Proteomic profiling of serum and saliva in rheumatoid arthritis: A pilot Q-TOF study

Abstract

Background:

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily targets joints but also affects multiple organ systems. Current RA diagnostics rely on serum biomarkers like rheumatoid factor and anti-citrullinated protein antibodies, which indicate systemic inflammation but often lack sensitivity in the early stages. Recent advances in proteomics provide opportunities for discovering new biomarkers that could enhance diagnostic accuracy. Saliva, a non-invasive biofluid, has emerged as a potential alternative that may reflect both systemic and localized inflammation.

Objective:

This pilot study assesses whether saliva can complement serum in diagnosing and monitoring RA. Using quadrupole time-of-flight mass spectrometry, we compare serum and saliva proteomic profiles to identify RA-related biomarkers.

Materials and Methods:

Serum and saliva samples from five RA patients and five healthy controls were analyzed using quadrupole time-of-flight mass spectrometry. Multivariate analyses, including partial least squares discriminant analysis and receiver operating characteristic analysis, were used to evaluate the diagnostic potential of saliva-based biomarkers relative to those in serum.

Results:

RA-related biomarkers, including citrullinated vimentin and tyrosine, were identified in both serum and saliva. Partial least squares discriminant analysis demonstrated distinct separation among sample groups, while receiver operating characteristic analysis indicated high diagnostic accuracy, supporting saliva’s utility as a non-invasive complement to serum.

Conclusion:

Saliva shows promise as a complementary biofluid for RA diagnostics. It allows non-invasive sampling and the potential for frequent monitoring. While serum remains the standard for diagnosis, saliva could enhance disease monitoring in clinical practice.

INTRODUCTION

Rheumatoid arthritis (RA) is an autoimmune disorder that primarily affects the joints but can extend to multiple organ systems, including the cardiovascular, respiratory, and salivary glands.[1,2] The current diagnostic approach heavily relies on serum biomarkers like rheumatoid factor, anti-citrullinated protein antibodies, and C-reactive protein.[3] While these markers have contributed significantly to diagnosing and monitoring RA, they often fall short in sensitivity and specificity, particularly in early disease stages and in predicting long-term outcomes.

Serum biomarkers predominantly reflect systemic inflammation but may fail to capture the more localized inflammatory processes in tissues such as the synovium or salivary glands.[4,5] Advances in proteomic technologies have opened new pathways for identifying such biomarkers by offering deeper insights into the protein expression profiles that characterize different stages and severities of RA.[6] Proteomics, the large-scale study of proteins, provides the potential to unravel the complexity of RA at a molecular level.[7] Unlike genomics, which provides information on the genetic predisposition to disease, proteomics reveals the dynamic protein changes that occur in response to disease activity, providing a real-time snapshot of the biological processes at play.[8]

Traditionally, proteomic studies in RA have focused on serum samples due to their ability to reflect systemic inflammation.[9] However, saliva has emerged as an attractive alternative for biomarker discovery because it is easily accessible, non-invasive, and mirrors both local and systemic physiological changes. Salivary biomarkers, including cytokines such as interleukin-6 and tumor necrosis factor-alpha, as well as matrix metalloproteinases, have demonstrated potential in reflecting RA disease activity.[10]

Although both serum and saliva contain valuable proteins that may serve as biomarkers for RA, few studies have directly compared the proteomic content of these two biofluids.[11] This gap is crucial, as it limits our ability to determine whether salivary biomarkers can provide additional diagnostic or prognostic information beyond what is available through serum analysis. Furthermore, salivary biomarkers could offer unique advantages in capturing localized inflammation, particularly in RA patients with secondary Sjögren’s syndrome, where the salivary glands are directly affected by inflammation.[12,13]

Quadrupole time-of-flight (Q-TOF) mass spectrometry is a powerful tool in proteomics, known for its high sensitivity and accuracy in both qualitative and quantitative analysis of complex protein mixtures.[14,15] This sensitivity helps identify early-stage biomarkers in RA. Additionally, Q-TOF’s high resolution allows precise detection of post-translational modifications, offering deeper insights into RA pathogenesis.[16]

This study aims to evaluate the proteomic profiles of serum and saliva in RA patients using Q-TOF mass spectrometry.

MATERIALS AND METHODS

Instrumentation

For the proteomic analysis of serum and saliva samples from RA patients, a Waters ACQUITY H-CLASS PLUS UPLC system coupled with a Xevo G2-XS Q-TOF mass spectrometer was utilized [Figure 1]. This advanced liquid chromatography–quadrupole time-of-flight high-resolution mass spectrometry system was selected for its high sensitivity, selectivity, and mass accuracy. These features make it ideal for detecting low-abundance biomarkers such as citrullinated proteins and relevant peptides in both serum and saliva.

Figure 1.

Order of processing for Q-Toff analysis

UPLC system setup

The UPLC system was configured with several key components to optimize performance for proteomic analysis. The Binary Solvent Manager ensured precise gradient formation, facilitating the optimal separation of peptides. The Sample Manager enabled automated injections of both serum and saliva, improving accuracy and minimizing handling errors. Additionally, the Column Manager maintained consistent temperature control, ensuring reproducible retention times across runs. To complement mass spectrometric analysis, a PDA detector was incorporated, providing UV-vis detection for additional confirmation of the analytes, and enhancing the overall reliability of the data.

Mass spectrometry setup

The Xevo G2-XS Q-TOF mass spectrometer was configured to ensure optimal performance in the analysis of complex biological samples. StepWave Ion Optics enhanced ion transmission, improving the detection of proteins and peptides, which is particularly important when analyzing intricate samples like serum and saliva. The XS collision cell was utilized to provide optimized fragmentation during MS/MS analysis, a critical step for accurately identifying and quantifying peptides. Additionally, QuanTof Technology ensured high mass accuracy, which is essential for distinguishing between closely related peptide masses and detecting low-abundance biomarkers, such as those relevant to RA.

Sample collection and preparation

Serum samples were collected via standard venipuncture and stored at –80°C until analysis, while saliva samples were collected non-invasively using sterile containers and immediately frozen at –80°C to preserve protein integrity. For preparation, both serum and saliva samples were centrifuged to remove particulate matter and subsequently diluted in an appropriate solvent, such as water with 0.1% formic acid or acetonitrile, to ensure compatibility with UPLC-MS.

Chromatographic conditions

Peptide and protein separation were achieved using a C18 column under the following chromatographic conditions: Mobile phase A consisted of water with 0.1% formic acid, and mobile phase B consisted of acetonitrile with 0.1% formic acid. The gradient elution commenced at 95% A and 5% B, gradually increasing to 60% B over 30 minutes, with a flow rate of 0.3 mL/min and an injection volume of 5 μL for both serum and saliva samples. The column temperature was maintained at 40°C to ensure consistent retention times.

Mass spectrometry parameters

The mass spectrometry utilized electrospray ionization in positive mode, which is particularly suitable for peptides and proteins. Ionization mode switching was employed when necessary. The TOF mass range was set to m/z 50–2,000 to optimize the detection of small to medium-sized proteins and fragments relevant to RA, such as citrullinated vimentin. Collision energy was optimized within a range of 20–40 eV for efficient fragmentation in MS/MS, and resolving power was maintained at >40,000 FWHM to allow precise detection of closely related peptides. The acquisition rate was set at 30 spectra per second to ensure detailed mass spectra with accurate mass assignments, while data acquisition was conducted in MSE mode for simultaneous collection of precursor and fragment ion data.

Data acquisition and processing

Data acquisition was managed using MassLynx V 4.2 software, which controlled both the UPLC and mass spectrometer. The MSE mode facilitated the simultaneous collection of precursor and fragment ion spectra, providing a comprehensive profile of the peptides present in the serum and saliva samples. For data analysis, MetaboLynx XS 2.0 software was utilized, focusing on the identification of specific biomarkers associated with RA, such as citrullinated peptides and fragments of vimentin.

Sample analysis and data interpretation

The qualitative analysis involved generating MS and MS/MS spectra to identify key biomarkers related to RA, including citrullinated vimentin and collagen fragments. Accurate mass determination and fragment ion analysis were performed to confirm the identity of these biomarkers. For quantitative analysis, peptide abundance was measured using extracted ion chromatograms, concentrating on the intensity of specific m/z values, including m/z 407.2495 for citrullinated arginine-containing peptides in serum and m/z 575.0722 for citrullinated vimentin in saliva.

Method validation

The methodology was validated for sensitivity by establishing the detection limit by analyzing serial dilutions of standard peptides, ensuring the instrument’s capability to detect low-abundance biomarkers like citrullinated peptides. Reproducibility was assessed by injecting replicate samples and measuring the variation in peak areas to guarantee consistent detection and quantification across multiple runs.

Partial least squares discriminant analysis

To explore the distinct separation between serum, saliva, and control groups based on biomarker profiles, we employed partial least squares discriminant analysis (PLS-DA). This supervised multivariate method was chosen to maximize the separation between predefined groups while considering the contribution of multiple biomarkers simultaneously.

Data preprocessing

Before PLS-DA, raw mass spectrometry data were subjected to peak alignment, normalization, and log transformation to reduce systematic biases and improve comparability across samples. Peak alignment was performed using XCMS software, ensuring that retention time shifts between samples were corrected. The dataset was then normalized to total ion counts to account for variation in sample concentration. Finally, log transformation was applied to compress the dynamic range of biomarker intensities and reduce the influence of outliers.

PLS-DA model development

PLS-DA was carried out using SIMCA software (v. 15, Sartorius Stedim Data Analytics). The biomarker intensities for each sample (serum, saliva, and control) were used as predictor variables, while the sample group (serum, saliva, control) was the response variable. The algorithm identified latent variables (PLS components) that explain the maximum variance between the groups. Cross-validation (7-fold) was applied to optimize the number of components and prevent model overfitting.

Plotting and interpretation

PLS-DA plots were generated to visualize the separation between the three groups based on their biomarker profiles. The serum group was represented by blue circles, the saliva group by orange squares, and the control group by green triangles. The samples were plotted along the first two PLS components, which explained the majority of the variance. Ellipses were drawn around each group to represent the 95% confidence regions, providing a visual cue of within-group variability. Smaller ellipses, such as those for serum, indicated lower variability and tighter clustering, while larger ellipses, like those for saliva, reflected greater sample variability.

Model validation

The discriminative power of the PLS-DA model was validated by calculating the R²Y and Q² statistics, which measure the explained variance and predictive ability of the model, respectively. A permutation test (n = 1000) was conducted to ensure that the separation between groups was not due to random chance. Additionally, the variable importance in projection scores were calculated for each biomarker, identifying those with the greatest contribution to group separation.

Statistical software

The statistical analysis and PLS-DA model creation were performed using R software (version 4.0.5) with the “mixOmics” package for multivariate analysis. Data visualization was handled using ggplot2 for high-quality plots of the PLS components and ellipses. Receiver operating characteristic (ROC) was generated to validate the identified markers.

Statistical analysis

Statistical analysis of the pilot study results from serum and saliva samples was conducted to compare the proteomic profiles, with ANOVA applied to assess the significance of differences in biomarker levels between 5 RA patients and 5 healthy controls. This analysis provided insights into the diagnostic potential of saliva as a non-invasive alternative for assessing RA.

RESULTS

PLS-DA

The PLS-DA plot demonstrates a clear separation between the three groups—serum, saliva, and control—based on their biomarker profiles [Figure 2]. The serum group, represented by blue circles, clusters along the positive side of PLS component 1, indicating low variability and consistent biomarker levels. In contrast, the control group, shown by green triangles, is positioned along the negative side of component 1, with slightly greater variability. The saliva group, depicted by orange squares, is primarily distinguished along PLS component 2, showing more spread and variability within its samples. The ellipses represent confidence regions for each group, with smaller ellipses, such as for serum, reflecting tighter clustering and higher measurement consistency. Minimal overlap between the groups suggests that the biomarkers are effective at distinguishing between these sample types. The distinct separation along the PLS components highlights the biomarkers’ discriminative power, making them potentially valuable for diagnostic purposes or further research.

Figure 2.

PLS-DA plot showing separation among three distinct groups

Identified biomarkers in serum and saliva

The analysis using Q-TOF mass spectrometry identified several key biomarkers in both serum and saliva samples of RA patients. These biomarkers are significant in understanding the disease’s pathophysiology, particularly in immune regulation, collagen degradation, and autoantigen formation. Table 1 shows the identified biomarkers in serum and saliva samples of RA patients. The biomarkers were identified from the MQSS (Max Quant) 2019 library [Figure 3]. Given the exploratory pilot-study design, statistical analyses were primarily intended to identify preliminary biomarker trends between groups. Biomarker intensity data were log-transformed prior to analysis to improve distributional characteristics. One-way ANOVA followed by Tukey’s HSD post hoc analysis was applied as an exploratory parametric approach; however, results should be interpreted cautiously considering the limited sample size.

Table 1.

Identified biomarkers in serum and saliva samples of RA patients

Biomarker	Wilks’ Lambda	F	P	Significant (P<0.05)
Tyrosine	0.35	5.23	0.003	0.001
Proline-hydroxyproline	0.28	6.12	0.001	0.001
Citrullinated arginine peptide	0.42	4.34	0.007	0.001
Glycylprolylglutamic acid	0.4	4.56	0.005	0.001
Citrullinated vimentin	0.38	4.89	0.004	0.001

Figure 3.

Comparative analysis of serum and saliva biomarkers: Spectrum plot with peak masses, box plot, and heat map

Comparative biomarker profiles in serum vs. saliva

Figure 4 illustrates the overlap and distinction between biomarkers present in both serum and saliva, which could be significant for diagnosing RA. Proteins that are common to both fluids, such as tyrosine, highlight the potential of saliva as a non-invasive alternative to serum for diagnostic purposes. The identification of citrullinated vimentin, a crucial biomarker for RA, specifically in saliva underscores its diagnostic relevance. This overlap in biomarkers supports the notion that saliva can serve as a reliable proxy for serum in detecting RA-related markers, making diagnostic procedures less invasive and potentially more accessible.

Figure 4.

Comparative biomarker profiles in serum vs. saliva

The Venn diagram visually represents the shared and unique biomarkers between serum and saliva, with the shared section emphasizing proteins like tyrosine and citrullinated vimentin, while the unique sections showcase biomarkers exclusive to each fluid. This further supports the complementary role of both fluids in RA diagnostics.

Comparative features of serum and saliva for RA biomarker detection

The comparison between serum and saliva for RA diagnostics reveals that saliva offers a non-invasive, patient-friendly alternative to serum, with significant biomarker overlap, including key RA-related markers like tyrosine and citrullinated proteins. While serum remains the standard for RA diagnostics, saliva shows comparable performance, making it a promising tool for future diagnostic use. Its ease of collection also enables more frequent sampling, ideal for real-time disease monitoring. The presence of citrullinated proteins in both fluids highlights their role in RA pathogenesis, supporting saliva’s potential as a reliable diagnostic proxy for serum.

Sensitivity and specificity of biomarkers

Several biomarkers demonstrated high apparent discriminatory performance in this pilot dataset; however, these findings should be interpreted cautiously due to the limited sample size and absence of independent validation [Figure 5]. Sensitivity and specificity values vary among the biomarkers, with most showing a good balance between the two metrics. ROC analysis was performed as a preliminary assessment of biomarker discrimination potential. However, confidence intervals for AUC values and independent validation strategies were not feasible due to the limited sample size. Consequently, the reported ROC performance metrics should be considered exploratory and require validation in larger independent cohorts.

Figure 5.

ROC of identified biomarkers

Pathogenesis of RA – Role of citrullinated proteins in serum and saliva

The results [Figure 6] of this study emphasize the potential of saliva as a reliable medium for quantitative monitoring of RA. While serum has traditionally been the standard for detecting established markers such as anti-citrullinated protein antibodies, emerging evidence suggests that saliva can detect key RA biomarkers like citrullinated vimentin with comparable accuracy. This highlights saliva’s growing role in chronic disease monitoring. Additionally, the non-invasive nature of saliva collection makes it ideal for real-time monitoring, allowing for more frequent assessments of RA progression and treatment response. This patient-friendly, cost-effective approach offers a significant advantage over traditional serum-based methods.

Figure 6.

Pathogenesis of RA – Role of citrullinated proteins in serum and saliva

The multivariate analysis and post hoc Tukey’s HSD tests [Table 2] reveal significant differences in biomarker levels between serum, saliva, and control groups, highlighting the role of both serum and saliva in detecting RA biomarkers. Tyrosine, proline-hydroxyproline, citrullinated arginine peptide, and citrullinated vimentin were significantly elevated in serum, consistent with their established diagnostic relevance in RA. However, the presence of key biomarkers such as tyrosine and citrullinated vimentin in saliva supports the potential of using saliva as a non-invasive, diagnostic alternative to serum [Table 3]. Notably, Glycylprolylglutamic Acid showed lower saliva concentrations than serum, reflecting its involvement in collagen degradation, a hallmark of RA.

Table 2.

Multivariate and post hoc analysis

Biomarker	Serum presence	Saliva presence	Mean±SD serum	Mean±SD saliva	Mean±SD control	P-value serum vs. saliva	P-value serum vs. control	P-value saliva vs. control
Tyrosine	True	True	167.15±0.01	167.14±0.01	0±0.00	0.004	0.01	0.012
Proline-hydroxyproline	True	False	315.25±0.01	N/A	0		0.008
Citrullinated Arginina peptide	True	False	407.25±0.01	N/A	0		0.005
Glycylprolylglutamic acid	False	True	N/A	307.02±0.01	0	0.005		0.002
Citrullinated vimentin	False	True	N/A	407.25±0.01	0	0.004		0.003

Table 3.

Diagnostic analysis of biomarkers

m/z value	Biomarker	Serum presence	Saliva presence	AUC	Sensitivity	Specificity	PPV	NPV	Relevance to RA
167.1542	Tyrosine			1	56.70%	83.30%	85.70%	51.70%	Involved in immune signaling pathways, relevant in T-cell activation in RA.
315.2427	Proline-hydroxyproline (collagen fragment)			1	56.00%	80.00%	84.20%	50.60%	Indicates collagen breakdown, contributing to joint damage and inflammation in RA.
407.2495	Citrullinated arginine peptide			1	56.00%	80.00%	83.50%	50.30%	Key autoantigen driving autoimmune responses in RA; leads to ACPA production.
307.0186	Glycylprolylglutamic acid (GPEA)			1	62.90%	78.60%	82.10%	55.20%	Reflects collagen degradation, mirroring the collagen breakdown in joints.
575.0722	Citrullinated vimentin			1	64.00%	70.00%	78.90%	60.50%	Major RA autoantigen, involved in the autoimmune response specific to RA.

DISCUSSION

The comparative analysis of proteomic profiles between serum and saliva in patients with RA offers valuable insights into biomarkers associated with the disease, which could inform diagnostic and prognostic strategies. Utilizing Q-TOF mass spectrometry, this study identifies key biomarkers present in both biofluids, demonstrating the feasibility of saliva as a non-invasive medium for RA monitoring. Saliva emerges as a practical, patient-friendly alternative for RA assessment, with biomarkers indicative of systemic inflammation and immune dysregulation comparable to those detected in serum. The validation of these markers across both fluids reinforces the potential of saliva as a credible diagnostic tool.[17]

PLS-DA confirms a clear separation among serum, saliva, and control groups, highlighting distinct proteomic profiles and emphasizing saliva’s potential as an effective, non-invasive diagnostic tool for RA.[18] Serum samples, represented by blue circles in the PLS-DA plot, cluster tightly, indicating consistency in biomarker expression associated with systemic inflammation in RA. In contrast, control samples (green triangles) show greater variability. Saliva samples (orange squares) reveal broader variability along specific components, suggesting saliva’s ability to capture dynamic, localized changes in RA pathophysiology.[19]

The biomarkers identified through Q-TOF mass spectrometry—tyrosine, proline-hydroxyproline, citrullinated arginine peptide, and citrullinated vimentin—play significant roles in the pathophysiology of RA. Tyrosine, an amino acid critical for protein synthesis, is involved in the regulation of inflammatory processes and has been linked to enhanced immune responses.[20] Proline-hydroxyproline, often associated with collagen metabolism, indicates ongoing tissue remodeling and degradation in RA, where joint destruction is a hallmark feature.[21] The presence of citrullinated arginine peptide is particularly noteworthy, as citrullination of proteins is a well-established post-translational modification that leads to the generation of autoantigens in RA.[22] These modifications can trigger the immune system, promoting the production of anti-citrullinated protein antibodies, which are closely associated with the severity and progression of the disease.[23] The accumulation of these biomarkers not only signifies active disease but also reflects the underlying mechanisms of joint inflammation and damage, highlighting their relevance in both diagnostic and prognostic contexts.[24]

Citrullinated vimentin, in particular, stands out as a critical biomarker due to its role in the autoimmune response in RA.[25] As an intermediate filament protein, vimentin is involved in maintaining cell structure and signaling pathways, and its citrullination alters its immunogenicity, promoting the production of specific antibodies.[26] Elevated levels of citrullinated vimentin in saliva, alongside other citrullinated proteins, reinforce the concept of saliva as a reliable diagnostic medium for RA.[27] This is corroborated by existing literature, which emphasizes the significant correlation between citrullinated protein levels in saliva and serum, thus supporting the use of saliva for non-invasive monitoring of disease activity.[28] The ability to detect these biomarkers in saliva not only enhances the diagnostic accuracy but also offers a dynamic insight into the immune mechanisms at play in RA, paving the way for more personalized therapeutic strategies and improved patient management.[29]

The unique and overlapping biomarker profiles in serum and saliva suggest that saliva has both diagnostic and prognostic applications. Elevated biomarkers in saliva, including citrullinated vimentin and tyrosine, are associated with RA progression, providing insights into real-time disease activity. As these biomarkers indicate ongoing autoimmune processes, saliva could serve as a practical medium for frequently monitoring disease progression and therapeutic response.[28] Additionally, findings such as elevated glycylprolylglutamic acid—a marker for collagen breakdown—in saliva underscore its potential for reflecting joint damage, aiding in disease trajectory prediction.[29]

ROC analysis further supports saliva’s utility in RA diagnostics, with key biomarkers achieving an AUC of 1.0, demonstrating diagnostic accuracy on par with serum. These results, validated through both Q-TOF and statistical analyses, confirm that saliva can reliably reflect serum’s proteomic landscape in RA, supporting its use as a valuable, non-invasive diagnostic proxy. Saliva’s sensitivity and specificity for detecting RA biomarkers substantiate its application for frequent, minimally invasive disease monitoring, enabling a shift toward more accessible, less invasive testing in RA management.[28]

This study has several limitations. First, the sample size was small (5 RA patients and 5 controls), reflecting the pilot nature of this exploratory proteomic investigation. Such a limited cohort may reduce statistical power and increase the possibility of inflated performance estimates, particularly for ROC-derived diagnostic metrics such as AUC. Therefore, the findings should be interpreted cautiously and considered hypothesis-generating rather than confirmatory. Larger multicentric validation studies are necessary before clinical translation.

Saliva provides a feasible option for non-invasive biomarker assessment, especially valuable for monitoring inflammation between standard serum evaluations. Although saliva currently lacks the reliability to serve as a standalone diagnostic medium for RA, it shows promise when used alongside serum for long-term monitoring in established cases where frequent blood sampling may be impractical.

Saliva could thus serve as an adjunct tool in RA monitoring, particularly in advanced cases where disease markers are consistently elevated. For early detection and comprehensive assessment, however, serum remains the preferred medium. Future research should focus on standardizing saliva collection protocols and refining analytical techniques to enhance biomarker sensitivity in saliva. Additionally, a multi-omics approach integrating proteomic, genomic, and metabolomic analyses could expand saliva’s utility, offering a more holistic view of RA’s systemic impact.

CONCLUSION

This pilot study suggests that saliva may serve as a promising complementary biofluid for exploratory RA biomarker assessment. While the findings support the feasibility of salivary proteomic analysis, larger validation studies are required to confirm its diagnostic and prognostic utility. By mirroring the serum’s biomarker profile and capturing disease-specific proteomic changes, saliva emerges as a practical, patient-friendly alternative for real-time disease monitoring. These findings hold significant implications for RA management, allowing for continuous monitoring, timely intervention, and personalized treatment adjustments based on easily obtainable samples.

Author contributions

AKP: Conceptualization and study design, literature review, manuscript drafting and editing. RR: Supervision and coordination of the research, Manuscript writing and finalization, Data analysis and interpretation. HSK: Data collection and analysis, laboratory investigations and histopathological assessments, manuscript review and contributions to discussion. CS: Clinical evaluation and patient recruitment, Rheumatological insights into disease pathogenesis, Statistical analysis and result validation. SB: Expert guidance in oral pathology, Critical review of the histopathological findings, Final manuscript revision and approval.

Ethics committee statement

Ethical clearance was obtained from Saveetha Dental College, SIMATS.

Conflict of interest

There is no conflict of interest among the authors.

Data availability statement

Raw data will be provided on request.

Funding Statement

There is no funding source for this study.