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1. Introduction
Humanity has been inspired from nature along its evolution, since ancient times. Each lively being has its own rules and magnificent knowledge. This capability of all lively beings gives inspiration to the human being in order to find solutions to the problems that he/she faces.
Most of the engineering designs have been inspired by nature. With the design of high-speed trains, the problem was “boom effect,” created by the trains, when entering the tunnel. This noise was because of the air pressure created on the front side of the train. This problem was solved with an excellent nature design, with kingfisher beak [1].
For more than half a century, algorithms have also been using inspirations from nature for computing and solving the problems related to computer science. The first optimization algorithm mimicking nature was genetic algorithm (GA). Genetic algorithm used the selection, mutation and crossover, finding the diverse solutions to complex problems [2]. Today, we have very powerful algorithms inspired by nature to optimize the problems.
Particle swarm optimization (PSO) is another population-based algorithm inspired by nature. Improved by James Kennedy and Russell C. Eberhart, PSO simulates the bird flocking and fish schooling foraging behaviors for the solution of a continuous optimization problem [3].
Biogeography-based optimization (BBO) algorithm is an evolutionary algorithm that simulates the formation of the biogeographies. BBO, improved by Simon [4], simulates the habitants’ immigration or emigration behaviors according to the suitability of the habitat for them.
Gray wolf optimizer (GWO) is also a nature-inspired population-based optimization algorithm originally proposed for the solution of the continuous optimization problems. GWO simulates the hunting behaviors of the gray wolves [5].
Optimization is a kind of programming, solving several problems including function minimization, clustering and feature selection. Clustering is an unsupervised machine learning method that groups the entities with a given number of categories according to their similarities. It is certain that clustering must maximize the similarities of the objects inside the same groups and also maximize the dissimilarity among the other groups’ objects. Clustering can be defined as an optimization problem with this perspective. A classical example of clustering is given in Figure 1.
Figure 1.
Patient clusters according to their systole and diastole blood pressures.
Clustering is a very common technique used for data analysis especially for the applications of summarization, abstracting the data and segmentation [6]. A very common application of clustering is data analysis [7]. Cluster centroids give brief information for the attributes of each cluster. This knowledge is used for information discovery and general classification. Another application of clustering is collaborative filtering [8]. The users, grouped in the same cluster, are accepted similar likes and dislikes. Data and image segmentation are another application of clustering [9].
Today, clustering is commonly used for biological data [10], medical data [11], social network [12, 13] and wireless sensor network data [14] and big data [15] for different kinds of applications stated above.
In this chapter, the reader will learn how he/she can apply optimization algorithms for clustering problems. In the next section, the clustering is defined as an optimization problem. Nature-inspired optimization algorithms, genetic algorithm, particle swarm optimization algorithm, biogeography-based optimization algorithm and gray wolf optimization algorithm have been explained in Section 3. Clustering with nature-inspired algorithms has been studied for a very basic and popular dataset given in Section 4. And the results have been submitted in Section 5.
2. Clustering as an optimization problem
Clustering is grouping the data into the clusters according to their similarities. Similarity is defined mathematically with a measure. The more the attributes of two data are near to each other, the less distance is between data. Namely, distance is inversely proportional to similarity. Different distance measures have been defined for clustering. Euclidean, Manhattan, Mahalanobis and Minkowski are the most [16, 17] used distance metrics. The most popular metric for continuous features is the Euclidean distance [18]. Euclidean distance is used while clusters are compact and the dimension of the data is low. For large dimension, Minkowski distance is preferred.
In this chapter, the objective function is defined based on Euclidean distance metric for comparison. Let us assume the two data points in D dimension space, X and Y. The distance between X and Y is calculated with Euclid and pth order of Minkowski distance, as given in Eqs. (1) and (2), respectively.
De=∑i=1DXi−Yi2E1
Dm=∑i=1DXi−YippE2
The object of clustering is to assign data to the clusters that minimize the sum of the distances from the data to centroids of the clusters. So the objective function with Euclid distance is defined as given in Eq. (3).
Fobj=∑j=1K∑k=1DXik−Cjk2for∀Xi∈CjE3
where N presents the number of data; K presents the number of fully separated clusters; D presents the number of dimension of data; Xik presents the ith data kth feature; Cjk 1, 2,…, K presents the center of the cluster j of kth feature.
The positions of centroids are independent variables. So if applied data dimension is D and the number of cluster is K, the number of independent variables for objective function is KXD. Namely, K centroid positions with D dimension are the independent variables of objective function. The objective is to find centroid positions that minimize the distance. The calculation of the objective function is shown schematically in Figure 2.
Figure 2.
Minimum distance for optimum centroid positions.
3. Nature-inspired optimization algorithms
In this chapter, some of the most cited and successful algorithms have been selected for comparing the clustering performances. Genetic algorithm, particle swarm optimization algorithm, biogeography-based optimization algorithm and gray wolf optimization algorithm have been selected. These algorithms have common features. All of them run a group of solutions.
Each solution is called as individual, particle, island and wolf, respectively. In this chapter, the number of solutions is given as S. Each solution has independent variables. So, the number of independent variables is called V, which is equal to KXD for clustering problem. The clustering problem is handled as an unconstrained optimization problem in this chapter as given in Eq. (4).
Fobj=fC1,C2,....CVE4
In the problem, the initial cluster centroids as independent variables are assigned randomly between the lower and upper values of data. After the independent variables are created randomly, the objective function value is calculated as given in Eqs. (3) and (4). Attaining for S initial solution, V random cluster centroids for each S solution are assigned. Solutions are called Fobj1, Fobj2, …, FobjS. The optimization algorithm starts with these initial solutions and evaluates and improves the solutions, until the stopping condition is true. From one iteration to the other, the algorithm converges to the best solution.
Before the algorithms are explained in detail, the general properties of population (swarm)-based optimization algorithms and specific namings are listed in Table 1.
3.1 Genetic algorithm
Genetic algorithm is one of the most studied and powerful optimization algorithms, used for the solution of both combinatorial and continuous optimization problems. The main idea behind GA is “survival of the fittest.” So, the algorithm is based on the evolution of the individuals from one generation to the next.
After the optimization problem is modeled and its independent variables, constraints and objective function are specified, genetic algorithm parameters are adjusted for the problem. After the algorithm starts with an initial population, fitness value of each individual in the population is calculated. The selection process for the next generation is realized with some selection methods in such a way that best individuals have more chance than the worse ones. Tournament selection, roulette wheel selection and rank selection are some of the selection methods [2].
After selection of the parents, crossover is applied for the parents. In GA, in reverse to the real evolution, the number of population is constant, the number of child is selected as two, and the best individuals are copied like genetic cloning. Crossover operation is applied with a crossover rate. Zero crossover rate means the children will be the copy of their parents, one crossover rate means the children will be completely different from their parents. After crossover, mutation is applied with a very low mutation rate. Mutation is the permanent changes in genes, in order not to get trapped in local minimum. After the new generation is attained, the fittest ones are selected among the latest population. And algorithm stops after a number of generations. Stopping condition is generally selected as maximum number of generation.
Population-based optimization algorithms and their naming for common terms of optimization.
Generation = 1 Specify max_generation value Generate S initial solution While Generation < max_generation Evaluate Fitness function values Select best solutions for the next generation Apply crossover for selected individuals Apply mutation for selected individuals New Population = selected individuals Generation = Generation + 1; end
Table 2.
The pseudocode of GA.
3.2 Particle swarm optimization
PSO is another most studied optimization algorithm, used for the solution of continuous optimization problems introduced by Eberhart and Kennedy [19]. The bird flocking or fish schooling moves in a multidimensional space in such a way that they find the food in a shortest path. The main idea behind the PSO is the behavior of the particles in a swarm. Each particle has a position in a multidimensional space, and they exchange information among them. The particles move in a space using social and cognitive information. When the algorithm stops, the best position has been found.
The algorithm starts after initial positions and initial velocities of particles have been assigned. The dimension size of the particle position in PSO is the number of independent variables. Fitness value of each particle in the swarm has been calculated. The particles update their velocities according to velocity formula. Although two different velocity formulas have been defined, there are two main parameters in both formulas, representing the social and cognitive behaviors of the particles. In swarm, particles update their velocities according to both the best position in the swarm and to their best. In this way, from one iteration to the other, PSO converges the optimum solution of the problems. PSO is the fast convergent optimization algorithm and requires less memory and there are a few parameters to adapt. In the first velocity formula, there was no inertia weight [19]. Inertia weight is introduced by Shi and Eberhart [20]. Inertia weight balances the algorithm’s local and global search ability. Inertia weight specifies the percentage of contribution of previous velocity to its current velocity. The velocity and position formulas for PSO are given in Eqs. (5) and (6), respectively.
vik+1=wvik+c1randpbesti−xik+c2randgbest−xikE5
xik+1=xik+vik+1E6
where w presents the inertia weight, vik presents the velocity of ith particle for kth iteration, xik presents the position of ith particle for kth iteration, pbesti presents the local best solution of ith particle, gbest presents the global best solution, rand() presents uniform random number, and c1 and c2 present the cognitive and social parameters.
Constriction factor (K) is used by Clerc [21]. Constriction factor assures the convergence of the PSO. The velocity and position formulas with constriction factor for PSO are given in Eqs. (7)–(9), respectively.
vik+1=Kvik+φ1randpbestik−xik+φ2randgbest−xikE7
K=22−φ−φ2−4φφ=φ1+φ2φ>4E8
xik+1=xik+vik+1E9
where φ1 and φ2 are individual and social parameters. The pseudocode of PSO is given in Table 3.
Create P initial particle position Do For I = 1:P Evaluate Fitness function values If fitness(Pi) < Pbest(I) Pbest(I) = Pi end If fitness(Pi) < Gbest Gbest = Pi end end Until stopping condition is true
Table 3.
The pseudocode of PSO.
3.3 Biogeography-based optimization
BBO applies biogeography mathematical foundations to solve the optimization problems. Biogeography observes the distribution of species in geographic space and tries to find the reason of the biodiversities in geography. Species migrates from one habitat to the other, trying to find the most suitable habitat. So if this biogeographic movement is simulated well, it can be applicable to solve an optimization problem. Geographical areas that are suitable for biological species are said to have a high habitat suitability index (HSI) [4]. The features, such as land area, temperature and rainfall show the suitability of the biogeography and called as suitability index variables (SIVs) independent variables of the optimization problem and HSI represents the fitness function. Species living in a geography that has high HSI emigrates to nearby habitats, which has low species, since this biogeography is already nearly saturated. BBO has been used for clustering in some studies [22, 23]. As seen in Table 3, since BBO algorithm uses three loops, BBO runs slower than the other algorithms like PSO and GWO. So some strategies have been used in the studies that make BBO run faster. The pseudocode of BBO is given in Table 4.
Initialize the SIVs of N habitat Calculate HSI values of each habitat Sort them and find best HSI for i = 1 to maximum_iteration for i = 1:N for k = 1:dim CandidateNewHabitat = Habitat Select Source Habitat Apply migration with a probability Apply mutation with a probability end end Calculate HSI values for new habitat Sort CandidateNew Habitat Create NewHabitat from Habitat bests and CandidateNewHabitat Update Best Solution Ever Found End
Table 4.
The pseudocode of BBO.
3.4 Gray wolf optimizer
Gray wolf optimizer, a population-based, nature-inspired algorithm, simulates the hunting behaviors of gray wolves [5]. Gray wolves live in groups, and there is a hierarchy among them. Their hunting strategy has three steps: encircling the prey, circling the prey and hunting the prey. This process is adapted for the optimization problem solution. The wolves move in d-dimensional space in order to search their prey. The position of the wolves presents d the independent variables. After they find the prey, they encircle their preys and lastly they hunt. Encircling behavior presents the converging of the solution and hunting presents the optimum point. The algorithms start with the creation of the initial positions of the wolves. The positions are evaluated with the fitness function. Since there is no knowledge about the position of the prey in problem, the best three solutions are selected, in order to update the next positions of the wolves. Instead of saving only one global best solution in memory, GWO saves three best solutions. This property makes the algorithm powerful for global best finding. GWO is applied successfully in feature selection [24], training multilayer perceptrons [25] and clustering [26, 27, 28].
Initialize the Gray Wolf Population Initialize parameter A,a,C Calculate each wolf fitness value Specify first,second and third best solutions while (t < max_iteration) for each wolf Update the position end Update a,A,C Update fitness of each wolf Update first, second and third best solutions t = t + 1; end
Table 5.
The pseudocode of GWO.
4. Clustering performances of the algorithms
As stated in another chapter, the object of clustering is to assign data to the clusters that minimize the sum of the distances from the data to centroids of the clusters. So the objective function value is accepted evolution metric for clustering. In Tables 8 and 9, Fobj column is given for the other algorithms’ clustering performance comparison. In this section, the clustering performances of the algorithms have been compared for IRIS dataset. The parameters, used in the simulation, have been given in Table 6.
Parameters
PSO
GA
GWO
BBO
Population size
5–30
5–30
5–30
5–30
Maximum iteration
100–500
100–500
100–500
100–500
Crossover rate
—
0.8
—
—
Mutation rate
—
0.01
—
—
Self-adjustment rate
1.49
—
—
—
Social adjustment rate
1.49
—
—
—
Inertia weight
1.1
—
—
—
a
—
—
2 → 0
Habitat modification probability
—
—
—
1
Mutation probability
—
—
—
0.005
Elitism rate
—
—
—
0.05
Immigration probability
—
—
—
[0–1]
Table 6.
Parameters of the optimization algorithms.
The benchmark dataset is quite well-known as IRIS dataset [29]. The dataset has four attributes and three class as given in Table 7.
Attributes
Classes
Sepal length in cm
Iris setosa
Sepal width in cm
Iris versicolour
Petal length in cm
Petal width in cm
Iris virginica
Table 7.
The attributes and classes of the IRIS dataset.
All simulations have been implemented on a personal computer with Intel Core Duo 3.0 GHz and 8 GB RAM. Each algorithm has been simulated 30 times and results have been saved. The average, best and worst clustering performances have been calculated from 30 runs. As it has been seen, population size and the maximum iteration number are two important parameters, in order to get best solutions in the nature-inspired optimization algorithms. So in order to get optimum values, two parameters must be selected in such a way that both solution time and optimum value must be optimized. With this aim, firstly population size is selected as constant and the number of maximum iteration is changed as 100, 200, 300, 400 and 500. But only the results for iteration number 100, 200 and 300 have been shown in Table 8, so that the rows of the table aren’t too many.
Algorithm
Iteration number
Time (s)
Fobj
K-means
100
Average
0,014958
85,24,339
Min
0,002778
78,85,144
Max
0,195,176
142,7541
GA
Average
12,675
890,8601
Min
1,112,884
182,4538
Max
1,139,856
2071,767
PSO
Average
1,373,846
268,877
Min
1,338,447
97,33,318
Max
1,335,599
681,3707
BBO
Average
2,637,282
749,6682
Min
1,331,544
184,2395
Max
2,717,847
2044,928
GWO
Average
1,365,047
243,7228
Min
1,317,441
83,71,005
Max
1,440,723
692,7803
GA
200
Average
2,693,518
771,0638
Min
3,119,822
204,5146
Max
2,432,806
2390,792
PSO
Average
343,235
190,5667
Min
3,455,119
80,04687
Max
1,558,096
681,3707
BBO
Average
6,591,394
769,89
Min
4,468,819
125,669
Max
5,675,291
1717,338
GWO
Average
3,352,038
204,6926
Min
3,352,038
81,44,311
Max
6,069,114
774,6732
GA
300
Average
3,190,949
876,8533
Min
2,670,085
290,7814
Max
0,584,012
2148,225
PSO
Average
3,582,182
301,8806
Min
4,470,383
80,00451
Max
111,881
862,6507
BBO
Average
7,955,762
674,7325
Min
8,167,651
198,4093
Max
8,294,291
1412,369
GWO
Average
3,901,675
169,6811
Min
384,877
79,77,414
Max
3,964,712
681,3854
Table 8.
IRIS clustering results of the algorithms for population size = 5.
Secondly, population size is changed as 10, 20 and 30, while maximum iteration number is constant and equal to 200. The results have been shown in Table 9.
Algorithm
Pop size
Time (s)
Fobj
K-means
10
Average
0,014958
85,24,339
Min
0,002778
78,85,144
Max
0,195,176
142,7541
GA
Average
5,298,525
316,0649
Min
5,094,453
99,18,655
Max
5,589,196
931,0285
PSO
Average
552,843
126,6415
Min
5,541,587
78,86,165
Max
5,511,388
176,8169
BBO
Average
11,19,563
295,1141
Min
10,73,245
99,54,019
Max
13,60,106
837,8205
GWO
Average
6,707,655
141,2761
Min
5,483,527
79,59,426
Max
908,555
226,1199
GA
20
Average
11,11,173
124,9446
Min
10,03661
78,8596
Max
13,36,941
164,6155
PSO
Average
8,826,531
123,2611
Min
5,475,998
78,85,145
Max
13,67,359
152,348
BBO
Average
21,39,521
162,8367
Min
21,10,746
79,57,156
Max
22,08803
227,7017
GWO
Average
10,61,837
136,0126
Min
10,43,832
78,90,892
Max
11,68,964
237,9805
GA
30
Average
17,91,835
114,2972
Min
14,41,476
78,85,246
Max
23,24,536
152,3933
PSO
Average
13,68,435
105,8372
Min
7,912,835
78,85,144
Max
18,75,625
152,348
BBO
Average
37,57,738
106,0112
Min
32,82,001
78,85,754
Max
50,14,788
152,46
GWO
Average
17,19,025
124,1055
Min
16,05447
78,988
Max
19,14,221
153,6034
Table 9.
IRIS clustering results of the algorithms for iteration number = 200.
As it has been seen in Table 8, the minimum objective value for iteration number = 100 and the population size = 5 belongs to GWO. PSO is the second best algorithm for clustering. These minimum values found with GWO and PSO are not far from the minimum distance found with k-means. But the average values are quite far from the minimum objective value. So it can be said that both the population size and iteration number are not enough for finding near optimum values for clustering problems [30]. So the algorithms are not stable for these parameters. Average objective values for iteration numbers have been shown in Figure 3.
Figure 3.
Average objective values for iteration numbers = 100, 200, 300, 400 and 500.
As it has been seen in Figure 3, PSO and GWO are fast convergent algorithms. But GA and BBO are also showing similar characteristics, since they have a lot of parameters like mutation rate.
PSO is the best algorithm for clustering the data with minimum distance from centroid to each data for iteration number = 200. GWO is the second best algorithm for data clustering.
GWO is the best algorithm for clustering the data with minimum distance from centroid to each data for iteration number = 300 and PSO is the second. GWO and PSO are more stable than BBO and GA.
But it has been seen that this population size (population size = 5) is not enough for the algorithms’ convergence to the minimum distance for clustering.
As it has been seen in Table 9, the best stable values belong to PSO and GWO. But four of the algorithms are working well under the conditions population size = 30 and iteration number = 200. But clustering time is increasing with the number of population size. Clustering time and objective function value for population size = 5, 10, 20 and 30 have been shown in Figures 4 and 5, respectively.
Figure 4.
Average objective function value for population size = 5, 10, 20 and 30.
Figure 5.
Average clustering time values for population size = 5, 10, 20 and 30.
As it has been seen in Figure 4, PSO and GWO can produce near optimal solutions for small population size.
However, BBO and GA require many people to effectively operate their mechanisms, such as crossing and mutation. GA and BBO catch the performances of the PSO and BBO after the population size is more than 20.
As it has been seen in Figure 5, BBO clustering time is highly increasing with the population size. Solution time for PSO, GWO and GA is changing less, while the population size increase.
Lastly, clustering time variation with iteration number has been shown in Figure 6. As it has been seen, GA and PSO clustering time are robust than BBO and GWO, depending on the number of iterations.
Figure 6.
Average clustering time values for iteration number = 100–500.
As an example, GWO convergence curves for 30 runs have been shown in Figure 7.
Figure 7.
GWO convergence curves for clustering IRIS data.
5. Results
Clustering is one of the unsupervised machine learning methods grouping data to the clusters. In this study, four well-known swarm-based, nature-inspired optimization algorithms have been used for clustering. In order to measure the clustering performance of the algorithms, sum of the distance values have been used. Clustering performance of the algorithms on IRIS dataset has been tested for comparison. As it has been seen in the tables, nature-inspired algorithms’ solution time is not comparable with k-means. Nature-inspired algorithms are very slow because of the swarm-based run. According to the tables, PSO and GWO are faster than BBO and GA owing to the mutation and other parameters. Both PSO and GWO have fewer parameters to adapt, and they are faster and more stable than BBO and GA. In this study, no adaptation is applied for any algorithm. So if special adaptation is applied for those algorithms, the clustering performance of the algorithms will increase.
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Introduction",level:"1"},{id:"sec_2",title:"2. Clustering as an optimization problem",level:"1"},{id:"sec_3",title:"3. Nature-inspired optimization algorithms",level:"1"},{id:"sec_3_2",title:"3.1 Genetic algorithm",level:"2"},{id:"sec_4_2",title:"3.2 Particle swarm optimization",level:"2"},{id:"sec_5_2",title:"3.3 Biogeography-based optimization",level:"2"},{id:"sec_6_2",title:"3.4 Gray wolf optimizer",level:"2"},{id:"sec_8",title:"4. Clustering performances of the algorithms",level:"1"},{id:"sec_9",title:"5. Results",level:"1"}],chapterReferences:[{id:"B1",body:'Available from: https://www.gtac.edu.au/the-kingfisher-and-the-bullet-train-in-the-news/. Latest access'},{id:"B2",body:'Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms. Vol. 1. Elsevier; 1991. pp. 69-93'},{id:"B3",body:'Kennedy J, Eberhart R. Particle Swarm Optimization. 1995. pp. 1942-1948'},{id:"B4",body:'Simon D. Biogeography-based optimization. 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1. Introduction
The vectors of leishmaniasis are dipterans belonging to the Psychodidae family, belonging to the genera Phlebotomus (Old World), and Lutzomyia (New World), with wide distribution in warm and temperate climates [1]. Only female sandflies are hematophagous and when infected become vectors [2], they can contaminate, in addition to humans, other mammals such as domestic dogs and cats, making them important reservoirs of the protozoan [3]. These vectors have been more active in the twilight and post-dusk, sheltering during the day in humid, shaded places and well protected from the winds, for example, wild animal burrows, wood holes, bamboo cavities [4].
Protozoan parasites of the genus Leishmania are the causative agents of leishmaniasis, a group of neglected tropical diseases whose clinical manifestations vary depending on the infectious species of Leishmania and weakness of the host [5]. Leishmaniasis presents an unstable epidemiological pattern, presenting unpredictable fluctuations in the number of cases in each region. In the Old World, it was initially described as a dermal condition known as Rish-e-Balkhi (Balkh Wound) as well as “kala-azar”. In the New World, leishmaniasis parasites were first described in 1909 by Adolpho Carlos Lindenberg, Antonio Carini, and Ulysses de Freitas Paranhos in skin lesions of patients with ‘Bauru’s ulcers’ in the state of São Paulo, Brazil [6]. Currently, there are three groups of parasites of the genus Leishmania classified into different subgenera and these vary depending on which parts of the vector’s gut are colonized by the parasites [7].
It is now known that leishmaniasis can present different characteristics that vary from skin lesions (such as erythematous or hypopigmented macules, papules, nodules, and patches) to visceralization, depending on the species of infecting parasite and the immune response developed by the host (Figure 1). However, it is known that cultural, environmental, and socioeconomic factors play an important role. Furthermore, due to the outbreak of tegumentary leishmaniasis in conflict zones in the Middle East, it reveals that war, ecological disasters, and forced migration are other factors associated with leishmaniasis risk factors [6]. Leishmaniasis-causing protozoa have two main life cycle morphologies: the amastigote phase [without apparent flagellum], which is intracellular in the mammalian host, and the promastigote phase [presence of flagellum in the anterior position of the cell] in the fly. The promastigote phase presents five main forms: procyclic, nectomonad, leptomonad, haptomonad, and metacyclic [7]. The growth of the flagellum in the promastigote occurs in several cell cycles. There are clear implications for the mechanisms of regulation of flagellum length, life cycle stage differentiation, and trypanosomatid division in general, and post-genomic analyzes of Leishmania cell biology have contributed to a better understanding of these mechanisms, not only regarding cell differentiation but also to the molecular mechanisms behind the protozoan infection, both in the vector and in the hosts [8].
Figure 1.
Representative scheme of the genus Leishmania classification illustrating three subgenres. The species presented include some of the more investigated that are the focus of biomedical research. They were colored by occurrences in the old world (blue boxes) and new world (red boxes), and the without colors occur in both regions. Parasites of the Leishmania and Viannia subgenus infect mammals, while Sauroleishmania infects reptiles as vertebrate hosts. Adapted from [1, 7].
Studies indicate that leishmaniasis parasites have adaptation mechanisms that allow the optimal activity of each life stage at its corresponding environmental pH [9]. For example, at pH 7.0 it produces morphologically mixed populations of promastigotes in the stationary phase, but it also includes a subpopulation with similar morphology to the metacyclic (Figure 2) [18, 19].
Figure 2.
Representative scheme of Leishmania differentiation process inside the sand fly vector. AM = amastigote form, the decrease in temperature and an increase in pH is detected by the cell and stimulate cell differentiation [10, 11], through modulation of the expression of genes linked to cell functions [12]. PP = pro-cyclic promastigote form, the secretion of chitinol enzymes aids in the escape from peritrophic membrane allowing the fixation on the vector intestine wall [13], the decrease in pH linked to the increase in glucose in the medium stimulates differentiation and migration according to the gradient of glucose concentration [14] by modulating the expression of genes linked to different cellular functions [12]. NP = Nectomonad Promatigote form, migrate to the thoracic portion of the midgut and begin to secrete PSG [15], as well as a decrease in the expression of several genes [12]. LP = Leptomonad Promatigote form, PSG secretion and detection of decreased oxygenation and pH actives signals for cell differentiation [16] into HP = Haptomonad Promatigote which attaches to the thoracic midgut wall and produces the PSG gel [17], or differentiates into MP = Metacyclic Promatigote form, the infecting phase, which migrates to the anterior portion of the sand fly intestine and infects the host during the next meal [14]. The increase is represented as blue arrows and the decrease is represented as red arrows.
2. Amastigote form
Amastigote means “without apparent flagellum”. The flagellum in amastigotes is internal and non-functional [7, 20]. This phase is a response to the phagocytation by its host’s defense cells, presenting itself in an intracellular form inside the phagolysosome [21].
After the blood-feeding, digestive enzymes, including trypsin, chymotrypsin, aminopeptidase, carboxypeptidase, and alpha-glycosidase degrade ingested infected cells and expose amastigote forms to the peritrophic membrane. The change in conditions from the mammalian host to the vector’s gut, active membrane receptors that detect the change in the environment as the pH increases, from ~4.0 to 5.5 in the phagolysosome to ~6.8 to 7.4 in the midgut vector [14, 21, 22] and temperature decrease, stimulate the development of the parasite into promastigote form [10, 11].
An important response of the parasite to this environmental change is the modulation of enzyme activity in the midgut, assigning different roles to these molecules than that suggested for the mucin-like structures, which appear to protect the parasite surface against the proteolytic enzymes [14]. The secretion of chitinase and N-acetylglucosaminidase enzymes protects from the intense enzymatic activity resulting from digestion, allowing the escape of peritrophic membrane towards the intestinal wall of the vector [13].
Several intracellular signals are triggered and are directly related to the transition from amastigote to promastigote. Relative expression studies revealed increased expression of several genes related to: (calmodulin binding; Cyclic nucleotide biosynthetic process; GTPase activity; GTP binding; DNA association; Nucleosome activity; Nucleosome assembly; Synthesis-coupled proton transport ATP; Mitochondrial proton transporter ATP synthase; Intracellular signal transduction; Dinein complex; Protein complex; Protein heterodimerization activity; Protein polymerization; Proteolysis; Phosphorus-oxygen lyase activity; Calcium-dependent cysteine-type endopeptidase] and a decrease in the expression of genes related to: (Antioxidant activity; Peroxiredoxin activity; Cysteine-type peptidase activity; DNA catabolic process; Triglyceride lipase activity) [12].
Transformation of amastigotes to promastigotes occurs within 12–18 h. These initially transformed promastigotes are termed procyclic and remain short, ovoid, and only slightly mobile [14].
3. Promastigote forms
3.1 Procyclic form
A procyclic promastigote is similar to a cell in G1 or post-S phase that has inherited the new short flagellum [23]. Its morphological characteristic is a body length of 6.5–11.5 μm and the flagellum is shorter than the body length and can have variable body width [20]. The intense multiplication of these forms starts at approximately 18–24 h [14], the divisor promastigotes are found in rosettes with flagella directed towards the center. In promastigotes, the flagellum extends from the cell body, hits and moves the organism, emerging from the anterior end of the cell [7].
Membrane protein classes of the parasite enable the attachment of the procyclic promastigote to the midgut wall of the vector and compatibility between Leishmania species with the vector species. The main membrane glycoconjugates, including their unique and common structures, are lipophosphoglycans–LPG, glycophosphatidylinositol lipids–GIPLs, glycoprotein 63–gp63, secreted acid phosphatases–sAP, secreted proteophosphoglycans–sPPG [14].
The fact that significant differences in LPG-mediated binding were observed when different vector species were compared suggest that the molecules that serve as parasite attachment sites can vary between different species of sandflies.
Serum digestion products destroy incompatible Leishmania species, furthermore, studies suggest that inter- and intraspecies-specific polymorphisms in the LPG phosphoglycan domains may result in species- and strain-restricted intestinal binding and thus determine vector competence. Species- and strain-specific and may therefore provide the evolutionary pressure for structural LPG polymorphisms [14, 24]. Developmental-regulated modifications in LPG structure control the specificity of the midgut adhesion stage [25, 26]. Recent findings indicate that non-LPG-mediated fixation is used by some other species of Leishmania [27, 28, 29, 30, 31].
The gp63, also known as leishmanolysin, is a 63 kDa zinc metalloproteinase containing a GPI anchor and is expressed on the surface of promastigotes of several Leishmania species. It plays an important role in the annexation of the leishmaniasis protozoan and has stood out in several studies related to the understanding of the development and virulence of the parasite [31, 32, 33, 34, 35, 36].
Gut-associated lectins or lectin-like molecules, which have been described for sandflies and presented as signaling sites conducive to parasite fixation [37, 38, 39].
Alternatively, lower affinity and less specific interactions, mediated by shared covering structures and/or flagellar proteins, may be sufficient for the parasite to resist the expulsive force it is exposed to in the vectors. Directing the anterior migration of unattached promastigotes to the thoracic midgut and stomodeal valve has generally been attributed to a glucose concentration gradient [14].
3.2 Nectomonad form
During 36–60 h, rapid multiplication continues, accompanied by the transformation of promastigotes into a long, slender, highly mobile form called nectomonads [14]. Nektós, comes from the Greek and means: “who swims”. Its morphological characteristic is the body length greater than or equal to 12 μm with variable body width and flagellar length [20]. A nectomonad promastigote is like an S-phase cell [23]. The cell differentiation signals triggered, in comparison to the procyclic ones, a significant decrease in the expression of genes related to: Nucleosome activity and assembly; protein heterodimerization; DNA association; core; kinetochore; administration of calmodulin [12].
3.3 Leptomonad form
By 60–72 h, an enormous number of nectomonads are found bundled up in the anterior portion of the abdominal midgut, with many attached via their flagella to the microvilli of the epithelial cells. The anterior migration of promastigotes to the region of the cardia [middle thoracic intestine] and stomodeal valve proceeds until a large accumulation of parasites behind the valve is reached. A leptomonad promastigote is similar to a cell in the same stages of the cell cycle as a procyclic promastigote, but which has inherited the older, longer flagellum [23]. Leptos, comes from the Greek and means: “slender, thin, small”. Its morphological characteristic is body length 6.5–11.5 μm, with flagellum greater than body length and variable body width [20]. Found lining the surface of the stomodeal valve and there can be differentiated haptomonad and metacyclic promastigotes [40].
3.4 Haptomonad form
It is the transformation of leptomonads into short, broad forms called haptomonads, which are occasionally seen to divide [7]. It comes from the Greek haptein, and means: “to hold, denoting contact or combination”, the morphological characteristic of haptomonads is the discoid expansion of the tip of the flagellum, with body shape and variable flagellar length [20]. The haptomonad forms bind through hemidesmosomes to the thin cuticular layer called the intima of the stomodeal valve or to each other through the secretion of a viscous gel-like matrix that restricts its motility [17].
The main component of the gel secreted by promastigotes (PSG) is a high molecular weight glycoprotein called filamentous proteophosphoglycan [15]. The identification of PSG strengthened the hypothesis of vector valve blockage, because the gel-forming properties of the filamentous proteophosphoglycan–fPPG may provide the physical obstruction necessary to cause regurgitation in the vector during repast [7].
The gelatinous nature of PSG, together with its high cell density, can cause local oxygen depletion, and anaerobiosis is also known to stimulate metacyclogenesis [16]. Furthermore, after differentiating leptomonad promastigotes in the middle of the PSG plug, metacyclic promastigotes can migrate to either pole, concentrating on the former in response to a chemotactic suggestion. The possibility of Leishmania responding to sugars or saliva released from the culture that could form a gradient in the midgut remains to be addressed [20].
3.5 Metacyclic form
The name “metacyclic” comes from the Greek Meta and means: “Between”. They are morphologically classified as short, slender, body length less than or equal to 8 μm, body width less than or equal to 1 μm, and highly active with a flagellum at least twice the length of the cell body and are generally not seen in the division [7, 20].
When compared with the gene expression in the form of neptomonads, we can observe the regulation of several cellular activities, with the negative expression of genes related to rRNA processing and the small subunit process (SSU) [12].
Metacyclic promastigotes, originating from the foregut or behind the stomodeal valve to the esophagus, pharynx, and proboscis, are inoculated during the meal, where they initiate the infection in the mammalian host [14]. However, there are at least three known components that lead to infection by the leishmaniasis protozoa: the metacyclic promastigotes themselves, which are obviously essential for transmission; sand fly saliva; and the gel secreted by promastigotes–PSG. Sandfly saliva is a well-established disease exacerbation factor [41], at least for tegumentary leishmaniasis. This is due to the fact that it contains potent compounds with vasodilatory and anti-hemostatic properties [42]. Co-inoculation of saliva with parasites has been shown to worsen the disease in several studies, and this is due to the modulatory capacity of the immune response to contribute to parasite survival and replication [43, 44, 45]. Likewise, PSG has also been shown to contribute to the worsening of the disease, being directly related to the increase in the number of metacyclic promastigote parasites co-inoculated with saliva [27]. The presence of parasites in the salivary glands of sandflies has already been reported by some studies and, therefore, it has been proposed as a fact of great relevance for transmission [46, 47].
4. Molecular aspects of the infection
The first interactions between Leishmania and the host’s immune response are closely linked to the evolution of the disease or protection against the protozoan, and the vector’s saliva directly contributes to these interactions [48]. Sandfly saliva is composed of active molecules that cause an imbalance in homeostasis at the host site, and aid repast [49]. The saliva of these arthropods contains a vast repertoire of pharmacologically active molecules that hinder the host’s hemostatic, inflammatory, and immunological responses [48, 49]. When sand fly saliva is injected into the host’s skin, it induces infiltration of inflammatory cells [50] and antibody production [51, 52, 53]. These disturbances in tissue physiology may also favor the release of Leishmania parasites, as the key to the success of Leishmania parasitism is the ability to evade host immune responses [48]. In this setting, immune complexes are formed [53] in the early stages of exposure. In addition, sand fly saliva also modulates costimulatory molecules and cytokine release by antigen-presenting cells [54, 55, 56].
Several active compounds with pharmaceutical properties have already been isolated from the saliva of sand flies such as the anticoagulant compound of Lufaxine (Inhibitor of Factor Xa from Lutzomyia longipalpis). This recombinant protein has potent and specific anticoagulant activity against factor Xa, a serine protease that cleaves prothrombin to generate thrombin and is involved in both the extrinsic and intrinsic coagulation pathway [57], preventing the activation of receptor 2 activated by protease and thereby inhibiting inflammation and thrombosis in C57BL/6 mice [58].
The action of the LuloHya compound, which acts as a hyaluronidase [55], has also been reported, and when co-inoculated with the parasites provides a more successful infection by Leishmania [59, 60, 61]. The Lundep protein, on the other hand, acts as an endonuclease and helps in the survival of parasites by inhibiting neutrophil traps (NET) in addition to preventing the activation by contact of FXIIa in human plasma [56, 60].
One of the most studied salivary peptides is a potent vasodilator known as maxadilan (MAX). In addition to vasodilation, this compound can also act as an immunomodulator in the host. It can up-regulate cytokines associated with a type 2 response (IL-10, IL-6, and TGF-β) and down-regulate type 1 cytokines (IL-12p70 and TNF-α), NO, and CCR7. This increased parasite survival in the vertebrate host in the early stages of infection [55, 56]. Studies involving the inhibition of human complement by the saliva of the sand fly Lutzomyia longipalpis showed the existence of inhibitors of the classical pathway in this species. As the anti-complement compound Salo [62] and is also considered as a potential transmission-blocking vaccine candidate against leishmaniasis [63].
Pharmacologically active molecules such as Maxadilan in L. longipalpis or PP-1 PP-2A inhibitors [Protein phosphorylation and dephosphorylation reactions, mediated by protein kinases and PPs, respectively, trigger signal transduction events that control diverse cellular responses to internal and external signals [64, 65] present in the saliva of P. papatasi, probably evolved to facilitate blood-feeding. However, as with many other biomolecules, salivary factors also exhibit other activities. In this case, Leishmania parasites benefit from the immunomodulatory effects of certain salivary factors to facilitate their establishment in the hostile environment of vertebrate skin [66].
Taken together, these data indicate that saliva is an endless issue, and several factors remain to be defined and how blocking these molecules is an open field for alternative tools against transmission [48, 49, 67]. Figure 3 briefly illustrates the main aspects of how the infection of leishmaniasis parasites occurs in the host.
Figure 3.
Schematic representation of the leishmaniasis stages of infection. Parasite infection: Leishmania sp. enters through the lesion caused by the proboscis during the meal and infect local macrophages. Stimulated by compounds with vasodilating and anti-hemostatic properties present in the vector’s saliva, an inflammatory reaction begins in the region where more immune cells are recruited to the site and can also be infected by protozoa in metacyclic form. Once phagocytosed, the protozoa become different in the amastigote form in the phagosome. Growth and survival of Leishmania sp.: Infected macrophages secrete anti-inflammatory and pro-inflammatory mediators, initiate immune response mechanisms, neutrophils release cytokines and reactive oxygen species–ROS in the region and monocytes, which differ into macrophages and dendritic cells, which become infected and migrate to other tissues. The increase is represented as blue arrows and decrease is represented as red arrows.
Recognition of the parasite by the host’s immune system cells is the key to triggering effective Leishmania-specific immunity [5]. However, the parasite can persist in the host’s myeloid cells, evading, delaying, and manipulating the host’s immunity to escape host resistance and ensure its transmission [5].
Neutrophils are the first to infiltrate infection sites, where they generate an inflammatory response that restricts the parasite and acts to protect the organism, fighting infection through a series of mechanisms, being considered important modulators of leishmaniasis [68]. They are responsible for the formation of web-like structures called neutrophil extracellular traps (NETs) that can capture and/or kill microorganisms [68]. However, for some species of Leishmania, neutrophils can act as carriers that facilitate the silent infection of macrophages [69, 70, 71]. The ‘Trojan Horse’ model is based on the silent transmission of the parasite from neutrophils to macrophages and dendritic cells when macrophages and cells phagocyte from apoptotic neutrophils that are contaminated by Leishmania [70]. This model is evidenced in the reported ability of some Leishmania species, such as L. major and L. braziliensis [72, 73], to induce neutrophil apoptosis.
Macrophages are the main effector population involved in parasite elimination [5]. However, macrophages are the main host cells where the parasites grow and divide. The parasites infect, multiply gradually, and finally destroy macrophages releasing large numbers of viable amastigotes in the region [74]. Once inside the macrophage, and depending on the Leishmania species, the parasites delay the formation and maturation of phagosomes, preventing phagosome acidification and the action of proteases, while guaranteeing the nutrients necessary for its survival. Furthermore, the parasites modulate the cytokine secretion pattern and inhibit the generation of NO and ROS, while extending the survival of infected macrophages [5]. Genomic and transcriptomic analyzes have largely contributed to the understanding of the biology of Leishmania and revealed to us about the complex interactions that occur within the parasite–host-vector triangle, these interactions are responsible for the rapid activation and deactivation of various signaling pathways that lead to functions of macrophages [e.g., phagocytosis, chemokine secretion, and prostaglandin secretion] [75, 76]. Extracellular matrix interactions, metabolic changes, modulation of gene expression and several mechanisms that are still being studied have revealed how cell–cell interaction occurs and why leishmaniasis is such a complex disease as shown in Figure 4. The elimination of parasites by macrophages requires the preparation and development of an adaptive effector Th1 immunity driven by specific subtypes of dendritic cells [5].
Figure 4.
Diagram representing the major reports about modulation of internal reactions in macrophages infected by the parasite causing leishmaniasis. Leishmania sp. internalization and cell differentiation is successfully achieved, mediated by modulating the expression of genes linked to various cellular functions [12] and by the alteration of signaling events in the host cell, leading to increased production of autoinhibitory molecules such as TGF-beta and decreased induction of cytokines such as IL12 for protective immunity. The production of nitric oxide is also inhibited. furthermore, defective expression of major histocompatibility complex (MHC) genes silences subsequent macrophage-mediated T cell activation, resulting in abnormal immune responses [77]. SHP-1 down-regulates JAK2, Erk1/Erk2 MAP, NF-B, IRF-1, and AP-1 kinases, thereby inhibiting IFN-inducible macrophage functions (e.g., nitric oxide, IL-12 production, and immunoproteasome formation), STAT1 degradation by the proteasome is dependent on PKC and other phosphatases (eg, phosphatase IP3 and calcineurin) and surface parasite molecules such as LPGs play a key role in altering several secondary pathways, for example, PKC, Ca+2 and phosphatidyl inositol), regulating important phagocyte functions such as NO and superoxide production [75]. The increase is represented as blue arrows and the decrease is represented as red arrows.
Studies analyzing neutrophils infected by L. major parasites have shown that, when phagocytosed by cells in the skin tissue, they have the ability to inhibit the maturation and migration of dendritic cells, resulting in a delay in the development of adaptive immunity [72, 78, 79]. Dendritic cells are essential for the generation of a Th1-mediated immune response, fundamental for the control of leishmaniasis [80, 81, 82]. These parasites can act at different levels to inhibit dendritic cells, including modulation of the MAPK pathway, decreased antigen presentation capacity and IL-12 secretion, this inhibition being mediated by the activation of protein tyrosine phosphatase (PTPs) [83, 84]. In summary, the internalization of the opsonized protozoan by dendritic cells via FcγR (Fcγ receptor) promotes dendritic cell activation and IL-12 production. Furthermore, there is a down-regulation of costimulatory molecules, CD40 and CD86 after infection and gp63 cleaves the SNAREs protein (soluble NSF binding protein receptor), preventing the assembly of the NADPH oxidase complex [5]. An analysis of the gene expression of lesions with Cutaneous Leishmaniasis showed increased P27 [85] and decreased expression of the A2 gene [86]. IL-10 is important for the persistence of the parasite in the lesion, preventing its complete elimination from the lesion, despite the presence of a protective immune response [87]. Furthermore, circulating antibody is crucial for susceptibility to the development of tegumentary leishmaniasis [88] and a progressive increase in tissue IL-10 expression during infection suggests a role in susceptibility [89]. The amastigotes from the cutaneous leishmaniasis lesion are coated with IgG, and the internalization of opsonized amastigotes by macrophages induces the production of IL-10 and a consequent increase in the intracellular growth of the parasite [90].
Tissue damage is promoted by inappropriate epidermal signals driven by dendritic cells. Furthermore, studies indicate that nTregs are essential for the development and maintenance of persistent skin infection and reactivation of infections caused by the Leishmania parasite [91]. Understanding which dendritic cell populations are critical to triggering and achieving immunity to Leishmania and how parasites inhibit its activation and migration will help to improve a rational design of vaccines aimed at neutralizing the parasite’s virulence factors, along with the use of the most appropriate adjuvants [5]. These recurrent injuries may result from the Koebner phenomenon [92] which refers to skin lesions appearing in lines of mechanical trauma, seen in some skin diseases such as psoriasis.
Antimicrobial peptides are innate immunity mechanisms that contribute to host defense. LL-37 is a peptide derived from human cathelicidin (CAP180, a multifunctional regulator of the innate and adaptive immune response, having a leishmanicidal activity, increasing phagocytosis in dendritic cells and macrophages, and acting as an activator or suppressor of the adaptive immune response depending on the concentration [91].
Natural regulatory T cells rapidly accumulate in the dermis, where they suppress, both through IL-10 dependent and independent mechanisms, the capacity of CD4 + CD25 effector T cells to eliminate the parasite from the site [91]. One of the immunopathological consequences of active visceral leishmaniasis in humans is the suppression of T cell responses mainly to the Leishmania antigen [93]. The immune responses induced during visceral leishmaniasis in experimental data are markedly different from those induced in cutaneous leishmaniasis [94]. Furthermore, gene expression studies of tissues infected with visceral leishmaniasis reveal the modulation of the expression of genes P27, Ufm1 [85] and A2 [95]. A spectrum of clinical manifestations occurs in visceral leishmaniasis, ranging from asymptomatic or oligosymptomatic disease to progressive disease with severe manifestations such as hepatosplenomegaly, fever, pancytopenia, and hypergammaglobulinemia [96].
These particularities must have to be studied in order to permit the understanding of how different Leishmania species could promote different forms of the disease can generate such different immune responses [94].
5. Conclusion
The morphological development of the parasite has been regulated by the environment in which it is found, being perceived by chemotactic receptors that identify these environmental changes (e.g. pH and oxygenation), modulating several genes and thus triggering various intracellular processes, processes that depend on the stage of development that the parasite is at when receiving such stimuli. About the molecular aspects involved in the infection, we can say that current research has indicated a strong relationship between the immune response and the way in which leishmaniasis will manifest itself.
In order to benefit the socio-economically vulnerable individuals affected by leishmaniasis, many young researchers start their studies in leishmaniasis from an early age in scientific initiation programs, often conducting their studies well beyond the PhD. These young researchers are the audience that this book chapter is dedicated. Because we believe that the study and understanding of the life cycle of Leishmania are mandatory for all researchers who intend to dedicate their careers to the different aspects of this important disease. From epidemiological studies to the development of new therapies, a good understanding of the parasite’s life cycle is essential for the success of all initiatives.
\n',keywords:"neglected disease, protozoan, leishmania, amastigote, promastigote",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80467.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80467.xml",downloadPdfUrl:"/chapter/pdf-download/80467",previewPdfUrl:"/chapter/pdf-preview/80467",totalDownloads:57,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 11th 2021",dateReviewed:"December 23rd 2021",datePrePublished:"February 14th 2022",datePublished:"April 13th 2022",dateFinished:"February 14th 2022",readingETA:"0",abstract:"According to WHO, Leishmaniasis is a complex neglected disease caused by a protozoa parasite from over 20 Leishmania species transmitted by more than 90 sandfly species, showing three main forms: visceral, cutaneous, and mucocutaneous. The efficient prevention and control of leishmaniasis are very difficult to achieve, depending on the combination of different intervention strategies, usually resulting in failure. Additionally, the correct diagnostics require the combination of clinical signs with laboratory tests, and only a few therapeutical options are available for patients. To improve this scenario, greater efforts in research for control and treatment are needed. For this purpose, the study and understanding of the life cycle of Leishmania are mandatory for all researchers who intend to dedicate their careers to the different aspects of this important disease. In order to support beginning researchers in the study of leishmaniasis, we propose in this review an update in the current knowledge about the major molecular aspects involved in the development of dimorphic forms of Leishmania parasites that replicate in the gut of sandflies (promastigotes) and in mammalian cells (amastigotes) and the relationship with host’s immune system.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80467",risUrl:"/chapter/ris/80467",signatures:"Natanael Endrew Souto Maior Torres Bonfim, Ana Lígia Barbour Scott and Leonardo de Azevedo Calderon",book:{id:"10891",type:"book",title:"Leishmaniasis",subtitle:"General Aspects of a Stigmatized Disease",fullTitle:"Leishmaniasis - General Aspects of a Stigmatized Disease",slug:"leishmaniasis-general-aspects-of-a-stigmatized-disease",publishedDate:"April 13th 2022",bookSignature:"Leonardo de Azevedo Calderonon",coverURL:"https://cdn.intechopen.com/books/images_new/10891.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83968-082-3",printIsbn:"978-1-83968-081-6",pdfIsbn:"978-1-83968-092-2",isAvailableForWebshopOrdering:!0,editors:[{id:"177382",title:"Dr.",name:"Leonardo de Azevedo",middleName:null,surname:"Calderon",slug:"leonardo-de-azevedo-calderon",fullName:"Leonardo de Azevedo Calderon"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"177382",title:"Dr.",name:"Leonardo de Azevedo",middleName:null,surname:"Calderon",fullName:"Leonardo de Azevedo Calderon",slug:"leonardo-de-azevedo-calderon",email:"leonardo.calderon@fiocruz.br",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/177382/images/system/177382.jpg",institution:{name:"Oswaldo Cruz Foundation",institutionURL:null,country:{name:"Brazil"}}},{id:"465571",title:"Dr.",name:"Natanael",middleName:null,surname:"Endrew Souto Maior Torres Bonfim",fullName:"Natanael Endrew Souto Maior Torres Bonfim",slug:"natanael-endrew-souto-maior-torres-bonfim",email:"dummy+14334225353246983@intechopen.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"465572",title:"Dr.",name:"Ana",middleName:null,surname:"Lígia Barbour Scott",fullName:"Ana Lígia Barbour Scott",slug:"ana-ligia-barbour-scott",email:"dummy+14253435332523446983@intechopen.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Amastigote form",level:"1"},{id:"sec_3",title:"3. Promastigote forms",level:"1"},{id:"sec_3_2",title:"3.1 Procyclic form",level:"2"},{id:"sec_4_2",title:"3.2 Nectomonad form",level:"2"},{id:"sec_5_2",title:"3.3 Leptomonad form",level:"2"},{id:"sec_6_2",title:"3.4 Haptomonad form",level:"2"},{id:"sec_7_2",title:"3.5 Metacyclic form",level:"2"},{id:"sec_9",title:"4. Molecular aspects of the infection",level:"1"},{id:"sec_10",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Akhoundi M, Kuhls K, Cannet A, et al. A historical overview of the classification, evolution, and dispersion of Leishmania parasites and Sandflies. PLoS Neglected Tropical Diseases. 2016;10(3):e0004349. DOI: 10.1371/journal.pntd.0004349'},{id:"B2",body:'Pimenta PFP, Saraiva EMB, Rowton E, et al. 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Science. 2008;321(5891):917-918. DOI: 10.1126/science.1162914'},{id:"B72",body:'Ribeiro-Gomes FL, Peters NC, Debrabant A, Sacks DL. Efficient capture of infected neutrophils by dendritic cells in the skin inhibits the early anti-Leishmania response. PLoS Pathogens. 2012;8(2):e1002536. DOI: 10.1371/journal.ppat.1002536'},{id:"B73",body:'Falcão SAC, Weinkopff T, Hurrell BP, Celes FS, Curvelo RP, Prates DB, et al. Exposure to Leishmania braziliensis triggers neutrophil activation and apoptosis. PLoS Neglected Tropical Diseases. 2015;9(3):e0003601. DOI: 10.1371/journal.pntd.0003601'},{id:"B74",body:'Handman E, Spira DT. Growth of Leishmania amastigotes in macrophages from normal and immune mice. Zeitschrift für Parasitenkunde. 1977;53(1):75-81. DOI: 10.1007/BF00383117'},{id:"B75",body:'Olivier M, Gregory DJ, Forget G. Subversion mechanisms by which Leishmania parasites can escape the host immune response: A Signaling point of view. Clinical Microbiology Reviews. 2005;18(2):293-305. DOI: 10.1128/CMR.18.2.293-305.2005'},{id:"B76",body:'Cantacessi C, Dantas-Torres F, Nolan MJ, Otranto D. The past, present, and future of Leishmania genomics and transcriptomics. Trends in Parasitology. 2015;31(3):100-108. DOI: 10.1016/j.pt.2014.12.012'},{id:"B77",body:'Kanehisa Lanboratories. Leishmaniasis – Reference pathway. 2020. Available from: https://www.kegg.jp/kegg-bin/show_pathway?map05140'},{id:"B78",body:'Ribeiro-Gomes FL, Romano A, Lee S, Roffê E, Peters NC, Debrabant A, et al. Apoptotic cell clearance of Leishmania major-infected neutrophils by dendritic cells inhibits CD8+ T-cell priming in vitro by Mer tyrosine kinase-dependent signaling. Cell Death & Disease. 2015;6(12):e2018-e2018. DOI: 10.1038/cddis.2015.351'},{id:"B79",body:'Peters NC, Pagán AJ, Lawyer PG, Hand TW, Henrique Roma E, Stamper LW, et al. Chronic parasitic infection maintains high frequencies of short-lived Ly6C+CD4+ effector T cells that are required for protection against Re-infection. PLoS Pathogens. 2014;10(12):e1994538. DOI: 10.1371/journal.ppat.1004538'},{id:"B80",body:'León B, López-Bravo M, Ardavín C. Monocyte-derived dendritic cells formed at the infection site control the induction of protective T helper 1 responses against Leishmania. Immunity. 2007;26(4):519-531. DOI: 10.1016/j.immuni.2007.01.017'},{id:"B81",body:'Ashok D, Schuster S, Ronet C, Rosa M, Mack V, Lavanchy C, et al. Cross-presenting dendritic cells are required for control of Leishmania major infection. European Journal of Immunology. 2014;44(5):1422-1432. DOI: 10.1002/eji.201344242'},{id:"B82",body:'Martínez-López M, Iborra S, Conde-Garrosa R, Sancho D. Batf3-dependent CD103 + dendritic cells are major producers of IL-12 that drive local Th1 immunity against Leishmania major infection in mice. European Journal of Immunology. 2015;45(1):119-129. DOI: 10.1002/eji.201444651'},{id:"B83",body:'Contreras I, Gómez MA, Nguyen O, Shio MT, McMaster RW, Olivier M. Leishmania-induced inactivation of the macrophage transcription factor AP-1 is mediated by the parasite metalloprotease GP63. PLoS Pathogens. 2010;6(10):e1001148. DOI: 10.1371/journal.ppat.1001148'},{id:"B84",body:'Iborra S, Martínez-López M, Cueto FJ, Conde-Garrosa R, Del Fresno C, Izquierdo HM, et al. Leishmania uses Mincle to target an inhibitory ITAM Signaling pathway in dendritic cells that dampens adaptive immunity to infection. Immunity. 2016;45(4):788-801. DOI: 10.1016/j.immuni.2016.09.012'},{id:"B85",body:'Sharma P, Gurumurthy S, Duncan R, Nakhasi HL, Salotra P. Comparative in vivo expression of amastigote up regulated Leishmania genes in three different forms of Leishmaniasis. Parasitology International. 2010;59(2):262-264. DOI: 10.1016/j.parint.2009.11.003'},{id:"B86",body:'Zhang WW, Mendez S, Ghosh A, Myler P, Ivens A, Clos J, et al. Comparison of the A2 gene locus in Leishmania donovani and Leishmania major and its control over cutaneous infection. 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DOI: 10.1128/IAI.66.1.18-27.1998'},{id:"B90",body:'Kane MM, Mosser DM. The role of IL-10 in promoting disease progression in Leishmaniasis. Journal of Immunology. 2001;166(2):1141-1147. DOI: 10.4049/jimmunol.166.2.1141'},{id:"B91",body:'Tapia FJ, Díaz NL, Rodríguez OL, Sánchez M. Tegumentary Leishmaniasis: Immunology and molecular biology. Gazeta Médica da Bahia. 2009;79(3):84-90'},{id:"B92",body:'Yaghoobi R, Maraghi S, Bagherani N, Rafiei A. Cutaneous leishmaniasis of the lid: A report of nine cases. Korean Journal of Ophthalmology. 2010;24(1):40-43. DOI: 10.3341%2Fkjo.2010.24.1.40'},{id:"B93",body:'Carvalho EM, Teixeira RS, Johnson WD. Cell-mediated immunity in American visceral leishmaniasis: Reversible immunosuppression during acute infection. Infection and Immunity. 1981;33(2):498-500. DOI: 10.1128/iai.33.2.498-500.1981'},{id:"B94",body:'Goto H, Lindoso JAL. Immunity and immunosuppression in experimental visceral leishmaniasis. Brazilian Journal of Medical and Biological Research. 2004;37(4):615-623. DOI: 10.1590/S0100-879X2004000400020'},{id:"B95",body:'Zhang W-W, Matlashewski G. Characterization of the A2-A2rel gene cluster in Leishmania donovani: Involvement of A2 in visceralization during infection. Molecular Microbiology. 2001;39(4):935-948. DOI: 10.1046/j.1365-2958.2001.02286.x'},{id:"B96",body:'Badaro R, Jones TC, Carvalho EM, Sampaio D, Reed SG, Barral A, et al. New perspectives on a subclinical form of visceral Leishmaniasis. The Journal of Infectious Diseases. 1986;154(6):1003-1011. DOI: 10.1093/infdis/154.6.1003'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Natanael Endrew Souto Maior Torres Bonfim",address:null,affiliation:'
Center for the Study of Biomolecules Applied to Health, Oswaldo Cruz Foundation (Fiocruz), Fiocruz Rondônia, Brazil
Laboratory of Computational Biophysical and Biology, Center for Mathematics, Computing and Cognition, Federal University of ABC, Brazil
Laboratory of Computational Biophysical and Biology, Center for Mathematics, Computing and Cognition, Federal University of ABC, Brazil
'},{corresp:"yes",contributorFullName:"Leonardo de Azevedo Calderon",address:"leonardo.calderon@fiocruz.br",affiliation:'
Center for the Study of Biomolecules Applied to Health, Oswaldo Cruz Foundation (Fiocruz), Fiocruz Rondônia, Brazil
Medicine Department, Federal University of Rondônia (UNIR), Brazil
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10,000 GBP Monograph - Long Form
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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Discoverability - electronic citation and linking via DOI
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Oguntibeju",authors:[{id:"169345",title:"Dr.",name:"Omolola",middleName:null,surname:"Ayepola",slug:"omolola-ayepola",fullName:"Omolola Ayepola"}]}],mostDownloadedChaptersLast30Days:[{id:"62672",title:"An Overview of Melatonin as an Antioxidant Molecule: A Biochemical Approach",slug:"an-overview-of-melatonin-as-an-antioxidant-molecule-a-biochemical-approach",totalDownloads:3828,totalCrossrefCites:22,totalDimensionsCites:41,abstract:"Melatonin is an endogenous hormone derived from tryptophan that is mainly released from the pineal gland in the dark. Melatonin regulates many biological functions such as sleep, circadian rhythm, immunity, and reproduction. Melatonin has a free radical scavenger, anti-inflammatory, and antioxidant effects. It scavenges reactive oxygen and nitrogen species and increases antioxidant defenses, thus it prevents tissue damage and blocks transcriptional factors of pro-inflammatory cytokines. Due to its small size and amphiphilic nature, it increases the efficacy of mitochondrial electron transport chain and reduces electron leakage. Melatonin prevents degenerative changes in the central nervous system in models of Alzheimer’s and Parkinson’s disease and reduces free radical damage to DNA which may lead to cancer and many other situations. Consequently, melatonin has beneficial effects including stimulation of antioxidant enzymes, inhibition of lipid peroxidation, and so it contributes to protection from oxidative damages.",book:{id:"7328",slug:"melatonin-molecular-biology-clinical-and-pharmaceutical-approaches",title:"Melatonin",fullTitle:"Melatonin - Molecular Biology, Clinical and Pharmaceutical Approaches"},signatures:"Aysun Hacışevki and Burcu Baba",authors:[{id:"248612",title:"Associate Prof.",name:"Aysun",middleName:null,surname:"Hacışevki",slug:"aysun-hacisevki",fullName:"Aysun Hacışevki"},{id:"248614",title:"Ph.D.",name:"Burcu",middleName:null,surname:"Baba",slug:"burcu-baba",fullName:"Burcu Baba"}]},{id:"75377",title:"Pathophysiologic Approach to Type 2 Diabetes Management: One Centre Experience 1980–2020",slug:"pathophysiologic-approach-to-type-2-diabetes-management-one-centre-experience-1980-2020",totalDownloads:777,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This overview summarizes the evolution of pathophysiologic treatment of diabetes type 2 (T2D) in the period of the last 40 years. Randomized Controlled Trials (RCT) and Real World Evidence (RWE) studies resulted in recent Statements of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) in the year 2020. Case reports and studies of a single-centre in Czech Republic are reported. The authors demonstrate the impact of (1) multiple doses of rapid insulin, (2) multiple doses of rapid or ultrarapid insulin analogs (3) continuous subcutaneous insulin infusion (CSII) (4) incretin receptor agonists, (5) fixed combination of insulin degludec with liraglutide (IDegLira) and (6) SGLT2 inhibitor dapagliflozin, on plasma glucose concentration, HbA1c, body mass and patient satisfaction. The importance of therapeutic patients’ education and technology (personal glucometers, continuous/flash glucose monitors, insulin pens/pumps) is emphasized. Most of the observations were already published. Hence, individually adopted education, lifstyle, technical equipment, incretin receptor agonists and/or metformin and/or gliflozins and/or insulin analogs appear to be the core of an effective pathophysiologic approach. Scientific conclusions from RCTs, RWE trials and own clinical case reports may prevail over clinical inertia and induce early implementation of effective methods into routine T2D treatment.",book:{id:"9517",slug:"type-2-diabetes-from-pathophysiology-to-cyber-systems",title:"Type 2 Diabetes",fullTitle:"Type 2 Diabetes - From Pathophysiology to Cyber Systems"},signatures:"Rudolf Chlup, Richard Kaňa, Lada Hanáčková, Hana Zálešáková and Blanka Doubravová",authors:[{id:"278357",title:"Prof.",name:"Rudolf",middleName:null,surname:"Chlup",slug:"rudolf-chlup",fullName:"Rudolf Chlup"},{id:"346119",title:"Dr.",name:"Richard",middleName:null,surname:"Kaňa",slug:"richard-kana",fullName:"Richard Kaňa"},{id:"346120",title:"BSc.",name:"Lada",middleName:null,surname:"Hanáčková",slug:"lada-hanackova",fullName:"Lada Hanáčková"},{id:"346121",title:"BSc.",name:"Hana",middleName:null,surname:"Zálešáková",slug:"hana-zalesakova",fullName:"Hana Zálešáková"},{id:"346122",title:"Dr.",name:"Blanka",middleName:null,surname:"Doubravová",slug:"blanka-doubravova",fullName:"Blanka Doubravová"}]},{id:"61064",title:"Secretions of Human Salivary Gland",slug:"secretions-of-human-salivary-gland",totalDownloads:2766,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"The salivary glands play an important role in our body by the virtue of its ability to secrete saliva. Saliva has a role to play in maintaining the health of the oral cavity and for carrying out physiological functions like mastication, taste perception, speech etc. It also acts as a mirror to the systemic status of an individual owing to its ability to act as a diagnostic fluid for detecting a number of conditions and diseases. Saliva is a potential noninvasive diagnostic fluid for detection of a number of biomarkers of disease and health. Advancement in diagnostic methods has helped in identifying biomarkers of disease in saliva. In order to understand and diagnose pathological changes, a thorough understanding of the salivary gland anatomy, physiology and regulation of its secretion is warranted. This chapter aims to provide the basic understanding of the secretions of saliva.",book:{id:"6246",slug:"salivary-glands-new-approaches-in-diagnostics-and-treatment",title:"Salivary Glands",fullTitle:"Salivary Glands - New Approaches in Diagnostics and Treatment"},signatures:"Anahita Punj",authors:[{id:"226076",title:"Dr.",name:"Anahita",middleName:null,surname:"Punj",slug:"anahita-punj",fullName:"Anahita Punj"}]},{id:"63301",title:"Role of PI3K/AKT Pathway in Insulin-Mediated Glucose Uptake",slug:"role-of-pi3k-akt-pathway-in-insulin-mediated-glucose-uptake",totalDownloads:3541,totalCrossrefCites:11,totalDimensionsCites:27,abstract:"Glucose uptake is regulated by several mechanisms, where insulin plays the most prominent role. This powerful anabolic hormone regulates the transport of glucose into the cell through translocation of glucose transporter from an intracellular pool to the plasma membrane mainly in metabolically active tissues like skeletal muscles, adipose tissue, or liver (GLUT4). This translocation occurs through multiple steps of PI3K/AKT signaling pathway. In this chapter, we will focus on molecular events leading to GLUT4 translocation, starting with activation of insulin receptors through signaling cascade involving phosphatidylinositol 3-kinase (PI3K) and protein kinase B (PKB) and finally, the action of their effectors. We will present regulatory mechanisms and modulators of insulin-mediated glucose uptake.",book:{id:"7061",slug:"blood-glucose-levels",title:"Blood Glucose Levels",fullTitle:"Blood Glucose Levels"},signatures:"Ewa Świderska, Justyna Strycharz, Adam Wróblewski, Janusz Szemraj, Józef Drzewoski and Agnieszka Śliwińska",authors:null},{id:"70711",title:"Fetal Growth Restriction",slug:"fetal-growth-restriction",totalDownloads:3104,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Fetal growth defect is classified into intrauterine growth restriction (IUGR) and small-for-gestational-age (SGA) fetus based on the estimated fetal weight percentile and Doppler hemodynamic parameters. IUGR pathophysiology and etiology are complex and diverse, highlighting placental insufficiency as a paradigm, which explains its association with other entities of great clinical importance such as preeclampsia. The poor long- and short-term perinatal and postnatal results associated with this context make it necessary to establish an early diagnosis and a therapeutic strategy, which can be challenging due to the compromise between the threat of intrauterine permanence and the prematurity problem. Consequently, a systematic and protocolized diagnostic-therapeutic management, based on scientific evidence, is necessary to determine whether obstetric intervention through a preterm delivery is advisable to improve the perinatal outcomes of these patients.",book:{id:"8224",slug:"growth-disorders-and-acromegaly",title:"Growth Disorders and Acromegaly",fullTitle:"Growth Disorders and Acromegaly"},signatures:"Edurne Mazarico Gallego, Ariadna Torrecillas Pujol, Alex Joan Cahuana Bartra and Maria Dolores Gómez Roig",authors:[{id:"202446",title:"Ph.D.",name:"Maria Dolores",middleName:null,surname:"Gómez Roig",slug:"maria-dolores-gomez-roig",fullName:"Maria Dolores Gómez Roig"},{id:"311835",title:"Dr.",name:"Edurne",middleName:null,surname:"Mazarico",slug:"edurne-mazarico",fullName:"Edurne Mazarico"}]}],onlineFirstChaptersFilter:{topicId:"178",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"May 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,annualVolume:11410,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,annualVolume:11414,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence"},{id:"23",title:"Computational Neuroscience",scope:"Computational neuroscience focuses on biologically realistic abstractions and models validated and solved through computational simulations to understand principles for the development, structure, physiology, and ability of the nervous system. This topic is dedicated to biologically plausible descriptions and computational models - at various abstraction levels - of neurons and neural systems. This includes, but is not limited to: single-neuron modeling, sensory processing, motor control, memory, and synaptic plasticity, attention, identification, categorization, discrimination, learning, development, axonal patterning, guidance, neural architecture, behaviors, and dynamics of networks, cognition and the neuroscientific basis of consciousness. Particularly interesting are models of various types of more compound functions and abilities, various and more general fundamental principles (e.g., regarding architecture, organization, learning, development, etc.) found at various spatial and temporal levels.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",keywords:"Single-Neuron Modeling, Sensory Processing, Motor Control, Memory and Synaptic Pasticity, Attention, Identification, Categorization, Discrimination, Learning, Development, Axonal Patterning and Guidance, Neural Architecture, Behaviours and Dynamics of Networks, Cognition and the Neuroscientific Basis of Consciousness"},{id:"24",title:"Computer Vision",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR"},{id:"25",title:"Evolutionary Computation",scope:"Evolutionary computing is a paradigm that has grown dramatically in recent years. This group of bio-inspired metaheuristics solves multiple optimization problems by applying the metaphor of natural selection. It so far has solved problems such as resource allocation, routing, schedule planning, and engineering design. Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization"},{id:"26",title:"Machine Learning and Data Mining",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence"},{id:"27",title:"Multi-Agent Systems",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Artificial Intelligence",id:"14"},selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"May 7th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:3,numberOfPublishedChapters:96,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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