Biochemical composition and characteristics of salivary FTIR spectra: Correlation analysis

Explorations of the chemical constitution and aqueous solution status of caries-arresting silver(I)-diammine fluoride and silver(I)-fluoride products using high-resolution 19F NMR analysis. Spectroscopic and SEM investigations of their interactions with human saliva: evidence for the in vivo salivary-catalysed autoconstruction of Ag/AgCl-based nanoparticles (IV-SCAN)-part I

Introduction

Silver(I)-diammine fluoride (SDF) and silver(I)-fluoride (SF) complexes have been successfully employed for the arrest of dental caries for many years. However, to date there are very few studies available reporting on the molecular structural compositional and solution status of these agents [typically applied as highly-concentrated 38% (w/v) solutions]. Here, we explored the solution status and chemical constitution of commercially-available SDF and SF products, and secondly investigated the multicomponent interplay of these products with biomolecules present in intact human whole-mouth salivary supernatants (WMSSs) in vitro.

Methods

High-resolution 19F NMR analysis was employed to explore SDF and SF product solutions, and to determine WMSS fluoride (F-) concentrations, whereas ammonia (NH3) release form SDF was tracked by 1H NMR spectroscopy. SEM and thin-film FTIR-ATR analyses were employed to explore the atomic and molecular compositions of sequentially-generated AgCl deposits and chromophoric Ag/AgCl nanoparticles (CSNPs); the time-dependent generation of the latter was followed spectrophotometrically.

Results

19F NMR spectra of aqueous SF solutions contained a very broad F- signal (Δv1/2 70 Hz), demonstrating that much of its solvated F- content was rapidly exchanging with Ag(I) on the NMR timescale, but those of SDF had a much sharper resonance, similar to that of "free" F- (4 Hz). Moreover, further NMR results revealed that a popular SDF product contained high molar excesses of both F- and NH3. Treatment of WMSSs with SDF and SF generated an off-white precipitate, which slowly developed into CSNPs at 23°C; SEM demonstrated high contents of both silver and chloride in this material (ca.1:1 atomic content ratio). FTIR-ATR analysis found that the CSNPs formed contained a range of salivary biomolecules, which appear to encapsulate the Ag/AgCl core (significant thiocyanate contents were also found). In conclusion, NMR results acquired demonstrated that SF, but not SDF, product solutions feature rapidly-exchanging F - between its "free" and Ag(I)-bound forms, and that SDF contains large excesses of both F- and its NH3 ligands. Characterised AgCl deposits and CSNPs were sequentially produced from the interactions of these complexes with WMSS biomolecules.

Discussion

In view of their well-known microbicidal and cariostatic properties, the observed autobioconstruction of CSNPs involving salivary catalysis is of much therapeutic significance.

A Study of the Association between Primary Oral Pathologies (Dental Caries and Periodontal Diseases) Using Synchrotron Molecular FTIR Spectroscopy in View of the Patient's Personalized Clinical Picture (Demographics and Anamnesis)

In this exploratory study, we searched for associations between the two most common diseases of the oral cavity-dental caries and periodontal diseases-taking into account additional factors, such as personalized clinical pictures (the individual risk factors of the patient), based on the method of a multivariate data analysis of the molecular changes in the composition of human gingival crevicular fluid (GCF). For this purpose, a set of synchrotron Fourier-transform infrared spectroscopy (FTIR) spectra of gingival crevicular fluid samples from patients with different demographics, levels of dental caries development and periodontal diseases, and the presence/absence of concomitant chronic diseases were obtained and analyzed. Using a set of techniques (v-, F-, Chi-square tests; a principal component analysis (PCA); and the hierarchical clustering of principal components (HCPCs)) implemented in the R package FactoMineR allowed us to assess the relationship between the principal components (PCs) and characteristics of the respondents. By identifying the features (vibrational modes in the FTIR spectra) that contribute most to the differentiation of the spectral dataset, and by taking into account the interrelationships between the patients' characteristics, we were able to match specific biological markers (specific molecular groups) to the two factors of interest-two types of oral pathologies. The results obtained show that the observed changes in the quantitative and qualitative composition of the modes in the infrared (IR) spectra of the GCF samples from patients with different dental caries developments and periodontal diseases present confirm the difficulty of identifying patient-specific spectral information. At the same time, different periodontal pathologies are more closely associated with other characteristics of the patients than the level of their caries development. The multivariate analysis performed on the spectral dataset indicates the need to take into account not only the co-occurrence of oral diseases, but also some other factors. The lack of this consideration (typical in lots of studies in this area) may lead to misinterpretations and consequently to a loss of data when searching for biological markers of certain oral diseases.

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.

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.

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.

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 may predict quality of life in psoriasis as measured by Fourier transform infrared spectroscopy (FTIR) and chemometrics

BACKGROUND

Psoriasis is a chronic skin disease, with several comorbidities, such as psoriatic arthritis, inflammatory bowel disease, metabolic syndrome, and impaired quality of life and work activity. The Dermatology Life Quality Index (DLQI) is the most commonly used quality of life index in psoriatic patients, as it is a marker of severe disease. This study evaluated the association between salivary Fourier transform Infrared Spectroscopy (FTIR) metabolic fingerprints and severity of psoriasis as measured by DLQI, using chemometric algorithms.

MATERIALS AND METHODS

Saliva was collected from 56 (27 with DLQI ≤ 10 [GI]; 29 with DLQI > 10 [GII]) psoriatic patients diagnosed and assessed by DLQI for disease severity by a dermatologist and analyzed by the transflectance technique in mid-infrared. Hierarchic cluster analysis (HCA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and orthogonal partial least squares (OPLS) algorithms were used to associate salivary FTIR spectra with the respective DLQI scores.

RESULTS

Second derivative (2D) discriminated GI and GII at 2522 cm^-1 (p < 0.0001). HCA and PCA partially discriminated GI from GII at 4000-450 cm^-1 (p = 0.042 and 0.00821, respectively). Data processing with 1^st derivative (1D), 3 latent variables (LV) and 1 orthogonal signal correction (OSC) component at 2550-1801 cm^-1 generated an FTIR/OPLS-DA model with 100% accuracy to classify the severity of psoriasis, and an FTIR/OPLS model to quantify DLQI (range 0-28) with high performance: root mean square error of prediction (RMSEP) < 0.01 and coefficient of determination (R²) > 0.9999.

CONCLUSIONS

Salivary FTIR combined with chemometric algorithms such as OPLS-DA and OPLS can be used as a clinical tool to classify or predict the severity of psoriasis according to DLQI in patients with confirmed psoriasis.

Predicting Satiety from the Analysis of Human Saliva Using Mid-Infrared Spectroscopy Combined with Chemometrics

The aim of this study was to evaluate the ability of mid-infrared (MIR) spectroscopy combined with chemometrics to analyze unstimulated saliva as a method to predict satiety in healthy participants. This study also evaluated features in saliva that were related to individual perceptions of human–food interactions. The coefficient of determination (R²) and standard error in cross validation (SECV) for the prediction of satiety in all saliva samples were 0.62 and 225.7 satiety area under the curve (AUC), respectively. A correlation between saliva and satiety was found, however, the quantitative prediction of satiety using unstimulated saliva was not robust. Differences in the MIR spectra of saliva between low and high satiety groups, were observed in the following frequency ratios: 1542/2060 cm⁻¹ (total protein), 1637/3097 cm⁻¹ (α-amino acids), and 1637/616 (chlorides) cm⁻¹. In addition, good to excellent models were obtained for the prediction of satiety groups defined as low or high satiety participants (R² 0.92 and SECV 0.10), demonstrating that this method could be used to identify low or high satiety perception types and to select participants for appetite studies. Although quantitative PLS calibration models were not achieved, a qualitative model for the prediction of low and high satiety perception types was obtained using PLS-DA. Furthermore, this study showed that it might be possible to evaluate human/food interactions using MIR spectroscopy as a rapid and cost-effective tool.