Phases of orthodontic tooth movement [27].
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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During the orthodontic procedure, the analysis of saliva/salivary fluid and Gingival crevicular fluid (GCF) may be examined to monitor biological process/progress.
In the orthodontic treatment, emphasis is on the point of patient care for the best results, by growth modification of the craniofacial region along with alveolar bone remodeling during fixed orthodontic procedure [1]. Induction of biologically active compounds occur inside the periodontium due to the orthodontic treatment which eventually induces cellular response in different microenvironment for biological response [1]. Orthodontic treatment is considered successful by three major factors namely periodontal health, oral hygiene and optimal orthodontic forces [1, 2]. Availability of newer techniques have reduced lateral effects like, pain, periodontal diseases, abbreviate the treatment period and limit iatrogenic damages like root resorption and development of nonvital teeth. The sequential events that occur following the orthodontic tooth movement (OTM) can be illustrated by the released molecules, regarded as biomarkers [1, 2]. Biomarkers are attributes that can be quantified, these can the indicators of biological processes which may be normal or pathogenic and/or other metabolic processes [3, 4]. Other important features of the biomarkers are specificity and sensitivity, which will have the ability to notify the biological conditions/changes occurring during any process/procedures [4]. Adequate knowledge about the cellular and biological processes makes it easier to understand the biological mechanics that can shorten the treatment time avoiding the detrimental effects linked to the orthodontic treatment due to its objective characteristics [3, 4].
In the orthodontic treatment, emphasis is on the point of patient care for the best results, by growth modification of the craniofacial region along with alveolar bone remodeling during fixed orthodontic procedure [1]. Induction of biologically active compounds occur inside the periodontium due to the orthodontic treatment. This induces cellular response in different microenvironment for biological response [1]. Orthodontic treatment is considered successful by three major factors namely periodontal health, oral hygiene and optimal orthodontic forces [1, 2]. Availability of newer techniques have reduced lateral effects like, pain, periodontal diseases, abbreviate the treatment period and limit iatrogenic damages like root resorption and development of nonvital teeth. The sequential events that occur following the orthodontic tooth movement (OTM) can be illustrated by the released molecules, regarded as biomarkers [1, 2]. Biomarkers are attributes that can be quantified, these can be the indicators of biological processes which may be normal or pathogenic and/or other metabolic processes [3, 4]. Other important features of the biomarkers are specificity and sensitivity, which will have the ability to notify the biological conditions/changes occurring during any process/procedures [4]. Adequate knowledge about the cellular and biological processes makes it easier to understand the biological mechanics that can shorten the treatment time avoiding the detrimental effects linked to the orthodontic treatment due to its objective characteristics [3, 4].
Forces induced by orthodontic therapy stimulates periodontium cells to release many chemical intermediaries like cytokines. The cytokines contribute immensely in the periodontal and alveolar bone remodeling, bone resorption and new bone deposition [5]. During the process of bone metabolism, the biomarkers are released into the circulation which indicates bone remodeling activity comprising of both osteoblastic deposition and osteoclastic resorption. Systemic circulation in the orthodontic patients indicates the skeletal maturity, this can be detected using biomarkers. And their detection locally, in saliva and gingival crevicular fluid (GCF), indicates the advancement of orthodontically induced alveolar bone remodeling [4]. Many research studies have been executed, suggestive of presence of array of molecules indicating the skeletal growth turnover. The assessment of molecular biomarkers of bone remodeling in the body fluids such as saliva, GCF etc., would guide the clinicians to arrive at a better treatment plan for orthodontic therapy at the ideal time and estimate the advent of the treatment [4, 5].
Saliva is considered a medium for the microbes and transport, which is affected by status of oral health and the quantity and types of bacteria present in the oral cavity. It is also comprising of innate immune factors and various salivary defense proteins. The fixed orthodontic therapy induces site-specific bone resorption and formation and cytokines which are released from periodontal ligament (PDL) cells.
The complex combination of serum, host inflammatory cells, structural cells of oral bacteria and the periodontium leads to the formation of the GCF which arises from the plexus of gingival blood vessels in the gingival corium, lying underneath to the epithelial lining of the dentogingival space. It can be isolated from healthy sulcus as well [3, 5].
The origination of GCF components is from blood, subgingival plaque and host tissues. Presence of the transudate of the gingival interstitial fluid due to the osmotic gradient in the healthy periodontium is observed. There will be a steady increase in the volume with inflammation and greater capillary permeability [6]. Previously GCF was known as continuous transudate but currently it is considered as inflammatory transudate [7]. GCF comprises of host-derived substances which includes cytokines, antibodies, tissue degradation products and enzymes [8]. The inflammatory exudate increases by more than 5-fold during the inflammatory conditions, such as periodontal disease and gingivitis [9].
Orthodontic forces result in a condition which can be described as a consequence of the orthodontic force, a condition persists that involves a series of inflammation and repair intended at converting it into normal tissues and [10] according to some reports GCF reflects the immune reactions arising from both orthodontic force application and periodontitis [11, 12].
Nowadays many biomarkers are detected using saliva. It was recently discovered that several new isoforms for Nerve Growth Factor (NGF), Brain derived Neurotropic Factor (BDNF) and Calcitonin gene-related peptide (CGRP) were found in the saliva [3]. Identification of these isoforms can be utilized to develop subtle ways that can be considered to be methods to detect and analyze markers related to pain. The nuclear factor kappa B ligand and of the nuclear factor kappa B/osteoprotegerin (RANK/RANKL/OPG) signaling pathway being one of the several key factors that initiated the commencement of osteoclasts. The recruitment, differentiation and survival of osteoclasts are facilitated by the osteoblasts [12] which secretes a molecular biomarker RANKL. Induction of differentiation of immature osteoclasts into functional cells are due to the binding of RANKL with RANK (expressed at the surface of the osteoclast). Osteoblasts produce OPG which acts as a soluble receptor for RANKL. This inhibits the terminal stages of osteoclast differentiation [12].
Pain and discomfort are inevitable during orthodontic treatment [13]. Conventionally, the degree of pain is assessed subjectively using many pain scales [14]. Assessing pain objectively using salivary physiological biomarkers would benefit the clinician for appropriate pain diagnosis and management [13, 15]. The role of saliva in the diagnostic and prognostics is side-lined due to unavailability of a standardized collection procedure, even though well accepted by patients [3].
The neuropeptide, NGF protects the neurons and regenerates them. It plays an important role in hyperalgesia and its concentration increases during inflammation which is up regulated in response to noxious stimuli [16]. The occurrence and development of pain and hyperalgesia are credited to be due to the role of CGRP and BDNF. CGRP and BDNF has been implicated in migraine and headache based on increased saliva and plasma concentrations during active pain periods [16, 17].
Only a few studies have investigated the levels of these above-mentioned neuropeptides in saliva [16, 17, 18]. Several patients describe much longer periods of pain and discomfort which are common during the first 1 or 2 days of the orthodontic treatment. Scheurer et al. reported that even after 7 days of insertion of a fixed appliance, 25% of all investigated patients still reported pain [19]. According to measurements at 4 h and 24 h, the intensity of pain generally increases with time, but falls to normal levels after 7 days of the orthodontic treatment [19]. Biomarkers can be used to characterize the sequential events following OTM. The rate, amount, and the activity of the released substances/biomarkers indicates the activity of individual cells and the metabolic activity involved in the tissues and organs [1]. These potential biological markers can be collected from different tissue samples. The sampling is done as per the required biomarker and the biological processes to be studied [1, 20]. Several possible biomarkers representing many of these biological changes during specific phenomenon like pain experienced during orthodontic treatment pain, bone remodeling, inflammation and root resorption have also been proposed [20]. The clinical application can be developed from the knowledge of biomarkers that can accelerate the orthodontic treatment as well.
There are two types of tooth movement namely: OTM and Physiological tooth movement. The physiological tooth movement occurs slowly in the cancellous bone in buccal direction or the cortical bone [21]. In contrast, OTM can occur both rapidly or slowly, it depends on the rate, physical characteristics and amount of the force application and the biological response of the Periodontal Ligament (PDL) [22]. The orthodontic force application can change the dental and paradental tissues, including the PDL, alveolar bone, dental pulp and gingiva resulting in pressure and tension sites at the tooth region [23].
Perinetti et al. [24] through their research study state that, one bone remodeling cycle involves four main phases namely: activation, bone resorption, reversal, and bone formation. Recent studies have exhibited that several enzymes are expressed during these phases which have been designated as biomarkers during bone remodeling namely Tartrate-resistant Acid Phosphatase (TRAP), Alkaline Phosphatase (ALP), Lactate Dehydrogenase (LDH), Aspartate Aminotransferase (AST), and many more.
Orthodontic therapy involves the supervision, guidance and correction of growing and maturing dentofacial structures which is based on the principle that if the teeth is subjected to prolonged pressure, consequently it will lead to remodeling of the bone. This OTM is exemplified by the remodeling of dental and paradental tissues [21].
In 1962, Burstone [25] stated the three phases of tooth movement, when rates of tooth movement are plotted against time:
Initial phase
Lag phase
Post-lag phase.
Initial phase: is described by immediate and quick movement which occurs in time period between 24 h to 48 h after the preliminary orthodontic force application to the tooth. This rate is largely recognized to the tooth movement in the PDL space.
Lag phase: The duration of this phase lasts up to 20–30 days which relatively shows little to no tooth movement. In this phase the region in which compression is applied, such region demonstrates the PDL hyalinisation. No subsequent tooth displacement occurs until the cells remove the necrotic tissues completely.
Post-lag phase: follows the lag phase, where the rate of tooth movement increases [26].
Pilon et al. [27, 28] divided the curve of tooth movement into four phases (Table 1). The GCF of tooth movement contain the biomarkers that indicate these phases and signaling pathways. Considerable increased levels of concentrations of cytokines responsible for inflammation and prostaglandins are observed.
Phase I (initial) | 24 h–2 days Initial tooth movement within the socket | Acute inflammatory response Vasodilation-migration of leucocytes-release of cytokines-cell signaling molecules (metabolic products of paradental remodeling) |
Phase II (arrest) | 20–30 days Movement stops | Chronic inflammation Continuation of migration of leucocytes Paradental remodeling |
Phase III (acceleration) | 40 days of accelerated tooth movement after initial force application | Another period of acute inflammation superimposing the on-going chronic inflammation |
Phase IV (linear) | Overall tooth movement | Recruitment of macrophages, fibroblasts, osteoblasts, and osteoclasts Alkaline phosphatase activity |
Phases of orthodontic tooth movement [27].
The tissue changes that are involved during OTM includes compression region (which involves osteoblasts), tension region (which involves osteoclasts), pulp tissues and dental root [29]. Several possible biological factors or biomarkers representing these biological changes during particular phenomenon that is, bone remodeling. Inflammation and root resorption, have been identified. Similarly, lactic acid dehydrogenase (LDH) and dentin sialophosphoprotien (DSPP) are also potentially observed. A research study suggests that using sampling from four different sampling procedures, that is, saliva, GCF, tissue (biopsy), and serum, the biomarkers indicative of the ongoing biological processes can be identified [29]. The suggested amount and concentration of biomarkers during OTM are the best and practical sampling or testing procedure indicative of the biological phenomenon. The amount of precise force application and duration that should be used for each tooth during OTM can be decided based on the knowledge of these biomarkers. Ultimately, it produces an optimal treatment with mild side effects or accelerate the treatment [29].
Most of the laboratory diagnostic procedures involves the analysis of the cellular and biochemical constituents of the blood. Saliva can be used in the diagnosis of several diseases. This can be feasible and valuable for children and older adults due to the ease of collection of the fluid [30]. Saliva can be classified into two types: gland-specific saliva and whole saliva. It is feasible to collect saliva specific to different glands from individual salivary glands namely: parotid, submandibular, sublingual and minor salivary glands. Since the secretions from both the submandibular and sublingual salivary glands enter the oral cavity only through single duct known as Wharton’s duct [31], hence collection of the saliva from submandibular and sublingual glands separately is challenging.
Orthodontists generally aims to gain ideal orthognathic conditions with fewer treatment times i.e.; shorter treatment time with longer treatment intervals with fewer appointments [32]. Conversely, when heavier force is applied to accelerate tooth movement, the oxygen tension in the periodontium will be conceded due to reduced vascular supply [33]. This will risk or expose the healthy supporting alveolar bone and periodontal structure leading to the slow progress of the treatment. In order to supervise the orthodontic tooth movement in a non-invasive approach in human beings, the alterations that appear during the examination of the profile and levels of various cytokines, enzymes, growth factors, and proteoglycans in saliva and GCF. Evidences support the elevated levels of several biomarkers or cytokines, that is, interleukin (IL)-1β, IL-6, epidermal growth factor (EGF), prostaglandin (PG) and proteoglycans, in the saliva and GCF [34, 35, 36]. Components of GCF namely ALP, TRAP, LDH and AST have been recognized to be potential biomarkers during OTM [37, 38, 39, 40]. Study conducted by Shahrul et al. [41] showed that ALP, TRAP, and LDH also existed in saliva. Orthodontic treatment using a surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) approach the effects of the orthodontic treatment on salivary proteins has been performed as per the study by Zhang et al. [42]. The outcome of this approach determined the relatively low molecular weight proteins but not the identity of these proteins. Only the expression profile was cross-examined.
Saliva has protective and anti-microbial properties and contains a variety of growth factors [43, 44]. Saliva helps in the easy digestion of the food since it has lubricating functions [45]. The role of saliva and different salivary constituents responsible for its functions are summarized below in Table 2 [30].
Functions salivary components involved:
|
The major functions of saliva [30].
Salivary protein concentration ranges from 2 to 5 mg/mL which constitutes about 3% of the protein concentration of blood. Numerous proline-rich glycoproteins, immunoglobulin A and amylase are the major secretory proteins of the parotid glands, other antibacterial salivary proteins include lysozymes, peroxidases and lactoferrin. Submandibular and sublingual glands contribute mucous glycoproteins to oral fluid. Pathological analysis can be carried out using the saliva produced by specific glands. Since saliva contains constituents of other serum, whole saliva is used for the diagnosis of systemic diseases. The gingival fluid flows into the oral cavity. The constituents of gingival fluid can be derived from the local vasculature of the salivary glands. Analysis of saliva perhaps be useful for the diagnosis of different hereditary disorders, endocrine disorders, malignant, autoimmune and infectious diseases, as well as in the assessment of therapeutic levels of drugs and in the monitoring of illicit use of drugs.
Evaluation of the fluids from the individual salivary glands can help in detecting the infection and obstruction. Mixture of oral fluids can be present in the whole saliva or mixed. This may include the secretions from both the major and minor salivary glands including several constituents of non-salivary origin. The fluids of non-salivary origin may include expectorated bronchial and nasal secretions, GCF, serum and blood derivatives from oral wounds [46, 47, 48, 49].
Evaluation of the systemic disorders is done by the salivary analysis of the whole saliva collected with or without stimulation. There are two procedures to collect the saliva using stimulation, they are gustatory stimulation (i.e., application of citric acid on the subject’s tongue [50]; or by masticatory action (i.e., from a subject chewing on paraffin). Constituents, pH and concentration of the fluid depends on the quantity of saliva collected by the stimulation method. Unstimulated saliva is collected without gustatory, masticatory, exogenous or mechanical stimulation. The factors that affect the salivary flow rate depends on the degree of hydration, olfactory stimulation, exposure to light, body positioning, seasonal and diurnal factors. There are two appropriate methods to collect the saliva, they are by the draining or drooling method, in which saliva can drip off the lower lip. In the second methods which is the spitting method the subject expectorates saliva into a test tube [51].
Specialized epithelial cells make up the salivary glands, based on their structure, these cells can be divided into two specific regions: the ductal and acinar regions. During the primary salivary secretion, the ductal cells actively absorb most of the Na+ and Cl− ions and secrete small amounts of K+ and HCO3− and some proteins. This modifies the primary salivary secretion into a hypotonic final salivary secretion when it enters the oral cavity [52].
In the acinar region most of the protein synthesis and secretion takes place, this is where the oral fluid is generated as well. The amino acids enter the acinar cells through active transport. After the intracellular protein synthesis, secretory stimulation releases most proteins, these are stored in storage granules [53, 54]. Description of three models for acinar fluid secretion area are available, which include the active passage of anions into the lumen and passage of osmotic gradient water from the interstitial fluid to the salivary lumen [55, 56]. Fluid obtained at the initial stage is isotonic in nature. This is derived from the local vasculature whereas the acinar cells are water-permeable, and the ductal cells are impermeable. The autonomic nervous system(sympathetic and parasympathetic system) controls the salivary secretion and its signaling mechanism involves the binding of neurotransmitter (primarily acetylcholine and norepinephrine) to plasma membrane receptors and signal transduction
Certain markers in the serum cannot be relied upon but with the salivary samples, the levels of certain markers are consistent than its manifestation. The components of the biomarker molecules and normal saliva have similar physicochemical characteristics. Hence the diffusion of lipophilic molecules into the saliva is easier than the lipophobic molecules. The molecules involved in the biological processes reach the saliva through different mechanisms. Hence extraction of molecules using the methods such as ultrafiltration and active transport have also been proposed for many substances recently. Whereas previously passive diffusion was the most common mechanism for drugs and hormones.
For accurate diagnosis, an appropriate relationship must be established between the biomarker concentration in serum and its concentration in saliva. Normalcy in the salivary gland function is necessary for the collection of salivary molecules or cytokines with diagnostic value. The flow rate of the saliva and its concentration is expected to vary between individuals and in the same individual under various conditions. Erratic collection of the serum markers is possible in the whole saliva due to the oral wounds and through GCF flow. Effectiveness of such samples may be questionable as these parameters may interfere in the correct diagnosis based on the salivary constituents [60]. Apart from some of the systemic disorders, other factors like the medications and radiation will affect salivary gland function, the quantity and consequently the composition of saliva [61, 62]. The whole saliva may contain some proteolytic enzymes that come from the host and there may be presence of enzymes derived from the oral microorganisms [63]. These enzymes can affect the stability, consistency and reliability of certain diagnostic biomarkers. Degradation of some molecules happened during intracellular diffusion into saliva. The diagnostic functional value of the marker may be affected by any condition or medication.
Despite the limitations of saliva to be used for diagnostic purposes, it is becoming popular as per the evidences suggested by many recent researches. Commercially available salivary diagnostic tests are currently being used by patients, researchers, and clinicians. Objective and qualitative detection that is the detection of the presence or absence of a biomarker using saliva is possible but it is not a feasible option for the quantitative diagnosis. Saliva also plays a major role in eliciting and monitoring the hormone levels, especially steroids which facilitates repeated sampling in short time intervals, which may be particularly important for hormone monitoring and avoiding patient and clinician compliance problems.
Nevertheless, before a salivary diagnostic test can replace more conventional methods, the diagnostic values of a new salivary test must be compared with the gold-standard. The effectiveness of a new test must be determined in terms of specificity, sensitivity, reproducibility and the correlation with an established disease diagnostic criterion.
The known functions of proteins identified by proteomic analysis are summarized in Table 3. Using the proteomics approaches, these proteins have been identified previously [32, 64, 65]. There has been no reports of the changes in the protein expression in relation to the orthodontic treatment, tooth movement and its forces. The Table 3 lists the functions of proteins and their predictive role in the OTM.
Protein S100-A9 (S100 calcium-binding protein A9) (Calgranulin-B) |
|
Serum albumin precursor |
|
Immunoglobulin J chain |
|
Ig alpha-1 chain C region |
|
Cysteine-rich secretory protein 3 precursor (CRISP-3) |
|
Hemoglobin subunit beta (Hemoglobin beta chain) (Beta-globin) | Role in the transportation of oxygen from the lung to the various peripheral tissues. |
14-3-3 protein |
|
Summary of identified proteins and their known functions [30].
During acute and chronic inflammation Protein S100-A9 (S100-A9) is a calcium-binding protein that functions as a proinflammatory mediator. It is found in high concentration in an inflamed tissue. Previous research described that S100-A9 was concerned in chondrocytic and osteoblastic maturation, matriceal calcification and was noticeable in osteoclasts [66]. Therefore, the presence of osteoclast indicates an active process of bone resorption. It is also been stated that it regulates cartilage destruction and joint inflammation during antigen-induced arthritis [67]. A recent study showed an obvious downregulation of S100-A9 protein, this indicates that there may not be an involvement of the protein during bone resorption in fixed orthodontic tooth movement. Its involvement may be in the inflammatory conditions as suggested by the data from the previous study. As per the study, on day 14 of the treatment, the inflammatory process was not active. The orthodontic treatment for 14 days resulted in the downregulation of this above protein indicated by the suppression of inflammation.
Immunoglobulin J chain (IgJ) is a constituent of IgA or IgM, whereas Ig Alpha-1 chain C region (IgAC) is a major immunoglobulin class in the body secretions. Both are common elements of the immune response in humans. Acute inflammatory responses were noticed in the periodontal tissues surrounding the mechanically stressed teeth, in the early phase of the OTM [24]. Recent study also exhibited an obvious downregulation of both IgAC and IgI after 14 days of the fixed orthodontic therapy. Previous study also did not show any increase of LDH after 14 days of orthodontic activation [41]. After these studies researchers suggested that no further inflammation had occurred at that period.
CRISP-3 shows the presence of the exocrine secretion and secretory granules of neutrophil in them. It has notable functions in innate immunity [68] and it is a potential biological marker for prostate cancer [69]. According to the results from the recent study after 14 days of orthodontic treatment, the presence of this protein was noticed. There is not much clarity regarding its relationship with orthodontic tooth movement.
ALB and hemoglobin subunit beta (HBB) are serum proteins. These serum proteins are responsible for the increase in subjects with periodontal disease [70]. ALB is considered as a major zinc transporter in the plasma which regulates the colloidal osmotic pressure of blood [71]. The HBB is a subunit of hemoglobin containing two beta units and four subunits with two alpha. Transport of oxygen from the lung to different peripheral tissues involves the alpha and beta subunit carrying an ion-containing molecule (heme) in each of them [72]. HBB is commonly found in the red blood cells (RBCs) but its function(s) in saliva is still unknown. Recent study showed that ALB was present only at day 0 of treatment. And its role(s) in orthodontic tooth movement is also still unclear.
On Day 0, an adaptor protein—14-3-3
Orthodontic and dentofacial orthopedic therapeutic appliances used in the treatment and correction of various maxillofacial and dento-maxillary anomalies with skeletal and dental problems most frequently believe that the orthodontic force application of high intensity can induce a localized inflammatory process around the tooth and tooth supporting structures. Due to the presence of this inflammatory process there is an increased synthesis of free radicals secondarily produced which is followed by the oxidative stress [74]. In the literature till date, there are very limited number of human studies and evidences on the oxidative stress and oxidative damage that may occur due to an aseptic inflammation in tissues caused because of orthodontic tooth movements [74].
A recent study performed in 2009 by Olteanu et al. [60] compared and determined the amount of oxidative stress markers in the saliva of patients, they were treated using orthodontic appliances for a predefined stipulated time period. The time periods that were predefined for determination were: before and after 1 h of treatment, 24 h and 7 days after the initiation of the treatment. At 24 h, maximum variation was observed in the concentrations of the saliva markers of the oxidative stress for ceruloplasmin and malondialdehyde (MDA). And for the hydrogen donors at one hour respectively, and at the 7 days from the placing of an appliance, the concentrations of markers were similar to the values observed in the initial phase of the tests. Concentration of saliva markers for oxidative stress showed changes but this cannot prove pathological processes prevalent in the patients with orthodontic appliances at the level of the oral cavity.
Recently, the research study performed using saliva by Ozcan et al. [75] had the objective to evaluate the changes in some oxidative stress markers for the determining of oxidative stress damage that occurs in the process of bone and tissue remodeling, including dysfunction of periodontal tissue caused by orthodontic tooth movement. At certain time intervals, the unstimulated saliva samples of patients with fixed orthodontic appliances were collected. The time intervals followed were: just before treatment, at the 1st month of treatment and at the 6th month of treatment. Investigations were conducted using spectrophotometric method to detect nitric oxide (NO) and MDA. The TNF-α, IL-1β, and 8-OHdG levels were detected using ELISA method. In the results, at any of the predefined time period, the study did not show any significant change in the saliva in all biochemical parameters. In another study involving the unstimulated saliva samples of individuals with fixed orthodontic appliances, there were no significant differences observed when compared to the control group in kynurenine concentration [76]. And the data presented in the study at least at the first 6 months of the treatment indicate that orthodontic materials and orthodontic tooth movement used in orthodontic treatment do not cause oxidative damage in the oral cavity.
Nowadays many biomarkers are detected using saliva. It was recently discovered that several new isoforms for Nerve Growth Factor (NGF), Brain derived Neurotropic Factor (BDNF) and Calcitonin gene-related peptide (CGRP) were found in the saliva [3]. Identification of these isoforms can be utilized to develop subtle ways that can be considered to be methods to detect and analyze markers related to pain.
Pain and discomfort are inevitable during orthodontic treatment [6]. Conventionally, the degree of pain is assessed subjectively using many pain scales [7]. Assessing pain objectively using salivary physiological biomarkers would benefit the clinician for appropriate pain diagnosis and management [6, 8].
These potential biological markers can be collected from different tissue samples. The sampling is done as per the required biomarker and the biological processes to be studied [1, 9]. Several possible biomarkers representing many of these biological changes during specific phenomenon like pain experienced during orthodontic treatment pain, bone remodeling, inflammation and root resorption have also been proposed [20]. The clinical application can be developed from the knowledge of biomarkers that can accelerate the orthodontic treatment as well.
Development of objective markers of nociception and pain helps in diagnosis of pain and its management. Standardization of pain assessment objectively is important to avoid bias in research studies. Tools that are sensitive and specific to pain fulfilling certain criteria’s like being observer-independent, not reliant on the patient’s ability to communicate and not influenced by disease characteristics are needed in developing an objective method of pain assessment [13]. A review by Cowen et al. [13] states that the objective biomarkers of nociception or pain which have been validated for clinical use, although there are currently promising strategies like monitoring changes in the autonomic nervous system, biopotentials, neuroimaging and composite algorithms. There is a serious need for theoretically promising and clinically useful objective marker for assessment of pain. Restricted use of physiological markers as ‘objective’ measures of pain and nociception is due to the lack of evidence in support of its use as biomarker. Biomarker research in saliva for pain and nociception as part of clinical phenotyping should be watched closely.
Fleming et al. [14] suggests that unlike the bracket type, the subjective pain experience at 4 h, 24 h, 3 days, and 7 days following fixed orthodontic appliance placement [14]. The subjective pain was recorded using Visual Analog Scale (VAS) [14].
In cross sectional study conducted by Jasim et al. [3], the levels of nerve NGF, CGRP and BDNF were determined using novel western blotting-based technology using Capillary Isoelectric Focussing (CIEF) Immunoassay. Glutamate and substance P (SP) was determined using ELISA. Numerous new isoforms were found for NGF, CGRP and BDNF in saliva. In expression and chemiluminescence levels, the isoform pattern showed significant difference between different collection methods. In this study, new sensitive methods to study pain related markers in saliva were developed. And this study was the first to:
detect NGF, CGRP, BDNF, Glutamate and S P (SP) in five different salivary types,
to develop a new protocol/method for analysis of different isoforms of NGF, CGRP and BDNF,
show quantifiable levels of several isoforms of NGF, CGRP and BDNF in human saliva, and finally.
establish a correlation between the glutamate level in stimulated whole saliva and plasma.
Activation of the orthodontic appliance induces painful sensations due to the inflammatory process, this occurs as part of the tooth movement related to tissue remodeling. It is established that the immunoreactive neuron C-fos is involved in the transmission of nociceptive information expressed bilaterally in the lateral parabranchial nucleus. And ipsilaterally in the trigeminal subnucleus caudalis past the initial 24 h of orthodontic force application. Similarly, fos-like immunoreactive neurons were distributed in other brain regions such as the neocortex, thalamic nucleus and dorsal raphe [77]. Nociceptive information by tooth movement is modulated and transmitted in several regions of the brain. Endogenous pain control systems are activated by these stimuli, including descending monoaminergic pathways [78].
Initial studies suggested that through dopaminergic and serotonergic systems, the nociception is nociception is regulated [77]. Subsequently, another experiment performed showed an increase in serotonin turnover in the medulla, indicating the bulbospinal serotonergic pathway activation by nociceptive neurological response [78]. Therefore, an indirect nociceptive mechanism operating during tooth movement occurs that suggests a continuous and delayed nociceptive response, which is expected to regulate the masticatory function during active tooth movement.
A recent case report published data on administration of MK-801 in rats (a non-competitive antagonist of N-methyl-Daspartate receptors), intraperitonially before tooth movement. The results suggested the N-methyl-D-aspartate receptors blockade along with neuronal suppression of sensory nuclear complex of the trigeminal nerve branch. Subsequently, these effects were found to increase the neuronal activity in the descending antinociceptive system, including dorsal raphe nucleus, nuclear raphe magnus, ventrolateral PAG, and Edinger-Westphal nucleus. Following, during orthodontic tooth movement, these results indicated a pharmacological way to decrease pain perception [79].
Salivary biomarkers as a measure has the potential to be an objective approach and a diagnostic tool for the studies related to pain. However, there is a need of estimating the different collection methods and develop more profound techniques for analysis. These biomarkers have a crucial part in objectively assessing the pain.
GCF is an exudate that can be collected from the gingival sulcus in periodontium, which provides a prospective source of factors or biomarkers associated with the changes and destruction in the underlying periodontium that generally occurs during the orthodontic force application during fixed orthodontic treatment [80].
Due to its non-invasive nature and ease of repetitive sampling from the same site with the help of filter paper strips, gingival washings, platinum loops and micropipettes, GCF is commonly collected for the examination and check the levels and concentration of these biomarkers during the orthodontic force application. This fluid is easily available as that of saliva in the oral cavity and is usually used to analyze various biochemical markers [80].
The importance of GCF biomarkers in periodontal effects is tabulated in Table 4.
|
|
|
List of GCF biomarkers and their role in orthodontic tooth movement.
GCF, Gingival crevicular fluid; IGF-1, Insulin-like growth factor-1; GAG, Glycosaminoglycans; PMN, Polymorphonuclear neutrophil; TNF, Tumor necrosis factor; PDL, Periodontal ligament.
GCF which can be described as a transudate or an exudate arises at the gingival margin where its flow rate is of 0.05–0.20/min which indicates gingival inflammation [81]. Various biochemical markers such as prostaglandin production and the action of various extracellular and intracellular factors, such as IL-6, IL-1, TNF, epidermal growth factors, cathepsin, aspartate aminotransferease, microglobulin, alkaline phosphatase, and lactate dehydrogenase are analysed by this GCF.
Various cell mediators or enzymes are produced due to the remodeling changes in the PDL and the alveolar bone that can be used as the biomarkers of orthodontic treatment [66, 82]. The initial works conducted by Embery, Waddington [83] and Last et al. [84], proved the presence of many proteoglycan, tissue proteins and GAGs in GCF and also reported presence of chondroitin-4-sulphate in GCF from the pressure side of tooth movement. Biological alteration is caused in deep-seated tissues due to that the increase in chondroitin-4-sulphate since the orthodontic model is a nonplaque and non-disease-related process.
Uematsu et al. [85, 86] found elevated levels of IL-1, IL-6, TNF, epidermal growth factors, TGF 2 and microglobulin during the orthodontic treatment in the GCF. Lee et al. [87] and Grieve et al. [88] also reported stated the similar finding for IL-1, and PGE2. Lowney et al. [89] for TNF. Griffiths et al. [90] demonstrated the presence of osteocalcin in GCF from teeth which is subjected to the orthodontic treatment force application. A recent study by Insoft et al. [91] also found an increased level of alkaline phosphatase during the first 3 weeks of orthodontic treatment, whereas acid phosphatase increased in successive weeks. Perinetti et al. [92] also determined the aspartate aminotransferase along with the alkaline phosphatase activity in GCF. Orthodontic force application induced an increase in the lactate dehydrogense activity in GCF as per a recent study by Serra et al. [93]. After the study it is proposed to be a sensitive factor or biomarker for periodontal metabolism. Sugiyama et al. [82] suggested that cathepsin B involved in ECM degradation and reported its increase in the amount in GCF.
After an orthodontic force presentation for 4–8 h Apajalahti et al. [94] found a significantly higher amount of MMP-8 in GCF. They suggested that MMP-8 reflects the enhanced periodontal remodeling, this is the effect of increased expression and activation of GCF. From the studies, it was concluded that presence of such markers in GCF during the orthodontic studies are useful in identifying the bone-remodeling activities. Therefore, GCF can be considered a promising topic for future research, as these investigations have already begun to provide an insight into the progressive aspects of remodeling.
According to the QUOROM statement suggestions, a systematic review was conducted by Allgayer et al. [95] in 2014 by strictly adhering to the guidelines suggested by PROSPERO [95]. Several key databases namely PubMed, Embase, Cochrane library, MEDLINE, and Web of Science were searched in May 2014 using the MESH terms ‘Orthodontics, Corrective’, ‘IL-17’, or ‘helper 17 cell’, or ‘helper T Cells’, or ‘TH 17’, ‘IL 17’, ‘IL-23’, ‘crevicular fluid’, ‘IL 23’, and using the free text terms ‘GCF, gingival crevicular fluid, regulatory proteins, tooth displacement, cytokines, inflammatory factors, root resorption and canine distalization’, and, by reference tracking an additional search was performed. The search results from each database were compiled, combined and the duplicate results were eliminated (Table 5).
Reference/article | Sample (n) Age (yr) | Sampling (points) | Method | Cytokine | Peak | Results |
---|---|---|---|---|---|---|
Alikhani et al 2014 | 20 19.5-33.1 | 0, 24 h, 28 d | Canine distalization | IL-1β, IL-1α, IL-6, IL- 8, TNF, MCP-1 | Tend to increase | |
Enhos et al 2014 | 20 18+-3 | 0, 24, 48, 168 h, 30 d | Canine distalization | RANKL OPG | OPG and RANKL levels vary as a result of force application | |
Gastel et al 2011 | 24 14+-1 | 0, 365 d | Headgear | IL-2 IL-4 IL-6 IL-8 IL-10 GM-CSF INF TNF MCP-1 IP-10 | No significant alteration No significant alteration Tend to increase Tend to increase No significant alteration No significant alteration No significant alteration No significant alteration No significant alteration No significant alteration | |
Tzannetou 2008 | 9 10-18 | 0, 24 h, 7 d, 14 d, 21 d | Hyrax RME | IL-1β | Increased during active RME; remained high in retention phase | |
Karacay et al 2007 | 10 15+-1 | 0, 1, 24, 168 h | Canine distalization | TNF-α | 1 d | Hybrid: no change with time Distraction: higher than hybrid |
Iwasaki 2006 | 10 15+-4 | 0, 1, 3, 7, 14, 28, 42, 56, 70, 84 d | Canine distalization | IL-1β IL-1ra | Tooth movement rate relate to IL-1 gene polymorphisms | |
Dudic 2006 | 18 9-14 | -7 d, 0, 1 min, 1 h, 1 d, 7 d | Separation elastics | IL-1β PGE2 SP | 1 d 1 d 1 d | Higher levels at tension sites Higher levels at tension sites |
Basaran 2006 | 18 16-19 | 0, 7 d, 21 d, 6 mns, +7 d, +21 d | Canine distalization | IL-1β TNF-α | Tend to increase Tend to increase | |
Kawasaki 2006 | 15 15+-3 15 31+-4 | 0, 1, 24, 168 h | Canine distalization | RANKL OPG | 1 d 1 d | Lower ratio in adults Lower ratio in adults |
Nishijima 2006 | 10 15+-2 | 0, 1, 24, 168 h | Canine distalization | RANKL OPG | 1 d 1 d | No change at 1 h or 168 h No change at 1 h or 168 h |
Basaran 2006 | 17 17+-2 | 0, 7 d, 21 d, 6 mns, +7 d, +21 d | Canine distalization | IL-2 IL-6 IL-8 | Tend to increase at 7 d, back at 21 d Tend to increase at 7 d, back at 21 d Decrease at 7 d, back to baseline at 21 d | |
Giannopoulou 2006 | 18 9-14 | 0, 1, 24, 168 h | Separation elastics | IL-1β PGE2 | 1 d | Associated with pain at 24 h Weak association with pain at 1 h |
Yamaguchi 2006 | 9 22-2 | 0, 1, 4, 8, 24, 72, 120, 168 h | Canine distalization | IL-1β | 1 d | Correlated with P-substance |
Toia 2005 | 6 10-13 | 0, 4 h, 10 d | Aligning | IGF | 4 h | Decreased 10 d after time-dependent decrease |
Hoshino-Itoh 2005 | 10 23+-3 | 0, 1, 24, 168 h | Canine distalization | PAI | 1 d | Increased only at 24 h Increased only at 24 h |
Iwasaki 2005 | 10 10-30 | -28, -14, 0, 1, 3, 14, 28, 42, 56, 70, 84 d | Canine distalization | IL-1β IL-1ra | The ratio of both correlate with the rate of tooth movement | |
Tuncer 2005 | 10 15-17 | 0, 1, 24 h, 6 d, 10 d, 30 d | Canine distalization | IL-8 | Tend to increase in early phase | |
Lee 2004 | 10 18-22 | 0, 1, 24, 168 h | Canine distalization | IL-1β PGE2 | 1 d 1 d | Continuous: 168 h back to baseline Interrupted: higher level at reactivation Continuous: 168 h back to baseline Interrupted: remained high for 1 wk |
Ren 2002 | 43 10-14 41 21-27 | 0, 24 h | Aligning | IL-6 PGE2 | Upregulated in both age groups Increased only in youngsters Increased only in adults | |
Iwasaki 2001 | 7 12-16 | -28, -14, 0, 1, 3, 14, 28, 42, 56, 70, 84 d | Canine distalization | IL-1β IL-1ra | 28 d periodic changes, no site difference No periodicity, no site difference | |
Tzannetou 1999 | 9 10-18 | 0, 24 h, 7 d, 14 d, 21 d | Hyrax RME | IL-1β | Increased during active RME; remained high in retention phase | |
Uematsu 1996 | 12 14 | 0, 1, 24, 168 h | Canine distalization | IL-1β IL-6 TNF-α | 1 d 1 d 1 d | No change at 1 h or 168 h No change at 1 h or 168 h No change at 1 h or 168 h No change at 1 h or 168 h |
Uematsu 1996 | 12 14 | 0, 1, 24, 168 h | Canine distalization | TGF-β1 | 1 d | No change at 1 h or 168 h |
Lowney 1995 | 20 12-36 | 0, 5 min | Canine distalization | TNF-α | More than twofold increase | |
Grieve 1994 | 10 24-27 | 0, 1, 24, 48, 168 h | Aligning | TNF-α PGE | 1 d 1 d | Increased at 1 h, back to baseline at 168 h Stay high at 48 h, back to baseline at 168 h |
A brief insight on those studies assessing biomarkers or the cytokines in gingival crevicular fluid (GCF) in orthodontics.
The results of this systemic review provide an insight by identifying the 115 potentially relevant studies. Table 5 below represents an overview of the outcomes. Among these studies, further analysis of the titles, abstracts, and full texts revealed that for this systematic review the major 25 studies were relevant [95]. Many studies were performed on mixed samples of young adults and adolescents, and two related studies reported the levels of GCF cytokines in different age groups (Table 5). More than 20 subjects were found to be recruited for only 3 studies. Out of 25 studies, seventeen of them used the maxillary canine as their study tooth, and the orthodontic force system was distalization of the canine with either continuous arch wires or sectional wires. The other eight studies addressed insertion of separation elastics (two), Hyrax appliance (two), aligning movement (three), and cervical headgear (one).
MMPs are considered to be the main endogenous chemical mediators of the pathologic destruction of tissues in periodontitis [96]. Extensive research studies have been performed to assess the levels of MMPs in GCF [97] and saliva and also, they have been found to be elevated in patients suffering with periodontitis compared to subjects with healthy periodontium. Additionally, after the periodontal therapy there was drastic decrease in the levels of MMPs in GCF. The levels of MMP-8, 9 MMP-3,10 and MMP-13,11,12 have been linked with the periodontal disease progression in GCF. MMPs also play a dominant role in the remodeling process of PDL during orthodontic tooth movement.
A study in dogs by Redlich et al. [96, 98] suggests an increase in the activity of MMP-1 and mRNA levels in the compression side of the gingiva during orthodontic tooth movement. Similarly, a study performed on rats also demonstrates an increased expression of MMP-13, mRNA, and MMP-8 in the PDL during active tooth movement [13]. Using MMP inhibitors orthodontic tooth movement can be prevented or delayed in mice and rats [96, 98]. A few studies performed on humans [96] have enumerated the presence of MMPs in GCF during orthodontic tooth movement and have stated the alterations in their levels during the orthodontic force application. Additionally, in orthodontic patients treated with fixed appliances, the total collagenase activity in the GCF has been revealed to be 10-fold that of control GCF [99].
Surprisingly, it is found that the effects of orthodontic force application on teeth affected by periodontal disease has not been significantly studied. However, a few retrospective and longitudinal studies [97, 99] indicated that the orthodontic force application can be tolerated by periodontally compromised teeth without any additional damage to the periodontium.
Present dissertation deals with brief insight into the biomarkers, connected to orthodontic treatment and progression. Suitable biomarkers may be used to distinguish the sequence of events following OTM. Biophysical mechanisms are involved in the displacement of tooth in the periodontal space and shift of stimulus from continuous force application. The assessment of these biological mechanisms can be done by the evaluation of rate and amount of synthesis of biomarkers in periodontium. This knowledge of the ongoing process occurring in periodontal tissues during orthodontic and dentofacial orthopedic therapies in turn may help us to make proper choice of mechanical orthodontic loading, the amount of orthodontic force application during the treatment and period of treatment may be shortened. It also helps to avoid adverse consequences such as bone loss or root resorption associated with orthodontic treatment. The essential goal is to develop a screen test based on these factors or biological markers that could be used non-invasively and easily by the orthodontist at chairside to monitor the ongoing process and detect early root resorption. Several sensitive, salivary biomarkers are available to detect the biomechanical changes occurring during orthodontic tooth movement and pain occurring during fixed orthodontic therapy. Further focussed research might help to analyze the sensitivity and reliability of these biomarkers (cytokines), which in turn can lead to the development of chairside tests to assess the pain experienced by patients during orthodontic therapy and finally the outcome of the fixed orthodontic therapy. There is an enormous scope for research in this field which may be a boon for future orthodontic treatment and modalities.
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Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. 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He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"14",type:"subseries",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11410,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). 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