ATR-FTIR spectrum analysis of saliva samples from COVID-19 positive patients

Infrared spectroscopy combined with chemometrics in transflectance mode: An alternative approach in the photodiagnosis of COVID-19 using saliva

The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has required the search for sensitive, rapid, specific, and lower-cost diagnostic methods to meet the high demand. The gold standard method of laboratory diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR). However, this method is costly and results can take time. In the literature, several studies have already described the potential of Fourier transform infrared spectroscopy (FTIR) as a tool in the biomedical field, including the diagnosis of viral infections, while being fast and inexpensive. In view of this, the objective of this study was to develop an FTIR model for the diagnosis of COVID-19. For this analysis, all private clients who had performed a face-to-face collection at the Univates Clinical Analysis Laboratory (LAC Univates) within a period of six months were invited to participate. Data from clients who agreed to participate in the study were collected, as well as nasopharyngeal secretions and a saliva sample. For the development of models, the RT-PCR result of nasopharyngeal secretions was used as a reference method. Absorptions with high discrimination (p < 0.001) between GI (28 patients, RT-PCR test positive to SARS-CoV-2 virus) and GII (173 patients who did not have the virus detected in the test) were most relevant at 3512 cm-1, 3385 cm-1 and 1321 cm-1 after 2nd derivative data transformation. To carry out the diagnostic modeling, chemometrics via FTIR and Discriminant Analysis of Orthogonal Partial Least Squares (OPLS-DA) by salivary transflectance mode with one latent variable and one orthogonal signal correction component were used. The model generated predictions with 100 % sensitivity, specificity and accuracy. With the proposed model, in a single application of an individual's saliva in the FTIR equipment, results related to the detection of SARS-CoV-2 can be obtained in a few minutes of spectral evaluation.

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

Two-trace two-dimensional correlation spectra (2T2D-COS) analysis using FTIR spectra to monitor the immune response by COVID-19

There is a growing trend in using saliva for SARS-CoV-2 detection with reasonable accuracy. We have studied the responses of IgA, IgG, and IgM in human saliva by directly comparing disease with control analyzing two-trace two-dimensional correlation spectra (2T2D-COS) employing Fourier transform infrared (FTIR) spectra. It explores the molecular-level variation between control and COVID-19 saliva samples. The advantage of 2T2D spectra is that it helps in discriminating remarkably subtle features between two simple pairs of spectra. It gives spectral information from highly overlapped bands associated with different systems. The clinical findings from 2T2D show the decrease of IgG and IgM salivary antibodies in the 50, 60, 65, and 75-years COVID-19 samples. Among the various COVID-19 populations studied the female 30-years group reveals defense mechanisms exhibited by IgM and IgA. Lipids and fatty acids decrease, resulting in lipid oxidation due to the SARS-CoV-2 in the samples studied. Study shows salivary thiocyanate plays defense against SARS-CoV-2 in the male population in 25 and 35 age groups. The receiver operation characteristics statistical method shows a sensitivity of 98% and a specificity of 94% for the samples studied. The measure of accuracy computed as F score and G score has a high value, supporting our study's validation. Thus, 2T2D-COS analysis can potentially monitor the progression of immunoglobulin's response function to COVID-19 with reasonable accuracy, which could help diagnose clinical trials. KEY MESSAGES: The molecular profile of salivary antibodies is well resolved and identified from 2T2D-COS FTIR spectra. The IgG antibody plays a significant role in the defense mechanism against SARS-CoV-2 in 25-40 years. 2T2D-COS reveals the absence of salivary thiocyanate in the 40-75 years COVID-19 population. The receiver operation characteristic (ROC) analysis validates our study with high sensitivity and specificity.

Exploration of macromolecular phenotype of human skeletal muscle in diabetes using infrared spectroscopy

Introduction

The global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents.

Materials and methods

In this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach.

Results

A thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles.

Conclusion

We have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.

A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders.

Dynamic response antibodies SARS-CoV-2 human saliva studied using two-dimensional correlation (2DCOS) infrared spectral analysis coupled with receiver operation characteristics analysis

COVID-19 has affected the entire world due to the rapid spread of SARS-CoV-2, mainly through airborne particles from saliva, which, being easily obtained, help monitor the progression of the disease. Fourier transform infrared (FTIR) spectra combined with chemometric analysis could increase the diagnostic efficiency of the disease. However, two-dimensional correlation spectroscopy (2DCOS) is superior to conventional spectra as it helps to resolve the minute overlapped peaks. In this work, we aimed to use 2DCOS and receiver operating characteristic (ROC) analyses to compare the immune response in saliva associated with COVID-19, which could be important in biomedical diagnosis. FTIR spectra of human saliva samples from male (575) and female (366) patients ranging from 20 to 82 ± 2 years of age were used for the study. Age groups were segregated as G1 (25-40 ± 2 years), G2 (45-60 ± 2 years), and G3 (65-80 ± 2 years). The results of the 2DCOS analysis showed biomolecular changes in response to SARS-CoV-2. 2DCOS analyses of the male G1 + (1579,1644) and -(1531,1598) crossover peaks evidenced changes such as amide I > IgG. Female G1 crossover peaks -(1504,1645), (1504,1545) and -(1391,1645) resulted in amide I > IgG > IgM. The asynchronous spectra in 1300-900 cm-1 of the G2 male group showed that IgM is more important in diagnosing infections than IgA. Female G2 asynchronous spectra -(1027,1242) and + (1068,1176) showed that IgA > IgM is produced against SARS-CoV-2. The G3 male group evidenced antibody changes in IgG > IgM. The absence of IgM in the female G3 population diagnoses a specifically targeted immunoglobulin associated with sex. Moreover, ROC analysis showed sensitivity (85-89 % men; 81-88 % women) and specificity (90-93 % men; 78-92 % women) for the samples studied. The general classification performance (F1 score) of the studied samples is high for the male (88-91 %) and female (80-90 %) populations. This high PPV (positive predictive value) and NPV (negative predictive value) verify our segregation of COVID-19 positive and negative sample groups. Therefore, 2DCOS with ROC analysis using FTIR spectra have the potential for a non-invasive approach to monitoring COVID-19.

Unlocking the Diagnostic Potential of Saliva: A Comprehensive Review of Infrared Spectroscopy and Its Applications in Salivary Analysis

Infrared (IR) spectroscopy is a noninvasive and rapid analytical technique that provides information on the chemical composition, structure, and conformation of biomolecules in saliva. This technique has been widely used to analyze salivary biomolecules, owing to its label-free advantages. Saliva contains a complex mixture of biomolecules including water, electrolytes, lipids, carbohydrates, proteins, and nucleic acids which are potential biomarkers for several diseases. IR spectroscopy has shown great promise for the diagnosis and monitoring of diseases such as dental caries, periodontitis, infectious diseases, cancer, diabetes mellitus, and chronic kidney disease, as well as for drug monitoring. Recent advancements in IR spectroscopy, such as Fourier-transform infrared (FTIR) spectroscopy and attenuated total reflectance (ATR) spectroscopy, have further enhanced its utility in salivary analysis. FTIR spectroscopy enables the collection of a complete IR spectrum of the sample, whereas ATR spectroscopy enables the analysis of samples in their native form, without the need for sample preparation. With the development of standardized protocols for sample collection and analysis and further advancements in IR spectroscopy, the potential for salivary diagnostics using IR spectroscopy is vast.

Canine visceral leishmaniasis diagnosis by UV spectroscopy of blood serum and machine learning algorithms

Visceral leishmaniasis (VL) is a zoonotic disease caused by the protozoan Leishmania infantum, and dogs are considered the main urban hosts for future disease transmission. The first and most effective control against the spread of disease relies on identifying infected animals, followed by their treatment or sacrifice, to reduce the protozoan reservoirs. Despite the availability of various diagnostic tests for VL in dogs the development of a quick and accurate diagnosis is essential from a public health and ethical point of view. Here we analyze the use of UV-Vis spectroscopy as an alternative diagnostic method for VL diagnosis by using the antigen-antibody interaction in canine blood serum and machine learning algorithms. The main UV spectra in the 220 to 280 nm range exhibit nine electronic absorption bands, but no significative difference could be identified between the positive and negative group spectra. Finally, UV pre-proceed spectra by SNV (standard normal variate) were submitted to principal component analysis followed by Linear SVM algorithm, the prediction model was tested in a leave-one-out cross-validation and external validation test reaching an overall accuracy of 75%.

Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases

Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.

ZnO-Based Electrochemical Immunosensor to Assess Vaccine-Induced Antibody-Mediated Immunity against Wild-Type and Gamma SARS-CoV-2 Strains

The evaluation of serological responses to COVID-19 is crucial for population-level surveillance, developing new vaccines, and evaluating the efficacy of different immunization programs. Research and development of point-of-care test technologies remain essential to improving immunity assessment, especially for SARS-CoV-2 variants that partially evade vaccine-induced immune responses. In this work, an impedimetric biosensor based on the immobilization of the recombinant trimeric wild-type spike protein (S protein) on zinc oxide nanorods (ZnONRs) was employed for serological evaluation. We successfully assessed its applicability using serum samples from spike-based COVID-19 vaccines: ChAdOx1-S (Oxford-AstraZeneca) and BNT162b2 (Pfizer-BioNTech). Overall, the ZnONRs/ spike-modified electrode displayed accurate results for both vaccines, showing excellent potential as a tool for assessing and monitoring seroprevalence in the population. A refined outcome of this technology was achieved when the ZnO immunosensor was functionalized with the S protein from the P.1 linage (Gamma variant). Serological responses against samples from vaccinated individuals were acquired with excellent performance. Following studies based on traditional serological tests, the ZnONRs/spike immunosensor data reveal that ChAdOx1-S vaccinated individuals present significantly less antibody-mediated immunity against the Gamma variant than the BNT162b2 vaccine, highlighting the great potential of this point-of-care technology for evaluating vaccine-induced humoral immunity against different SARS-CoV-2 strains.

The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy

Saliva is an easily sampled matrix containing a variety of biochemical information, which can be correlated with the individual health status. The fast, straightforward analysis of saliva by vibrational (ATR-FTIR and Raman) spectroscopy is a good premise for large-scale preclinical studies to aid translation into clinics. In this work, the effects of saliva collection (spitting/swab) and processing (two different deproteinization procedures) were explored by principal component analysis (PCA) of ATR-FTIR and Raman data and by investigating the effects on the main saliva metabolites by reversed-phase chromatography (RPC-HPLC-DAD). Our results show that, depending on the bioanalytical information needed, special care must be taken when saliva is collected with swabs because the polymeric material significantly interacts with some saliva components. Moreover, the analysis of saliva before and after deproteinization by FTIR and Raman spectroscopy allows to obtain complementary biological information.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

Roles of Bothrops jararacussu toxins I and II: Antiviral findings against Zika virus

Zika virus is the etiologic agent of Zika fever, and has been previously associated with cases of microcephaly, drawing the attention of the health authorities worldwide. However, no vaccine or antiviral are currently available. Phospholipases A₂ (PLA₂) isolated from snake venoms have demonstrated antiviral activity against several viruses. Here we demonstrated the anti-ZIKV activity of bothropstoxins-I and II (BthTX-I and II) isolated from Bothrops jararacussu venom. Vero E6 cells were infected with ZIKVPE243 in the presence of compounds for 72 h, when virus titers were evaluated. BthTX-I and II presented strong dose-dependent inhibition of ZIKV, with a SI of 149.1 and 1.44 × 10⁵, respectively. These toxins mainly inhibited the early stages of the replicative cycle, such as during the entry of ZIKV into host cells, as shown by the potent virucidal effect, suggesting the action of these toxins on the virus particles. Moreover, BthTX-I and II presented significant activity towards post-entry stages of the ZIKV replicative cycle. Molecular docking analyses showed that BthTX-I and II potentially interact with DII and DIII domains from ZIKV Envelope protein. Our findings show that these PLA₂s could be used as useful templates for the development of future antiviral candidate drugs against Zika fever.

Discrimination of dyslipidemia types with ATR-FTIR spectroscopy and chemometrics associated with multivariate analysis of the lipid profile, anthropometric, and pro-inflammatory biomarkers

BACKGROUND

Obesity, dyslipidemia, and low-grade inflammatory state form a triad of self-sustaining metabolic dysfunction. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is a simple, rapid, and non-destructive technique that generates spectral fingerprints of biomolecules that can be correlated with metabolic changes. We verified the efficiency of ATR-FTIR spectroscopy in blood plasma (n = 74) to discriminate the types of dyslipidemias and suggest metabolic inflammatory changes.

METHODS

Principal Component Analysis (PCA) was performed on the biochemical and anthropometric data to verify whether the dyslipidemia types share a similar biochemical profile plausible of discrimination in chemometric modeling. To discriminate the types of dyslipidemias based on spectral data, Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) was used and validated with leave-one-out cross-validation.

RESULTS

Although no significant difference was obtained between the types of dyslipidemia and normal subjects by CRP, leptin, and cfDNA, there was a significant difference between normal subjects vs combined hyperlipidemia (CH) + hypercholesterolemia (HCL) + hypertriglyceridemia (HTG) (p < 0.05) by the 1245 cm^-1 peak [ν_as(PO₂^-)] (possible indication of chronic inflammation by increased cfDNA). The area under the curve of the region between 1770 and 1720 cm^-1 was significantly increased for CH in relation to other dyslipidemias and normal subjects. Furthermore, there were significant differences for the main representative peaks of lipids, proteins, carbohydrates, and nucleic acids between the types of dyslipidemias and between the types of dyslipidemias and normal subjects. The OPLS-DA model achieved 100 % accuracy with 1 latent variable and Standard Error of Cross-Validation (SECV) < 0.004 for all types of dyslipidemia  and the control group.

CONCLUSIONS

Our results suggest that ATR-FTIR spectroscopy associated with chemometric modeling is a plausible applicant for screening the types of dyslipidemias. However, more extensive studies should be conducted to verify the real applicability in clinical analysis laboratories or medical clinics.

Advances in Electrochemical Biosensors Based on Nanomaterials for Protein Biomarker Detection in Saliva

The focus on precise medicine enhances the need for timely diagnosis and frequent monitoring of chronic diseases. Moreover, the recent pandemic of severe acute respiratory syndrome coronavirus 2 poses a great demand for rapid detection and surveillance of viral infections. The detection of protein biomarkers and antigens in the saliva allows rapid identification of diseases or disease changes in scenarios where and when the test response at the point of care is mandated. While traditional methods of protein testing fail to provide the desired fast results, electrochemical biosensors based on nanomaterials hold perfect characteristics for the detection of biomarkers in point-of-care settings. The recent advances in electrochemical sensors for salivary protein detection are critically reviewed in this work, with emphasis on the role of nanomaterials to boost the biosensor analytical performance and increase the reliability of the test in human saliva samples. Furthermore, this work identifies the critical factors for further modernization of the nanomaterial-based electrochemical sensors, envisaging the development and implementation of next-generation sample-in-answer-out systems.

ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm^-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.

Electrochemical Immunosensors Based on Zinc Oxide Nanorods for Detection of Antibodies Against SARS-CoV-2 Spike Protein in Convalescent and Vaccinated Individuals

Even after over 2 years of the COVID-19 pandemic, research on rapid, inexpensive, and accurate tests remains essential for controlling and avoiding the global spread of SARS-CoV-2 across the planet during a potential reappearance in future global waves or regional outbreaks. Assessment of serological responses for COVID-19 can be beneficial for population-level surveillance purposes, supporting the development of novel vaccines and evaluating the efficacy of different immunization programs. This can be especially relevant for broadly used inactivated whole virus vaccines, such as CoronaVac, which produced lower titers of neutralizing antibodies. and showed lower efficacy for specific groups such as the elderly and immunocompromised. We developed an impedimetric biosensor based on the immobilization of SARS-CoV-2 recombinant trimeric spike protein (S protein) on zinc oxide nanorod (ZnONR)-modified fluorine-doped tin oxide substrates for COVID-19 serology testing. Due to electrostatic interactions, the negatively charged S protein was immobilized via physical adsorption. The electrochemical response of the immunosensor was measured at each modification step and characterized by scanning electron microscopy and electrochemical techniques. We successfully evaluated the applicability of the modified ZnONR electrodes using serum samples from COVID-19 convalescent individuals, CoronaVac-vaccinated with or without positive results for SARS-CoV-2 infection, and pre-pandemic samples from healthy volunteers as controls. ELISA for IgG anti-SARS-CoV-2 spike protein was performed for comparison, and ELISA for IgG anti-RBDs of seasonal coronavirus (HCoVs) was used to test the specificity of immunosensor detection. No cross-reactivity with HCoVs was detected using the ZnONR immunosensor, and more interestingly, the sensor presented higher sensitivity when compared to negative ELISA results. The results demonstrate that the ZnONRs/spike-modified electrode displayed sensitive results for convalescents and vaccinated samples and shows excellent potential as a tool for the population's assessment and monitoring of seroconversion and seroprevalence.

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.

Comparison of the Immune Response in Vaccinated People Positive and Negative to SARS-CoV-2 Employing FTIR Spectroscopy

Various immunopathological events characterize the systemic acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Moreover, it has been reported that coronavirus disease 2019 (COVID-19) vaccination and infection by SARS-CoV-2 induce humoral immunity mediated by B-cell-derived antibodies and cellular immunity mediated by T cells and memory B cells. Immunoglobulins, cytokines, and chemokines play an important role in shaping immunity in response to infection and vaccination. Furthermore, different vaccines have been developed to prevent COVID-19. Therefore, this research aimed to analyze and compare Fourier-transform infrared (FTIR) spectra of vaccinated people with a positive (V-COVID-19 group) or negative (V-Healthy group) real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) test, evaluating the immunoglobulin and cytokine content as an immunological response through FTIR spectroscopy. Most individuals that integrated the V-Healthy group (88.1%) were asymptomatic; on the contrary, only 28% of the V-COVID-19 group was asymptomatic. Likewise, 68% of the V-COVID-19 group had at least one coexisting illness. Regarding the immunological response analyzed through FTIR spectroscopy, the V-COVID-19 group showed a greater immunoglobulins G, A, and M (IgG, IgA, and IgM) content, as well as the analyzed cytokines interferon-gamma (IFN-γ), tumor necrosis factor-alpha (TNF-ɑ), and interleukins 1β, 6, and 10 (IL-1β, IL-6, and IL-10). Therefore, we can state that it was possible to detect biochemical changes through FTIR spectroscopy associated with COVID-19 immune response in vaccinated people.

An Emerging Strategy for Muscle Evanescent Trauma Discrimination by Spectroscopy and Chemometrics

Trauma is one of the most common conditions in the biomedical field. It is important to identify it quickly and accurately. However, when evanescent trauma occurs, it presents a great challenge to professionals. There are few reports on the establishment of a rapid and accurate trauma identification and prediction model. In this study, Fourier transform infrared spectroscopy (FTIR) and microscopic spectroscopy (micro-IR) combined with chemometrics were used to establish prediction models for the rapid identification of muscle trauma in humans and rats. The results of the average spectrum, principal component analysis (PCA) and loading maps showed that the differences between the rat muscle trauma group and the rat control group were mainly related to biological macromolecules, such as proteins, nucleic acids and carbohydrates. The differences between the human muscle trauma group and the human control group were mainly related to proteins, polysaccharides, phospholipids and phosphates. Then, a partial least squares discriminant analysis (PLS-DA) was used to evaluate the classification ability of the training and test datasets. The classification accuracies were 99.10% and 93.69%, respectively. Moreover, a trauma classification and recognition model of human muscle tissue was constructed, and a good classification effect was obtained. The classification accuracies were 99.52% and 91.95%. In conclusion, spectroscopy and stoichiometry have the advantages of being rapid, accurate and objective and of having high resolution and a strong recognition ability, and they are emerging strategies for the identification of evanescent trauma. In addition, the combination of spectroscopy and stoichiometry has great potential in the application of medicine and criminal law under practical conditions.

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%.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect’s allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

DETERMINATION OF COVID-19 VIRUSES IN SALIVA USING FOURIER TRANSFORM INFRARED SPECTROSCOPY

The rapid spread of severe syndrome coronavirus 2 (SARS-CoV-2) has led to the coronavirus disease 2019 (COVID-19) worldwide pandemic. Scientists and researchers all over the world studied different methods in order to accelerate the testing results. In this review, we present some of the most important papers related to the determination of COVID – 19 in saliva using the Fourier Transform Infrared Spectroscopy technique.

Saliva as a sample type for SARS-CoV-2 detection: implementation successes and opportunities around the globe

INTRODUCTION

Symptomatic testing and asymptomatic screening for SARS-CoV-2 continue to be essential tools for mitigating virus transmission. Though COVID-19 diagnostics initially defaulted to oropharyngeal or nasopharyngeal sampling, the worldwide urgency to expand testing efforts spurred innovative approaches and increased diversity of detection methods. Strengthening innovation and facilitating widespread testing remains critical for global health, especially as additional variants emerge and other mitigation strategies are recalibrated.

AREAS COVERED

A growing body of evidence reflects the need to expand testing efforts and further investigate the efficiency, sensitivity, and acceptability of saliva samples for SARS-CoV-2 detection. Countries have made pandemic response decisions based on resources, costs, procedures, and regional acceptability - the adoption and integration of saliva-based testing among them. Saliva has demonstrated high sensitivity and specificity while being less invasive relative to nasopharyngeal swabs, securing saliva's position as a more acceptable sample type.

EXPERT OPINION

Despite the accessibility and utility of saliva sampling, global implementation remains low compared to swab-based approaches. In some cases, countries have validated saliva-based methods but face challenges with testing implementation or expansion. Here, we review the localities that have demonstrated success with saliva-based SARS-CoV-2 testing approaches and can serve as models for transforming concepts into globally-implemented best practices.

Towards the direct detection of viral materials at the surface of protective face masks via infrared spectroscopy

The ongoing COVID-19 pandemic represents a considerable risk for the general public and especially for health care workers. To avoid an overloading of the health care system and to control transmission chains, the development of rapid and cost-effective techniques allowing for the reliable diagnosis of individuals with acute respiratory infections are crucial. Uniquely, the present study focuses on the development of a direct face mask sampling approach, as worn (i.e., used) disposable face masks contain exogenous environmental constituents, as well as endogenously exhaled breath aerosols. Optical techniques—and specifically infrared (IR) molecular spectroscopic techniques—are promising tools for direct virus detection at the surface of such masks. In the present study, a rapid and non-destructive approach for monitoring exposure scenarios via medical face masks using attenuated total reflection infrared spectroscopy is presented. Complementarily, IR external reflection spectroscopy was evaluated in comparison for rapid mask analysis. The utility of a face mask-based sampling approach was demonstrated by differentiating water, proteins, and virus-like particles sampled onto the mask. Data analysis using multivariate statistical algorithms enabled unambiguously classifying spectral signatures of individual components and biospecies. This approach has the potential to be extended towards the rapid detection of SARS-CoV-2—as shown herein for the example of virus-like particles which are morphologically equivalent to authentic virus—without any additional sample preparation or elaborate testing equipment at laboratory facilities. Therefore, this strategy may be implemented as a routine large-scale monitoring routine, e.g., at health care institutions, nursing homes, etc. ensuring the health and safety of medical personnel.

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.

Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Discriminates the Elderly with a Low and High Percentage of Pathogenic CD4+ T Cells

In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700–1500 cm⁻¹ region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700–1500 cm⁻¹ region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800–900 cm⁻¹. Exosome spectra in the 1700–1500 and combined 3000–2800 and 1800–900 cm⁻¹ regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800–900 cm⁻¹ region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

Salivary Ferritin Changes in Patients with COVID-19

High ferritin serum levels can be found in patients with macrophage activation syndrome, and increased serum ferritin due to cytokine storm have been reported in severe COVID-19 patients. Saliva is being increasingly used in COVID-19 tests as a diagnostic sample for virus detection and quantification. This study aimed to evaluate the possible changes in ferritin in saliva in COVID-19 patients. In addition, the effects of different inactivation SARS-CoV-2 treatments in ferritin measurements in saliva, the correlation between ferritin in saliva and serum, and the possible effects of correction of ferritin values by total protein were assessed. Ferritin was measured in saliva from healthy (n = 30) and COVID-19 (n = 65) patients with severe, (n = 18) or mild (n = 47) disease, depending on the need for nasal flow oxygen or assisted respiration. Ferritin was also measured in paired serum and saliva samples (n = 32) from healthy and COVID-19 patients. The evaluated inactivation protocols did not affect the assay's results except the addition of 0.5% SDS. Significantly higher ferritin was found in the saliva of COVID-19 patients (median; 25-75th percentile) (27.75; 9.77-52.2 µg/L), compared with healthy controls (4.21; 2.6-8.08 µg/L). Individuals with severe COVID-19 showed higher ferritin values in saliva (48.7; 18.7-53.9) than mild ones (15.5; 5.28-41.3 µg/L). Significant correlation (r = 0.425; p < 0.001) was found between serum and saliva in ferritin. Ferritin levels were higher in COVID-19 patients in serum and saliva, and the highest values were found in those patients presenting severe symptomatology. In conclusion, ferritin in saliva has the potential to be a biomarker to evaluate severity in patients with COVID-19.