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\n
1. Introduction
\n
This chapter presents a study of the biomechanical behavior of dental implant prosthodontics in a mandible under masticatory loads using numerical models made by the finite elements method (FEM). This method is widely used in the study of dental implants and has proven very useful in analyzing the distribution of strains and stress in the entire bone tissue, implant, and crown structure [1].
\n
These studies are equally relevant in achieving optimal design of the implant, in osseointegration study, in oral rehabilitation study by developing various loading and orientation versions of the implant, and so on [2].
\n
Numerical analysis of implants is useful for doctors in a deeper and thorough understanding of the characteristics of the state of stress and strain in the bone structure to ensure success of implants [3].
\n
FEM is a numerical method developed by engineers and substantiated by mathematicians to find approximate solutions to complex structure problems. This method is also successfully used in medicine for biological structures and, in particular, in dentistry.
\n
The method consists of dividing (mesh) the problem structure domain in smaller areas (finite element) connected by nodes, called mesh; finding the solution to these subdomains, finite elements (FEs), considering both loads (forces, pressures, etc.) applied to the structure boundary, as well as the boundary conditions and finally assembling these elementary solutions for finding global solutions.
\n
FEM is considered by many specialists as one of the most common numerical methods for structural analysis. Regardless of their type and complexity, FEM is used in industry and science, taking increasingly greater scale in medicine and, certainly, its usefulness being quite rigorously outlined in dentistry and all of its specialties [4].
\n
The large variety of types of finite elements, of loads that can be taken into consideration, of material modes of behavior (linear or non-linear), of the application regime (static, elastic, or dynamic), of the types of analysis (structural response, modal analysis, fatigue, fracture, optimization, etc.) gives the method and, therefore, the user the possibility to carry out an accurate calculation for any problem, with results as close as possible to reality [5].
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FEM possibilities are closely linked to the performance of programs and computers. Currently, software products of analysis by FEM are made, which, under increasingly high hard performance, enables the possibility that the size or difficulty of the problem to be solved to no longer constitutes an insurmountable obstacle.
\n
Correctly solving the structure analysis problems by FEM is based primarily on creating a numerical model as suitable as possible; this model should be based on the correct understanding of the problem to be solved and equally on the knowledge of the theoretical foundations of the method.
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The great versatility and efficiency of FEM determined its utilization in areas as highly diverse as mechanical structures, fluids, thermal processes, and recently in areas of medicine such as orthopedics, stress flow, dental medicine, and so on.
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Therefore, FEM is currently one of the most powerful tools for investigating many phenomena of the most complex and highly diverse areas.
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The FEM uses as a starting point an integral model of the phenomenon under consideration [6, 7]. It applies separately for a series of small parts of a continuous structure obtained by the meshing process, known as finite elements, connected to each other at points called nodes [8]. These finite elements must be designed so that their ensemble reconstructs as closely as possible the actual analyzed structure. In principle, these connections must be designed as to allow a numerical convergence to the exact solution when the structure is discretized in finite elements with dimensions increasingly reduced.
\n
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1.1. Finite elements analysis steps
\n
Finite elements analysis of a structure should take the following steps [6–8]:
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\n
1. Dividing analysis area in finite elements (mesh)
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Meshing a structure means that it is subdivided in a seldom number of finite elements or in a numerical integration point mesh, interconnected in their exterior nodes. During this operation, the types of finite elements to be used will be chosen and their distribution among the meshed area is established, resulting in their number, size, and form.
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Meshing is done by computer. The finite element type is defined by several characteristics, such as dimension (one-, two-, three-dimensional), the number of the element’s nodes, the associated approximation functions, and others. Choosing the finite element is of great importance for the necessary of internal memory, for the required calculation effort of the computer, and for results quality.
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In case of two-dimensional structures, for modeling, triangular or quadrilateral finite elements can be used; the triangular elements ensure better opportunities in terms of contours geometry approximation, while quadrilateral elements accurately reproduce the distribution of stress. It is appropriate to use the elements as close as possible of the equilateral triangle or square. The use of very obtuse triangles or rectangle angles is not recommended, with too elongated elements. Similarly, in three-dimensional structures, tetrahedral or parallelepipedic types of finite elements can be used.
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The starting point for the mathematical construction of various finite elements methods is represented by respecting the following principles:\n
considering approximations based on the use of simple elements for which we have provided a solution;
increasing accuracy of the calculation by refining the mesh.
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\n
\n
1.2. Effect of meshing on the numerical results
\n
Calculation results (displacements strain and stress), which are obtained by FEM, are dependent of the meshing choosing. For this reason, there are situations, especially in the case of complicated geometries when the problem addressed with these methods should be investigated in several meshing variants and subsequently sorting out the results [8].
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On the other hand, the effect of errors increases with the number of elements used. Numerical errors are due to truncation, rounding, and of input data errors.
\n
To study the influence of meshing, the most common method is to halve the mesh and compare it, and if the results are negligible, the analysis is considered acceptable. To effectively achieve more accurate results by successive refining of the finite elements mesh, the following criteria must be considered:\n
Each previous mesh should be reflected in the new one;
Each point of the structure must always be found in a finite element;
The approximation function (type of element) should remain the same when passing from one element to another.
\n
Nevertheless, it must be noted that from a certain number of finite elements, results can no longer be improved by increasing their number, and changing the type of finite element used becomes compulsory.
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The model obtained by meshing the structure into finite elements must meet the following requirements:\n
to represent with sufficient fidelity the actual behavior of the structure;
to allow easy generation of results (displacements, strains, stress);
to not involve an excessively high labor for input data preparation or results processing and hence a very long work time for the computer and a great part of its memory.
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Some of these requirements are becoming less restrictive following the improvement of software and technical performance of computers.
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It should be emphasized that computer programs are capable of performing automatic meshing, being able to perform even an automatic refinement of meshing in areas where there are large gradients of stresses (strains).
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2. Establishment of finite element equations (elemental equations)
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Material behavior within a finite element is described by finite element equations called elemental equations. These form a system of equations of the element. Basic equations can be derived directly on a variational way, through, for example, residues method (Galerkin) or by the energy balance method.
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\n
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3. Assembling the elemental equations in the equations system of the structure
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The whole structure behavior is modeled by assembling systems of finite elements equations in the system of structure equations, which physically means that the structure balance is conditioned by the balance of finite elements. The assembly requires that in the common nodes of the elements, the unknown function or functions to have the same value.
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\n
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4. Implementation of boundary conditions and solving the system of the structure equations
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The equations system obtained from implementing appropriate boundary conditions suitable for the problem under consideration is solved by one of the common procedures, for example, by Gauss elimination or by Cholesky method, and so on, obtaining function values in nodes. These are called primary or first-order unknowns.
\n
\n
\n
5. Performing additional calculations to determine the secondary unknowns
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In some problems, after finding the primary unknowns, the analysis closes. In other problems, however, knowing only the primary unknown is not sufficient, the analysis must proceed with the determination of the secondary unknowns. These are higher-order derivatives of the primary unknowns. Thus, for example, in dental implants analysis, primary unknowns are the nodal displacements. With their help, in this step, secondary unknowns are determined, represented by strains and stress [6, 8].
\n
\n
\n
2. Material and method
\n
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2.1. The use of FEM in dental implants studies
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FEM is very useful in the study of dental implants because it determines stress, strain, and displacement both in the implant, crown, as well as in the bone tissue. This calculation plays a crucial role both in the optimal design of implants [9] and in determining the factors that control the whole process of post-implant osseointegration [3].
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In recent years, studies that are based on the use of FEM in oral implantology became very numerous and are dedicated either to oral rehabilitation or to analysis of the state of stress in and around the implant in the peri-implant bone (cortical and trabecular bone). We can mention works that use two-dimensional (2D) representations of the implant and mandible or maxilla and consider a geometry of the implant-bone system and axial-symmetric loads [10] or numerous three-dimensional (3D) studies [11], which model the bone geometry or loads application in a proper way. On the other hand, there are studies that consider a more realistic modeling of mastication, namely cyclical dynamic loads directed in an occlusal angle [12].
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The importance of FEM numerical analysis in the study of dental implants involves several aspects. The method is equally useful both for clinicians, by investigating alternative treatments, and for dental implants producers [13]. They can change the macro-design according to clinical benefits. The purpose of improving the design and use of dental implants is represented by the bone absorption as reduced as possible in the region around the implant, certain micro-displacement of the blunt, a better distribution of the loading on the implant structures [14]. All these properties are often related to the biomechanical behavior and should be investigated not only in clinical trials but also in FEM studies [2].
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A new design of dental implants and materials must be subjected to thorough investigation and compared to traditional structures. FEM analysis allows the comparison of old and new treatment modelings, taking into account the limitations and deeper understanding of the applicability areas [15].
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FEM studies are not generally designed for clinicians without the help of engineers. Typically, clinicians have limited knowledge of modeling and simulation using computers and therefore they need the help of engineers. Therefore, collaboration between a clinician and an engineer in this field is very useful. The clinician should devote too much time to acquire adequate knowledge on techniques of computational for the preparation and development of a numerical model implementation. However, the clinician must manage the study and provide necessary directions and indications concerning the dental implant parts, bone physiology, masticatory forces, the implant bonds with the bone, generally, the dental implantology matter. Also, the clinician should make every effort to maintain contact with the engineer to achieve effective assessment and adaptation of the numerical model [16].
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The number of elements and nodes can be increased to achieve a more detailed modeling. But this can be time consuming, which could hinder the development of the model and complicate the computational calculations. Therefore, the engineer must clearly understand the purpose of the study, the boundaries, the limitations that can be applied to the model, and the number of elements can be increased only in the area of interest, therefore in the vicinity of the implant.
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The difficulties in finite element modeling in dental implants study are linked on one hand to the assignment of material properties for biological materials: cortical bone, trabecular bone, gingiva; on modeling of loads that act on the bone-tissue-implant structure [17], and also on the actual faithful realization of the geometric model of the implant, which can be extremely laborious in the case of accurate modeling [11, 18].
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As previously mentioned, 3D models are widely used. Given the nature of applied loads, unbalance of loads, and of geometric structures involved as well as their complexity, 2D models are unsatisfactory, 3D models being rather preferred [13, 19]. 2D models [20] cannot simulate the behavior of real structures as realistic as 3D models, so, the latest research focuses on 3D models [3]. The present paper continues the modern trends of recent years of using detailed 3D models that are much closer to reality both in geometric modeling accuracy loads application and in boundary conditions.
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This is the case of the present study, which consists of the calculation and analysis of the state of stress and strain and displacement of the implant and its interaction with the mandibular bone.
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In implants cases, the most critical area is that in which there is maximum stress concentration [9]. This is the neck of the implant and the surrounding area, namely the cervical edge (marginal bone) [18]. Therefore, this area should be kept as intact as possible in order to maintain a structural and functional bone-implant interface [21].
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\n
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2.2. Numerical analysis program
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Modeling and simulation of dental implants behavior are made using Solid Works program regarding the geometric model development to simulate the implant and bone-tissue structure and Cosmos program for numerical solving of the obtained scheme [22].
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Cosmos program is one of the best professional programs of FEM analysis of the continuum mechanics. This program was used in the version of its integration into the Solid Works product—one of the most powerful media of structure modeling—thus ensuring an efficient and accessible interface, both during data preprocessing and during the post-processing of obtained results using graphs, isocurves, isosurfaces to represent the values of the studied entities fields, and so on.
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Since implementing the geometry model of dental implant, crown, and bone tissue requires special preprocessing resources, SolidWorks software was used. The geometric model made with this program was exported and used for the Cosmos program calculations.
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The present study analyzes the insertion of a dental implant in a section of the mandible, focusing primarily on highlighting and predicting areas of stress concentration both in the trabecular bone and in the cortical bone for different clinical situations and loads caused by mastication. These areas are the most vulnerable areas, where potential failures, fractures, or damage structure may occur.
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2.3. The geometric model of the dental implant
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2.3.1. General considerations
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The first step of finite element modeling is to provide implant-crown-bone-tissue structure by creating a geometric model. The next step is choosing materials for structural components [23, 24]. Their properties are available in SolidWorks materials library and studies based on experimental determinations [19, 25].
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In our case, the analysis model is static and materials are considered elastic. We indicate the material constants for the different materials involved in the modeled structure, such as Young elasticity module (E), Poisson coefficient (ν), the density, tensile strength, and yield strength. The third step is the application of restrictions related to boundary conditions and then applying the loads to simulate the mastication forces in the considered problem.
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Both the geometric model and the complete finite elements model of the implant components and of the bone tissue were performed with great accuracy, considering the construction and functional details (radius of fillet, fillets, releases, and contact) for the results to be as close to reality as possible. All of this can be seen in Figures 1–12.
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2.3.2. Geometric model components
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The case of a structure consisting of a 13.5-mm long implant, with a diameter of 3.8 mm with two threaded zones (fine step and big step) placed in a section of the mandible with extension of about 20 mm from the implant axis as shown in Figures 1(a) and 1(b), was considered [26].
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The geometric model consists on one hand of the biological material, mandibular part, which comprises the cancellous bone, cortical bone, and gingiva tissue, and on the other hand of the implant, prosthetic blunt, and crown. The implant is usually made of titanium alloy and crown of ceramic [27]. In the studied Denti implant case, the blunt is made of magnesium alloy (Figure 1b).
Figure 1.
Geometric model: (a) overview and (b) section.
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The geometric model of the bone tissue may also be considered in a simpler geometrical shape, for example, in the form of a cylinder, which comprises a layer of trabecular bone lined both in the upper and in the lower sides by a thinner layer of cortical bone, as shown in Figure 2(a). The geometric model of the bone tissue could be simplified by considering a geometric shape in the form of a cylinder, but which contains a layer of trabecular bone bounded only at the top by a thinner layer of cortical bone, as shown in Figure 2(b), which represents bone constituents who contact the implant.
Figure 2.
Geometric model, bone tissue, simplified bone versions, with the implant and the crown inserted: (a) trabecular bone placed between two cortical bone disks (marked in red); (b) trabecular bone superior bounded by a disk of cortical bone (marked in red).
\n
For structures shown in Figures 1(a,b) and 2(a,b), various calculations by applying appropriate boundary conditions and some axial and lateral loads between 100 and 400 N have been carried out, which are simulating the masticatory forces [28].
\n
Calculations revealed no significant differences either in values obtained for stress, strains, and displacements, or in their localization which is why it can be considered for subsequent calculations the simpler geometry of the bone tissue as shown in Figure 2(a,b). As the time of creating the geometric model and the running time is much lower in simplified geometry, considering a simplified geometry model is useful for quick guidance calculations, which allow running many cases, with variation of the different factors taken into account, for example, different values, localizations, and positions of the loading forces (simulating masticatory forces), types of material with different values of the material constants, and so on.
\n
For problems that require highly accurate calculations in areas of interest, it is preferable to chose a geometry as close to reality as possible.
\n
The analyzed structure components are implants, crown, and mandible portion. All these components were created by a computer and were used in the finite element calculation [26, 29].
\n
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2.3.2.1. Implant
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It is cone-shaped with two threaded zones (see Figure 3).
Figure 3.
The geometric model of the implant.
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The interior of the implant (see Figure 4) allows installation without a threaded zone of the intermediate part (blunt) (see Figure 5) and at the top has a hexagonal bore for mounting with an Allen key in the mandible. The thread of the inner part is used to assemble the implant with the prosthetic blunt by a titanium screw M2 (see Figure 6).
Figure 4.
Longitudinal section through the geometric model of the implant where implant system parts can be observed.
Figure 5.
Geometric model of abutment.
Figure 6.
Geometric model of screw.
\n
\n
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2.3.2.2. Crown
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This component is made from ceramic and has been modeled by a geometric shape as close to the real one as possible (Figure 7).
Figure 7.
Geometric model of crown in section.
\n
\n
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2.3.2.3. Bone tissue
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This is the portion of the mandible around the implant at a distance from it which does not influence the stress and strain of the studied ensemble.
\n
Adoption of the mandible portion size was based on the Saint-Venant’s principle (the new distribution of forces produces appreciable differences in stress in the area of application, but remain with no effect or insignificant effect at large distances from the place of forces application) and by preliminary numerical simulations which confirmed the correct choice of size [30].
\n
Modeling the mandible portion took into account the different structure of the bone portion (cortical and trabecular) by assigning suitable material properties in respective areas (see Figure 8) [17].
Figure 8.
Geometric model of bone as a piece of mandible, in section: cancellous bone portion bordered by the upper and lower thinner layers of cortical bone.
\n
For example, in the case of simplified modeling of the bone tissue in a cylindrical shape, the cortical bone is shown in Figure 9 and the trabecular bone is shown in Figure 10.
Figure 9.
The geometric model of the cortical bone component in the simplified version in the form of a cylinder. The implant thread with a small step can be seen.
Figure 10.
Geometric model of cancellous bone component in the form of a simplified cylinder version. The implant thread with a large step can be seen.
\n
For the same example, the whole geometric model, consisting of the trabecular bone side, cortical bone, implant, and crown, is shown in Figure 11, and a complete section in Figure 12.
Figure 11.
The geometric model of the assembly, the visible portion in the image is represented by compact bone (a), trabecular bone (b), and crown (c).
\n
Figure 12.
The longitudinal section of the system, composed of bone portions represented by compact bone, trabecular bone, the implant (d) fixed along the two portions of bone, the clamping screw (e), blunt (f), and crown.
\n
All components were modeled respecting all elements and technical details (threads, taperings, releases, etc.).
\n
The assembly was designed to transmit the mastication force from the crown to blunt and then to the upper part of the implant; mastication force transmission mechanism does not intervene in any threaded part [31].
\n
\n
\n
2.4. The dental implant finite elements model
\n
\n
2.4.1. The finite elements
\n
3D finite elements model used for the study of an implant in a mandible portion was built using SolidWorks software and uses tetrahedral elements both in the implant and in the bone. In the following, we represent one meshing option that easily allows viewing of the considered details.
\n
The FE model is made of several parts corresponding to implant components, as shown in Figures 13 (FE model of the implant) and 14 (FE model of implant-crown assembly) [26].
Figure 13.
FE model of implant and abutment.
Figure 14.
FE model of implant-crown assembly.
\n
Figures 15 and 16 represent a complete FE model of the implant and mandibular portion, respectively, complete FE model for the implant and mandibular cylindrical portion.
Figure 15.
Complete FE model of crown, implant, and mandible portion.
Figure 16.
Simplified FE model (cylindrical mandible portion, implant, crown).
\n
Usually, when a problem is to be modeled, first, simple models are created, beginning with the geometry for a quick development, calculation, and running, considering different scenarios (e.g., loading size, localization of loading application, etc.). Further, more complicated models are created, in order to bring it as close as possible to reality. The main difference between the two models is the easy way to construct, to handle, to run for a much shorter time, and so on, the simpler one. But the complete model offers more accurate information, especially the localizations of critical zones, despite the difficulty of its construction and a longer running time.
\n
The features of present numerical studies obtained on the two models, full (portion of mandible) and simplified (cylindrical), are presented in Table 1.
\n\n
\n
Information about the mesh
\n
Full model
\n
Simplified model
\n
\n\n\n
\n
Element size
\n
0.4 mm
\n
0.4 mm
\n
\n
\n
Tolerance
\n
0.02 mm
\n
0.02 mm
\n
\n
\n
Number of elements
\n
216904
\n
165382
\n
\n
\n
Number of nodes
\n
318030
\n
243874
\n
\n
\n
Meshing time (hh; mm; ss):
\n
00:07:44
\n
00:01:18
\n
\n\n
Table 1.
Mesh information.
\n
The large number of finite elements relative to the size of the implant is explained by the existence of fine threads in the structural elements. Meshing fineness appeared as a need for a closer-to-reality modeling of constructive forms of great finesse, as threads, releases, and so on.
\n
\n
\n
2.4.2. Material models and their characteristics
\n
In this paragraph, the material types and the characteristics of the material are presented for each component, namely the bone tissue (composed of trabecular bone and cortical bone), implant, and crown.
\n\n
\n
No.
\n
Component name
\n
Material
\n
Weight
\n
Amount
\n
\n\n\n
\n
1
\n
Crown
\n
Ceramics
\n
0.000223658 kg
\n
9.72426 × 10−008 m3
\n
\n
\n
2
\n
Prosthetic blunt
\n
Magnesium alloy
\n
8.17675 × 10−005 kg
\n
4.08837 × 10−008 m3
\n
\n
\n
3
\n
Trabecular bone
\n
Trabecular bone
\n
0.00162148 kg
\n
1.08099 × 10−006 m3
\n
\n
\n
4
\n
Cortical bone
\n
Mandibular cortical bone
\n
0.000677504 kg
\n
3.38752 × 10−007 m3
\n
\n
\n
5
\n
Implant 3.8 × 11.5
\n
Titanium alloy Al- 4VS Ti6
\n
0.000436557 kg
\n
9.85725 × 10−008 m3
\n
\n
\n
6
\n
Screw
\n
Titanium alloy Al- 4VS Ti6
\n
0.000172112 kg
\n
3.88621 × 10−008 m3
\n
\n\n
Table 2.
Data on the used materials.
\n
\n\n
\n
Ceramics (crown)
\n
\n\n\n
\n
Constant name
\n
Value
\n
Units
\n
\n
\n
Elastic module
\n
2.2059 × 10+011
\n
N/m2
\n
\n
\n
Poisson coefficient
\n
0.22
\n
\n
\n
\n
Shear modulus
\n
9.0407 × 10+010
\n
N/m2
\n
\n
\n
Density
\n
2300
\n
kg/m3
\n
\n
\n
Traction resistance
\n
1.7234 × 10+008
\n
N/m2
\n
\n
\n
Yield strength
\n
5.5149 × 10+008
\n
N/m2
\n
\n
\n
Thermal expansion coefficient
\n
1.08 × 10−005
\n
/Kelvin
\n
\n
\n
Thermal conductivity
\n
1.4949
\n
W/(m.K)
\n
\n
\n
Specific heat
\n
877.96
\n
J/(kg. K)
\n
\n\n
Table 3.
Material constants used for ceramics.
\n
\n\n
\n
Magnesium alloy (prosthetic abutment)
\n
\n\n\n
\n
Constant name
\n
Value
\n
Units
\n
\n
\n
Elastic module
\n
4.2 × 10+010
\n
N/m2
\n
\n
\n
Poisson coefficient
\n
0.33
\n
\n
\n
\n
Shear modulus
\n
7.7 × 10+010
\n
N/m2
\n
\n
\n
Density
\n
2000
\n
kg/m3
\n
\n
\n
Tensile strength
\n
4.2 × 10+008
\n
N/m2
\n
\n
\n
Yield strength
\n
1 × 10+008
\n
N/m2
\n
\n
\n
Thermal expansion coefficient
\n
1.5 × 10−005
\n
/Kelvin
\n
\n
\n
Thermal conductivity
\n
24
\n
W/(m.K)
\n
\n
\n
Specific heat
\n
590
\n
J/(kg. K)
\n
\n\n
Table 4.
Material constants used for magnesium alloy.
\n
These data are provided in [1, 32, 33] and by the technical presentation of implants used in the calculation. They were used as input to perform the numerical calculations.
\n
For an easier presentation, data are summarized in tables as follows: type of material for each component, mass, and volume are shown in Table 2, and in Tables 3–7 are represented characteristics of material that is used, respectively, for crown, for intermediate piece of implant, implant, trabecular bone, and cortical bone.
Material constants used for cortical bone (mandible portion).
\n
\n
\n
2.4.3. Contact modeling
\n
In this analysis, we considered a model as close to real conditions as possible. In this regard, we paid special attention to the modeling of contact.
\n
Contact zones are the threaded portions of the implant, mandibular segment, and screw. Contact phenomenon in these areas is modeled by special finite elements—contact elements, according to the actual behavior [33].
\n
The contact model was used between adjacent surfaces of the threaded zones without allowing them for interpenetrating. In this way, up to seven pairs of contact surfaces were defined.
\n
This contact modeling corresponds to a complete osseointegration, which corresponds to a period of implant analysis of more than 6 months after its insertion.
\n
\n
\n
2.5. Boundary conditions
\n
In general, structural analysis, boundary conditions are set in displacements and/or forces in those regions where these entities of the structure are considered to be known [6, 8].
\n
These regions are considered restricted to remain fixed (if they have null displacements and/or rotations) during the numerical simulation or they may have non-zero values for specified displacements and/or rotations.
\n
Non-zero displacement restrictions should be placed on boundaries of model for maintaining the balance of the solution.
\n
Also, restrictions should be placed in nodes that are away from the region of interest, in our case, in the vicinity of the implant. This is in order to prevent overlapping of the stress or strain field associated with the reaction forces to the bone-implant interface.
\n
The models created with FEM presented in this study (Figure 17, complete model; Figure 18, cylindrical section of a simplified model), the lateral faces of bone tissue and the lower ones, were considered without moving, namely fixed (displacements, nodes are blocked on those faces in all directions).
Figure 17.
Boundary conditions for FE model (green arrows).
Figure 18.
Boundary conditions for the simplified FE model (green arrows).
\n
\n
\n
2.6. Load application
\n
Loosing marginal bone in the peri-implantation region may be a result of excessive occlusal forces [34]. To determine and understand this correlation, intensive research, including engineering principles, biomechanical relationships of living tissues, and mechanical properties of bone around the implant, is necessary.
\n
In this context, the loads setting in an FE model is an important part of the study. Each component of the model is contributing to the final analysis after loading. In other words, from beginning to end, all FE analysis steps contribute to the ability to extrapolate masticatory forces in the area around the implant and prosthesis.
\n
Masticatory forces can be forces of compression, traction, bending, and shear. Compressive forces try to push materials into each other, the traction forces pull them apart, separate entities and shear forces cause slides. Most detrimental forces that can increase stress around the bone-implant interface and prosthesis are traction and shear. These forces tend to damage the integrity of the material and cause stress concentration [2, 34].
\n
In general, the prosthesis-implant ensemble adapts to compressive forces. Under effective mastication, repetitive model of cyclical forces transmitted via dental implant causes load of the peri-implant bone. This generates a stress around the ridge and prosthesis. However, random cyclic forces of mastication are not easily simulated. Therefore, most FE studies use axial and/or non-axial forces [35].
\n
Non-axial loadings generate a distinctive stress particularly in the cortical bone ridge. So, for a realistic simulation it is necessary to use a combination of vertical and/or oblique (axial and non-axial) forces. As mentioned, oblique loads are more destructive for the peri-implant bone region and clinically disruptive for the prosthesis.
\n
Masticatory force size can be variable depending on age, sex, and para-functional habits, and can vary from anterior to posterior [36]. Loadings simulating mastication forces generate stress concentrations that must be evaluated.
\n
The loadings applied in the present study were provided by own experimental determinations (maximum values of masticatory forces women/men) and by information analysis from other studies [37–39]. In this study, we used both axial and non-axial loads. More precisely, more sets of static loads were considered, namely axial loads, non-axial (Figure 19); axial and non-axial (Figure 20), applied to the crown.
Figure 19.
Non-axial load application (arrows indicate the application direction).
Figure 20.
Axial and non-axial load application (arrows indicate the application direction).
\n
\n
\n
3. Results of FEM analysis of some clinical situations of implant prosthodontics rehabilitations
\n
\n
3.1. Analysis of states of stress, strain, and displacement in the implant and in the bone tissue
\n
Under the action of mastication forces, stress concentration increases in the prosthesis and bone. The stress is a representation of internal forces which are acting within a deformable body; it is a measure of the average force per unit of surface within the internal body where forces act. These internal forces occur in response to the application of external loadings on the considered body. Internal resistance after applying external loads cannot be measured. Therefore, a simple method is to measure the forces applied to the cross section.
\n
The FEM analysis of dental implants usually calculated von Mises stress (equivalent stress), a scalar quantity (number) characterizing stress size. This is very important in formulating the criteria of damage, plasticity, strength, and so on. This analysis is used to assess the effect of load forces on the region in the vicinity of dental implant or crown [34].
\n
There is the convention that specifies that positive values of stress mean traction stress, while negative values of stress mean compressive stress.
\n
FEM numerical calculation results are shown as diagrams in which different colors signify areas of physical quantities considered of equal value, the minimum value areas to areas with maximum values. In Figures 21–23, the red-colored areas represent critical areas and blue-colored areas represent areas with the least loading.
Figure 21.
von Mises stress field in the longitudinal plane section for axial force.
Figure 22.
Displacement field in the longitudinal plane section for axial force.
Figure 23.
Equivalent strain field in the longitudinal plane section for axial force.
\n
In our analysis, loads that simulate axial and non-axial type masticatory forces were considered, with a range between 20 and 140 N.
\n
In Figures 21–23, the most favorable areas are those with minimum values of stress, strain, and displacement, while areas with the greatest damage, high risk are characterized by higher values.
\n
We will present the most important results regarding the distribution of the following entities of the study: von Mises stress field, displacement field, strain field, the safety factor (SF) for axial loadings, non-axial loadings, and axial and non-axial loading simultaneously applied.
\n
\n
3.1.1. Axial loadings
\n
When axial loadings are applied, the results concerning the stress field distribution for the entire structure are shown in Figure 21, the displacement field in Figure 22, and the strain field in Figure 23.
\n
For example, Figure 21 confirms the correctness of dimensions adopted for mandibular bone by the fact that stress in marginal areas of it is practically null.
\n
Figures 21–23 show fields symmetry due to the application of axial loads and they were obtained for a load value of 100 N.
\n
We note that the case of axial loads is an ideal case, in reality this case is combined with non-axial loads.
\n
\n
\n
3.1.2. Non-axial loadings
\n
For non-axial loadings, we present the results for the von Mises stress field (Figure 24), displacement field (Figure 25), and strain field (Figure 26).
Figure 24.
Von Mises stress field in longitudinal plane section for non-axial loadings.
Figure 25.
Displacement field in longitudinal plane section.
Figure 26.
Strain field in longitudinal plane section for non-axial loadings for non-axial loadings.
\n
Figures 24–26 confirm instead the asymmetry for stress, strain, and displacement fields due to the application of non-axial forces. Results from the figures presented here are obtained for a non-axial load of 120 N and represent the distribution of von Mises field stress, displacements, and strains of the entire bone-implant-crown system.
\n
The distribution of these quantities—stress, displacements, and strains—is more suggestive if we consider their distribution separately only for bone tissue or for implant.
\n
Figure 27.
Von Mises stress in the bone tissue.
\n
Thus, we present the results for the bone tissue: the von Mises stress distribution in Figure 27, displacements distribution in Figure 28; for implant: the von Mises stress distribution in Figure 29 and von Mises stress distribution of the implant in the longitudinal section in Figure 30.
\n
Figure 28.
Displacements in the bone tissue.
Figure 29.
Von Mises stress in the implant.
Figure 30.
Von Mises stress in the longitudinal plane section of the implant.
\n
From the results shown in Figures 27 and 28, it is observed that to obtain large displacements and stresses, which may become critical in the upper cortical bone of the implant neck, it is necessary to apply non-axial forces in the opposite direction.
\n
\n
\n
3.1.3. Axial and non-axial loadings applied simultaneously
\n
A more realistic simulation of masticatory forces can be achieved by the simultaneous applications of axial (120 N) and non-axial loadings (20 N) (Figure 31). This case of application of forces is closer to the actual situation [26].
Figure 31.
The simultaneous application of axial and non-axial forces.
\n
In this case, for instance, the displacement distribution for the whole bone-implant-crown system is presented in Figure 32, while Figure 33 shows the distribution of displacements only in the bone tissue.
Figure 32.
Displacements in the case of application of axial and non-axial loads in the whole system.
Figure 33.
Displacements in the case of application of axial and non-axial loads in bone tissue.
\n
The comparison of these results with those obtained in the previous paragraph is very useful, as they allow an analysis concerning the localizations on which the forces are acting on. It appears that, for example, the maximum von Mises stress if only non-axial loads in Section 3.1.2 is 5.197 × 108 Pa, unlike the calculations in this paragraph, the maximum value of von Mises stress is 1.288 × 108 Pa. This fact is due to modeling of the masticatory forces which are applied over a larger area, case in which there is lower concentration (case of Section 3.1.3). If the application forces surface is smaller, then the maximum stress concentration is higher (case of Section 3.1.2).
\n
The stress is directly proportional to the force and inversely proportional to the surface area on which the force is applied. It is important to determine the surface area on which the loads are applied, as that fact influences considerably the calculation of maximum stress magnitude. For example, the area of the occlusal surface on which the rehabilitation is carried out is less than 4 mm, so that the amount of stress in much rehabilitation is in the range of MPa [40].
\n
\n
\n
3.2. Safety factor determination
\n
The CosmosWorks program has a section destined for determining the distribution of the factor of safety (FOS) or safety factor as a ratio between allowable stress limit values and actual stress values obtained by FEM calculation [22]. If SF < 1, the stress level is higher than the material limit and the structure is likely to fail [22].
\n
Images are suggestive and may indicate the need for constructive appropriate solutions to eliminate dangerous areas that have a low safety factor.
\n
Figures 34 and 35 present safety factor distributions for implant determined by the von Mises method and Tresca, respectively, in the case of axial loading, and in Figures 36 and 37, the safety factor distributions for implant, determined by the von Mises and Tresca method in the case of non-axial loads, respectively.
Figure 34.
Safety factor distributions in the longitudinal section, von Mises criterion, axial load.
Figure 35.
Safety factor distributions in the longitudinal section, Tresca criterion, axial load.
Figure 36.
SF distribution in longitudinal section, the von Mises criterion, non-axial loads.
Figure 37.
SF distribution in longitudinal section, the criterion Tresca, non-axial loads.
\n
From these figures, the obtained results show that the most strained zone is the contact area of the crown and fine thread parts from the cortical bone area (red-colored areas in Figures 34 and 35). Between the two alternative calculations, there are small differences, both in terms of factor value and in terms of its distribution.
\n
A suggestive description of the SF distribution can be achieved by considering the bone tissue only (Figure 38).
Figure 38.
SF distribution in the bone tissue, Tresca criteria for non-axial loads.
\n
From the obtained results of Figures 36, 37 and 38, asymmetry is observed due to the non-axial forces application, and also an area with very low safety factor in cervical bone area adjacent to the implant neck, on opposite application of oblique force.
\n
\n
\n
4. Discussions
\n
3D FEM analysis is very efficient for assessing the biomechanical behavior of a structure made of bone, implant, and crown under various loading conditions. In the past four decades, numerous studies have shown that FEM applied in dentistry is a very successful method used to investigate critical issues related to stress distribution [41]. Using detailed 3D models may be extremely useful in understanding the critical issues related to choosing the rehabilitation and application of procedures.
\n
Results of a FEM analysis cannot be implemented directly in clinical situations, but it can design a model so as to simulate a real situation as well as possible [42]. Some limitations of the study using FEM are, however, the adoption of simplifications and assumptions.
\n
FEM analysis should be interpreted with care. In most cases, the numerical studies of oral implantology use isotropic material, and not orthotropic, or anisotropic, as would be plausible [43].
\n
On the other hand, the model by FEM is a static situation at a moment of loading application and not an actual clinical situation. In reality, structure loading is rather dynamic and cyclical. Materials used in various branches of dentistry are supposed isotropic, homogeneous, and elastic, and so remain after loadings, which do not reflect the real situation [43, 44]. For example, periodontal ligament has non-linear mechanical properties and bone tissue is heterogeneous. The numerical results obtained should be more precise and rigorous if the material would be considered anisotropic and inhomogeneous, but they would lead to complex mathematical calculations on one hand, but more difficult, it would require more complicated laboratory experiments to determine material constants, especially for materials of living tissue type [45].
\n
The results obtained by FEM use von Mises plasticity criterion, which is used in engineering rather for ductile material, for which the compression stress is equal to the traction stress as steel and aluminum. By contrast, brittle materials such as ceramics, cements, or composites resins had a compressive strength value significantly greater than the tensile strength [7].
\n
The structure response is different under the action of asymmetric loads. When the structure is loaded compressively, no significant displacements occur due to higher compressive strength. The situation is different when loading is asymmetrical and when traction stress occurs. When a further lateral load is applied, traction stress is generated in areas with higher values than when vertical loads are applied in the same areas [43].
\n
An increase in load does not cause a change in the stress distribution, only an increase in values. A loading that a structure is subjected can cause micro-cracks in certain parts of the structure, but not an immediate rupture [36].
\n
Most dental breaks of used materials are caused by traction stress. A prevention of this phenomenon can be avoided by adjusting the occlusal surfaces. Masticatory forces vary between 11 and 150 N, while the maximum value of force is 200 N anterior, 350 N posterior, and 1000 N in bruxism [46, 47].
\n
New developments in computer technology and modeling techniques make FEM a reliable and accurate approach in dealing with biomechanical applications.
\n
In clinical practice, it may be considered that as these applications are being made by computer, assumptions and critical limitations can clearly affect the application of these results on a real scenario. Another aspect of the analysis by FEM is overemphasis results, due to the simplification applied to simulation models.
\n
Although usually an advanced computing technology is used to obtain numerical results, there are many factors that affect clinical features such as macro- and micro-design, material properties, loading conditions, and boundary conditions.
\n
Consequently, correlating the obtained FEM results with preclinical and clinical studies of long duration can help to validate results obtained by numerical simulations.
\n
Note that several versions of meshing were created, to a fineness of it that does not lead to errors in the solution 3.5% higher than similar studies conducted by other authors [3].
\n
Loading simulations must be as realistic as possible, containing simultaneous axial forces and non-axial [26, 29].
\n
Results from our FEM analysis are consistent with data from the literature for similar models [48, 49]. Regarding optimizing dental implants, the following can be mentioned [50, 51]:\n
– Placing the implant should be done with caution so this would be as much as possible in the cortical bone area, as strain and stress values in the trabecular bone zone is very small, they may cause atrophy in the surrounding area.
– The neck of the implant should be long enough so that it departs from the soft tissue and any implant adherence has no effect on the mucous membrane. Any inflammation of the soft tissue and/or marginal bone resorption can jeopardize the stability of the implant.
– The neck of the implants should be quite robust, since the maximum stress concentrations occur in the neck of the implant. If the implant is not quite strong in this region, it can affect the integrity of the implant.
– There is a high risk of overload in the mesial and distal areas. This should be considered in patients where there is a narrow ridge of bone.
– Since loading does not necessarily mean it has overcome bone resistance, continuous loading more likely causes fatigue damage (micro-cracks in bone, marginal bone resorption). From a mechanical point of view, the presence of bone defects seems disadvantageous due to lack of bone support. Instead, the peri-implant bone stresses and strains are not only depending on the in vivo loading but also determined by bone quality (mechanical properties of bone) and amount (thickness of the cortical bone, density of the trabecular bone), periodontal state, oral hygiene, and other factors that may play an important role in marginal bone remodeling.
\n
\n
\n
5. Conclusions
\n
The present study constitutes a FEM calculation of stress, displacement, and strain in implant and surrounding bone, which is used to assess risk factors from a biomechanical point.
\n
FEM studies have certain advantages over clinical studies, preclinical, and in vitro. First, patients are in no way affected by the application of new materials and new treatment that have not been previously tested. In the biomedical field, FEM is an important tool because it avoids the need for a traditional specimen, instead using a mathematical model that eliminates the need for a large number of teeth. FEM analysis helps in preparing the design, indicates the right materials, or a combination therefore to be used in various load conditions to reduce material consumption and/or failure of the clinical practice.
\n
FEM analyses are useful for clinicians, although they require additional time to work, but they are a useful tool in predicting the implant choosing: type, shape, size. We believe that in the future, it will develop further, being available and accessible to a large number of doctors.
\n
FEM simulations can be extended in numerous directions, such as parametric studies for the key factors involved in the analysis of the success/failure of the dental implant treatments; the analysis of the implant prostheses: bridges on the implants, connected crowns, and individual crowns on the implants, in order to formulate exclusion criteria regarding the success of one of the therapeutic alternatives; comparative analysis of the various models of implants in terms of their stability and highlighting the factors involved in it.
\n
\n
Acknowledgments
\n
I would like to express my gratitude to Prof. Dr. E. Avram for his support for the geometrical model development and the FEM calculation. I also like to thank Prof. Dr. V. Năstăsescu for his high professionalism and expertise on FEM use.
\n
\n',keywords:"finite elements methods, implant prosthodontics, biomechanics, stress distribution, numerical solutions",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/52382.pdf",chapterXML:"https://mts.intechopen.com/source/xml/52382.xml",downloadPdfUrl:"/chapter/pdf-download/52382",previewPdfUrl:"/chapter/pdf-preview/52382",totalDownloads:937,totalViews:434,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,dateSubmitted:"March 28th 2016",dateReviewed:"July 20th 2016",datePrePublished:null,datePublished:"December 14th 2016",dateFinished:null,readingETA:"0",abstract:"This chapter is devoted to the study of behavior of functional loadings for implant prosthetics rehabilitation by finite elements method (FEM). It presents a numerical calculation of stress, displacement, and strain in implant and surrounding bone, which is used to assess risk factors from a biomechanical point. The masticatory forces are simulated by axial and/or non-axial loads, and they are responsible for the biomechanical response of the bone-tissue-implant-crown system. This chapter represents an analysis of this response in view of highlighting the factors involved in implant stability and success. The safety factor for different loading cases is calculated as well. A good agreement with other study results and clinical studies is obtained.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/52382",risUrl:"/chapter/ris/52382",book:{slug:"perusal-of-the-finite-element-method"},signatures:"Iulia Roateşi",authors:[{id:"187359",title:"Associate Prof.",name:"Iulia",middleName:null,surname:"Roatesi",fullName:"Iulia Roatesi",slug:"iulia-roatesi",email:"iulia.roatesi@gmail.com",position:null,institution:{name:"Universitatea Titu Maiorescu",institutionURL:null,country:{name:"Romania"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Finite elements analysis steps",level:"2"},{id:"sec_1_3",title:"1. Dividing analysis area in finite elements (mesh)",level:"3"},{id:"sec_3_2",title:"1.2. Effect of meshing on the numerical results",level:"2"},{id:"sec_3_3",title:"2. Establishment of finite element equations (elemental equations)",level:"3"},{id:"sec_4_3",title:"3. Assembling the elemental equations in the equations system of the structure",level:"3"},{id:"sec_5_3",title:"4. Implementation of boundary conditions and solving the system of the structure equations",level:"3"},{id:"sec_6_3",title:"5. Performing additional calculations to determine the secondary unknowns",level:"3"},{id:"sec_9",title:"2. Material and method",level:"1"},{id:"sec_9_2",title:"2.1. The use of FEM in dental implants studies",level:"2"},{id:"sec_10_2",title:"2.2. Numerical analysis program",level:"2"},{id:"sec_11_2",title:"2.3. The geometric model of the dental implant",level:"2"},{id:"sec_11_3",title:"2.3.1. General considerations",level:"3"},{id:"sec_12_3",title:"2.3.2. Geometric model components",level:"3"},{id:"sec_12_4",title:"2.3.2.1. Implant",level:"4"},{id:"sec_13_4",title:"2.3.2.2. Crown",level:"4"},{id:"sec_14_4",title:"2.3.2.3. Bone tissue",level:"4"},{id:"sec_17_2",title:"2.4. The dental implant finite elements model",level:"2"},{id:"sec_17_3",title:"Table 1.",level:"3"},{id:"sec_18_3",title:"Table 2.",level:"3"},{id:"sec_19_3",title:"2.4.3. Contact modeling",level:"3"},{id:"sec_21_2",title:"2.5. Boundary conditions",level:"2"},{id:"sec_22_2",title:"2.6. Load application",level:"2"},{id:"sec_24",title:"3. Results of FEM analysis of some clinical situations of implant prosthodontics rehabilitations",level:"1"},{id:"sec_24_2",title:"3.1. Analysis of states of stress, strain, and displacement in the implant and in the bone tissue",level:"2"},{id:"sec_24_3",title:"3.1.1. Axial loadings",level:"3"},{id:"sec_25_3",title:"3.1.2. Non-axial loadings",level:"3"},{id:"sec_26_3",title:"3.1.3. Axial and non-axial loadings applied simultaneously",level:"3"},{id:"sec_28_2",title:"3.2. Safety factor determination",level:"2"},{id:"sec_30",title:"4. Discussions",level:"1"},{id:"sec_31",title:"5. Conclusions",level:"1"},{id:"sec_32",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Gultekin B.A., Gultekin P.,Yalcin S., Application of finite element analysis in implant dentistry. Finite Element Analysis New Trends and Developments. Rijeka, Croatia: Intech, 2012: pp. 21–54.'},{id:"B2",body:'Vanegas-Acosta J.C., Landinez P.N., Garzon-Alvarado D.A., Casale R.M., A finite element method approach for the mechanobiological modeling of the osseointegration of a dental implant. 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Dental Materials, 2009. 25(5): pp. 678–90.'},{id:"B6",body:'Avram E., Năstăsescu V., Finite element method in fluid mechanics. 1995: Military Technical Academy Press.'},{id:"B7",body:'Huebner K.H., Dewhirst D.L., Smith D.E., Byrom T.G., The finite element method for engineers. 2008: John Wiley & Sons.'},{id:"B8",body:'Zienkiewicz O., Taylor R., The finite element method for solid and structural mechanics. 2005: Butterworth-Heinemann.'},{id:"B9",body:'Bahuguna R., Anand B., Kumar D., Aeran H., Anand V., Gulati M., Evaluation of stress models in bone around dental implant for different blunt angulations under axial and oblique loading: A finite element analysis. Natl J Maxillofac Surg, 2013. 4(1): pp. 46–51.'},{id:"B10",body:'Bijjargi S., Chowdhary R., Stress dissipation in the bone through various crown materials of dental implant restoration: a 2-D finite element analysis. J Investig Clin Dent, 2013. 4(3): pp. 172–7.'},{id:"B11",body:'Siadat H., Hashemzadeh S., Geramy A., Bassir S.H., Alikhasi M., The effect of offset implant placement on the stress distribution around a dental implant: a three-dimensional finite element analysis. J Oral Implantol, 2015. 41(6): p.646–651.'},{id:"B12",body:'Ogawa T., Ogawa M., Koyano K., Different responses of masticatory displacements after alteration of occlusal guidance related to individual displacement model. J Oral Rehab, 2001. 28(9): pp. 830–41.'},{id:"B13",body:'Chang C.L., Chen C.S., Yeung T.C., Hsu M.L., Biomechanical effect of a zirconia dental implant-crown system: a three-dimensional finite element analysis. Int J Oral Maxillofac Implants, 2012. 27(4): pp. 49–57.'},{id:"B14",body:'Vivan Cardoso M., Vandamme K., Chaudhari A., De Rycker J., Van Meerbeek B., Naert I., Duyck J., Dental implant macro-design features can impact the dynamics of osseointegration. Clin Implant Dent Relat Res, 2015. 17(4):639–645.'},{id:"B15",body:'Kasai K., Takayama Y., Yokoyama A., Distribution of occlusal forces during occlusal adjustment of dental implant prostheses: a nonlinear finite element analysis considering the capacity for displacement of opposing teeth and implants. Int J Oral Maxillofac Implants, 2012. 27(2): pp. 329–35.'},{id:"B16",body:'Tang C.B., Liul S.Y., Zhou G.X., Yu J.H., Zhang G.D., Bao Y.D., Wang Q.J., Nonlinear finite element analysis of three implant-blunt interface designs. Int J Oral Sci, 2012. 4(2): pp. 101–8.'},{id:"B17",body:'Marcian P., Borak L., Valasek J., Kaiser J., Florian Z., Wolff J., Finite element analysis of dental implant loading on atrophic and non-atrophic cancellous and cortical mandibular bone—a feasibility study. J Biomech, 2014. 47(16): pp. 3830–6.'},{id:"B18",body:'Lee J.S., Cho I.H., Kim Y.S., Heo S.J., Kwon H.B., Lim Y.J., Bone-implant interface with simulated insertion stress around an immediately loaded dental implant in the anterior maxilla: a three-dimensional finite element analysis. Int J Oral Maxillofac Implants, 2012. 27(2): pp. 295–302.'},{id:"B19",body:'Liang R., Guo W., Qiao X., Wen H., Yu M., Tang W., Liu L., Wei Y., Tian W., Biomechanical analysis and comparison of 12 dental implant systems using 3D finite element study. Comput Meth Biomech Biomed Eng, 2015. 18(12): pp. 1340–8.'},{id:"B20",body:'Desai S.R., Desai M.S., Katti G., Karthikeyan I., Evaluation of design parameters of eight dental implant designs: a two-dimensional finite element analysis. Niger J Clin Pract, 2012. 15(2): pp. 176–81.'},{id:"B21",body:'Huang Y.M., Chou I.C., Jiang C.P., Wu Y.S.,Lee S.Y., Finite element analysis of dental implant neck effects on primary stability and osseointegration in a type IV bone mandible. Biomed Mater Eng, 2014. 24(1): pp. 1407–15.'},{id:"B22",body:'Lombard M., SolidWorks surfacing & complex shape modeling bible, 2013: Wiley.'},{id:"B23",body:'Verri F.R., Batista V.E., Santiago J.F., Jr., Almeida D.A., Pellizzer E.P., Effect of crown-to-implant ratio on peri-implant stress: a finite element analysis. Mater Sci Eng C Mater Biol Appl, 2014. 45: pp. 234–40.'},{id:"B24",body:'Winter W., Klein D., Karl M., Effect of model parameters on finite element analysis of micromotions in implant dentistry. J Oral Implantol, 2013. 39(1): pp. 23–9.'},{id:"B25",body:'Ramos Verri F., Santiago Junior J.F., de Faria Almeida D.A., de Oliveira G.B., de Souza Batista V.E., Marques Honorio H., Yoshito Noritomi P., Piza Pellizzer E., Biomechanical influence of crown-to-implant ratio on stress distribution over internal hexagon short implant: 3-D finite element analysis with statistical test. J Biomech, 2015. 48(1): pp. 138–45.'},{id:"B26",body:'Roateși I., Hutu E., Ghergic D.L., Dental implant modeling by FEM. Roman J Stomatol, 2014. 60(4): pp. 211–17.'},{id:"B27",body:'Shigemitsu R., Yoda N., Ogawa T., Kawata T., Gunji Y., Yamakawa Y., Ikeda K., Sasaki K., Biological-data-based finite-element stress analysis of mandibular bone with implant-supported overdenture. Comput Biol Med, 2014. 54: pp. 44–52.'},{id:"B28",body:'Chang C.L., Chen C.S., Huang C.H., Hsu M.L., FEA of the dental implant using a topology optimization method. Med Eng Phys, 2012, 34(7): pp. 999–1008.'},{id:"B29",body:'Roateşi I., Hutu E., Ghergic D.L., Stress and strain calculation in implant and surrounding bone by FEM. Roman J Stomatol, 2014, 60(4): pp. 227–34.'},{id:"B30",body:'Piccioni M.A.R.V., Campos E.A., Saad J.R.C., de Andrade M.F., Galvão M.R., Rached A.A., Application of the finite element method in dentistry. RSBO Revista Sul-Brasileira de Odontol, 2013, 10(4): pp. 369–77.'},{id:"B31",body:'Quaresma S.E., Cury P.R., Sendyk W.R., Sendyk C., A finite element analysis of two different dental implants: stress distribution in the prosthesis, blunt, implant, and supporting bone. J Oral Implantol, 2008, 34(1): pp. 1–6.'},{id:"B32",body:'Dee K.C, Puleo D.A., Bizios R., An introduction to tissue-biomaterial interactions, 2002: Wiley-Liss, John Wiley & Sons, Inc., Publication.'},{id:"B33",body:'Geng, J.P., Yan W., Xu W., Application of finite element analysis in implant dentistry, 2008: Springer.'},{id:"B34",body:'Cheng H.Y., Chu K.T., Shen F.C., Pan Y.N., Chou H.H., Ou K.L., Stress effect on bone remodeling and osseointegration on dental implant with novel nano/microporous surface functionalization. J Biomed Mater Res A, 2013, 101(4): pp. 1158–64.'},{id:"B35",body:'Borcic J., Braut A., Finite element analysis in dental medicine. 2012: Intech Publication.'},{id:"B36",body:'Raabe D., Harrison A., Alemzadeh K., Ireland A., Sandy J. Capturing motions and forces of the human masticatory system to replicate mastication and to perform dental wear experiments. in 24th International Symposium on Computer-Based Medical Systems (CBMS), 2011: IEEE.'},{id:"B37",body:'O’Mahony A., Bowles Q., Woolsey G., Robinson S.J., Spencer P., Stress distribution in the single-unit osseointegrated dental implant: finite element analyses of axial and off-axial loading. Implant Dent, 2000, 9(3): pp. 207–18.'},{id:"B38",body:'Geng J.P., Tan K.B., Liu G.R., Application of finite element analysis in implant dentistry: a review of the literature. J Prosthet Dent, 2001, 85(6): pp. 585–98.'},{id:"B39",body:'Morneburg T.R., Proschel P.A., Measurement of masticatory forces and implant loads: a methodologic clinical study. Int J Prosthodont, 2002, 15(1): pp. 20–7.'},{id:"B40",body:'Huthmann S., Staszyk C., Jacob H.G., Rohn K., Gasse H., Biomechanical evaluation of the equine masticatory action: calculation of the masticatory forces occurring on the cheek tooth battery. J Biomech, 2009, 42(1): pp. 67–70.'},{id:"B41",body:'El-Mekawy N., Fouad M.M., El-Hawary Y.M., Al-Shahat M.A., El-Gendy R., Scanning electron microscopy observations of osseointegration failures of dental implants that support mandibular overdentures. Implant Dent, 2013, 22(6): pp. 645–9.'},{id:"B42",body:'Roateşi I., Three dimensional finite element method modeling of dental implants biomaterials. Roman J Mater, 2015, 45(3): pp. 282–89.'},{id:"B43",body:'Khorshid H.E., Hamed H.A.F., Aziz E.A., The effect of two different immediate loading protocols in implant-supported screw-retained prostheses. Implant Dentistry, 2011. 20(2): pp. 157–66.'},{id:"B44",body:'Kumar Y.S., Pant B., Darunkumar Singh K., Thickness effects on maximum von-Mises stress of a cement mantle in total hip replacement—a finite element study. J Appl Biomater Biomech, 2009, 7(2): pp. 111–5.'},{id:"B45",body:'Keyak J.H., Rossi S.A., Prediction of FEMoral fracture load using finite element models: an examination of stress-and strain-based failure theories. J Biomech, 2000, 33(2): pp. 209–14.'},{id:"B46",body:'Spyrakos C.C., Finite element modeling. 1994: WVU Press.'},{id:"B47",body:'Bălăcel E., Lăcătuşu Ş., Topoliceanu C., Bălăcel I., Iovan G., Ghiorghe A., Mathematics model analysis of biomechanical behaviour of three dental materials to loading related to bruxism. Roman J Oral Rehab, 2011, 3(3): p. 26.'},{id:"B48",body:'Lin C.L., Kuo Y.C., Lin T.S., Effects of dental implant length and bone quality on biomechanical responses in bone around implants: a 3-d non-linear finite FEA. Biomed Eng Appl Basis Commun, 2005, 17: pp. 44–9.'},{id:"B49",body:'Van Staden R.C., Guan H., Loo Y.C., Johnson N.W., Meredith N., Comparative analysis of two implant-crown connection systems—a finite element study. Appl Osseointegration Res, 2008, 6: pp. 48–53.'},{id:"B50",body:'Misch C.E., Contemporary implant dentistry. 2007: Elsevier Health Sciences.'},{id:"B51",body:'Cicciu M., Bramanti E., Cecchetti F., Scappaticci L., Guglielmino E., Risitano G., FEM and von Mises analyses of different dental implant shapes for masticatory loading distribution. Oral Implantol (Rome), 2014. 7(1): pp. 1–10.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Iulia Roateşi",address:"iulia.roatesi@gmail.com",affiliation:'
Faculty of Dental Medicine, Titu Maiorescu University, Bucharest, Romania
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Jalalabadi",slug:"r.-jalalabadi"}]},{id:"33905",title:"The Finite Volume Method in Computational Rheology",slug:"the-finite-volume-method-in-computational-rheology",signatures:"A.M. Afonso, M.S.N. Oliveira, P.J. Oliveira, M.A. Alves and F.T. Pinho",authors:[{id:"118484",title:"Dr.",name:"Mónica",middleName:null,surname:"Oliveira",fullName:"Mónica Oliveira",slug:"monica-oliveira"},{id:"118485",title:"Prof.",name:"Alexandre M.",middleName:null,surname:"Afonso",fullName:"Alexandre M. Afonso",slug:"alexandre-m.-afonso"},{id:"118486",title:"Prof.",name:"Paulo J.",middleName:null,surname:"Oliveira",fullName:"Paulo J. Oliveira",slug:"paulo-j.-oliveira"},{id:"118487",title:"Prof.",name:"Manuel A.",middleName:null,surname:"Alves",fullName:"Manuel A. Alves",slug:"manuel-a.-alves"},{id:"118488",title:"Prof.",name:"Fernando T.",middleName:null,surname:"Pinho",fullName:"Fernando T. Pinho",slug:"fernando-t.-pinho"}]},{id:"33906",title:"Rayleigh-Bénard Convection in a Near-Critical Fluid Using 3D Direct Numerical Simulation",slug:"rayleigh-b-nard-convection-in-a-near-critical-fluid-using-3d-direct-numerical-simulation",signatures:"Accary Gilbert",authors:[{id:"116772",title:"Dr.",name:"Gilbert",middleName:null,surname:"Accary",fullName:"Gilbert Accary",slug:"gilbert-accary"}]},{id:"33907",title:"A Concept of Discretization Error Indicator for Simulating Thermal Radiation by Finite Volume Method Based on an Entropy Generation Approach",slug:"a-concept-of-discretization-error-indicator-for-simulating-thermal-radiation-by-finite-volume-method",signatures:"H. C. Zhang, Y. Y. Guo, H. P. Tan and Y. Li",authors:[{id:"12841",title:"Prof.",name:"Heping",middleName:null,surname:"Tan",fullName:"Heping Tan",slug:"heping-tan"},{id:"12842",title:"Dr.",name:"Haochun",middleName:null,surname:"Zhang",fullName:"Haochun Zhang",slug:"haochun-zhang"},{id:"119388",title:"Prof.",name:"Yao",middleName:null,surname:"Li",fullName:"Yao Li",slug:"yao-li"},{id:"139388",title:"Mr.",name:"Yangyu",middleName:null,surname:"Guo",fullName:"Yangyu Guo",slug:"yangyu-guo"}]},{id:"33908",title:"Volume-of-Fluid (VOF) Simulations of Marangoni Bubbles Motion in Zero Gravity",slug:"volume-of-fluid-vof-simulations-of-marangoni-bubbles-motion-in-zero-gravity",signatures:"Yousuf Alhendal and Ali Turan",authors:[{id:"115974",title:"Dr.",name:"Yousuf",middleName:null,surname:"Alhendal",fullName:"Yousuf Alhendal",slug:"yousuf-alhendal"},{id:"139857",title:"Mr.",name:"Ali",middleName:null,surname:"Turan",fullName:"Ali Turan",slug:"ali-turan"}]},{id:"33909",title:"Mass Conservative Domain Decomposition for Porous Media Flow",slug:"mass-conservative-domain-decomposition-for-porous-media-flow",signatures:"Jan M. Nordbotten, Eirik Keilegavlen and Andreas Sandvin",authors:[{id:"118785",title:"Prof.",name:"Jan",middleName:null,surname:"Nordbotten",fullName:"Jan Nordbotten",slug:"jan-nordbotten"},{id:"119283",title:"Dr.",name:"Eirik",middleName:null,surname:"Keilegavlen",fullName:"Eirik Keilegavlen",slug:"eirik-keilegavlen"},{id:"119291",title:"MSc.",name:"Andreas",middleName:null,surname:"Sandvin",fullName:"Andreas Sandvin",slug:"andreas-sandvin"}]},{id:"33910",title:"On FVM Transport Phenomena Prediction in Porous Media with Chemical/Biological Reactions or Solid-Liquid Phase Change",slug:"on-fvm-transport-phenomena-prediction-in-porous-media-with-chemical-biological-reactions-or-solid-li",signatures:"Nelson O. Moraga and Carlos E. Zambra",authors:[{id:"23421",title:"Dr.",name:"Carlos",middleName:null,surname:"Zambra",fullName:"Carlos Zambra",slug:"carlos-zambra"},{id:"118112",title:"Dr.",name:"Nelson",middleName:null,surname:"Moraga",fullName:"Nelson Moraga",slug:"nelson-moraga"}]},{id:"33911",title:"Finite Volume Method for Streamer and Gas Dynamics Modelling in Air Discharges at Atmospheric Pressure",slug:"finite-volume-method-for-streamer-and-gas-dynamics-modelling-in-air-discharges-at-atmospheric-pressu",signatures:"Olivier Ducasse, Olivier Eichwald and Mohammed Yousfi",authors:[{id:"33761",title:"Prof.",name:"Mohammed",middleName:null,surname:"Yousfi",fullName:"Mohammed Yousfi",slug:"mohammed-yousfi"},{id:"119639",title:"Dr.",name:"Olivier",middleName:null,surname:"Ducasse",fullName:"Olivier Ducasse",slug:"olivier-ducasse"},{id:"119652",title:"Prof.",name:"Olivier",middleName:null,surname:"Eichwald",fullName:"Olivier Eichwald",slug:"olivier-eichwald"}]},{id:"33912",title:"Conjugate Gradient Method Applied to Cortical Imaging in EEG/ERP",slug:"conjugate-gradient-method-applied-to-cortical-imaging-in-eeg-erp",signatures:"X. Franceries, N. Chauveau, A. Sors, M. Masquere and P. Celsis",authors:[{id:"118013",title:"Dr.",name:"Nicolas",middleName:null,surname:"Chauveau",fullName:"Nicolas Chauveau",slug:"nicolas-chauveau"},{id:"118021",title:"Prof.",name:"Xavier",middleName:null,surname:"Franceries",fullName:"Xavier Franceries",slug:"xavier-franceries"},{id:"118023",title:"MSc.",name:"Aurelie",middleName:null,surname:"Sors",fullName:"Aurelie Sors",slug:"aurelie-sors"},{id:"118024",title:"Prof.",name:"Mathieu",middleName:null,surname:"Masquere",fullName:"Mathieu Masquere",slug:"mathieu-masquere"},{id:"118025",title:"Dr.",name:"Pierre",middleName:null,surname:"Celsis",fullName:"Pierre Celsis",slug:"pierre-celsis"}]},{id:"33913",title:"Wood Subjected to Hygro-Thermal and/or Mechanical Loads",slug:"wood-subjected-to-hygro-thermal-and-or-mechanical-loads",signatures:"Izet Horman, Dunja Martinović, Izet Bijelonja and Seid Hajdarević",authors:[{id:"117850",title:"Prof.",name:"Dunja",middleName:null,surname:"Martinovic",fullName:"Dunja Martinovic",slug:"dunja-martinovic"},{id:"118045",title:"Dr.",name:"Izet",middleName:null,surname:"Horman",fullName:"Izet Horman",slug:"izet-horman"},{id:"119580",title:"Prof.",name:"Izet",middleName:null,surname:"Bijelonja",fullName:"Izet Bijelonja",slug:"izet-bijelonja"},{id:"119581",title:"MSc.",name:"Seid",middleName:null,surname:"Hajdarević",fullName:"Seid Hajdarević",slug:"seid-hajdarevic"}]},{id:"33914",title:"Integrated Technology for CAD Modeling and CAE Analysis of a Basic Hydraulic Cylinder",slug:"integrated-technology-for-cad-modeling-and-cae-analysis-of-a-basic-hydraulic-cylinder",signatures:"Radostina Petrova and Sotir Chernev",authors:[{id:"118470",title:"PhD.",name:"Radostina",middleName:"Vasileva",surname:"Petrova",fullName:"Radostina Petrova",slug:"radostina-petrova"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"74620",title:"Molecular Mechanisms of Distinct Diseases",doi:"10.5772/intechopen.95114",slug:"molecular-mechanisms-of-distinct-diseases",body:'\n
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1. Introduction
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Distinct diseases have different etiology pattern and this chapter covers the chromosomal diseases, cancer, neurodegenerative diseases, pulmonary diseases, obesity-induced insulin resistance, lymphoblastic leukemia, viral immunology and infectious diseases. These communicable and non-communicable diseases negatively affect structure-function of the organism and specific symptoms are associated with these conditions. Pathogens or internal dysfunctions may lead these diseases. The chapter provides pathology of selected diseases from each class along with the molecular mechanisms.
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1.1 Chromosomal diseases
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1.1.1 Down syndrome
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Down syndrome (DS) is the most common chromosomal genetic disorder. The disease is caused by the trisomy of human chromosome 21 (HSA21) and is also the most genetic mental disability [1]. The HSA21 mosaic can also lead to DS. Maternity age is an important aspect in the formation of an individual with DS [2]. The main cause of this disease is the absence of normal chromosome separation during meiosis and the production of gametes with two copies of chromosome copies instead of a single copy. As a result, DS individuals have trisomy 21 in some body cells, and a normal number of chromosomes in others. This is called mosaicism and is seen in approximately 4% of DS individuals. The term mosaicism was first reported in 1961 [3] and can occur in two ways: either a normal zygote is exposed to an early mitotic error following fertilization, which results in trisomy 21 in some cells, or an early mitotic error in some cells allows it to return to normal karyotype [4].
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HSA21 is the most studied human chromosome, and since the long arm of chromosome 21 has been fully sequenced, a significant progress has been made in understanding its functional genomic units. HSA21 is the smallest chromosome and the overall gene density per megabase is about 15 genes per Mb (for the human genome) [5]. HSA21 is also very rich in long encoding RNA (lncRNA) genes, and, one of the poorest for genes encoding microRNA (miRNA). Also, the gene density is average for pseudogenes encoding the protein per Mb [6]. HSA21 is a weak chromosome in non-encoding RNAs (ncRNAs) and long nuclear elements (LINE). Interestingly, HSA21 shows significant enrichment for proteins found in cytoskeleton structures. These cytoskeletal proteins are known to play a role in neurological disorders, especially Alzheimer’s neuropathology [7].
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Individuals with DS occasionally develop the myeloproliferative disorder (TMD), a disease that is mostly unique to DS. Almost all TMD cases were found to contain somatic mutations on the X chromosome, in the GATA1 transcription factor [8]. Certain features of DS contain genes on other chromosomes causing gene and trisomy mutations and working together to reveal the disorder in HSA21. Studies have shown that the formation of Trisomy 21 precedes the formation of GATA1 mutations [1]. This may indicate that Trisomy 21 either increases genomic discrepancy leading to GATA1 mutations, or it supplies a selected medium for hematopoietic cells containing GATA1 mutations.
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1.1.2 Molecular mechanism
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Many hypotheses have been proposed to explain the genotype–phenotype relationship in DS. One of these is the ‘gene dosage effect’ hypothesis putting forward that the phenotypes arise directly from the dosage imbalance of the genes. Overlapping this hypothesis, the ‘DS Critical Region’ (DSCR) was announced in the 1990s. [9, 10]. Many of the DS features can be called into a subset of the critical genes in the DSCR region, suggesting that DS phenotypes are mainly caused by the dosage imbalance of only a few genes on HSA21. Genomic regions affecting the presence of certain DS phenotypes have been identified and high-resolution genetic maps of DS features have been created [11]. Olson et al. studied the DSCR regions in mice to test its hypothesis. They concluded that dosage imbalance of some individual genes on HSA21 directly affects certain phenotypes, but they stated that more studies are needed [12].
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The “amplified developmental instability” hypothesis suggests that dosage imbalance of the HSA21 gene leads to a non-specific impairment of cellular homeostasis [10]. Extra chromosome materials may also contribute to phenotypes by disrupting chromosomal regions. Some data on monozygotic twins for TS21 suggest that differential expression between normal and trisomic twins can be regulated across chromosome domains. This study shows that some DS phenotypes can be enlightened by the modification of the chromatin structure in the nucleus [13]. Monozygotic twins affected by DS but showing incompatible phenotypes have been reported in some cases, suggesting the role of epigenetics in the phenotypic variability of DS. For example, DNA methylation (controlling gen expression) has been shown to change in Trizomy of chromosome 21 (TS21) samples [14].
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1.1.3 Turner syndrome
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Turner syndrome (TS) is a disorder in mosaic karyotypes associated with complete or partial loss of the X chromosome. Seen especially in women, TS is associated with short stature, delayed puberty, ovarian dysgenesis, infertility, congenital malformations of the heart, type 1 and type 2 diabetes mellitus, osteoporosis, and autoimmune disorders. It occurs in almost every 2500 live female births. Fetuses affected by TS are 99% estimated to result in fetal death. Approximately half have monosomy X (45, X) and 10% have a repeat (isochromosome) of the long arm of the X chromosome. Most of the rest has a mosaic in more cell lines for 45X. TS, which is associated with a missing X chromosome, was first identified about 100 years ago [15].
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Related genes: Shox gene (short length homeobox protein-coding) located on X and Y chromosomes, it is a gene responsible for TS phenotype. This gene does not undergo X inactivation, and a decrease in the expression of SHOX explains some of the TS-related growth deficits. The gene product controls the expression of natriuretic peptide B (NPBB) and FGFR3 (fibroblast growth factor receptor 3) and regulates the proliferation and of chondrocytes, and also cooperates with SOX5, SOX6 and SOX9 and some other genes [16].
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The TS genome is hypo-methylated with less hypermethylation sites and there are RNA expression changes that affect the X chromosome genes and autosomal genes compared to women who are 46 XX. Known escape genes are expressed differently in individuals with TS and other X chromosome genes such as RPS4X and JPX (CD40LG and KDM5C) in particularly, KDM5C (encoding lysine-specific demethylase 5C) can participate in the transcriptional profile of neuronal genes and play role in different neurocognitive profiles [17]. 40S ribosomal protein S4 (RPS4X) also plays an important role in TS, bringing together multiple protein complexes. In addition, the Y paralog of RPS4X (RPS4Y) may also have a role since it is normally expressed as duplicates [18].
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Many different studies show that women with TS have increased mortality compared to the pool of a wide variety of related diseases [19]. The most obvious increase in morbidity is caused by autoimmunities like diabetes mellitus or thyroiditis, osteoporosis, cardiovascular diseases, hypertension, congenital malformations, especially endocrine diseases including heart diseases, digestive system and anemia [20].
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1.1.4 Genotype-phenotype
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It is still unclear which chromosomal regions or genes make up the phenotypical properties of TS. The physical symptoms of TS were thought to be due to the absence of normal sex chromosomes before inactivation of the X chromosome, or the haplo-insensitivity of the genes in the pseudo-autosomal regions of the aneuploidy [21]. It is thought that a complete phenotype results in the loss of short arm (Xp) in the X chromosome. Aneuploidy itself can cause growth failure. Loss of a region in Xp22.3 was found to be related to neurocognitive problems in TS [22]. Loss of the SRY gene locus in the short arm of the Y chromosome leads to the phenotype of TS, even if it does not cause a population of 45 X cells. It has also been suggested that an area in Xp11.4 is important for the development of lymphedema [23].
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1.2 Cellular proliferation: cancer
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Cancer can be defined as the uncontrolled cell growth with the most basic explanation. Cell stacks that grow uncontrollably are called tumors. Benign tumors grow much slower and usually do not metastasize, while malignant tumors can spread to other organs through metastasis, and lead to multiple organ damage and eventually death. Tumor cells acquire characteristic features such as sustaining growth signals in the process of cancer, avoiding growth suppressors, resisting cell death, ensuring replicative immortality, initiating angiogenesis, and activating invasion and metastasis [24].
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Cancer cells acquire these abilities in the process due to genetic instability and inflammation caused by environmental and hereditary effects. Many studies show that viruses, in addition to many environmental factors such as radiation and chemicals, induce cancer. Chronic inflammation has been shown to trigger oncogenic mutations, genetic instability, tumor growth, and angiogenesis through angiogenesis and cause local immunosuppression [25].
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Two types of gene groups involved in cancer are oncogenes, which trigger cellular growth and uncontrolled proliferation, causing increased genetic instability with increased expression and tumor suppressor genes that cause cancer as a result of decreased control of their expression, cell division, and growth. Proto-oncogenes include RAS, WNT, MYC, ERK, and TRK genes. A mutation that may occur on a proto-oncogene or a regulatory region of the gene (e.g., promoter region) can cause an increase in the amount of protein with the change in protein structure [26]. Expressions of oncogenes can also be regulated with miRNAs [27]. Mutations occurring in these regulatory miRNAs can cause activation of oncogenes [28]. Cancer cells increase cell growth-division by activation of oncogenes, as well as suppress preventive control mechanisms of tumor suppressor genes that control this process.
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Mutations in tumor suppressor genes cause loss of function. Therefore, they occur in both alleles. To inactivate the gene and its protein, wide-ranging effects, such as deletions, frame-shift mutations, insertions, should be seen rather than point mutations [29]. Tumor suppressor genes include retinoblastoma (RB) [30], TP53, BRCA1, BRCA2, APC, and PTEN. Many side factors such as transcription complexes, changes in cellular metabolism, microenvironment can guide the course of cancer [31].
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The development of cancer is a multi-stage process consisting of initiation, promotion, and progression. Cancer-inducing events are usually caused by genetic mutations. Mutant cell proliferates rapidly in the promotion stage and acquires features that allow malignant behavior in the progression stage. Production of telomerase and expression of p53 are examples of malignant behavior [32]. Then, the process proceeds in the form of dysplasia formation, where new blood vessels are formed (angiogenesis) with cellular transformation. Angiogenesis facilitates the intravasation of cancer cells after undergoing an epithelial-mesenchymal transition (EMT) [33]. EMT gives an invasive phenotype to cancer cells and is managed by various transcription factors (such as SNAI, SLUG, ZEB2, ETS1, TWIST) [34]. These transcription factors also regulate each other for the protection of EMT [35].
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1.2.1 Cancer cell metabolism
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Normal cells only use anaerobic glycolysis when oxygen is absent or limited, while cancer cells can convert glucose to lactate in the presence of oxygen. Otto Warburg discovered that cancer cells exhibit a differentiated metabolism ability [36]. Warburg effect is biochemical properties that help identify cancer cells. On the other side, cancer cells are generally highly glucose-dependent. Glucose intake of cells is enabled by overexpression of different isoforms of membrane glucose transporters in cancer cells [37]. It has been shown that the benefit of the Warburg effect for cancer cells is not just the formation of glycolytic ATP, but also the production of many glycolytic intermediates before anabolic processes such as NADPH and amino acids [38]. Cancer cells are also able to metabolize glutamine to synthesize some amino acids they need, use it as a nitrogen source and for fatty acid synthesis in hypoxic conditions [39]. Therefore, blood glutamine levels increase in some cancer cases [40]. Lactic acid is used to produce citric acid and maintain cancer progression in neighboring cancer cells. This is called the “Reverse Warburg effect” [41].
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Tumor micro-environment, consisting of fibroblasts, adipocytes, endothelial cells, and macrophages, is a good source for tumor growth. Tumors “steal” energy-rich metabolites from their micro-environment [42]. Monocarboxylate carriers (MCTs) are used for L-lactate transfer between cancer cells and their microenvironment [43]. Tumors have heterogeneous structures with hypoxic and aerobic regions. A “metabolic symbiosis” behavior has recently been found between the two regions [44]. Lactate is produced by glycolysis in hypoxic tumor cells. This product is obtained by aerobic cancer cells by MCT1. Aerobic cells convert lactate to pyruvate with lactate dehydrogenase isoform B (LDH-B) enzyme.
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When glucose consumption is not enough to meet the energy need of cancer cells, they begin the fatty acid oxidation (FAO) [45]. For example, prostate cancer, leukemia, and large B-cell lymphoma, increasing palmitate and FAO uptake in cells are among the most commonly used bioenergetic pathways [46, 47, 48]. Normal cells usually receive fatty acids by diet, while tumors show an increase in de novo fatty acid synthesis [45].
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Pyruvate plays a pivotal role in the regulation of metabolic reprogramming, especially in tumors [49]. Pyruvate dehydrogenase (PDH) converts cytosolic pyruvate into mitochondrial acetyl-CoA, which is the first substrate of the Krebs cycle. Pyruvate dehydrogenase kinase (PDK) negatively regulates PDH. This reaction slides glucose from oxidative to glycolytic metabolism [50]. Lactate dehydrogenase (LDH) is the primary metabolic enzyme converting pyruvate into lactate. LDH plays an important role in arranging food interchange between stroma and tumor. Studies have shown that inhibition of LDH is important for treating advanced carcinomas [51]. Mitochondrial hyperpolarization is a mutual property of several tumor cells [52]. Tumor cells, which have more negative mitochondrial structures, are more selective targets in drug therapies [53].
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1.2.2 Brain cancer
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Brain tumors are cancer tissues that grow abnormally and prevent the brain or central spinal system from performing its normal functions. Primary brain tumors originating from brain tissue can usually spread only to other parts of the brain, and occasionally to other organs. Tumors that form in another tissue in the body migrate to the brain are called metastatic or secondary brain tumors. These types of tumors occur more frequently than primary brain tumors. They are termed after their tissue of origin [54].
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The most prevalent primary tumor types in adults are glioma, astrocytomas, oligodendroglioma, meningioma, schwannoma, pituitary tumors, and central nervous system (CNS) lymphoma.
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1.2.3 Genetic background of brain cancer
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Retinoblastoma mutations are found in almost 75% of brain tumors and are mostly associated with glioblastoma, and Tp53 mutations are found in more than 80% of advanced gliomas [55]. Primary glioblastomas have EGFR tyrosine kinase mutations, tumor suppressor PTEN gene mutations, DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) protein abnormalities [56, 57]. While IDH1 mutations in the control mechanism of the citric acid cycle are seen in advanced glioblastomas, IDH2 mutations are usually shown in oligodendroglioma [58]. Mutations in the BRAF oncogene are common in pilocytic astrocytomas, pleomorphic xanthoastrocytomas, and gangliogliomas [55]. In some glioblastoma tumors, telomere length is maintained by mutations in the TERT promoter and ATRX gene [59].
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WHO groups glioma patients based on the presence of two genetic changes; first, mutations [60] in the family of genes encoding isocitrate dehydrogenase (IDH), and second, loss of two specific parts of the genome (1p and 19q co-deletion) [61]. The presence or absence of these changes gives a clue about the patient’s prognosis and appropriateness of various kinds of treatments.
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Approximately 40% of people with astrocytoma, oligodendroglioma, or IDH mutation bear a hereditary variation. This variation is a single nucleotide polymorphism (SNP) in the 8q24 region of the genome [62]. There is another SNP in the 11q23 region, which enhances the risk of IDH-mutant brain cancer. Approximately 5–8% of gliomas are familial, POT1 gene mutations have been found in 6 of 300 families with glioma [63].
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Non-coding RNAs (ncRNAs) play important roles in regulating tumor malignancy in glioma [64, 65, 66]. According to healthy brain tissue, mir-21 expression increases in glioma and mir-21 acts as an oncogene [67, 68]. It has been reported that mir-124 and mir-137 act as tumor suppressors in glioblastoma multiform cells [69]. Hotair, SOX2ot, CRNDE, Malat1, H19, GAS are lncRNAs that have been recently shown to regulate glioma [70, 71]. Glioma cells also express the circRNAs, for example, circBRAF, bircFBXW7, circSMARCA5. These regulate proliferation, migration, and invasion of glioma cells [72, 73, 74]. The exosomal ncRNAs, mir-21, mir-148a, lncRNA PU03F3, lncRNACCAT2 can be used as circulating biomarkers of glioma patients [75, 76, 77, 78]. circRNAs and the exosomal ncRNAs were also reported as potential biomarkers for the diagnosis and prognosis of glioma patients.
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1.3 Molecular pathology of neurodegenerative diseases with Alzheimer’s disease in focus
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As a characteristic of almost all neurodegenerative diseases, abnormal protein assembly gathers these diseases under the prion concept [79]. Prion protein, known as PrP, was introduced to define protein pathogens and distinguish them from viruses and was identified as a proteinaceous infectious particle known to resist inactivation. Even back at that time, its importance was foreseen in terms of shedding light on the etiologies of chronic degenerative diseases [80]. Self-propagation is an important characteristic of prions that is also observed in abnormal protein assembly in Alzheimer’s Disease (AD) [81, 82]. Aggregation of proteins in neurodegenerative diseases was believed to occur spontaneously in autonomous cells, however, it was later understood that this aggregation begins in a particular region and propagates across other regions developing the disease further. Transmission of these prion proteins across neuronal cells takes place trans-synaptically [82].
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As described more than a 100 years ago, abnormal protein assembly forms the basis of neurodegeneration with AD being one of the most common neurodegenerative diseases. The pathology of abnormal protein assembly starts with misfolding of native proteins that gather to form seeds which eventually lead to aggregation and development of protein fibrils. The pathophysiology of AD involves amyloid plaque inclusions of β-amyloid (Aβ) peptides and neurofibrillary lesions of tau protein. Tau inclusions may also be characteristics of other neurodegenerative diseases, which do not necessarily show the same implications. Altering the native forms of this protein may contribute to its pathology and cause damage to its host cell.
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Most cases of this disease are sporadic, while dominantly inherited mutations are also seen to a lesser extent. Back in the 1990s, missense mutations of APP, encoding amyloid precursor, were shown to cause AD [83, 84, 85, 86, 87]. Mutations in this gene also increase the aggregation tendency of encoded proteins. Many studies have demonstrated phenotypes associated with neurodegeneration when this protein is overexpressed.
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There are six isoforms of microtubule-associated protein tau ranging from 352 to 441 amino acids, encoded by the MAPT gene as a result of alternative mRNA splicing. One half has three repeats and the other has four repeats, altogether establishing the microtubule-binding domain and also the core of tau filaments in case of pathology [88]. All isoforms have been observed in the brains of AD patients. Diseases that have isoforms with only three or four repeats, but not both, lack the Aβ peptides seen in AD and therefore do not carry the symptoms specific to the disease [70]. Tau inclusions may be of a variety of conformations, which can also be caused by different mutations on the MAPT gene, explaining the existence of numerous tauopathies [89, 90, 91, 92, 93].
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Aβ peptides are encoded by the amyloid precursor protein gene, APP, and are widely expressed as type 1 transmembrane glycoproteins. As a result of alternative mRNA splicing, there are three major transcripts named APP695, APP751, and APP770 [94, 95]. β- and γ-secretase enzymes take part in the production of Aβ peptides in sequential endoproteolytic cleavage. β-secretase is responsible for cleaving the N-terminus of the peptide thus removing the portion that remains on the extracellular side. This cleaved peptide is endocytosed and intracellular aggregation builds up which is later released into the extracellular space [79]. γ-Secretase is a membrane-embedded enzyme that is able to cleave many transmembrane proteins including C-terminus of the Aβ peptide. It a complex enzyme of four proteins; presenilin (PS) forming the catalytic core, presenilin enhancer-2 (Pen-2) enabling maturation of PS, anterior pharynx-defective (Aph-1) stabilizing the complex, and nicastrin possibly being the receptor for the enzyme’s substrate [96, 97]. PS and Aph-1 each have two variants resulting in at least four different enzyme complexes, which give rise to various cleaved Aβ peptides. Additionally, γ-secretases cleave the peptide in three different sites. Different protein variants and cleavage sites produce Aβ peptides of different profiles, some of which may be more susceptible to aggregation [98].
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Overall, it is important to target the pathways leading to abnormal protein assembly and only then treatments may be proposed based on these mechanisms. Once the first protein inclusion is formed, it is essential to keep an eye on the time frame until the disease symptoms come forth. When techniques sensitive enough to catch the first protein inclusion are developed, then tracking its transformation into filaments can be helpful in designing novel preventive approaches. Understanding this cascade will also contribute to planning more efficient therapeutic methods.
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1.4 Cellular and molecular mechanisms of asthma and COPD
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Asthma and chronic obstructive pulmonary disease (COPD) are common disorders characterized by progressive chronic inflammation in the lungs. They have unique characteristics with dissimilarly involved cells, mediators, and inflammation. They also have distinct responses to corticosteroid treatment. Roughly 15% of COPD patients have characteristics of asthma [99]. Also, a comparable ratio of asthma patients has traits of COPD that is currently the fifth leading cause of death worldwide [100]. Many risk factors are linked to COPD including smoking tobacco, air pollution, indoor cooking while tobacco smoking (including passive smoking) making up around 80% of the cases [101]. There are many types of cells and mediators that have a significant effect during the pathogenesis of asthma and COPD.
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Macrophages have a crucial role in coordinating the inflammatory response activated by cigarette smoke extract in COPD cases [102]. They discharge inflammatory mediators including tumor necrosis factor (TNF)-α, IL-8, other CXC chemokines, monocyte chemotactic peptide (MCP)-1, LTB4 and reactive oxygen species (ROS) [103]. However, the role of macrophages in asthma is not certain. Allergens via low-affinity IgE receptors may activate macrophages causing an inflammatory response through the discharge of a definite arrangement of cytokines. On the other hand, macrophages also excrete anti-inflammatory mediators, such as IL-10 that is thought to decrease in subjects with intense asthma [104].
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Activated neutrophils were shown to be enhanced in some subjects with severe asthma and COPD in their sputum and airways [105]. Among the serine proteases secreted by neutrophils are neutrophil elastase (NE), cathepsin G, proteinase-3, matrix metalloproteinase (MMP)-8 and MMP-9, leading to alveolar destruction [103]. The mechanisms of neutrophilic inflammation in asthma and COPD are not clear. Demonstration of priming in COPD occurs at neutrophils in the peripheral circulation. Many chemotactic signals exhibit the capacity for neutrophil recruitment in COPD. These include LTB4, IL-8 and related CXC chemokines, comprising GRO-α (growth-related oncoprotein) and ENA-78 (epithelial neutrophil activating protein of 78 kDa) which are enhanced in COPD airways [106]. Although the mentioned mediators might be sourced from alveolar macrophages and epithelial cells, neutrophils have the capacity of being a vital source of IL-8 [107].
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Airway and alveolar epithelial cells in COPD can be a vital point of source of inflammatory mediators and proteases 5. Cigarette smoke activates epithelial cells which produce inflammatory mediators, including TNF-α, IL-1β, GM-CSF and IL-8 [108]. Epithelial cells play an important role in airways defense and tissue repair processes. Goblet cells, a type of epithelial cell, in mucus catch bacteria and inhaled particulates [109]. Epithelial cells release antioxidants and antiproteases. Immunoglobulin A is carried by epithelial cells, hence involved in adaptive immunity [110]. On a side note, native and adaptive immune reactions of the airway epithelium are triggered by cigarette smoke and damage by other harmful agents, increasing sensitivity to infection.
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The main role of dendritic cells is to introduce innate and adaptive immune reaction by activating macrophages, neutrophils, T and B lymphocytes among others [103].
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Lymphocytes are directly involved in the pathogenesis of both asthma and COPD. Both airway and parenchymal inflammation exist in asthma and COPD patients [111]. Most lung lymphocytes are T cells which are in the respiratory tract of ordinary humans. Activated T lymphocytes are characteristic in both asthma and COPD, but CD4+ type-2 T lymphocytes are the major player in asthma whereas CD8+ type-1 lymphocytes are specific to COPD [111]. CD4+ T lymphocytes can generate many cytokines involved in mediating cell functions and cell–cell communications. This is done through impressing physiologic cell properties such as proliferation, differentiation and activation of other immunocompetent cells, chemotaxis, and connective tissue metabolism [112]. On the other hand, CD8+ T lymphocytes exist in the respiratory mucosa and are activated in response to foreign antigens [111]. Specifically, the cells in the respiratory mucosa have an important role in anti-viral immunity. Another lymphocyte type is B cells which are the minority (<5%) lymphocytes. The main function of B cells located in the lungs is the production of immunoglobulins for local defense mechanisms [113].
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Apart from these mentioned cells, there are crucial molecular mediators in the pathogenesis of asthma and COPD. The first family of mediators is transforming growth factor (TGF) family. The TGF-β subfamily is composed of five parts that exhibits plenty of effects pertaining to asthma and COPD. A recent study shows that overexpression of TGF-β1 in mice causes Smad3-dependent pulmonary expression of procollagen, antiproteases and fibrosis [114]. TGF-β exhibits chemotactic signatures for monocytes, macrophages and mast cells. Research shows an abnormal pulmonary expression of TGF-β1 in subjects suffering from COPD. Protein and mRNA expression of TGF- β1 are abundant in the lung tissue, including airway epithelial cells, of mild to moderate COPD patients. TGF-β1 has the role in pathogenesis of COPD because of its increased expression in parallel to the number of macrophages [115].
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Another mediator family is the fibroblast growth factor (FGF) family with 23 members in humans. Their functional receptors are named from FGFR1 to FGFR5 [116]. FGFs have many functions such as development, tissue homeostasis, and repair. In addition to further growth factors, FGF-1, FGF-2, and FGF-7 and their receptors FGFR1 and FGFR2, are located abundantly in the lungs [101]. Research shows that increased expression degrees of FGF-1, FGF-2, and FGFR1 were detected in vascular and epithelial areas in the lungs of COPD patients. FGF-1 causes higher collagenase expression and lower collagen I expression in lung fibroblasts which prompt tissue remodeling.
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Another family of mediators is the vascular endothelial growth factor (VEGF) family. There are seven units in this family capable of attaching to related cellular receptors. VEGFs have many functions including paracrine acting, angiogenic factors, prompting mitogenesis, emigration, and permeabilization of the vascular endothelium [101]. VEGF and its receptors assist in tissue remodeling as well as disease intensity in incessant lung diseases such as asthma [117]. COPD patients have increased pulmonary VEGF expression in bronchial and alveolar epithelial located around the vascular smooth muscle and alveolar macrophages. Additionally, unlike healthy subjects, COPD patients exhibit elevated levels of VEGFR-1 and VEGFR-2 expression inside the endothelium [118]. Furthermore, VEGFR-2 and VEGF expressions are decreased in COPD patients. Compared to VEGFR-2, VEGFR-1 has a higher affinity for VEGF which leads to VEGFR-1 scavenging VEGF from VEGFR-2. This phenomenon culminates VEGFR-1 activation and in the case of endothelial apoptosis, increased MMP activity as well as vascular and alveolar decimation [101]. This suggests the importance of harmony among VEGF, VEGFR-1, and VEGFR-2 during the pathogenesis of COPD subordinary types.
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Finally, cytokines and chemokines are mediators supplying a chemotactic gradient which has the potential to activate macrophages, CD8+ T cells and neutrophils for COPD patients. It is known that inflammatory cells of both native and gained immune systems are significant in the COPD pathophysiology. This is where cytokines and chemokines are the key drivers [103, 119]. Different types of cytokines arrange chronic inflammation in asthma and COPD. T2 cytokines which are IL-4, IL-5, IL-9 and IL-13 interfere with allergic inflammation. Other types of cytokines including TNF-α and IL-1β accelerate the inflammatory response [120]. In asthma and COPD patients, chemokines are instrumental in drawing inflammatory cells from the circulation into the lungs [121].
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1.5 The endocrine system
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1.5.1 Molecular mechanism of obesity and obesity-induced insulin resistance
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Obesity is a serious health problem that has become epidemic all over the world, especially in developed countries. It is characterized by hypertrophied adipocytes that secrete various adipokines and hormones, chronic inflammation in all tissues, and systemic insulin resistance resulting in type 2 diabetes, hypertension, and hyperlipidemia. In addition to these metabolic diseases, it can cause diseases such as cancer, atherosclerosis, obstructive sleep apnea syndrome, steatohepatitis, and musculoskeletal problems [122]. The obesity rate is 20% in women and 18% in men in developed countries [123]. It affects complex metabolic pathways in all tissues as a result of chronic and progressive inflammation, leads to insulin resistance, endothelial dysfunction and lipotoxicity.
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The pathophysiology of obesity includes complex interactions of numerous adipokines, hormones and pro-inflammatory cytokines with the central nervous system and metabolic organs (such as liver, pancreas, and muscle) as a result of genetic-environmental interactions.
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Genetic etiology: Obesity is generally present in a polygenic etiology. Many studies have investigated the genetic background of body mass index (BMI) and waist/hip ratio (WHR), which are the best measurements of obesity. The results of these studies have been presented collectively in genome-wide association studies (GWAS) [124]. Although, single gene defects (monogenic) are rare in obesity, including especially melanocortin-4 receptor, leptin and leptin receptor genes [125].
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Dysregulation in hypothalamic control: The center of food intake and energy regulation in the central nervous system is the arcuate nucleus (ARC) in the hypothalamus besides the autonomic nervous system and brain stem. The balance between the opposing effects of orexigenic and anorexigenic neurons is important. Agouti-related protein (AgRP) and neuropeptide Y (NPY) (AgRP/NPY) neurons are orexigenic that promotes appetite and eating. Pro-opiomelanocortin–producing (POMC) peptide and cocaine-and-amphetamine–regulated transcript (CART), collectively known as POMC/CART neurons are anorexigenic that suppress appetite and eating. Oxygenic pathways that increase energy balance become more effective in obesity [126].
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Adipose tissue dysfunction and systemic inflammation; The most important pathophysiological mechanisms of obesity and obesity-related insulin resistance are adipocyte dysfunction (visceral adipose tissue; VAT) and low-grade chronic systemic inflammation. In particular, white adipocyte tissue in obese subjects contributes to the regulation of food intake, energy metabolism and other functions by secreting adipokines from adipose tissue, which provide the necessary signals to the central nervous system, hypothalamus, liver, pancreas, muscle tissue, and other systems to regulate appetite, food intake, and energy balance [125]. Leptin is the most important adipokine that stimulates anorexigenic POMC/CART neurons and induces production of pro-inflammatory cytokines (TNF-alpha and IL-6) by macrophages and monocytes. In the case of hyperleptinemia, leptin resistance develops by the inhibition of the JAK2/STAT3 signaling pathway, which later increases oxidative stress and inflammation, causing insulin resistance, hyperlipidemia and hypertension [127]. Resistin is a pro-inflammatory adipokine produced by the resistin gene (RETN), which activates SOCS3, causing the insulin signaling pathway to be inhibited and consequently induces insulin resistance [128]. Other adipokines like Retinol binding protein 4 (RBP4), Angiopoietin-like protein 2 (ANGPTL2), Visfantin, Adiponectin, Lipocalin 2, Serum Amyloid A, Angiotensinogen, Renin, Angiotensin-Converting Enzyme, Acylation-Stimulating Protein, and Vaspin, are increased, and adiponectin, and Apelin are decreased in obesity, altogether stimulating inflammation, lipolysis, releasing free fatty acid (FFA) and causing insulin resistance as a result [129].
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Gastrointestinal hormones and microbiota: Gastrointestinal hormones and gut microbiota play a significant role in the complex pathophysiology of obesity. Ghrelin produced in the stomach induces starvation and food intake by stimulating orexigenic AgPR/NPY neurons in the hypothalamus. Although the effect of ghrelin cannot be fully explained, it is thought to increase in obesity, stimulate growth hormone release (GH), increase gastrointestinal motility and insulin secretion [130]. Decreased GLP-1, Peptide YY, pancreatic polypeptide, and increased amylin and cholecystokinin cause appetite inhibition and gastric emptying delay, resulting in excess energy [129]. Besides hormones in the gastrointestinal tract, changes in microbiota-gut-brain axis and their effects on metabolic organs are also important. Occurring as a result of nutrition and gene–environment interactions; chronic systemic inflammation resulting from intestinal microbiota dysbiosis (increase in Firmicutes-Bacteroides ratio), microbial fermentation products, increase in short-chain fatty acid formation and intestinal permeability, decrease in butyrate-producing bacteria rate, leads to an increase in proinflammatory response in metabolic organs, impaired fat metabolism and glucose metabolism [131, 132].
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İmpaired insulin sensitivity and oxidative stress; The beginning of insulin resistance is the first step in the pathophysiology of T2D. Anabolic effects such as glycogen and protein synthesis, glucose transport, adipogenesis are formed by phosphatidylinositol-3-kinase (PI3K)/Akt pathway activation as a result of insulin binding to its receptor (INSR) synthesized in the pancreas [133]. On the other hand, insulin shows mitogenic effects with mitogen-activated protein kinases/Ras pathway (MAPK/Ras).
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Adipokines, FFA’s, pro-inflammatory cytokines (TNF-a, IL-18, IL-1β, IL-6), synthesized as a result of inflammation in adipose tissue in obesity, also cause systemic inflammation in metabolic tissues such as liver and muscle. As a result, decreased GLUT-4 expression, activation of Ser/Thr kinases with insulin receptor substrate (IRS) phosphorylation, production of ceramides and proinflammatory cytokines, suppressing of cytokine signaling-3 (SOCS-3) expression, insulin pathways and effects. On the other hand, increased production of reactive oxygen radicals and production of toxic doses NO with inducible nitric oxide synthase (iNOS) activation, affect mitochondrial and endoplasmic reticulum functions. Activation of pro-inflammatory pathways increased oxidative stress, mitochondrial dysfunction, ER stress affects lipid metabolism, insulin mechanisms of action and other metabolic pathways, causing insulin resistance, Type 2 diabetes, hypertension, and hyperlipidemia [134].
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Beta-cell dysfunction: In addition to peripheral insulin resistance in obesity, serious reductions in beta cell function are also observed. An increase in fat accumulation in islet cells due to chronic lipotoxicity disrupts the function of beta cells by blocking calcium channels. Chronic hyperglycemia due to disruption in glucose metabolism and systemic inflammation due to an increase in oxidative damage and lipotoxicity, disrupt insulin secretion pathways and cause changes in apoptosis gene expression. Hyperinsulinemia in obesity, impaired insulin signaling pathway, oxidative stress, lipotoxicity in islet cells, loss of beta-cell function and apoptosis may lead to the formation of type 2 diabetes [122, 135].
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Obesity has become a pandemic all over the world as a result of rapidly changing lifestyles and genetic heritage in the last century. Despite the findings in recent studies on the development and complications of obesity, it is difficult to say that the subject of etiology and pathophysiology is still not fully understood. Especially omics technologies, big data on environmental gene interactions, neuroendocrinology, and neuropsychological studies will reveal findings that open up different horizons. However, due to its complications from deadly metabolic diseases to cancer, rapid preventive measures should be taken, and effective treatment models should be developed.
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1.6 Alterations of blood cells: lymphoblastic leukemia
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Acute lymphoblastic leukemia (ALL) is a heterogeneous malignancy emerging from lymphoid precursors. It is characterized by the proliferation of immature lymphoid cells with somatic mutations including chromosomal rearrangements, and aneuploidy [136]. ALL has two peak points; first point occurs at ~5 years of age (80%), and the second point occurs at the age of ~50 (20%) [137]. The basic mechanism underlying the development of ALL is similar in children and adults, while they have the frequency of different genetic subtypes. Molecular analysis of genetic changes in leukemia disease provides a great advantage in order to understand prognosis and pathogenesis of ALL [138].
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The diagnosis of ALL depends on the presence of at least 20% lymphoblast in bone marrow. Immunophenotyping by flow cytometry (FCM) identifies the subtype of ALL that may be B-cell precursor (BCP), mature B-cell types, or T-cell ALL. Chromosomal abnormalities are a characteristic of lymphoblastic leukemia, which are found in B or T cell lineage. The most common abnormality found in adult B precursor ALL is the t(9;22) BCR-ABL translocation, while the t(12;21)(p13;q22) TEL-AML1 translocation is most commonly found in childhood B precursor ALL [139]. On the other hand, the discovery of mutations in the receptor tyrosine kinase FLT3 contributes to the understanding of leukemogenesis mechanism in hyperdiploid ALL (20% of cases). Based on this finding, targeting specific tyrosine kinase inhibition may be useful in the management of leukemia [140].
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1.6.1 Treatment in patients with lymphoblastic leukemia
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Small-molecule kinase inhibitors have a clear benefit in the treatment of many cancer types including leukemia. Imatinib mesylate, a small-molecule inhibitor of BCR-ABL kinase, is highly effective in the treatment of chronic myelogenous leukemia (CML) [141]. Although the single kinase inhibitor is a remarkable treatment option in a different type of leukemia, it will need to be combined with either other targeted therapy or chemotherapy because of the resistance to small-molecule inhibitor [142]. Unlike ALL, Chronic Lymphocytic Leukemia (CLL) is defined the accumulation of monoclonal B cell with a special immunophenotype in the bone marrow, blood, and other lymphoid organs where B lymphocytes express CD19, CD23, CD5, low-level CD20 and surface immunoglobulins [143]. The standard treatment procedure of ALL and CLL includes consolidation therapy following chemotherapy in pediatric patients. For adult patients, unlike pediatric patients, the allogeneic hematopoietic stem cell transplantation is frequently preferred as consolidation therapy [144]. Because the patients resistant to chemotherapy are not respond to treatment well enough, novel therapy approaches such as Chimeric antigen receptor-modified T cell (CAR-T) therapy have developed in order to overcome chemotherapy resistance and improving the outcome of patients [145]. CAR-T cells, as immunotherapeutic tools, are genetically engineered to express a chimeric antigen receptor recognizing an antigen that is located in the special cells such as a tumor [146]. CD19 antigen on B lymphocytes was considered the initial target for CAR-T cell therapy. However, specific antigen loss might cause the failure of CAR-T cell therapy in CLL. CD19–20 co-targeting CAR-T cells were designed to kill both CD19-positive and CD19-negative CLL and it was shown that these cells were very effective in killing CLL cells. In one of the first reported in pediatric ALL the clinical trials, CAR-T cells targeted the CD19 antigen of B cells are designed with CD3ζ and CD28 costimulatory domain [147].
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1.7 Viral immunology and infectious diseases
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1.7.1 The differences between HIV-1 and SARS-CoV-2 genome
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The origin of a pathogen has a crucial role in developing vaccines and blocking transmission. This may last many years due to its elusiveness as seen in HIV-1, SARS, and MERS [148, 149, 150]. According to a recent report, it was emphasized that SARS-CoV-2 is able to infect T cells, which are targeted by HIV [151]. Another report alleged that the motif insertions of spike glycoprotein, similar to HIV-1, may help increase the range of host cells of SARS-CoV-2. HIV-1 envelope glycoprotein contains mutable insertions and deletions not necessary for biological function. Only 1 and 2 insertions are matched in only a few HIV-1 strains and this reveals that four insertions are scarce. Thus, HIV-1 cannot be assumed as the source for those insertion sequences in the SARS-CoV-2 genome due to their inefficient identities and scarceness in the HIV-1 sequences [152].
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1.7.2 The origin of SARS-CoV2
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The reported cases showed that there have been 3,162,284 COVID-19 cases in at least 212 countries and approximately 7.1% of which was resulted in death as of April 30, 2020 [153]. It is known that SARS-CoV, MERS-CoV, and SARS-CoV-2 are the members of coronoviridae family of the Nidovirales order, which comprises a relatively positive-sense, single-stranded RNA genome of around 26–32 kb [154]. 5o-methylguanosine cap at the beginning, a 3o-poly-A tail at the end, and a total of 6–10 genes in between exist in their genome [155, 156].
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This family has extremely expressive instability and recombination rate, which is similar to RNA viruses, so it is practically unfeasible to prevent their distribution among humans and animals worldwide; nevertheless, the fact that the virus is exceedingly pathogenic to humans is closely related to random genetic recombination in the host. Although there is a strict genetical relation between SARS-CoV-2 and SARS-CoV, it is explicit that SARS-CoV-2 has a unique feature providing rapidly spread worldwide [157].
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SARS-CoV-2 genome sequence is much more resembles a SARS-like bat rather than SARS-CoV [158, 159]. Two open reading frames translating the replication- and transcription-related gene into two large non-structural polyproteins [156]. Ribosomal frameshifting contributes to translate two different but overlapping open reading frames. Besides these nonstructural proteins, the subgenomic RNA also encodes the viral genome packaging protein N (nucleocapsid), and the viral coating proteins M (membrane), E (envelope), and S (spike) as the structural proteins. Viral coating proteins, which interact with host surface receptors, is generally preferred as the therapeutic target blocking protein–protein interaction [160, 161]. TMPRSS2, the human serine protease, enables S Protein of both SARS-CoV and SARS-CoV-2 to prime, and these two viruses use the angiotensin-converting enzyme 2 (ACE2) receptor in order to bind the host cell as the first step of the viral entry mechanism. Unlike SARS-CoV and SARS-CoV-2, the cell entry of MERS-CoV depends on the binding of its own spike protein to DPP4 (dipeptidyl peptidase 4). The RT-PCR analysis of the throat swabs is essential to the diagnosis of COVID19 pneumonia, and it takes 3.5 h to provide the results [162].
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1.7.3 Current treatment approaches
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Clinical management puts emphasis on the importance of supportive care and prevention of complications due to a lack of specific treatment for COVID-19 pneumonia. On the other hand, potential antiviral therapies for the purpose of rapidly dealing with this pandemic are taking place on several clinical trials. These trials focused on three main targets that include enhancing the host immune system, blocking the virus spike protein-host cell surface receptor interaction, and vaccine development [163].
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1.7.4 Human Papillomavirus genome and treatment
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HPV genome, which is a double-stranded circular DNA, has the early (E) genes that are responsible for replication and transcription, and the late (L) genes that are responsible for viral capsid proteins. In the early stage of HPV infection, the highly expressed E1 and E2 proteins provide the maintaining of viral replication and transcription within the cervical cell [164].
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HPVs, unlike SARS coronaviruses, are non-enveloped viruses and don’t have a specific host cell receptor that initiates the viral infection. Additionally, HPVs have many different genotypes such as HPV type 16 and type 18 which are known as the reason for cervical cancer. HPV infection may cause low-grade cytological changes on Papanicolaou smears, or low-grade squamous intraepithelial lesions [165]. When malignant conversion considered, viral oncoproteins E6 and E7 attach, respectively, tumor suppressor protein p53 and Rb have a crucial role [166]. Until today, many vaccine developments studies have been carried out to protect HPV malignant type 16 and 18. For example, the clinical vaccine Gardasil 9 provides effective protection against vaginal, cervical, and vulvar diseases caused by HPV type 16,18 and also its 5 other different types [167].
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2. Conclusion
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The chapter outlined the unique mechanism of each disease. Depending of the origin of the disease; deficiency, hereditary, infectious and physiological diseases may be treated diversely but the perturbation effect can only be eliminated with proper intervention. Current amelioration may be improved by biochemical methods only if the molecular mechanism is clearly understood. Therefore, molecular medicine provides unique solutions to diagnose and treat disease by elucidating macromolecular interaction and abnormalities in cells and tissues. The chapter summarizes current findings and methods to alleviate and cure the diseases.
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Acknowledgments
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BYK, EK, KUC and ENYT acknowledge YOK100/2000 bursary and thanks to Turkish Council of Higher Education (YOK).
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Conflict of interest
None.
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Notes/thanks/other declarations
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Thanks to Assistant Prof. Dr. Lütfi Tutar for carefully reading the manuscript.
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Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Chromosomal diseases",level:"2"},{id:"sec_1_3",title:"1.1.1 Down syndrome",level:"3"},{id:"sec_2_3",title:"1.1.2 Molecular mechanism",level:"3"},{id:"sec_3_3",title:"1.1.3 Turner syndrome",level:"3"},{id:"sec_4_3",title:"1.1.4 Genotype-phenotype",level:"3"},{id:"sec_6_2",title:"1.2 Cellular proliferation: cancer",level:"2"},{id:"sec_6_3",title:"1.2.1 Cancer cell metabolism",level:"3"},{id:"sec_7_3",title:"1.2.2 Brain cancer",level:"3"},{id:"sec_8_3",title:"1.2.3 Genetic background of brain cancer",level:"3"},{id:"sec_10_2",title:"1.3 Molecular pathology of neurodegenerative diseases with Alzheimer’s disease in focus",level:"2"},{id:"sec_11_2",title:"1.4 Cellular and molecular mechanisms of asthma and COPD",level:"2"},{id:"sec_12_2",title:"1.5 The endocrine system",level:"2"},{id:"sec_12_3",title:"1.5.1 Molecular mechanism of obesity and obesity-induced insulin resistance",level:"3"},{id:"sec_14_2",title:"1.6 Alterations of blood cells: lymphoblastic leukemia",level:"2"},{id:"sec_14_3",title:"1.6.1 Treatment in patients with lymphoblastic leukemia",level:"3"},{id:"sec_16_2",title:"1.7 Viral immunology and infectious diseases",level:"2"},{id:"sec_16_3",title:"1.7.1 The differences between HIV-1 and SARS-CoV-2 genome",level:"3"},{id:"sec_17_3",title:"1.7.2 The origin of SARS-CoV2",level:"3"},{id:"sec_18_3",title:"1.7.3 Current treatment approaches",level:"3"},{id:"sec_19_3",title:"1.7.4 Human Papillomavirus genome and treatment",level:"3"},{id:"sec_22",title:"2. Conclusion",level:"1"},{id:"sec_23",title:"Acknowledgments",level:"1"},{id:"sec_26",title:"Conflict of interest",level:"1"},{id:"sec_23",title:"Notes/thanks/other declarations",level:"1"}],chapterReferences:[{id:"B1",body:'\nD. Patterson, “Molecular genetic analysis of Down syndrome,” Hum. Genet., vol. 126, no. 1, pp. 195-214, 2009.\n'},{id:"B2",body:'\nT. F. Williams and A. J. Dalton, “Dementia and aging adults with intellectual disabilities: A handbook,” Dement. Aging Adults with Intellect. Disabil. A Handb., pp. 1-488, 2014.\n'},{id:"B3",body:'\nC. M. Clarke and J. H. Edwards, “21-Trisomy / Normal,” pp. 1028-1030, 1961.\n'},{id:"B4",body:'\nA. Kuliev, Z. Zlatopolsky, I. Kirillova, J. Spivakova, and J. Cieslak Janzen, “Meiosis errors in over 20,000 oocytes studied in the practice of preimplantation aneuploidy testing,” Reprod. Biomed. Online, vol. 22, no. 1, pp. 2-8, 2011.\n'},{id:"B5",body:'\nM. Hattori, A. Fujiyama, and Y. 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Plenum et al., “Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes,” Nature, vol. 397, no. February, pp. 436-441, 1999.\n'},{id:"B149",body:'\nW. Li et al., “Bats are natural reservoirs of SARS-like coronaviruses,” Science (80-. )., vol. 310, no. 5748, pp. 676-679, 2005.\n'},{id:"B150",body:'\nE. I. Azhar et al., “Evidence for camel-to-human transmission of MERS coronavirus,” N. Engl. J. Med., vol. 370, no. 26, pp. 2499-2505, 2014.\n'},{id:"B151",body:'\nP. Pradhan et al., “Uncanny similarity of unique inserts in the 2019-nCoV spike protein to HIV-1 gp120 and Gag,” bioRxiv, p. 2020.01.30.927871, 2020.\n'},{id:"B152",body:'\nX. C., L. X., L. S., S. Y., G. S.-J., and G. F., “HIV-1 did not contribute to the 2019-nCoV genome,” Emerg. Microbes Infect., vol. 9, no. 1, pp. 378-381, 2020.\n'},{id:"B153",body:'\nWHO., “WHO. Coronavirus Disease 2019 (COVID-19): Situation Report − 105.,” WHO. Coronavirus Dis. 2019 Situat. Rep. − 105., p. 18, 2020.\n'},{id:"B154",body:'\nK. Pyrc et al., “Mosaic Structure of Human Coronavirus NL63, One Thousand Years of Evolution,” J. Mol. Biol., vol. 364, no. 5, pp. 964-973, 2006.\n'},{id:"B155",body:'\nD. W. E., V. D. N., F. D., and M. V.J., “SARS and MERS: Recent insights into emerging coronaviruses,” Nat. Rev. Microbiol., vol. 14, no. 8, pp. 523-534, 2016.\n'},{id:"B156",body:'\n“Coronaviridae - Figures - Positive Sense RNA Viruses - Positive Sense RNA Viruses (2011),” Int. Comm. Taxon. Viruses.\n'},{id:"B157",body:'\nJ. S. Mani et al., “Natural product-derived phytochemicals as potential agents against coronaviruses: A review,” Virus Res., vol. 284, 2020.\n'},{id:"B158",body:'\nA. Wu et al., “Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China,” Cell Host Microbe, vol. 27, no. 3, pp. 325-328, 2020.\n'},{id:"B159",body:'\nP. Zhou et al., “A pneumonia outbreak associated with a new coronavirus of probable bat origin,” Nature, vol. 579, no. 7798, pp. 270-273, 2020.\n'},{id:"B160",body:'\nM. Hoffmann et al., “SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor,” Cell, vol. 181, no. 2, pp. 271-280.e8, 2020.\n'},{id:"B161",body:'\nK. G. Andersen, A. Rambaut, W. I. Lipkin, E. C. Holmes, and R. F. Garry, “The proximal origin of SARS-CoV-2,” Nat. Med., vol. 26, no. 4, pp. 450-452, 2020.\n'},{id:"B162",body:'\nT. Adhanom Ghebreyesus, “WHO Director-General’s opening remarks at the media briefing on COVID-19,” World Heal. Organ., no. March, p. 4, 2020.\n'},{id:"B163",body:'\nZ. Wu and J. M. McGoogan, “Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases from the Chinese Center for Disease Control and Prevention,” JAMA - J. Am. Med. Assoc., vol. 323, no. 13, pp. 1239-1242, 2020.\n'},{id:"B164",body:'\nS. A. Southern and C. S. Herrington, “Molecular events in uterine cervical cancer,” Sex. Transm. Infect., vol. 74, no. 2, pp. 101-109, 1998.\n'},{id:"B165",body:'\nE.-K. Yim and J.-S. Park, “The Role of HPV E6 and E7 Oncoproteins in HPV-associated Cervical Carcinogenesis,” Cancer Res. Treat., vol. 37, no. 6, p. 319, 2005.\n'},{id:"B166",body:'\nM. Julia Gargano, PhD; Elissa Meites, MD, MPH; Meg Watson, MPH; Elizabeth Unger, MD, PhD; Lauri Markowitz, Human Papillomavirus. .\n'},{id:"B167",body:'\nNCI, “National Cancer Institute. Human Papillomavirus (HPV) Vaccine Fact Sheet,” 2015. [Online]. Available: http://www.cancer.gov/about-cancer/causes-prevention/risk/infectious-agents/hpv-vaccine-fact-sheet.\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Adnan Batman",address:null,affiliation:'
Division of Molecular Medicine, University of Health Sciences, Hamidiye Health Sciences Institute, Turkey
Division of Molecular Medicine, University of Health Sciences, Hamidiye Health Sciences Institute, Turkey
Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Hamidiye Faculty of Pharmacy, Turkey
'}],corrections:null},book:{id:"9569",title:"Methods in Molecular Medicine",subtitle:null,fullTitle:"Methods in Molecular Medicine",slug:"methods-in-molecular-medicine",publishedDate:"January 20th 2021",bookSignature:"Yusuf Tutar",coverURL:"https://cdn.intechopen.com/books/images_new/9569.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"178563",title:"Associate Prof.",name:"Timothy",middleName:"Jay",surname:"Price",email:"timothy.price@adelaide.edu.au",fullName:"Timothy Price",slug:"timothy-price",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"University of Adelaide",institutionURL:null,country:{name:"Australia"}}},booksEdited:[],chaptersAuthored:[{title:"BRAF Mutation in Colorectal Cancer",slug:"braf-mutation-in-colorectal-cancer",abstract:"The BRAF mutant colorectal cancer subgroup is a small population with unique clinicopathological and molecular features. This subgroup has been associated with particularly poor prognosis and advanced disease. The poor response of these patients to available treatments has driven much of the effort in trialling combination targeted treatments involving BRAF and MEK inhibitors. Most recently, an observed survival benefit with intensive triplet chemotherapy agents would encourage its use as first-line treatment in suitable candidates given that few of these patients proceed to second- or third-line treatments.",signatures:"Louisa Lo, Timothy Price, Joanne Young and Amanda Townsend",authors:[{id:"178563",title:"Associate Prof.",name:"Timothy",surname:"Price",fullName:"Timothy Price",slug:"timothy-price",email:"timothy.price@adelaide.edu.au"},{id:"178572",title:"Dr.",name:"Amanda",surname:"Townsend",fullName:"Amanda Townsend",slug:"amanda-townsend",email:"amanda.townsend@health.sa.gov.au"},{id:"178573",title:"Dr.",name:"Louisa",surname:"Lo",fullName:"Louisa Lo",slug:"louisa-lo",email:"Louisa.lo@health.sa.gov.au"},{id:"178574",title:"Dr.",name:"Joanne",surname:"Young",fullName:"Joanne Young",slug:"joanne-young",email:"joanne.young@adelaide.edu.au"}],book:{title:"Colorectal Cancer",slug:"colorectal-cancer-from-pathogenesis-to-treatment",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"170255",title:"Prof.",name:"Roberto",surname:"Ghiselli",slug:"roberto-ghiselli",fullName:"Roberto Ghiselli",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"178035",title:"Associate Prof.",name:"Deniz",surname:"Tural",slug:"deniz-tural",fullName:"Deniz Tural",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"178038",title:"Dr.",name:"Soichiro",surname:"Murata",slug:"soichiro-murata",fullName:"Soichiro Murata",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Yokohama City University",institutionURL:null,country:{name:"Japan"}}},{id:"178234",title:"Prof.",name:"Mario",surname:"Guerrieri",slug:"mario-guerrieri",fullName:"Mario Guerrieri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Marche Polytechnic University",institutionURL:null,country:{name:"Italy"}}},{id:"178250",title:"Dr.",name:"Josefa",surname:"Leon",slug:"josefa-leon",fullName:"Josefa Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Barcelona",institutionURL:null,country:{name:"Spain"}}},{id:"178611",title:"BSc.",name:"Jorge",surname:"Casado",slug:"jorge-casado",fullName:"Jorge Casado",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"178612",title:"Dr.",name:"Sandra",surname:"Ríos-Arrabal",slug:"sandra-rios-arrabal",fullName:"Sandra Ríos-Arrabal",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"181798",title:"Dr.",name:"Rumeysa",surname:"Ciftci",slug:"rumeysa-ciftci",fullName:"Rumeysa Ciftci",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"184205",title:"Dr.",name:"Monica",surname:"Ortenzi",slug:"monica-ortenzi",fullName:"Monica Ortenzi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"184206",title:"Dr.",name:"Giovanni",surname:"Lezoche",slug:"giovanni-lezoche",fullName:"Giovanni Lezoche",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"open-access-funding",title:"Open Access Funding",intro:"
IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\\n\\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\\n\\n
\\n\\t
Does your institution already have a budget for covering Open Access publication costs?
\\n\\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\\n
\\n\\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\\n\\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at oapf@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at oapf@intechopen.com
\n\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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