**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/bios12050266/s1, Figure S1: RG chromaticity diagram of the LbL and non-LbL model paper samples up to 14 cycles. The number of cycles of both models increases along the direction of the arrow. Inset pictures are optical micrographs of the cellulose paper and 14 cycles in both models; Figure S2: Histogram of the diameters of the Ag NPs overall; Figure S3: Cross-sectional SEM image of an Ag–chitosan nanocomposite with pore size measurements; Figure S4: Tensile strength of silver/chitosan nanocomposite layered papers from cellulose paper (0 cycles) to 14 cycles [53].

**Author Contributions:** Conceptualization, H.J.K. and H.N.; methodology, H.J.K.; validation, Y.K., H.J.K. and H.N.; formal analysis, Y.K.; investigation, Y.K.; resources, H.N.; data curation, Y.K.; writing—original draft preparation, Y.K.; writing—review and editing, H.J.K., S.H.L. and H.N.; visualization, Y.K. and H.J.K.; supervision, H.N.; project administration, Y.K.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No.<PHONE_NUMBER>).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available in https://figshare.com/ search?q=10.6084%2Fm9.figshare.19635069, (accessed on 16 March 2022).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method**

**Posen Lee 1, Tai-Been Chen 2,3, Chin-Hsuan Liu 1,4,\*, Chi-Yuan Wang 2, Guan-Hua Huang 3 and Nan-Han Lu 2,5,6**

**Abstract:** Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification.

**Keywords:** iso-block postural identity; OpenPose; fusion deep learning

**1. Introduction**

The OpenPose algorithm is a deep learning method in which part affinity fields (PAFs) are used to detect the two-dimensional (2D) postures of humans in images [1]. The relationship between posture stability, motor function, and quality of life has been determined [2,3]. Moreover, the OpenPose algorithm has been used for checking the medication situations of patients and for their physical monitoring [4,5]. The evaluation of the cardinal symptoms of resting tremor and bradykinesia for Parkinson's disease has been conducted using an OpenPose-based deep learning method [6,7]. Furthermore, in [8], the OpenPose framework was used to create a human behavior recognition system for skeleton posture estimation. Quantitative gait (motor) variables can be estimated and recorded using pose tracking systems (e.g., OpenPose, AlphaPose, and Detectron) [9]. These factors are useful for measuring the quality of life of older adults [10–12]. Moreover, parkinsonian motion features have been created using deep-learning-based 2D OpenPose models [13,14]. For people with autism spectrum disorder, skeleton posture characteristics are correlated with longterm memory in the field of action recognition [15–17]. The physical function of a patient

**Citation:** Lee, P.; Chen, T.-B.; Liu, C.-H.; Wang, C.-Y.; Huang, G.-H.; Lu, N.-H. Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method. *Biosensors* **2022**, *12*, 295. https://doi.org/10.3390/ bios12050295

Received: 9 March 2022 Accepted: 2 May 2022 Published: 3 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

should be assessed according to their health data obtained using a skeleton pose tracking device and gait analysis [18–21]. Many neurological and musculoskeletal disorders are associated with problems related to postural movement, which can be estimated using a pose-capturing device [22]. Therefore, noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. In this study, fusion deep learning was used to generate dynamic joint node plots (DJNPs) by using OpenPose-based methods, and skewness in walking was qualitatively analyzed using convolutional neural networks (CNNs) [23]. An iso-block postural identity (IPI) method was used to perform the quantified analysis of postural control and walking behavior. This proposed approach combines cell phone camera recordings, cloud storage, and fusion deep learning for postural estimation and classification.

#### **2. Materials and Methods**

#### *2.1. Research Ethics*

All the experimental procedures were approved by the Institutional Review Board of E-DA Hospital [with approval number EMRP52110N (04/11/2021)]. Verbal and written information on all the experimental details was provided to all the participants before they provided informed consent. Written informed consent was obtained from the participants prior to experimental data collection.

#### *2.2. Flow of Research*

In this study, videos walking toward and away from a cell phone camera were recorded using the camera (Step 1 in Figure 1). The videos were recorded at 24-bit (RGB), 1080p resolution, and 30 frames per second. The videos were uploaded to Google Cloud through 5G mobile Internet or Wi-Fi (Step 2 in Figure 1). The workstation used in this study downloaded a video, extracted a single frame from the video, and then applied a fusion artificial intelligence (AI) method to this frame (Step 3 in Figure 1). In the aforementioned step, single frames were extracted from an input video (Step 3A), frames with static walking were identified using an OpenPose-based deep learning method (Step 3B), and the joint nodes of the input video were merged into a plot (Step 3C). The obtained DJNP was categorized as representing straight or skewed walking (Step 3D). CNNs were used to classify DJNPs into one of the aforementioned two groups. Two types of deep learning methods were used in the fusion AI method adopted in this study: an OpenPose-based deep learning method and CNN-based methods. The OpenPose-based method is useful for estimating the coordinates of joint nodes from an input image [1]. The adopted CNNs are suitable for the classification of images with high accuracy and robustness.

#### *2.3. Participants*

A total of 35 young adults without any health problems were recruited to participate in a walking experiment. The age range was 20.20 ± 1.08 years. The inclusion criteria were healthy adults who were willing to participate and could walk more than 5 m. People with musculoskeletal pain (such as muscle soreness), those who had drunk alcohol or taken sleeping pills within 24 h before the commencement of the experiment, and individuals with limited vision (such as nearsighted people without glasses) were excluded from this study.

**Figure 1.** Flow of research.

#### *2.4. Experimental Design*

The experimental setup is depicted in Figure 2. The total length of the experimental space was greater than 7 m. The ground was level, free of debris, and smooth to ensure a straight and smooth walking path. The cell phone was placed 1 m above the ground (approximately equal to the height of a medium-sized adult holding a cell phone) and 2 m from the endpoint of the walking path. The entire body of a participant was recorded during the walk. The participants were required to wear walking shoes and not slippers while walking. Participants walked away from the cell phone and then turned back and walked toward the cell phone. The participants walked for 5 m toward and away from the camera three times each. One video was captured for each 5-m walk; thus, six videos were recorded for each participant. A series of single (static) frames was extracted from a video every 0.3 s. For example, for a 3-s input video, 10 frames were extracted to estimate the coordinates of joint nodes. A static frame of one DJNP was extracted per 0.3 s for one video. For example, a 10 s walking video with frame rate 30 (frames/second), the total static frame in one DJNP are 90 frames (i.e., 90 = 10 (second) × 30 (frames/second) × 0.3 (second)). Hence, the DJNP was a variety of frames according to the length of a walking video. The filmmakers are not medical experts but are trained in motion assessment. The video is analyzed by an expert in image analysis and an occupational therapist specializing in rehabilitation Table 1 lists the number of participants and the mean and standard deviation (STD) of velocity (m/s) and time (s) for each group.

**Table 1.** Information on the number of participants and the mean and standard deviation (STD) of velocity (m/s) and time (s) for each group.

**Figure 2.** Experimental setup (the cell phone was placed 1 m above the floor and 2 m from the participant).

#### *2.5. Measurement of Joint Nodes through Openpose-Based Deep Learning*

OpenPose is a well-known system that uses a bottom-up approach for real-time multiperson body pose estimation. In the proposed OpenPose-based method, PAFs are used to obtain a nonparametric representation for associating body parts with individuals in an image [1]. This bottom-up method achieves high accuracy in real time, regardless of the number of people in the image. It can be used to detect the 2D poses of multiple people in an image and to perform single-person pose estimation for each detection. In this study, the OpenPose algorithm was mainly used to output a heat map of joint nodes (Figure 3). The center coordinates of joint nodes were estimated by using the geometric centroid formula.

**Figure 3.** Dynamic joint node plot (DJNP) (right) obtained by merging the heat maps of joint nodes from t1 to t5 by using the OpenPose algorithm.

#### *2.6. Definition of the Control and Experimental Groups*

The data for the control group comprised DJNPs that indicated straightforward walking toward and away from the camera. The experimental group comprised DJNPs that indicated skewed walking. The data for the control and experimental groups comprised 102 and 108 DJNPs, respectively, which were classified using different CNNs.

#### *2.7. Classification Using Pretrained CNNs and Machine Learning Classifiers*

Pretrained CNNs were used to extract the features of DJNPs, and machine learning classifiers were used to construct classification models. The eight pre-trained CNNs used in this study were AlexNet, DenseNet201, GoogleNet, MobileNetV2, ResNet101, ResNet50, VGG16, and VGG19. Moreover, the three machine learning classifiers used in this study were logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM).

CNNs have a high learning capacity, which makes them suitable for image classification. They extract features and learn data according to variations in the breadth and depth of features. Table 2 lists the features that were extracted by CNNs and served as the inputs for the LR, NB, and SVM. A deep CNN network comprises five types of primary layers: a convolutional layer, a pooling layer, a rectified linear unit layer, fully connected layers, and a softmax layer. Information on the pretrained CNNs used in this study is provided in Table 2. The fully connected layers of the CNNs extracted and stored the features of the input image. In the present study, eight CNNs and three classifiers with four batch sizes and 20 random splits were adopted. The four batch sizes selected in this study for the CNNs were 5, 8, 11, and 14. The total number of investigated models was 8 (CNNs) × 3 (machine learning techniques) × 4 (batch size settings) × 20 (instances of random splitting) = 1920. Therefore, the 1920 models represent the 1920 possible combinations of one CNN, classifier, batch size, and random data split. CNNs have demonstrated utility and efficiency in image feature extraction in the fields of biomedicine and biology [23–27].

**Table 2.** Information on the adopted convolutional neural networks.

LR is a process of modeling the probability of a discrete outcome when an input variable is given. This process is often used to analyze associations between two or more predictors or variables. LR does not require the existence of a linear relationship between inputs and output variables. This method is useful when the response variable is binary, but the explanatory variables are continuous. LR is also an effective analysis method for classification problems. The LR method is used for the development of classification models in the field of machine learning because of its capacity to provide hierarchical or tree-like structures. Many fields have adopted LR for prediction and classification. LR is suitable for classification problems related to health issues, such as whether a person has a specific ailment or disease when a set of symptoms are given.

NB classifiers are based on Bayes' theorem with a naïve independence hypothesis between the adopted predictors or features. These classifiers are the most suitable ones for solving classification problems in which no dependency exists between a particular feature and other features of a certain class. NB classifiers offer high flexibility for linear or nonlinear relations among variables (features or predictors) in classification problems and provide increased accuracy when combined with kernel density estimation. NB classifiers exhibit higher performance for categorical input data than for numerical input data. These classifiers are easy to implement and computationally inexpensive, perform well on large datasets with high dimensionality, and are extremely sensitive to feature selection.

SVM classifiers are highly powerful classifiers that can be used to solve two-class pattern recognition problems. They transform the original nonlinear data into a higherdimensional space and then create a separating hyperplane defined by various support vectors in this space to maximize the margin between two datasets. Data can be linearly separated in the higher-dimensional space by using a kernel function. Many useful kernels are available to improve the classification performance and reduce the false rate. SVM is a supervised learning method for the classification of linear and nonlinear data and is generally used for the classification of high-dimensional or nonlinear data.

The computing time of using SVM is in linear time, rather than by expensive iterative approximation, which is performed by many other types of classifiers. The LR, NB, and SVM methods were applied as deep and machine learning methods to extract features of DJNPs and classify the postural control of the straight and skewed walking groups.

#### *2.8. Validation of Classification Performance*

The data for the control and experimental groups comprised 102 and 108 DJNPs, respectively. A random splitting schema was employed to separate the training (70%) and testing (30%) sets; 71 and 31 samples from the control group were used for training and testing, respectively, and 76 and 32 samples from the experimental group were used for training and testing, respectively. Testing sets and confusion matrices were used to evaluate the models with respect to the kappa value, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). These indices were sorted in the ascending order of the corresponding kappa value, and a radar plot was then generated to present the aforementioned indices of the adopted models.

#### **3. Results**

In this study, 70% of the samples of each group were randomly used to train the adopted classifiers, and the remaining 30% of samples were used to perform validation. Figure 4 shows a scatter plot for the specificity and sensitivity of the 1920 models for the validation dataset. The maximum specificity and sensitivity of 0.84 and 0.87, respectively, were achieved by the ResNet101 and NB classifiers, respectively.

**Figure 4.** Scatter plot for the specificity and sensitivity of the 1920 models for the validation dataset.

In Figure 5, a radar plot was constructed for six performance indices with the results sorted by the maximum kappa value for 96 models (the abbreviations of the investigated models are written in Appendix A). The best performing model was M53, which is a combination of ResNet101 and naïve Bayes. The kappa, accuracy, sensitivity (Sen), specificity (Spe), PPV, and NPV values were 0.71, 0.86, 0.87, 0.84, 0.84, and 0.87, respectively. All of the performance indices are over 0.7. The optimized model, ResNet101 with naïve Bayes, had acceptable agreement results and the highest accuracy.

**Figure 5.** Radar plot of the six performance indices sorted in the ascending order of the kappa value for 96 models (the abbreviations are explained in the Appendix A). Sen represents the sensitivity, and Spe represents the specificity.

Table 3 lists the 13 models with kappa values greater than 0.59. These models comprised four (30.8%) AlexNet models, three DenseNet201 models (23.1%), three ResNet101 models (23.1%), two VGG16 models (15.4%), and one VGG19 model (7.7%). AlexNet, DenseNet201, and ResNet101 accounted for 10 of the aforementioned 13 models (76.9%). SVM and NB were the main machine learning classifiers that performed well in this study. The numbers of the aforementioned 13 models with SVM and NB classifiers were 3 and 10, respectively. Thus, NB performed well. Finally, the batch sizes of the 13 models were 5, 8, 11, and 14 useable in this work.

**Table 3.** Models with kappa values greater than 0.59.

#### **4. Discussion**

*4.1. Measurement of Postural Control*

IPIs were used to measure the skewness or displacement. Figure 6 illustrates the fusion of a DJNP with the IPI generated for a series of time points. In this study, an IPI was created every 0.3 s, and all the IPIs were fused with DJNPs.

**Figure 6.** Iso-block postural identity (IPI) generated for a series of times and fusion of the IPI with a DJNP (right).

Figure 7 presents the skewness or displacement for a walking video at three time points (i.e., *t*0, *t*1, and *t*2). Figure 7A,C,D,F depict DJNPs and IPIs for skewed walking. Figure 7B,E depict DJNPs and IPIs for straight walking. These DJNPs can be used to measure skewness and horizontal postural movement.

The parameters Θ*r* and Θ*l* represent the angles of the right and left sides of the body during captured images, respectively (Figure 7E). The ratio of two angles (i.e., *SR* = Θ*l*/Θ*r*) was used to measure the skewness tendency. When this ratio is >1, the body tends to skew to the right. When *SR* = 1, the body is almost straight. When *SR* is <1, the body tends to skew to the left. The displacement of the body between two time points was quantified by estimating the distance covered between these time points. For example, in Figure 7B,E, *Dr,0,1* and *Dl,0,1* represent the displacements of the right and left sides of the body, respectively between *t*0 and *t*1. Similarly, *Dr*,1,2 and *Dl*,1,2 represent the displacements of the right and left sides of the body, respectively, between *t*1 and *t*2. Therefore, the ratio of *Dr*,*i*-1,*i* to *Dl*,*i*-1,*i* (i.e., *MD* = *Dr*,*i*-1,*i*/*Dl*,*i*-1,*i*, *i* = 0, 1, 2) could be used to determine the dominant side of body displacement. When *MD* was >1, the right side was the dominant side of displacement. When *MD* was 1, the walking posture was almost straight. Moreover, when *MD* was <1, the left side was the dominant displacement side.

**Figure 7.** Graphical representation of the skewness or displacement for a walking video at three time points (i.e., *t0, t1, t2*). (**A**,**D**), (**B**,**E**), and (**C**,**F**), respectively, present postural skew to the left, postural balance, and postural skew to the right with participants walking toward the camera.

#### *4.2. Literature for Health Issues and Postural Control during Walking*

Poor postural control during walking may indicate health problems. An individual's postural control considerably influences their quality of life [2,3]. Equipping participants with wearable devices that assess their posture can be challenging [4]. Nevertheless, this problem can be overcome by incorporating deep learning into Internet of things monitoring systems to effectively detect motion and posture [5]. Resting tremors and finger tapping have been detected using OpenPose-based deep learning methods [6,7]. Moreover, skeleton normality has been determined through the measurement of angles and velocities by using the aforementioned methods [8–10]. Such methods are useful for not only generating three-dimensional poses [11,12] but also for identifying the relationship between postural behavior and functional diseases, such as Parkinson's disease [6,13,14], autism spectrum disorder [15], and metatarsophalangeal joint flexions [16]. OpenPose-based deep learning methods can be used for skeleton, ankle, and foot motion [8,17] detection; physical function assessment [18,19]; and poststroke study [20].

Thus, noninvasive tracking devices play crucial roles in the recording [21], analysis, measurement, and detection of body posture, which may indicate health issues in real time.

#### **5. Conclusions**

In this study, fusion deep learning was applied to generate DJNPs by using an OpenPose-based method and quantify skewness by using CNNs. The adopted approach successfully incorporates cell phone camera recording, cloud storage, and fusion deep learning for posture estimation and classification. Moreover, the adopted IPI method can be used to perform a quantified analysis of postural control and walking behavior.

The research conducted in the present study can be considered preliminary. We developed the IPI method and attempted a quantified analysis of postural control and walking behavior to identify factors indicative of possible clinical gait disorders. However, at the time of writing, the research is in the preliminary phase and will remain as such until the automated analysis is completed through the IPI method. The highlights of our proposed method include its suitability for use with computer vision for identifying signs

of gait problems for clinical application, as well as its replacement of a dynamic joint node plot. In addition, the IPI method is straightforward and allows for real-time monitoring. A video of walking behavior can be conveniently recorded in real-time by using a mobile device. A user can easily remove the background from the video and generate dynamic joint node coordinates through fusion AI methods. The developed IPI method allows for use with computer vision to identify postural characteristics for clinical applications.

Future studies can apply the proposed approach to individuals with health problems to validate this approach.

**Author Contributions:** Conceptualization, P.L., T.-B.C. and C.-H.L.; Data curation, T.-B.C., G.-H.H. and N.-H.L.; Formal analysis, P.L., T.-B.C. and C.-H.L.; Investigation, C.-Y.W.; Methodology, P.L., T.-B.C. and C.-H.L.; Project administration, P.L.; Software, T.-B.C.; Supervision, P.L. and C.-H.L.; Writing—original draft, T.-B.C. and C.-H.L.; Writing—review and editing, P.L. and C.-H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** Ministry of Science and Technology of Taiwan funded this research under the grant numbers MOST 110-2118-M-214-001.

**Institutional Review Board Statement:** The study was conducted in accordance with the guidelines of the Declaration of Helsinki. All experimental procedures were approved by the Institutional Review Board of the E-DA Hospital, Kaohsiung, Taiwan (approval number EMRP52110N).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** The 96 combinations of investigated models with abbreviation are listed below.

#### **References**

## *Communication* **Masticatory Myoelectric Side Modular Ratio Asymmetry during Maximal Biting in Women with and without Temporomandibular Disorders**

**Felipe Acácio de Paiva 1, Kariny Realino Ferreira 2, Michelle Almeida Barbosa 2 and Alexandre Carvalho Barbosa 1,2,3,\***

**Abstract:** There is no consensus on the role of electromyographic analysis in detecting and characterizing the asymmetries of jaw muscle excitation in patients with temporomandibular disorders (TMD). To analyze the TMD patients (*n* = 72) in comparison with the healthy controls (*n* = 30), the surface electromyography (sEMG) of the temporalis anterior muscle (TA) and masseter muscle (M) was recorded while a maximal biting task was performed. The differences in the asymmetry of the relationship between the masseter muscles were assessed in a module to determine the sensitivity (Sn) of binomial logistic models, based on the dominance of the TA or the M muscle, in accurately predicting the presence of TMD. All assumptions were met, and comparisons between the groups showed significant differences for the TA muscle ratio (*p* = 0.007), but not for the M muscle ratio (*p* = 0.13). The left side was predominant over the right side in the TMD group for both the TA (*p* = 0.02) and M muscles (*p* = 0.001), while the non-TMD group had a higher frequency of the right side. Binary logistic regression showed a significant model (χ2 = 9.53; *p* = 0.002) for the TA muscle with Sn = 0.843. The model for the M muscle also showed significance (χ2 = 8.03; *p* = 0.005) with Sn = 0.837. The TMD patients showed an increased TA muscle ratio and asymmetry of left dominance, compared to the healthy subjects. Both of the binomial logistic models, based on muscle dominance TA or M, were moderately sensitive for predicting the presence of TMD.

**Keywords:** electromyography; facial pain; temporomandibular joint disorders; jaw muscles; diagnosis

#### **1. Introduction**

The prevalence of temporomandibular disorders (TMD) is 27–38% in the adult population [1]. Chronic TMD affects the patient's ability to work and interact in the social environment, and results in an impaired quality of life [2,3]. An accurate diagnosis of TMD is critical for treatment planning and increases the chances of successful outcomes. Evidence suggests that individuals with TMD have a lower rate of motor unit discharge in painful muscles [4–6] and early fatigue, compared with individuals without TMD [7,8]. Surface electromyography (sEMG) provides a direct and objective assessment of muscle excitation [4,5] and can aid in the diagnosis of TMD, particularly the assessment of masticatory muscle activity in patients with impaired function and altered jaw movement patterns [9,10].

Some studies suggest that the use of sEMG to assess masticatory muscle excitation can discriminate against women with and without TMD [5,11–13]. In this sense, some features of muscle functionality in TMD patients have been reported, but the results of specific thresholds for raw electromyography of the masticatory muscles showed poor responsiveness and accuracy in discriminating between healthy and TMD patients [14,15].

**Citation:** de Paiva, F.A.; Ferreira, K.R.; Barbosa, M.A.; Barbosa, A.C. Masticatory Myoelectric Side Modular Ratio Asymmetry during Maximal Biting in Women with and without Temporomandibular Disorders. *Biosensors* **2022**, *12*, 654. https://doi.org/10.3390/ bios12080654

Received: 13 July 2022 Accepted: 16 August 2022 Published: 18 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

One possible solution to this is to compare the excitation asymmetries between the sides and muscles to prioritize intervention [10]. However, no consensus has been reached on the usefulness of sEMG and the presence of muscle excitation asymmetries in TMD patients [14].

Aside from the above-mentioned issues, understanding the musculoskeletal impairments associated with TMD diagnoses is critical to providing effective treatments. Therefore, clarifying the relationship between the excitation of the major jaw muscles between sides during biting could provide insights into managing patients with this complex disorder. In this sense, the primary objective of the present study was to evaluate the difference in the modular relationship between the excitation of the side-to-side muscles by electromyography of the TA and M in TMD patients and healthy subjects, bilaterally. A secondary objective was to evaluate the possible differences, considering a nominal side-to-side predominance in both groups, and to establish possible predictive models with combined sensitivity and specificity analysis. The first hypothesis was that the TMD patients would have an increased asymmetry of ratio and dominance, compared with the healthy subjects. The second hypothesis was that a binomial logistic model, based on the dominance of the TA or M muscle, would be able to accurately predict the presence of TMD.

#### **2. Materials and Methods**

#### *2.1. Participants*

Participants (Table 1) were recruited through public invitations, via recruitment posters and personal contacts. A sample of 112 women were interested in participating. The inclusion criterion for the TMD group was a diagnosis of TMD arthralgia that was associated with myofascial pain, according to the Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD), for at least 6 months' duration. The RDC/TMD is internationally recognized as the gold standard for TMD diagnosis [16]. The assessment included external palpation, using a calibrated pressure algometer (MED.DOR pressure algometry, Governador Valadares, Brazil) [17]. Internal to the mouth, the index finger was calibrated using the pressure algometer to palpate the medial pterygoideus muscles. The inclusion criteria for both groups were having a minimum of 28 permanent teeth and an age between 18 and 45 years. All patients were evaluated by a dentist for periodontal problems. The exclusion criteria for both groups were as follows: a history of trauma to the face and temporomandibular joints; systemic diseases such as arthritis; fibromyalgia; pain due to confirmed migraine; headache or neck pain unrelated to TMD; ongoing use of analgesics, anti-inflammatory drugs, muscle relaxants or psychotropic medications; acute infections or other serious dental, ear, eye, nose, or throat conditions; and neurologic or cognitive deficits. After the initial screening, 10 participants were excluded. The 102 included participants were divided into 2 groups, according to RDC/TMD axis I: 1. The non-TMD group (*n* = 30 individuals without TMD), 2. The TMD group (*n* = 72 individuals with TMD). This cross-sectional study was approved by the Ethics Committee of the Federal College of Juiz de Fora (number 68457617.6.0000.5147). Participants were informed of the benefits and potential risks before signing a written informed consent form, before participating in the study. An a priori sample size was calculated based on a previous study. An effect size of 0.88 was considered (variable: side-to-side electrical excitation %), with α = 0.05 and a power (1-β) of 0.95. The analysis yielded an actual sample power of 0.949, with a total sample size of 60 participants. Considering a drop-out of 20%, the recommended sample included at least 72 participants. G-Power software (version 3.1.5, Franz Faul, College of Kiel, Düsseldorf, Germany) was used to calculate the sample size.

**Table 1.** Participants' Characteristics.

#### *2.2. Electromyography*

All the sEMG procedures and the device's specifications were in accordance with the recommendations of the International Society of Electrophysiology and Kinesiology (https://isek.org/, accessed on 25 June 2021). The conversion of analog to digital signals was performed by an A/D board with an input range of 16-bit resolution, a sampling frequency of 2 kHz, a joint-rejection module of more than 100 dB, a signal-to-noise ratio of less than 03 μV root mean square, and an impedance of 109 Ω. The sEMG signals were recorded as root mean square in μV, using surface Meditrace™ (Ludlow Technical Products, Gananoque, ON, Canada) Ag/AgCl electrodes with a diameter of 2 cm and a center-to-center spacing of 2 cm, placed in transverse alignment, parallel to the underlying fibers at a muscle site. Differential bipolar sensors were attached to the electrodes to reduce the constant noise. A reference electrode was placed on the left lateral humeral epicondyle. The sEMG signals were amplified and filtered (Butterworth 4th order, 20–450 Hz bandpass filter, 60 Hz notch filter). All information was recorded and processed using Miotec Suite® software (Miotec Biomedical Equipments, Porto Alegre, RS, Brazil) [7,8,18]. Before placement of the sEMG electrodes, the skin was cleaned with 70% alcohol to remove fatty residues, followed by exfoliation with a special sandpaper for the skin and a second cleaning with alcohol. As the TM and M electrodes' locations were not described in the Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM) site (http://seniam.org/, accessed on 25 June 2021), the electrodes were placed on both the left and right sides, according to previous studies' descriptions [7,8,18].

#### *2.3. Maximal Voluntary Isometric Contraction*

The excitation of M and TA muscles was assessed during a maximal bite force test. Each participant performed a 10 s maximal isometric contraction (MVIC), while biting on a load cell (maximum tension–compression = 200 Kgf, precision of 0.1 Kgf, maximum measurement error = 0.33%; Miotec™ Biomedical Equipment, Porto Alegre, RS, Brazil). Subjects were asked to sit comfortably while the adapted arms of the load cell were positioned on the incisors (Figure 1). A disposable material was used to cover the adapted arms for each subject. Forward head posture was controlled during all procedures by positioning the load cell closer to the participant so that participants could bite in their natural head posture. Standardized verbal commands ("begin", "continue biting", and "stop") were used by the same experimenter for all recordings. A 5 s familiarization period was followed by a 3 min pause before the task. The load cell was coupled and synchronized with the electromyograph.

**Figure 1.** The adapted load cell. (**A**) laboratory-grade load cell; (**B**) adapted arms.

#### *2.4. Data Extraction*

All data were extracted offline using Miotec Suite™ software (Miotec™, Biomedical Equipments). Because the load cell was synchronized with the electromyography channels, the trained rater determined the interval based on the increase in force. After three 1 s windows of rest were collected, onset was defined by three times the standard deviation from the average rest intervals, plus the mean itself (Figure 2). The interval began when the signal exceeded the onset threshold. Conversely, the end of the interval was defined by the same threshold. For statistical analysis, the mean values of the force intervals were used [7]. The sEMG ratio was calculated using the maximum value (in μV), divided by the minimum value (in μV), so that the difference could always be modular (with positive values). Thus, the difference values were displayed regardless of the side. To determine the sEMG side predominance, a nominal classification was used. If the voltage on the right side had the highest value, the participant's result was classified as "1". Conversely, if the left side was higher than the right side, the result was classified as "2".

#### *2.5. Statistical Analysis*

The between-group differences were assessed using the independent samples t-tests. The 95% confidence interval was also used to set the lower and the upper limits of significance. The effect size (ES) was set using Cohen's d coefficient. The magnitude of the ES was qualitatively interpreted using the following thresholds: <0.2, trivial; 0.2–0.6, small; 0.6–1.2, moderate; 1.2–2.0, large; 2.0–4.0, very large; and >4.0, huge [19]. The Chi-square test, with continuity correction, was used to verify the frequency differences for nominal side predominance. To provide a predictive model, binary logistic regressions were performed for M and TA analysis. The assumptions of the absence of multicollinearity (Variance Inflation Factor [VIF] less than 5) and outliers were evaluated. The best fit model was judged based on the values of the Chi-square test, Nagelkerk's R2 and odds ratio (OR), considering its confidence intervals (OR [95% CI]). In addition, the likelihood ratio test, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) values were inspected to assess the model fit (i.e., the lower the better). The sensitivity (Sn) and the specificity (Sp) were also investigated for each model. The correlation between the pain side and the outcome variables were assessed using Pearson's coefficient (r), accompanied by the adjusted coefficient of determination (r2), which is used to measure how well a statistical model predicts an outcome. They were qualitatively interpreted using the following thresholds: <0.1, trivial; 0.11–0.3, small; 0.31–0.5, moderate; 0.51–0.7, large; 0.71–0.9, very

large; and >0.9, nearly perfect [19]. All data analysis was performed using the JAMOVI software (v. <IP_ADDRESS>, The JAMOVI Project, 2022), with significance level set at 5%.

**Figure 2.** Example of the raw (image above–in μV) and processed RMS signal (image below–in μV).

#### **3. Results**

The between-group comparisons showed significant differences for the TA muscle ratio (TMD: 2.17 [1.74] μV vs. non-TMD: 1.50 [1.28] μV; *p* = 0.007; 95% CI: −0.43 to −0.04; ES = 0.35 [small]), but not for the M muscle ratio (TMD: 1.80 [1.46] μV vs. non-TMD: 1.29 [0.23] μV; *p* = 0.13; 95% CI: −0.23 to 0.02; ES = 0.19 [trivial]). In terms of frequency, the left side was predominant over the right side on the TMD group for both the TA muscle (46.6% vs. 31.4%; χ**2** = 5.41; *p* = 0.02) and the M muscle (46% vs. 30.9%; χ**2** = 11; *p* = 0.001), while the non-TMD group showed a higher frequency of the right side (TA: 13.5% vs. 8.4% and M: 17% vs. 6.4%). The side of pain was only significantly and positively correlated to the temporal ratio index (r = 0.30; *p* = 0.007).

The binary logistic regression showed a significant model (likelihood χ**2** = 9.53; *p* = 0.002; Nagelkerk's R2 = 0.119) for the TA muscle, with a 1.07 μV cutoff point. The VIF values confirmed the absence of collinearity (VIF = 1), and the low AIC (=136) and BIC (=141) confirmed the model fit. The model's Sn was 0.843, with an Sp of 0.431 for an OR of 4.08 (95% CI = 1.599 to 10.400). The M muscle regression model also showed significance, with a cutoff point of 1.11 μV (likelihood χ**2** = 8.03; *p* = 0.005; Nagelkerk's R2 = 0.056). The VIF (=1), AIC (=137), and BIC (=142) values confirmed the absence of collinearity and the model fit. The analysis highlighted an OR of 3.64 (95% CI = 1.429 to 9.255), with an Sn of 0.837 and an Sp of 0.415.

#### **4. Discussion**

The results showed that the TA muscle percentage was two times significantly higher in the TMD group, compared with the non-TMD group. Moreover, the left side was electromyographically predominant in the TMD patients. Conversely, the right side was predominant in the non-TMD participants. The results also showed a predictive value in distinguishing the TMD and non-TMD patients, when considering the TA (OR = 4.08) or the M's (OR = 3.64) frequency of asymmetry, with a high Sn to detect those with TMD. The primary hypothesis was partially confirmed because differences were observed only in the TA muscle.

Other studies examined the responsiveness of individual variables to distinguish the subjects with TMD from those without TMD. One study examined the pressure–pain threshold (PPT) in 200 subjects of both sexes, aged 19–27 years [20]. Each subject described the result of pressure algometry for the superficial and deep parts of the masseter muscle, the anterior and posterior parts of the temporalis muscle, and the tissues adjacent to the lateral and dorsal parts of the temporomandibular joint capsule, by selecting the pain intensity on a visual analog scale (VAS) each time. A receiver operating characteristic curve analysis showed a specificity of 95.3% in identifying healthy subjects and a sensitivity of 58.4% in identifying patients with TMD symptoms, at a cut-off point of 7.4 VAS and an accuracy of 68.1%. Another study examined a sample of 49 women who were divided into the following three groups: TMJ osteoarthritis, asymptomatic disk displacement, and control group [21]. The authors aimed to determine a cut-off point for PPT and determined the sensitivity and specificity. The specificity determined was 89.6% and the sensitivity was 70%, for a cutoff point of 1.36 kgf/cm2 (area under the curve = 0.90). In a previous study, PPT, sensitivity, and specificity were found to be 0.67 and 0.85 for the masseter muscle, and 0.77 and 0.87 for the temporal muscle, respectively, with a cutoff point one standard deviation below the mean PPT of subjects who did not have TMD [20]. Given these results, pressure algometry has severe limitations when used as a single diagnostic tool because of its limited ability to detect true positives (Sn).

The sEMG technique has also been studied, but methodological problems tend to compromise its ability to predict and distinguish the TMD patients from the healthy controls. A previous study showed altered coactivation and coordination strategies of the jaw muscles during mastication, resulting in higher relative energy expenditure and impaired differential recruitment [22]. Another study showed that women with TMD myalgia had greater jaw muscle work than healthy control subjects [9]. However, the same study showed that the activity of the temporalis anterior muscle (TA) and the masseter muscle (M) were similar when comparing the right and left sides in both the TMD and healthy groups, but the TMD group had greater M activity, compared to TA activity. Other studies examined the electromyographic muscle asymmetry between the sides when comparing TMD and healthy subjects [10,13], but the authors reported no differences at rest or during isometric contractions. One study showed the moderate accuracy (0.74–0.84) of raw sEMG in the TA, M, and the suprahyoid muscles in diagnosing TMD at rest, and in the suprahyoid muscles during maximal contraction on parafilm [11]. In addition, the sensitivity ranged from 71.3% to 80% and the specificity ranged from 60.5% to 76.6%. Such conflicting results are often due to different methods of analyzing the electromyographic signal; differences in how TMD patients have been previously diagnosed; and the inclusion of small samples, combined with the lack of sample size calculation. In the present study, the gold standard method for TMD diagnosis was used in conjunction with a large sample size and an a priori calculated power of 0.95. These procedures were performed to ensure further inference on the assessment of asymmetry, instead of the usual raw values for sEMG analysis. Despite the moderate Sp in the current analysis, the main objective was to identify the women with TMD, instead of their healthy counterparts. The analysis yielded values of over 80% sensitivity for the detection of TMD, using nominal side-predominance, with well-established cutoff points. The ideal Sn value is 100%, meaning that all individuals with TMD are detected [23]. However, this value is rarely achieved in clinical or even research

studies. Given the possible effect-size bias associated with an inappropriate sample size (type I error), a post-hoc power calculation would demonstrate the value of the results. In the present work, the two-sided post hoc power calculation returned that the power was 0.96 or 96%, with a maximum possible power of 1 or 100% considering the input ES of 0.35, with the sample of 102 participants.

An important issue that must be addressed is the predominance of pain to the left side (57%) over the right side (20%), and over the combination of both sides (23%) for the occurrence of pain (see Table 1). That predominance could be a possible confounding factor that may lead to an interpretation bias of the current results. In fact, the single significant correlation occurred between the pain location and the temporal ratio. However, it was classified as small (r = 0.30; *p* = 0.007), with a trivial coefficient of determination (r2 = 0.09). This result means that only 9% of the data fits the predicted model. As a positive correlation, the pain would increase following the increase in the temporal ratio in the TMD patients, but not in an exact linear trend. This means that muscle excitation would also weakly increase proportionally to the pain predominance. The impact of such a left predominance of pain in TMD patients should be further studied, but in the present work its relevance is questionable.

The current study did not aim to establish which side is predominant for all people with TMD, as this may vary according to the location, population age, and other factors to be set in further studies. Instead, the present results reinforce the sEMG as an important factor to account for TMD evaluation, using an alternative index and a nominal classification. As noticed, the imbalance is not restricted to TMD patients, but it is also present (in a different direction) for healthy people. However, the modular ratio showed the intensification of the index for TMD. A limitation of the present study was the cross-sectional design, which did not allow cause–effect inferences. The present study also focused on women in a limited age range. Those with other levels of functional limitations might show a different pattern than their male counterparts of the same age. Not all intrinsic factors were addressed (such as the predominant side for chewing), and the results could be biased by those issues. However, as it also constitutes a memory recall, it was not possible to be sure of such factors for the analysis. Instead, they were pondered by the most usual factors set in previous studies.

#### **5. Conclusions**

TMD patients exhibit increased TA muscle ratio and an asymmetry of left dominance, compared with healthy subjects. Both binomial logistic models, based on TA or M muscle predominance, were moderately sensitive to predicting the presence of TMD.

**Author Contributions:** Conceptualization, A.C.B. and M.A.B.; methodology, A.C.B.; software, K.R.F. and A.C.B.; validation, K.R.F., M.A.B. and F.A.d.P.; formal analysis, F.A.d.P. and A.C.B.; investigation, F.A.d.P., M.A.B. and K.R.F.; resources, A.C.B.; data curation, F.A.d.P.; writing—original draft preparation, A.C.B. and M.A.B.; writing—review and editing, A.C.B.; visualization, F.A.d.P.; supervision, A.C.B. and M.A.B.; project administration, M.A.B. and A.C.B.; funding acquisition, A.C.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Fundação de Amparo à Pesquisa de Minas Gerais–FAPEMIG, grant number APQ 02040/18. This study was also supported, in part, by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001. This research was funded by the Federal University of Juiz de Fora, supporting the APC.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Federal University of Juiz de Fora (protocol code 68457617.6.0000.5147), for studies involving humans.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data are available only upon a request to the authors.

**Acknowledgments:** Special thanks to the UFJF-GV Department of Physical Therapy, MSc in Health Sciences–UFJF/GV and PhD/MSc in Physical Education Programs–UFJF/UFV.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Review* **Salivary Diagnostics in Pediatrics and the Status of Saliva-Based Biosensors**

**Hayeon Min 1,†, Sophie Zhu 1,†, Lydia Safi 1, Munzer Alkourdi 1, Bich Hong Nguyen 2, Akshaya Upadhyay 1 and Simon D. Tran 1,\***

**Abstract:** Salivary biomarkers are increasingly being used as an alternative to diagnose and monitor the progression of various diseases due to their ease of use, on site application, non-invasiveness, and most likely improved patient compliance. Here, we highlight the role of salivary biosensors in the general population, followed by the application of saliva as a diagnostic tool in the pediatric population. We searched the literature for pediatric applications of salivary biomarkers, more specifically, in children from 0 to 18 years old. The use of those biomarkers spans autoimmune, developmental disorders, oncology, neuropsychiatry, respiratory illnesses, gastrointestinal disorders, and oral diseases. Four major applications of salivary proteins as biomarkers are: (1) dental health (caries, stress from orthodontic appliances, and gingivitis); (2) gastrointestinal conditions (eosinophilic esophagitis, acid reflux, appendicitis); (3) metabolic conditions (obesity, diabetes); and (4) respiratory conditions (asthma, allergic rhinitis, small airway inflammation, pneumonia). Genomics, metabolomics, microbiomics, proteomics, and transcriptomics, are various other classifications for biosensing based on the type of biomarkers used and reviewed here. Lastly, we describe the recent advances in pediatric biosensing applications using saliva. This work guides scientists in fabricating saliva-based biosensors by comprehensively overviewing the potential markers and techniques that can be employed.

**Keywords:** biosensors; biomaterials; oral diagnostics; pediatric population; salivary diagnostics; salivary biomarkers

#### **1. Introduction**

Currently, blood and urine tests are commonly carried out to assess one's immune status, neuropsychiatric state, and development status. However, invasive screening, diagnostic, and prognostic tests induce a high-stress level in the pediatric population and their parents. Recent research has shown that salivary biomarkers can be an alternative non-invasive test, particularly useful in reducing stress in children between 0 and 18. This study aims to screen the current research on the use of salivary biomarkers in diagnosing autoimmune diseases, developmental disorders, and cancer, as well as neuropsychiatric, pulmonary, gastrointestinal, and oral diseases. It is an effort to highlight the role of saliva as a sample for its utilization in biosensing applications (Figure 1).

**Citation:** Min, H.; Zhu, S.; Safi, L.; Alkourdi, M.; Nguyen, B.H.; Upadhyay, A.; Tran, S.D. Salivary Diagnostics in Pediatrics and the Status of Saliva-Based Biosensors. *Biosensors* **2023**, *13*, 206. https:// doi.org/10.3390/bios13020206

Received: 16 November 2022 Revised: 16 January 2023 Accepted: 23 January 2023 Published: 30 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** Prospects for saliva-based biosensors: various biomarkers have been identified in systemic conditions; discussed here are autoimmune disorders, developmental conditions, neuropsychiatric conditions, metabolic disorders, gastrointestinal disorders, pulmonary diseases, and oral diseases. For biosensing applications, they should be assessed for: their reproducibility at different time points, locations, and populations; specificity towards the marker and disease; sensitivity, meaning they should be able to detect as low a concentration of the markers as possible, ideally lower than a clinically available test; the response time of the assay, preferably shorter for quicker results; and validation in clinical conditions in larger population sizes.

#### **2. Materials and Methods**

*2.1. Study Design*

This study involved an Ovid database search with the following search strategy: (1) saliva.mp. (69,706 articles); (2) salivary diagnostics.mp. (172 articles); (3) saliva\*.mp. (131,830 articles); (4) oral.mp. (767,765 articles); (5) 1 or 2 or 3 or 4 (876,003 articles); (6) biomarkers.mp. or Biomarkers, Tumor/or Biomarkers/or Biomarkers, Pharmacological/(639,768 articles); (7) 5 and 6 (24,223 articles); (8) pediatrics.mp. or Pediatrics/or Child/(1,892,565 articles); (9) 7 and 8 (1285 articles).

#### *2.2. Study Participants*

The publication year was limited to "2015 to current", resulting in a total of 698 articles that fit the criteria above at the time of preparing this manuscript in August 2022. We established inclusion and exclusion criteria for this study to filter those articles further. Inclusion criteria: the research paper must focus on using salivary biomarkers to diagnose a specific disease or disorder in the pediatric population, between 0 years old and 18 years old. Exclusion criteria: review articles and case reports were excluded.

#### *2.3. Data Collection*

We selected 161 articles and divided them into 7 sections: pulmonary and gastrointestinal diseases; developmental disorders; infectious diseases; neuropsychiatric diseases; oral diseases; oncology; and autoimmune diseases. There were: 9 articles related to autoimmune diseases; 27 articles for developmental disorders; 7 articles for infectious diseases; 57 articles for neuropsychiatric diseases; 27 articles for oral diseases; 3 articles for oncology; and 31 for pulmonary and gastrointestinal diseases. Each section is further divided into 5 specific applications of salivary biomarkers: metabolomics, proteomics, genomics, transcriptomics, and microbiomics.

#### **3. Salivary Biomarkers as Diagnostic Tools in Autoimmune Disorders**

#### *3.1. Metabolomics*

A study by Aral et al. investigated the salivary oxidative status of children with Type I Diabetes Mellitus (T1DM) and its implications regarding the risk of periodontal disease (PD) development. The authors observed that, when compared to their healthy counterparts, children with T1DM have a greater Oxidative Stress Index (OSI) (*p* < 0.05) [1]. To explain this finding, the authors observe that chronic hyperglycemia impairs reactive oxygen species (ROS) elimination. The elevated ROS produces tissue damage, thus contributing to the development of PD. This finding highlights the potential of salivary OSI as a prognostic tool for PD in children with T1DM, which would enable physicians to intervene earlier to improve the course of the patient's periodontal disease and, by extension, the duration of their diabetes.

#### *3.2. Proteomics*

A study by Collin et al. compared the salivary levels of several cytokines in children with Juvenile Idiopathic Arthritis (JIA) to those in healthy children. While IL-8 levels initially seemed to be higher in children with JIA, the authors ultimately concluded that there was no significant difference in the level of any investigated cytokines across the two group [2]. To explain this finding, the authors highlight that the children with JIA were taking immunosuppressive therapy throughout the study, which could hold their immune activity, and thus salivary cytokine levels, in check. Despite this, it could be possible to create a prognostic tool for children with JIA based on salivary cytokine levels. Such a tool would help monitor the children's response to therapy and the prognosis of their symptoms.

Immunoglobulin-free light chains (FLCs) were identified as a strong predictor in the prognosis of Pediatric-Onset Multiple Sclerosis (POMS), and its relapse [3]. Detecting the higher levels of free light chains early can potentially enable physicians to initiate stronger therapy to reduce the severity of the children's symptoms. Additionally, compared to current prognostic modalities, saliva collection is significantly less invasive than a lumbar puncture; it avoids gadolinium's potentially harmful deposition in the brain from repeated MRIs.

In a similar fashion to the studies above, Gomez Hernandez et al. investigated the saliva of children with Sjogren's Syndrome (SS) for cytokines, chemokines, and biomarkers (CCBM) that can serve as a diagnostic instrument for the condition [4]. The authors found 43 CCBM levels significantly elevated in children with SS, compared to healthy controls. Judging that number is too high for practical use; the authors recommend a test based on

IL-27 and CCL-4 alone, or a quintuple test of IL-27, MIA, CCL-4, TNFRSF18 and TNFA, to aid in diagnosing SS. The authors encourage the pursuit of further studies to link these CCBM to Sjogren's, as identifying such salivary biomarkers, which would appear early before the onset of symptoms, would enable physicians to intervene early and avert the course of the disease. A common limitation of the studies reviewed here is the small sample size. Indeed, the authors of the above papers invite further research to consolidate their findings. Further, other markers pertaining to transcriptomics and genomics can be explored for such conditions (Table 1).

**Table 1.** Investigated autoimmune disorders and their respective biomarkers.

#### **4. Salivary Biomarkers as Diagnostic Tools in Developmental Conditions**

*4.1. Metabolomics*

Bone-specific alkaline phosphatase (B-ALP) is a bone formation marker whose levels fluctuate with puberty. Al-Khatieeb et al. hypothesized that the level of ALP and LDH could affect the compliance of patients to monoblock therapy for orthoskeletal treatment. A high level of these enzymes was observed but was not significant after performing statistical analysis [5]. In another study, ALP, protein concentration, and chronological age with cervical vertebral maturation stages (CVMS) were used as non-invasive biomarkers to determine skeletal maturity. Salivary levels of ALP in conjunction with chronological age were accurate predictors for CVMS in 53.2% of cases [6]. ALP levels were co-related with variable stages of skeletal maturity (*p* = 0.0003) [7].

The early diagnosis and intervention of those with Autism Spectrum Disorder (ASD) have been linked with enhanced functional outcomes. The diagnosis, intervention, and prognosis would be facilitated by biomarker sampling, particularly salivary zinc concentrations. Low salivary zinc in those with ASD may contribute to the pathogenesis of autism [8]. Salivary metabolites were found to be higher in children experiencing pain when compared to those not experiencing pain. Collecting these non-invasive biomarkers may contribute to future pain management interventions in children with ASD [9].

#### *4.2. Proteomics*

It was found that insulin-like growth factor (IGF-1) and insulin-like growth factor binding protein-3 (IGFBP-3) may be used to estimate skeletal maturity. It was found that low salivary IGF-1 levels were present during puberty and lowered as pubertal growth ended. As a result, these salivary levels may be used to predict the completion of skeletal growth and may help predict when children would be most amenable to orthodontic treatments [10,11].

Increased levels of salivary proinflammatory markers, such as interleukin-6 (IL-6) and c-reactive protein (CRP), have been associated with child maltreatment. IL-6 secretions have been found to follow a clear circadian rhythm, suggesting that a disrupted rhythm is indicative of a dysfunctional inflammatory system. These findings suggest that a disrupted IL-6 rhythm, and, as a result, a disrupted inflammatory system, may be linked with childhood traumas (*p* = 0.031) [12]. Mucosal secretory immunoglobulin A (s-IgA) plays a critical role in the immune system. It has been found that acute psychosocial stress stimulates s-IgA secretion in adults and children who have undergone maltreatment. This is suggestive of child maltreatment prematurely aging their immune system [13]. Familial social and economic instability in early childhood has been linked to immune system dysregulation, as is evidenced by DNA shedding of the Epstein-Barr virus (EBV) salivary biomarkers. Traumatic early childhood events resulted in a 100% increase in EBV DNA shedding among previously infected adolescents. This indicates that the salivary biomarkers of EBV may be indicative of childhood trauma [14].

The protein composition of saliva has been studied from early on to find associated markers for the diagnosis and prognosis of ASD [15,16]. The mass spectrometric analysis of a patient cohort of 132 proteins were increased in ASD-positive patients, 25 of which were associated with a severe to moderate group of the elevated proteins, suggested as biomarkers for ASD calmodulin-3, plastin-2, and protein s100-a7 [17].

#### *4.3. Genomics*

Those with Cleft Lip Palate (CLP), one of the most common congenital malformations of the head and neck, are found to be more likely to present with accompanying anomalies including Defects of Enamel (DDE). Matrix Metallopeptidase 2 (MMP2), a membranebound protein containing collagen-degrading capabilities, plays a crucial role in tooth formation and mineralization. The findings suggest that MMP2 may play a role in a genomic approach to establish any potential risks for those with CLP [18]. Supernumerary teeth are one of the most common dental anomalies seen in the pediatric population. An immunohistochemistry panel showed that those with supernumerary teeth presented with an enhanced expression of Wingless (Wnt) and Sonic Hedgehog (SHH) proteins, both of which are pro-tumorigenic proteins [19].

It has been shown that psychological stress elicits telomere shortening. Through salivary samples via PCR testing, it was found that families with an ASD child endure exceeding levels of psychological stress [20]. Using these biomarkers may contribute to the early diagnosis of any psychological stress and respective intervention, which would optimize mental well-being and the quality of life. Maternal stress affects an infant's neurocognitive development before, during, and post-pregnancy. Certain CpGs were found to be linked genetically, including YAP1, TOMM20, and CSMD1.

Additionally, two differentiated methylation groups (DMR), DAXX and ARL4D, were found to be related to maternal stress measures. An early assessment of maternal stress may contribute to preventative health measures for the infant, further optimizing their health and the quality of life [21]. Upon examining polymorphisms of the STS and SULT2A1 genes, dehydroepiandrosterone (DHEA) and its sulfated form, DHEA-A, and their relationship to Attention Deficit Hyperactivity Disorder (ADHD), it was found that the levels of DHEA were positive correlated with attention. This may highlight the potential role of ADHD and its pathogenesis with regard to STS polymorphisms and neurosteroid levels [22] (Table 2).

**Table 2.** Most commonly recurring developmental conditions and their respective biomarkers.

#### *4.4. Transcriptomics*

Circulating microRNA (miRNA) was compared to cerebrospinal fluid miRNA levels when assessing miRNA as a biomarker for pediatric concussions. Six miRNAs had similar changes in both CSF and salivary levels, including miR-182-5p, miR-221-3p, mir-26b-5p, miR-320c, miR-29c-3p, and miR-30e-5p. More specifically, concentrations of miR-320c were found to be directly correlated with attention deficit. This suggests that salivary miRNA may be an accurate potential biomarker for traumatic brain injuries [25].

Several miRNAs have been found to potentially be used as biomarkers in diagnosing and treating those with ASD. The most accurate miRNAs to differentiate between typically developing children and those with ASD were miR-23a-3p, miR-32-5p, and miR-628-5p [24].

#### *4.5. Microbiomics*

ASD has been linked with many comorbidities, including gastrointestinal abnormalities, dental disease, and allergies. Oral and gut microbiomes are suggested to play important roles in inflammation and immune dysfunction pathogenesis. More specifically, 16S rRNA gene amplicon sequencing helps distinguish the oral and gut macrobiotics of patients with ASD. Altering the oral and gut microbiome may potentially address and ameliorate co-morbidities associated with those with ASD [23].

#### **5. Salivary Biomarkers as Diagnostic Tools in Neuropsychiatry**

#### *5.1. Metabolomics*

A plethora of evidence supports that measuring salivary cortisol and alpha-amylase levels serves as stress biomarkers. The application of salivary stress biomarkers spans from pediatric dental anxiety to internalizing or externalizing disorders. The partial hypofunction of hypothalamus-pituitary-adrenal (HPA) was found to be associated with pediatric patients suffering from ADHD by assessing the cortisol levels to induced stress, which was found to be reduced in positive patients (*p* = 0.034) [26]. In addition, salivary immune biomarker secretory immunoglobin A (s-IgA) is often measured alongside HPA biomarkers to explore the interactions between stress, the HPA axis, and the immune system [27]. Obtaining samples through saliva is a great alternative to blood collection in the pediatric population, especially for children with cerebral palsy. In patients with cerebral palsy, the sympathetic nervous system predominates, causing vasoconstriction and making peripheral venous blood more difficult to access. A study demonstrated, with non-invasive

salivary diagnostics, that higher levels of inflammatory markers in patients with cerebral palsy are associated with excessive constipation and a reduced quality of life [28].

While using salivary stress biomarkers, such as cortisol and alpha-amylase, as a diagnostic tool is largely favorable, they are far from perfect. For instance, they cannot distinguish between acute fear and chronic anxiety [29]. Additionally, salivary cortisol concentrations reflect free serum cortisol concentrations, which is the unbound and biologically active 5% of total cortisol under basal conditions. On the other hand, measuring plasma cortisol concentration yields total cortisol. In other words, salivary cortisol can only be a surrogate for free serum cortisol concentrations [30]. Another study demonstrated that the validity of assessing manganese exposure through saliva samples needs further study. The correlation between manganese exposure in water and salivary levels is the weakest of the candidate biomarkers, including saliva, hair, and toenails [31].

#### *5.2. Proteomics*

Soluble salivary immune mediators such as C-reactive protein (CRP), interleukin-6 (IL-6), IL-1beta, IL-8, tumor necrosis factor-alpha (TNF-alpha), and secretory immunoglobin A (s-IgA), can be measured in salivary samples and serve as markers for systemic inflammation, including gingival inflammation [32]. In the case of children and youth with Obsessive-Compulsive Disorder (OCD), the elevated salivary levels of lysozyme, alphaamylase, secretory s-IgA, CRP, IL-6, IL-1beta, and TNF-alpha are found to be associated with OCD diagnosis and symptom severity [32,33]. Melatonin secretion can also be detected and collected in saliva to evaluate one's sleep [34], with the possibility of an at-home collection [35].

#### *5.3. Genomics*

Salivary extracellular vesicles (EVs) are reliable diagnostic biomarkers for detecting mild traumatic brain injury (TBI). Genetic profiling with the real-time PCR of salivary EVs showed that more than 50 genes, and more significantly three of them (CDC2, CSNK1A1, and CTSD), are upregulated in emergency department patients and in concussion clinic patients, compared to control groups (*p* < 0.05) **[36]**. Salivary DNA samples can also help researchers determine epigenetic changes in maltreated school-aged low-income children. The increase or the decrease in methylation varies depending on the sex of the child and the timing of maltreatment. In general, the mean difference in methylation between maltreated children and the control group was 6.2%. Epigenetic modifications are also seen in the glucocorticoid receptor gene (nuclear receptor subfamily 3, group C, member 1 genes), dopaminergic gene (ankyrin repeat and kinase domain containing 1) and alcoholmetabolizing gene (aldehyde dehydrogenase 2), which are particularly important for the development of psychopathology in maltreated children [37].

#### *5.4.  The 2018 article published in the Journal of Neurotrauma compared changes in miRNA concentration after childhood TBI in CSF and in saliva. A total of 214 miRNAs were detected in CSF, where 63% of them were also present in saliva, and 10% had parallel changes in both saliva and CSF. Six miRNAs had similar changes in both CSF and saliva: miR-182-5p, miR-221-3p, mir-26b-5p, miR-320c, miR-29c-3p, and miR-30e-5p; three of them were related to neuronal development. Attention difficulty reported by the child and parent was directly correlated with the concentration of miR-320c [25].

Another study explored the relationship between miRNA expression in saliva and detecting prolonged concussion symptoms [38]. Researchers could accurately identify patients with prolonged symptoms using levels of 5 miRNAs (miR-320c-1, miR-133a-5p, miR-769-5p, let-7a-3p, and miR-1307-3p) and logistic regression (area under the curve, 0.856; 95% CI, 0.822–0.890). One month after injury, the expression of three miRNAs was associated with specific symptoms: miR-320c-1 was associated with memory difficulty; miR-629 with headaches; and let-7b-5p with fatigue. Similarly, a study compared salivary miRNA expression in two groups of children: those with persistent post-concussive symptoms (PPCS); and those without PPCS, at three different time points (within one week of injury, one to two weeks post-injury and four weeks post-injury) [39]. The study analyzed a total of 827 miRNAs, 91 of which had higher expression levels than the calculated background threshold and were included in the differential gene expression analysis. Among these 91 miRNAs, 13 had significantly different expression levels in children with PPCS at all the time points (Table 3).

**Table 3.** Neuropsychiatric conditions and their respective biomarkers.

#### *5.5. Microbiomics*

A study measured the number of EBV copies shed in saliva and assessed depressive symptoms using the Center for Epidemiologic Studies-Depression scale in female and male adolescents aged 11–17 years. Their study showed that the presence of salivary shedding of reactivated EBV is linked to depressive symptoms in female adolescents only. In fact, the interaction between sex and depressive symptoms with EBV reactivation was statistically significant (*p* < 0.01) [40].

#### **6. Salivary Biomarkers as Diagnostic Tools in Metabolic, Gastrointestinal, and Pulmonary Diseases**

#### *6.1. Metabolomics*

A pilot cohort study revealed that hepatic steatosis, or non-alcoholic fatty liver disease (NAFLD)—an important feature linked to metabolic syndrome—can be detected by elevated salivary uric acid levels in children with obesity. Saliva was sampled using a non-invasive swab collection device and analyzed using gas chromatography-mass spectrometry. Moreover, salivary uric acid increased was associated with elevated homeostatic model assessment-insulin resistance values. Despite the small sample, its findings revealed the hopeful use of salivary markers as a screening and preventive tool for hepato-metabolic comorbidities, including hepatic steatosis, with high sensitivity, specificity, and accuracy among children with obesity [41].

#### *6.2. Proteomics*

Studies have confirmed a significant increase in CRP and insulin concentrations in the saliva of children with obesity. An observational study in Spain followed a cohort of children from ages 8 to 12 and measured the levels of circulating inflammatory cytokines to detect the signs of obesity-related inflammation and glucose intolerance, a predisposing factor responsible for the progression of diabetes and metabolic syndrome. Increased salivary CRP, leptin, and insulin levels in assays detected differences in the body-mass index, dietary characteristics, and physical activity levels in a sex-dependent manner [42]. A large cohort study constructed and evaluated a continuous approach to a scoring system for metabolic syndrome risk factors adapted to children. The diagnostic tool proposed by Shi et al. incorporates a combination of clinical parameters, such as waist circumference and systolic blood pressure. It uses salivary measures of fasting glucose and high-density lipoprotein cholesterol as surrogates of plasma measures. In addition to finding correlations between obesity and the salivary measures of insulin, CRP, and adiponectin, the progressive nature of their suggested scoring system, as opposed to the binary definition of the disease in adults, revealed a higher level of sensitivity for pediatric populations [43].

Evaluating systemic changes in saliva can also be a potential tool for diagnosing, predicting, and monitoring pulmonary diseases in pediatrics. Like obesity, salivary CRP is suggested to be a predictor of pediatric acute respiratory illness, more importantly in pediatric pneumonia. The clinical utility of salivary CRP can be seen in an acute pediatric setting and helps predict serum CRP above 100 mg/L with high specificity. This can replace the invasive use of venipuncture in young outpatients [44]. Finally, salivary biosensors can detect early childhood asthma and predict asthma exacerbations. In their analytical crosssectional study of children aged 6 to 12, Zamora-Mendoza et al. proposed a non-invasive method based on immunoassay and surface-enhanced Raman spectroscopy. The study identified the proteomic profile of bronchial inflammation, including elevated levels of IL-8, IL-10, and sCD163 [45]. Another small cohort study conducted by Okazaki et al. revealed that salivary surfactant protein D (SP-D) in children with asthma using ELISA was much higher than in healthy controls. This reflected the degree of bronchial inflammation in response to allergen exposure causing airway resistance and correlated with the increased indices measured by the forced oscillation technique. Increased salivary SP-D levels were associated with a higher degree of asthma exacerbation [46].

In the field of liver diseases, proteomic methodologies were also applied to look for a non-invasive method of monitoring. With the salivary cytokine profile using ELISA quantification, Davidovich et al. were able to reflect a similar pattern of serum inflammatory parameters describing the immunosuppressive status in liver-transplanted children undergoing tacrolimus therapy. The potential markers of inflammation in saliva using

proteomics were identified, such as the reduced levels of IL-6 and IL-10 and the elevated levels of IL-1b [47].

#### *6.3. Microbiomics*

Two studies examined oral microbiota dysbiosis using bacterial 16S rRNA gene sequence analysis in children with various liver diseases. Iwasawa et al. showed that subjects with Pediatric-Onset Primary Sclerosing Cholangitis (PSC) presented significantly decreased Haemophilus and increased Oribacterium colonies in their saliva, compared to healthy participants to patients with Ulcerative Colitis (UC). By observing significant differences between PSC and UC salivary bacterial profiles, microbiome markers in the saliva can help distinguish these two diseases and provide accurate diagnosis in a noninvasive manner [48]. Another cohort study performed microbial profiling from oral swabs in children diagnosed with non-alcoholic fatty liver disease. Systemic inflammation, cirrhosis, and advanced liver disease progression were associated with increased levels of oral *Veillonella* and *Prevotella* [49]; however, further microbial profiling is needed with larger heterogeneous sample sizes, as mentioned previously (Table 4).

**Table 4.** Common metabolic, gastrointestinal, and pulmonary conditions and their respective biomarkers.

#### **7. Salivary Biomarkers as Diagnostic Tools in Oral Diseases**

In clinical practice, caries, gingivitis, and periodontitis diagnosis is mostly based on clinical, visual, and radiological findings. The following section overviews research where various proteins, microbes, and metabolites, were associated with childhood caries or periodontal disease. This research can be a basis for biochemical diagnostic tools in dental settings. Adopting such a biochemical diagnosis can improve treatment outcomes by enabling dentists to tailor therapeutic interventions to a patient's specific biochemical needs (Table 5).

**Table 5.** Oral diseases and their respective studied biomarkers.

#### *7.1. Metabolomics*

In the case of dental caries, one study by Alqaderi et al. associated a later bedtime in Kuwaiti children with an increased incidence of caries [50]. To explain this association, the authors highlight that a later bedtime leads to changes in hormonal levels, notably a lower leptin level and a higher ghrelin level. This hormonal change leads to more frequent, carbohydrate-rich snacking in children, thus predisposing them to caries. This observation opens the door for cost-efficient, easy-to-implement sleep interventions in susceptible children to reduce their caries risk. Additionally, these hormonal changes can be detected relatively simply and non-invasively in saliva. Another study by Syed et al. associated a lower level of salivary nitric oxide (NO) and its metabolites with an increased risk of caries in children. This association can be explained by the antimicrobial role of NO in protection against caries [51]. Thus, screening children for salivary NO levels enables dentists to identify those at high-risk for caries, and tailor a dietary intervention with relatively easier compliance to raise their NO levels, and thus protect them from caries.

As for gingivitis and periodontal disease, Goodson et al. found an association between dietary phosphate intake and gingivitis in children. Indeed, phosphate acts as a local pro-inflammatory in the gingiva by reducing IL-4 production and raising that of IL-1b [58]. Thus, by screening children for salivary phosphate levels, we can identify those at high-risk for gingivitis and intervene by reducing their dietary intake to protect them from caries. Lastly, a study by Sridharan et al., focusing on periodontal disease in children with Type I Diabetes Mellitus (T1DM), observed that children with a higher alkaline phosphatase level had a greater gingival index and pocket pocking depth [59]. Thus, evaluating the salivary level of alkaline phosphatase in children with T1DM enables dentists to intervene early to avert the course of the disease and improve the children's outcomes.

#### *7.2. Proteomics*

This sub-section will also focus only on caries lesions. A study by de-Sousa et al. [52] found an association between the activity of carbonic anhydrase-VI (CA-VI) and caries incidence in children. Indeed, a lower activity level of salivary CA-VI predisposes children to caries and can thus be used as a prognostic factor for caries development. Another work by Jurczak et al. associated higher levels of salivary b-defensin-2 and histatin 5, two proteins with antimicrobial properties, with more advanced carious lesions [53]. Higher levels of 1β, IL-6, IL-8, IL-10, and TNF-a are seen to be increased in gingivitis and cariesprone children [54,55]. Lastly, while this does not constitute a salivary biomarker, Hertel et al. observed a higher level of Mucin 5b, Mucin 7, and IgA, in the pellicle of caries-active children compared to their caries-free counterparts [60].

While the authors of the studies above recognize a limitation in their work, namely their small sample sizes, they encourage the pursuit of larger-scale trials to confirm their observed associations. Much like we would test gene panels to identify a defective gene in an individual, to diagnose and prognose a suspected genetic disorder, one could do a "salivary biomarker screen", which would include the various metabolites, microbes, and proteins associated with oral disease. Such a screening tool would enable dentists to pinpoint a patient's biochemical risk factors for oral disease and develop individualized treatment plans for their patients.

#### *7.3. Microbiomics*

This sub-section focuses exclusively on childhood caries. One study by Kim et al. observed that caries-free Korean children had greater diversity in their oral microbiota than their caries-active counterparts [61]. Additionally, children with caries had greater proportions of bacteria that exhibit pro-caries behavior, such as *Streptococcus* and *Granulicatella*, which invade epithelial cells, as well as *Staphylococcus* and *Veillonella*, which are involved in D-alanine metabolism. In contrast, caries-free children had greater proportions of bacteria that antagonize caries formation, notably *Neisseria* and *Lautropia*, which are involved in linoleic acid metabolism, and *Capnocytophaga*, which is involved in flavonoid biosynthesis. Another study by Hemadi et al. observed higher salivary levels of *Streptococcus mutans spp.*, *C. albicans,* and *Prevotella* spp. in children with caries, compared to their caries-free counterparts [56]. Lastly, Manzoor et al. observed higher levels of sugar fermenters, such as *Paludibacter* and *Labrenzia*, in children with caries [57].

#### **8. Current Status and Advances in Saliva-Based Biosensors**

The importance of saliva as a diagnostic tool came into light during the advent of the COVID-19 pandemic. Its non-invasiveness and the ease of collection ensured the safety of healthcare workers; nonetheless, nasopharyngeal swabs remained the gold standard. It evidenced how saliva can become an essential part of diagnostic procedures. Antibody antigen-based reactions can be efficiently utilized and detected by colorimetric or spectrophotometric changes. A prototype for a saliva-based MMP8 detector using specific antibodies and surface acoustic wave (SAW) technology was developed for periodontal disease and was seen to have comparable sensitivity and specificity to the standard ELISA-based methods [62] (Figure 2).

**Figure 2.** A typical saliva-based biosensor. Briefly, it consists principally of three components, a surface (carbon-based or metal) to accommodate the biorecognition element (probes, antibodies, or enzymes), which captures the target (genomic material, antibodies, proteins, etc.). The molecular interactions induce colorimetric or spectrometric changes, which can be observed with their corresponding detection technologies. Image used with permission from Goldoni et al., 2021 [63].

Stefan-van Staden et al. proposed a new method of assay of estradiol (E2), testosterone (T2), and dihydrotestosterone (DHT) based on stochastic microsensors that allow the earlier detection and prevention of hormonal, metabolic, and developmental problems in children. This could replace the classical measurement of electrochemical markers using ELISA or liquid chromatography-tandem mass spectrometry. The biosensor is designed using diamond paste as the matrix and three different electroactive materials: maltodextrin, a-cyclodextrin, and 5,10,15,20-tetraphenyl-21H,23H porphyrin [64]. The response to stochastic microsensors is based on channel conductivity, which is the degree of alteration of a current traversing a channel under a controlled potential of 125 mV when a steroidal hormone binds onto the channel wall. By manipulating the channel conductivity of analytes, stochastic sensors are described as having high reliability and selectivity, which could overcome the limitations of the standard method, such as its insufficient sensitivity for children's saliva. The results demonstrated that, compared to the standard method using ELISA, the stochastic biosensor could perform reliable pattern recognition, identification, and the quantification of the three hormones at very low concentrations in saliva samples [64].

Lukose et al. also highlighted optical technologies using photonics to rapidly screen abnormal health conditions and viral infections rapidly and universally through saliva. The article explored optical spectroscopy techniques that more precisely involve the UVvisible-infrared region, including surface plasmon resonance, Raman, IR spectroscopy, and laser-induced fluorescence [65]. For instance, a previous section of our paper discussed the potential use of surface-enhanced Raman spectroscopy to detect the proteomic characteristics of asthma in children. Non-invasive, photonic-based biosensors constitute a hopeful field of new diagnostic tools due to their capacity to provide the remote, contactless evaluation and identification of conditions, including communicable diseases such as COVID-19. In addition to detecting viral, bacterial, and parasitical infections, photonics can be applied in a wide range of health conditions, such as various cancers, oral diseases, metabolic and systemic diseases, as well as the detection of drug-related biomarkers in saliva [65]. Finally, Lukose and al. suggested the possibility of a multi-modal approach

in which a combination of photonics tools, accompanied by artificial intelligence, could further optimize the specificity and sensitivity of these promising biosensors.

#### **9. Conclusions**

We have reviewed several methodologies that performed with good sensitivity and specificity, as well as additional advantages covering some of the important limitations of standard diagnostic tests performed today in the pediatric population. Acknowledging its non-invasiveness and ubiquitous applications, saliva as a probing biofluid sample remains highly attractive. It requires further investigations with larger samples and better analytical and comparative evidence. The pursuit of further studies on these biomarkers can be highly impactful. Indeed, salivary diagnostic/prognostic tools are less invasive and less harmful than current tools and enable physicians to intervene early, altering the course of the disease and significantly reducing suffering and disability in patients.

While sensitivity and specificity to the target molecules are satisfactory in the studies explored, the specificity to a particular condition remains a challenge. For example, proinflammatory markers can be commonly identified in autoimmune disorders, stress, poor oral health, and pulmonary conditions. Identifying a marker highly specific to each condition is imperative. Nonetheless, diagnosing infectious diseases like COVID-19, and periodontal diseases, using microbiome-specific screens, specific proteins in ASD, and miRNAs in various systemic conditions, are promising as salivary biosensor applications.

**Author Contributions:** Conceptualization, A.U., S.Z., H.M., L.S., M.A., B.H.N. and S.D.T.; writing original draft preparation, S.Z., H.M., L.S., M.A. and A.U.; writing—review and editing, A.U., H.M., S.Z., B.H.N. and S.D.T.; supervision, S.D.T.; project administration, A.U., S.D.T. and B.H.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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