capable of performing the identification quickly, accurately, and inexpensively is necessary. Thus, this work aimed to analyze the feasibility of a new technique for identifying SARS-CoV-2 through the use of optical spectroscopy in the visible and near-infrared range (Vis-NIR) combined with machine learning algorithms. Spectral signals were obtained from nasopharyngeal swab samples previously analyzed using the RT-PCR method. The specimens were provided by the Molecular Diagnosis Laboratory of Covid-19 at Univasf. A total of 314 samples were analyzed, comprising 42 testing positive and 272 testing negative for Covid-19. Digital signal processing techniques, such as Savitzky-Golay filters and statistical methods were used to eliminate spurious elements from the original data and extract relevant features. Supervised machine learning algorithms such as SVM, Random Forest, and Naive Bayes classifiers were used to perform automatic sample identification. To evaluate the performance of the models, a 5-fold cross-validation technique was applied. With the proposed methodology, it was possible to achieve an accuracy of 75%, a sensitivity of 80%, and a specificity of 70%, in addition to an area under the ROC curve of 0.81, in the identification of nasopharyngeal swab samples from previously diagnosed individuals. From these results, it was possible to conclude that Vis-NIR spectroscopy is a promising, fast and relatively low cost technique to identify the SARS-CoV-2.

A review on current diagnostic tools and potential optical absorption spectroscopy for HFMD detection

Hand, Foot, and Mouth Disease (HFMD) is an outbreak infectious disease that can easily spread among children under the age of five. The most common causative agents of HFMD are enterovirus 71 (EV71) and coxsackievirus A16 (CVA16), but infection caused by EV71 is more associated with fatalities due to severe neurological disorders. The present diagnosis methods rely on physical examinations by the doctors and further confirmation by laboratories detection methods such as viral culture and polymerase chain reaction. Clinical signs of HFMD infection and other childhood diseases such as chicken pox, and allergies are similar, yet the genetics and pathogenicity of the viruses are substantially different. Thus, there is an urgent need for an early screening of HFMD using an inexpensive and user-friendly device that can directly detect the causative agents of the disease. This paper reviews current HFMD diagnostic methods based on various target types, such as nucleic acid, protein, and whole virus. This was followed by a thorough discussion on the emerging sensing technologies for HFMD detection, including surface plasmon resonance, electrochemical sensor, and surface enhanced Raman spectroscopy. Lastly, optical absorption spectroscopic method was critically discussed and proposed as a promising technology for HFMD screening and detection.

Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning

Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in organic compounds. A complex and time-consuming interpretation of massive unknown spectra usually requires knowledge of chemistry and spectroscopy. This paper presents a new deep learning method for transforming IR spectral features into intuitive imagelike feature maps and prediction of major functional groups. We obtained 8272 gas-phase IR spectra from the NIST Chemistry WebBook. Feature maps are constructed using the intrinsic correlation of spectral data, and prediction models are developed based on convolutional neural networks. Twenty-one major functional groups for each molecule are successfully identified using binary and multilabel models without expert guidance and feature selection. The multilabel classification model can produce all prediction results simultaneously for rapid characterization. Further analysis of the detailed substructures indicates that our model is capable of obtaining abundant structural information from IR spectra for a comprehensive investigation. The interpretation of our model reveals that the peaks of most interest are similar to those often considered by spectroscopists. In addition to demonstrating great potential for spectral identification, our method may contribute to the development of automated analyses in many fields.

Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses-A Review

The sudden outbreak of the COVID-19 pandemic led to a huge concern globally because of the astounding increase in mortality rates worldwide. The medical imaging computed tomography technique, whole-genome sequencing, and electron microscopy are the methods generally used for the screening and identification of the SARS-CoV-2 virus. The main aim of this review is to emphasize the capabilities of various optical techniques to facilitate not only the timely and effective diagnosis of the virus but also to apply its potential toward therapy in the field of virology. This review paper categorizes the potential optical biosensors into the three main categories, spectroscopic-, nanomaterial-, and interferometry-based approaches, used for detecting various types of viruses, including SARS-CoV-2. Various classifications of spectroscopic techniques such as Raman spectroscopy, near-infrared spectroscopy, and fluorescence spectroscopy are discussed in the first part. The second aspect highlights advances related to nanomaterial-based optical biosensors, while the third part describes various optical interferometric biosensors used for the detection of viruses. The tremendous progress made by lab-on-a-chip technology in conjunction with smartphones for improving the point-of-care and portability features of the optical biosensors is also discussed. Finally, the review discusses the emergence of artificial intelligence and its applications in the field of bio-photonics and medical imaging for the diagnosis of COVID-19. The review concludes by providing insights into the future perspectives of optical techniques in the effective diagnosis of viruses.

Polarimetric imaging for the detection of synthetic models of SARS-CoV-2: A proof of concept

OBJECTIVE

To conduct a proof-of-concept study of the detection of two synthetic models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using polarimetric imaging.

APPROACH

Two SARS-CoV-2 models were prepared as engineered lentiviruses pseudotyped with the G protein of the vesicular stomatitis virus, and with the characteristic Spike protein of SARS-CoV-2. Samples were prepared in two biofluids (saline solution and artificial saliva), in four concentrations, and deposited as 5-µL droplets on a supporting plate. The angles of maximal degree of linear polarization (DLP) of light diffusely scattered from dry residues were determined using Mueller polarimetry from87 samples at 405 nm and 514 nm. A polarimetric camera was used for imaging several samples under 380-420 nm illumination at angles similar to those of maximal DLP. Per-pixel image analysis included quantification and combination of polarization feature descriptors in 475 samples.

MAIN RESULTS

The angles (from sample surface) of maximal DLP were 3° for 405 nm and 6° for 514 nm. Similar viral particles that differed only in the characteristic spike protein of the SARS-CoV-2, their corresponding negative controls, fluids, and the sample holder were discerned at 10-degree and 15-degree configurations.

SIGNIFICANCE

Polarimetric imaging in the visible spectrum may help improve fast, non-contact detection and identification of viral particles, and/or other microbes such as tuberculosis, in multiple dry fluid samples simultaneously, particularly when combined with other imaging modalities. Further analysis including realistic concentrations of real SARS-CoV-2 viral particles in relevant human fluids is required. Polarimetric imaging under visible light may contribute to a fast, cost-effective screening of SARS-CoV-2 and other pathogens when combined with other imaging modalities.

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Robustness of FTIR-Based Ultrarapid COVID-19 Diagnosis Using PLS-DA

The World Health Organization (WHO) declared the Omicron variant (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen responsible for the Coronavirus disease 2019 (COVID-19) pandemic, as a variant of concern on 26 November 2021. By this time, 42% of the world's population had received at least one dose of the vaccine against COVID-19. As on 1 October 2022, only 68% of the world population got the first dose of the vaccine. Although the vaccination is incredibly protective against severe complications of the disease and death, the highly contagious Omicron variant, compared to the Delta variant (B.1.617.2), has led the whole world into more chaotic situations. Furthermore, the virus has a high mutation rate, and hence, the possibility of a new variant of concern in the future cannot be ruled out. To face such a challenging situation, paramount importance should be given to rapid diagnosis and isolation of the infected patient. Current diagnosis methods, including reverse transcription-polymerase chain reaction and rapid antigen tests, face significant burdens during a COVID-19 wave. However, studies reported ultrarapid, reagent-free, cost-efficient, and non-destructive diagnosis methods based on chemometrics for COVID-19 and COVID-19 severity diagnosis. These studies used a smaller sample cohort to construct the diagnosis model and failed to discuss the robustness of the model. The current study systematically evaluated the robustness of the diagnosis models trained using smaller (real and augmented spectra) and larger (augmented spectra) datasets. The Monte Carlo cross-validation and permutation test results suggest that diagnosis using models trained by larger datasets was accurate and statistically significant (Q ² > 99% and AUROC = 100%).

Current trends in COVID-19 diagnosis and its new variants in physiological fluids: Surface antigens, antibodies, nucleic acids, and RNA sequencing

Rapid, highly sensitive, and accurate virus circulation monitoring techniques are critical to limit the spread of the virus and reduce the social and economic burden. Therefore, point-of-use diagnostic devices have played a critical role in addressing the outbreak of COVID-19 (SARS-CoV-2) viruses. This review provides a comprehensive overview of the current techniques developed for the detection of SARS-CoV-2 in various body fluids (e.g., blood, urine, feces, saliva, tears, and semen) and considers the mutations (i.e., Alpha, Beta, Gamma, Delta, Omicron). We classify and comprehensively discuss the detection methods depending on the biomarker measured (i.e., surface antigen, antibody, and nucleic acid) and the measurement techniques such as lateral flow immunoassay (LFIA), enzyme-linked immunosorbent assay (ELISA), reverse transcriptase-polymerase chain reaction (RT-PCR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), microarray analysis, clustered regularly interspaced short palindromic repeats (CRISPR) and biosensors. Finally, we addressed the challenges of rapidly identifying emerging variants, detecting the virus in the early stages of infection, the detection sensitivity, selectivity, and specificity, and commented on how these challenges can be overcome in the future.

Clinical studies of detecting COVID-19 from exhaled breath with electronic nose

The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.

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.

Potential of ATR-FTIR-Chemometrics in Covid-19: Disease Recognition

The COVID-19 pandemic has caused major disturbances to human health and economy on a global scale. Although vaccination campaigns and important advances in treatments have been developed, an early diagnosis is still crucial. While PCR is the golden standard for diagnosing SARS-CoV-2 infection, rapid and low-cost techniques such as ATR-FTIR followed by multivariate analyses, where dimensions are reduced for obtaining valuable information from highly complex data sets, have been investigated. Most dimensionality reduction techniques attempt to discriminate and create new combinations of attributes prior to the classification stage; thus, the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. In this work, we developed a method for evaluating SARS-CoV-2 infection and COVID-19 disease severity on infrared spectra of sera, based on a rather simple feature selection technique (correlation-based feature subset selection). Dengue infection was also evaluated for assessing whether selectivity toward a different virus was possible with the same algorithm, although independent models were built for both viruses. High sensitivity (94.55%) and high specificity (98.44%) were obtained for assessing SARS-CoV-2 infection with our model; for severe COVID-19 disease classification, sensitivity is 70.97% and specificity is 94.95%; for mild disease classification, sensitivity is 33.33% and specificity is 94.64%; and for dengue infection assessment, sensitivity is 84.27% and specificity is 94.64%.

Tools shaping drug discovery and development

Spectroscopic, scattering, and imaging methods play an important role in advancing the study of pharmaceutical and biopharmaceutical therapies. The tools more familiar to scientists within industry and beyond, such as nuclear magnetic resonance and fluorescence spectroscopy, serve two functions: as simple high-throughput techniques for identification and purity analysis, and as potential tools for measuring dynamics and structures of complex biological systems, from proteins and nucleic acids to membranes and nanoparticle delivery systems. With the expansion of commercial small-angle x-ray scattering instruments into the laboratory setting and the accessibility of industrial researchers to small-angle neutron scattering facilities, scattering methods are now used more frequently in the industrial research setting, and probe-less time-resolved small-angle scattering experiments are now able to be conducted to truly probe the mechanism of reactions and the location of individual components in complex model or biological systems. The availability of atomic force microscopes in the past several decades enables measurements that are, in some ways, complementary to the spectroscopic techniques, and wholly orthogonal in others, such as those related to nanomechanics. As therapies have advanced from small molecules to protein biologics and now messenger RNA vaccines, the depth of biophysical knowledge must continue to serve in drug discovery and development to ensure quality of the drug, and the characterization toolbox must be opened up to adapt traditional spectroscopic methods and adopt new techniques for unraveling the complexities of the new modalities. The overview of the biophysical methods in this review is meant to showcase the uses of multiple techniques for different modalities and present recent applications for tackling particularly challenging situations in drug development that can be solved with the aid of fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, and small-angle scattering.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

Diagnostic Tools for Rapid Screening and Detection of SARS-CoV-2 Infection

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely impacted human health and the health management system globally. The ongoing pandemic has required the development of more effective diagnostic strategies for restricting deadly disease. For appropriate disease management, accurate and rapid screening and isolation of the affected population is an efficient means of containment and the decimation of the disease. Therefore, considerable efforts are being directed toward the development of rapid and robust diagnostic techniques for respiratory infections, including SARS-CoV-2. In this article, we have summarized the origin, transmission, and various diagnostic techniques utilized for the detection of the SARS-CoV-2 virus. These higher-end techniques can also detect the virus copy number in asymptomatic samples. Furthermore, emerging rapid, cost-effective, and point-of-care diagnostic devices capable of large-scale population screening for COVID-19 are discussed. Finally, some breakthrough developments based on spectroscopic diagnosis that could revolutionize the field of rapid diagnosis are discussed.

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

OBJECTIVE

To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

MATERIAL AND METHODS

Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

RESULTS

The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.

CONCLUSION

The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective

The coronavirus disease 2019 (COVID-19) pandemic is a worldwide health anxiety. The rapid dispersion of the infection globally results in unparalleled economic, social, and health impacts. The pathogen that causes COVID-19 is known as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A fast and low-cost diagnosis method for COVID-19 disease can play an important role in controlling its proliferation. Near-infrared spectroscopy (NIRS) is a quick, non-destructive, non-invasive, and inexpensive technique for profiling the chemical and physical structures of a wide range of samples. Furthermore, the NIRS has the advantage of incorporating the internet of things (IoT) application for the effective control and treatment of the disease. In recent years, a significant advancement in instrumentation and spectral analysis methods has resulted in a remarkable impact on the NIRS applications, especially in the medical discipline. To date, NIRS has been applied as a technique for detecting various viruses including zika (ZIKV), chikungunya (CHIKV), influenza, hepatitis C, dengue (DENV), and human immunodeficiency (HIV). This review aims to outline some historical and contemporary applications of NIRS in virology and its merit as a novel diagnostic technique for SARS-CoV-2.

Micro-Fourier-transform infrared reflectance spectroscopy as tool for probing IgG glycosylation in COVID-19 patients

It has been reported that patients diagnosed with COVID-19 become critically ill primarily around the time of activation of the adaptive immune response. However the role of antibodies in the worsening of disease is not obvious. Higher titers of anti-spike immunoglobulin IgG1 associated with low fucosylation of the antibody Fc tail have been associated to excessive inflammatory response. In contrast it has been also reported that NP-, S-, RBD- specific IgA, IgG, and IgM are not associated with SARS-CoV-2 viral load, indicating that there is no obvious correlation between antibody response and viral antigen detection. In the present work the micro-Fourier-transform infrared reflectance spectroscopy (micro-FTIR) was employed to investigate blood serum samples of healthy and COVID-19-ill (mild or oligosymptomatic) individuals (82 healthcare workers volunteers in “Instituto de Infectologia Emilio Ribas”, São Paulo, Brazil). The molecular-level-sensitive, multiplexing quantitative and qualitative FTIR data probed on 1 µL of dried biofluid was compared to signal-to-cutoff index of chemiluminescent immunoassays CLIA and ELISA (IgG antibodies against SARS-CoV-2). Our main result indicated that 1702–1785 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {cm}^{-1}$$\end{document}cm-1 spectral window (carbonyl C=O vibration) is a spectral marker of the degree of IgG glycosylation, allowing to probe distinctive sub-populations of COVID-19 patients, depending on their degree of severity. The specificity was 87.5 % while the detection rate of true positive was 100%. The computed area under the receiver operating curve was equivalent to CLIA, ELISA and other ATR-FTIR methods (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>0.85$$\end{document}>0.85). In summary, overall discrimination of healthy and COVID-19 individuals and severity prediction as well could be potentially implemented using micro-FTIR reflectance spectroscopy on blood serum samples. Considering the minimal and reagent-free sample preparation procedures combined to fast (few minutes) outcome of FTIR we can state that this technology is suitable for fast screening of immune response of individuals with COVID-19. It would be an important tool in prospective studies, helping investigate the physiology of the asymptomatic, oligosymptomatic, or severe individuals and measure the extension of infection dissemination in patients.

Spectroscopy: a versatile sensing tool for cost-effective and rapid detection of novel coronavirus (COVID-19)

The deadly novel coronavirus SARS-CoV-2 is responsible for COVID-19, which was first recognized in Wuhan, China, in December 2019. Rapid identification at primary stage of the novel coronavirus, SARS-CoV-2, is important to restrict it and prevent the pandemic. Real-time RT-PCR assays are the best diagnostic tests presently available for SARS-CoV-2 detection, which are highly sensitive, even though expensive equipment and trained technicians are necessary. Furthermore, the method has moderately long time bound. This deadly viral infection can also be detected by applying various spectroscopic techniques as spectroscopy can provide fast, precise identification and monitoring, leading to the overall understanding of its mutation rates, which will further facilitate antiviral drug development as well as vaccine development. It is an innovative and non-invasive technique for combating the spread of novel coronavirus. This review article demonstrates the application of various spectroscopic techniques to detect COVID-19 rapidly. Different spectroscopy-based detection protocols and additional development of new, novel sensors and biosensors along with diagnostic kits had been described here stressing the status of sensitive diagnostic systems to handle with the COVID-19 outbreak.

GRAPHICAL ABSTRACT

Spectroscopy: A versatile sensing tool for cost-effective and rapid detection of novel Coronavirus (COVID-19).

Optical imaging spectroscopy for rapid, primary screening of SARS-CoV-2: a proof of concept

Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.

Pathophysiological Response to SARS-CoV-2 Infection Detected by Infrared Spectroscopy Enables Rapid and Robust Saliva Screening for COVID-19

Fourier transform infrared (FTIR) spectroscopy provides a (bio)chemical snapshot of the sample, and was recently used in proof-of-concept cohort studies for COVID-19 saliva screening. However, the biological basis of the proposed technology has not been established. To investigate underlying pathophysiology, we conducted controlled infection experiments on Vero E6 cells in vitro and K18-hACE2 mice in vivo. Potentially infectious culture supernatant or mouse oral lavage samples were treated with ethanol or 75% (v/v) Trizol for attenuated total reflectance (ATR)-FTIR spectroscopy and proteomics, or RT-PCR, respectively. Controlled infection with UV-inactivated SARS-CoV-2 elicited strong biochemical changes in culture supernatant/oral lavage despite a lack of viral replication, determined by RT-PCR or a cell culture infectious dose 50% assay. Nevertheless, SARS-CoV-2 infection induced additional FTIR signals over UV-inactivated SARS-CoV-2 infection in both cell and mouse models, which correspond to aggregated proteins and RNA. Proteomics of mouse oral lavage revealed increased secretion of kallikreins and immune modulatory proteins. Next, we collected saliva from a cohort of human participants (n = 104) and developed a predictive model for COVID-19 using partial least squares discriminant analysis. While high sensitivity of 93.48% was achieved through leave-one-out cross-validation, COVID-19 patients testing negative on follow-up on the day of saliva sampling using RT-PCR was poorly predicted in this model. Importantly, COVID-19 vaccination did not lead to the misclassification of COVID-19 negatives. Finally, meta-analysis revealed that SARS-CoV-2 induced increases in the amide II band in all arms of this study and in recently published cohort studies, indicative of altered β-sheet structures in secreted proteins. In conclusion, this study reveals a consistent secretory pathophysiological response to SARS-CoV-2, as well as a simple, robust method for COVID-19 saliva screening using ATR-FTIR.

Features of Pathobiology and Clinical Translation of Approved Treatments for Coronavirus Disease 2019

Background

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is currently the most important etiological agent of acute respiratory distress syndrome (ARDS) with millions of infections and deaths in the last 2 years worldwide. Several reasons and parameters are responsible for the difficult management of coronavirus disease-2019 (COVID-19) patients; the first is virus behavioral factors such as high transmission rate, and the different molecular and cellular mechanisms of pathogenesis remain a matter of controversy, which is another factor.

Summary

In the present review, we attempted to explain about features of SARS-COV-2, particularly focusing on the various aspects of pathogenesis and treatment strategies.

Key Messages

We note evidence for the understanding of the precise molecular and cellular mechanisms of SARS-CoV-2 pathogenesis, which can help design the appropriate drug or vaccine. Additionally, and importantly, we reported the updated issues associated with the history and development of treatment strategies such as, drugs, vaccines, and other medications that have been approved or under consideration in clinics and markets worldwide.