Behavioral evaluation values of central auditory processing between control and experimental groups.
\r\n\tStatistical machine learning specifically poses some of the most challenging theoretical problems in modern statistics, the crucial among them being the general problem of understanding the link between inference and computation. This book intends to provide the reader with a comprehensive overview of linear method for regression, non linear method for regression, deep learning, unsupervised learning, artificial neural network, and support vector machine (SVM).
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"a3fb79b0a4a302d6318df11534e1ec85",bookSignature:"Dr. Andino Maseleno",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8661.jpg",keywords:"Linear Method for Regression, Non Linear Method for Regression, Deep Learning, Unsupervised Learning, K-Means Clustering, Hierarchichal Clustering, Principal Component Analysis, Artificial Neural Network, Learning in Neural Network, Convolutional Neural Network, Support Vector Clustering, Multiclass SVM",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 3rd 2018",dateEndSecondStepPublish:"July 24th 2018",dateEndThirdStepPublish:"September 22nd 2018",dateEndFourthStepPublish:"December 11th 2018",dateEndFifthStepPublish:"February 9th 2019",remainingDaysToSecondStep:"3 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"219663",title:"Dr.",name:"Andino",middleName:null,surname:"Maseleno",slug:"andino-maseleno",fullName:"Andino Maseleno",profilePictureURL:"https://mts.intechopen.com/storage/users/219663/images/system/219663.jpg",biography:"Dr. Andino Maseleno is a research fellow at the Institute of Informatics and Computing Energy, Universiti Tenaga Nasional, Malaysia. He was a visiting fellow in Centre for lifelong learning, Universiti Brunei Darussalam, Brunei Darussalam, in July 2016 till March 2017. He received the B.S. in Informatics Engineering from UPN 'Veteran” Yogyakarta, Indonesia in 2005, M.Eng. in Electrical Engineering from Gadjah Mada University, Indonesia in 2009, and Ph.D. in Computer Science from Universiti Brunei Darussalam, Brunei Darussalam in 2015.",institutionString:"Universiti Tenaga Nasional",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"606",title:"Machine Learning and Data Mining",slug:"numerical-analysis-and-scientific-computing-machine-learning-and-data-mining"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"177731",firstName:"Dajana",lastName:"Pemac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/177731/images/4726_n.jpg",email:"dajana@intechopen.com",biography:"As a Commissioning Editor at IntechOpen, I work closely with our collaborators in the selection of book topics for the yearly publishing plan and in preparing new book catalogues for each season. This requires extensive analysis of developing trends in scientific research in order to offer our readers relevant content. Creating the book catalogue is also based on keeping track of the most read, downloaded and highly cited chapters and books and relaunching similar topics. I am also responsible for consulting with our Scientific Advisors on which book topics to add to our catalogue and sending possible book proposal topics to them for evaluation. Once the catalogue is complete, I contact leading researchers in their respective fields and ask them to become possible Academic Editors for each book project. Once an editor is appointed, I prepare all necessary information required for them to begin their work, as well as guide them through the editorship process. I also assist editors in inviting suitable authors to contribute to a specific book project and each year, I identify and invite exceptional editors to join IntechOpen as Scientific Advisors. 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Research has shown that recurrent episodes can induce changes or delay the development of the central auditory nervous system, leading to central auditory processing disorder (CAPD). In this chapter, we present results obtained in the behavioral and electrophysiological evaluation of the auditory processing of children and adolescents with OM over the first few years of life. In addition, we discuss aspects of the auditory rehabilitation process itself.
\nLanguage plays an essential role in perceptual organization, including the reception and structuring of information, learning, and social interactions. Language enables us to communicate with each other and acquire and transmit experience and knowledge. The development of speech and language requires a functional auditory system capable of detecting sound, paying attention, remembering, discriminating, and perceiving location. Any interruption to development will lead to significant functional impairments, not only in language but also in cognitive, intellectual, cultural, and social development [1, 2].
\nCentral auditory processing (CAP) is defined as the efficiency and effectiveness with which the central auditory nervous system uses auditory information. It refers to the perceptual processing of auditory information and to the neurobiological activity underlying this processing that gives rise to electrophysiological auditory potentials [3, 4]. The efficient analysis and interpretation of normal auditory information involves several subprocesses and skills, and includes neural mechanisms underlying a range of auditory behaviors such as sound localization and lateralization; auditory discrimination; recognition of auditory patterns; temporal aspects of the hearing (integration, discrimination, resolution, temporal masking); auditory performance in the presence of competing acoustic signals (which includes dichotic listening); and decoding degraded acoustic signals [5, 6].
\nThis whole process involves a complex system of neurons located in several stations of the auditory system. The initial analysis of the stimulus occurs in the peripheral auditory system, constituted by the external and middle ear, responsible for the capture, transduction, and processing of the sound stimulus. The stimulus arrives first at the cochlear nucleus and encephalic trunk, followed by the upper olivary complex, lateral lemniscus, inferior colliculus, and medial geniculate body, and finally reaches the primary area of auditory reception in the temporal lobe of each hemisphere. From the primary auditory cortex of each hemisphere, the signals travel to other regions of the brain-the association areas-both in the same hemisphere and in the opposite hemisphere. As the auditory information travels by ipsi- and contralateral routes, it undergoes increasingly complex levels of processing. This processing occurs both hierarchically and serially, as well as in parallel or overlapping. The result of combining serial and parallel processing makes the system highly efficient and redundant. In addition to ascending pathways, there are also descending pathways that can moderate the response to a received acoustic stimulus [7, 8].
\nCentral auditory processing disorder (CAPD) is a dysfunction of the central auditory nervous system that leads to hearing difficulties. It can lead to, or be associated with, changes in language, learning, cognition, or other communicative functions [3, 4, 5, 9]. In the pediatric population, there are several possible causes of the disorder, among them otitis media [10, 11].
\nOtitis media with effusion (OME) is a clinical entity characterized by the presence of effusion in the middle ear, without perforation of the tympanic membrane, but with an acute infection that lasts for a period of at least 3 months. The condition is common enough to be called an “occupational hazard of early childhood” [12] because about 90% of children have OM before school age and they develop, on average, four episodes of OM per year. OM may occur during an upper respiratory infection or occur spontaneously because of poor Eustachian tube function or an inflammatory response following a previous OM, most often between the ages of 6 months and 4 years [13, 14]. In the first year of life, 50% of children will experience OM, increasing to 60% by age 2. When primary school children aged 5–6 years were screened for OM, about 1 in 8 was found to have fluid in one or both ears [15] Figure 1a–d.
\n(a–d) Otitis media with effusion (OME). Personal collection.
Most episodes of OM resolve spontaneously within 3 months, but about 30–40% of children have repeated OM episodes and 5–10% of episodes last 1 year [13]. At least 25% of OM episodes persist for 3 months and may be associated with hearing loss which is usually noticed by parents or teachers as inattention, needing to ask several times, disinterest, and poor school achievement.
\nOM impairs sound transmission to the inner ear by reducing mobility of the tympanic membrane and ossicles, thereby reflecting acoustic energy back into the ear canal instead of allowing it to pass freely to the cochlea.
\nDiagnosis is performed by otoscopy and confirmed by a basic audiological evaluation. Under otoscopy, a retracted, opaque tympanic membrane with reduced mobility is seen. In the vast majority of cases, a yellowish liquid line, sometimes with air bubbles, is visible through the tympanic membrane. In the audiological evaluation, the result can range from normal hearing to moderate conductive hearing loss (HL of 0–55 dB) [16]. The mean hearing loss associated with OM in children is 28 dB, while a lesser proportion (~20%) exceeds 35 dB, with a type B tympanometric curve characteristic of effusion. Auditory losses are characterized by being fluctuating, temporary, and asymmetrical [17]. The mild degree of loss is sufficient to impair certain auditory functions, and the fluctuating nature (which may change to periods of normal hearing) leads to variable stimulation of the central auditory nervous system. The effect is to make it difficult to perceive sounds, and leads to diffuse cognitive and linguistic abilities affecting both speech and the perception of phonemes; school performance also suffers [18]. In addition, the fluid in the middle ear can cause noise near the cochlea, producing a distorted perception of sounds.
\nDepending on the clinical history and functional conditions of the child’s middle ear, treatment involves either clinical or surgical management. In small children with OME, the most common surgical procedure is tympanotomy with ventilation tube placement, which drains fluid from the middle ear and thus restores hearing. Diagnosis and treatment is essential, since in an acute episode of OM fluids can remain in the middle ear for 3–12 months; in 10–30% of children, the fluid remains for 2–3 months. Thus, a child who has had three to four OME episodes before the age of three can have had 12 months of conductive hearing loss, which is a third of the period considered critical for development and learning [19]. The periods of auditory deprivation during the active periods of OME over the first years of life can delay the maturation of the structures in the CANS and consequently impair auditory abilities associated with central auditory processing.
\nTherefore, evaluation of auditory processing is fundamental in children with a history of otitis media in order to allow diagnosis, intervention, and guidance.
\nTo evaluate central auditory processing in children with a history of OM, it is recommended that a battery of test procedures be used by which the mechanisms and auditory abilities involved in the analysis and interpretation of sounds can be investigated. Due to the complexity of CANS, no single test is sufficient to explore its nature [3, 4]. Since the 1950s, numerous tests have been developed to evaluate central hearing function. These tests differ in that each presents different types of stimuli (verbal or nonverbal) and involves presentation to one or both ears (monaural or binaural). Each test is designed to evaluate a particular auditory mechanism or auditory ability and consequently probes different areas and functions of the CANS. Below the tests are divided into categories according to the way in which the stimuli are presented to the ears, the nature of the auditory tasks involved, and the method or approach used. Other currently accepted classifications involve categorizing them as binaural interaction tests, dichotic tests with verbal and non-verbal sounds (binaural integration and separation), monaural tests using low redundancy stimuli, time processing tests, and electroacoustic and electrophysiological procedures [20].
\nA comprehensive assessment allows for correct quantification and qualification of the various CANS mechanisms and dysfunctions and provides important information for planning and managing treatment.
\nResearch by Colella-Santos et al. [11] involved 50 children (28 boys, 22 girls, mean age 11.2 years) with a documented history of bilateral SOM in the first 6 years of life and who had bilateral tympanostomy tube insertion (experimental group, EG); a control group (CG) consisted of 40 children (17 boys, 23 girls, mean age 10.7 years) with no history of otitis media. All children had auditory thresholds within normal limits on the day of evaluation and had a type A tympanometric curve. They were all evaluated with the tests described below [21, 22, 23]. The tests were the dichotic digits test, synthetic sentence identification test, gaps-in-noise test, and frequency pattern test. Details are as follows Figure 2a and b.
\n(a–b) Tympanostomy tube insertion. Personal collection.
The DD test as developed in Brazil consists of four presentations of a list of two-syllable digits in Brazilian Portuguese, in which four different digits are presented simultaneously, two in each ear. The list contains 40 randomly arranged pairs of digits presented at 50 dB HL. The digits used to form the numbers are four, five, seven, eight, and nine. The participants are instructed to listen to two numbers in each ear and repeat all the numbers they hear. The order does not matter. The dichotic digits test verifies binaural integration ability [21].
\nThe SSI test consists of the presentation of 10 Brazilian Portuguese sentences at 40 dB HL, in the presence of a competing children’s story in the same ear at a signal-to-noise ratio of 0, −10, or −15 dB. The task of the subject is to listen to the sentence and point to it in a frame. The ability analyzed in this test is figure-ground discrimination [21].
\nThe FPT test is composed of three 150 ms tones presented at 50 dB HL and separated by 200 ms. The tones in each triplet are combinations of two sinusoids, 880 and 1122 Hz, which are designated as low frequency (L) and high frequency (H), respectively. Thus, there are six possible combinations of the three-tone sequence (LLH, LHL, LHH, HLH, HLL, and HHL). The subjects are instructed that they will hear sets of three consecutive tones that vary in pitch. Their task is to repeat the pattern by humming and verbalizing the frequency pattern (e.g., high–low–high). The FPT test checks temporal ordering ability [22].
\nThe GIN test consists of a series of 6-second segments of broad-band noise presented at 50 dB HL with 0–3 gaps embedded within each segment. The gaps vary in duration from 2 to 20 ms. The gap-detection threshold is defined as the shortest duration that is correctly identified at least four out of six times. The participants are instructed to indicate each time they perceive a gap. The GIN test measures temporal resolution ability [23].
\nTo establish a difference between the right and left ears of subjects in the EG, it was necessary that there was a statistically significant difference in both the Dichotic Digits (p = 0.001) and GIN (p = 0.004) tests. No significant difference was found for gender in the behavioral tests. It was observed that the EG had lower mean responses than the CG for the DD test of approximately 5% in both ears; for the FPT 9.6% (humming) and 30% (naming); and 8% for the SSI test. For the GIN test, there was a statistically significant difference in the gap-detection threshold between the groups, with the highest threshold obtained in the EG compared to the CG (the higher the threshold, the worse the performance).
\nIn summary, there was a negative effect of OM on the auditory skills of figure-background discrimination, resolution, and temporal ordering. The poorer results in CAP behavioral tests in the EG participants can be explained by the fact that OM, by generating a fluctuating auditory threshold and causing temporary auditory deprivation, hampers the maturation of auditory abilities (such as binaural integration, resolution, temporal ordering, and discrimination) which are fundamental for understanding speech. During this period of auditory deprivation due to episodic OM, the CANS received inconsistent and incomplete auditory information. That is, the period between clinical assessment and the decision to perform surgery may have been too long Table 1.
\nProcedure | \nEar | \nControl group | \nExperimental group | \n\n | ||||
---|---|---|---|---|---|---|---|---|
N | \n∑ (%) | \nSD | \nN | \n∑ (%) | \nSD | \np-value | \n||
DD | \nR | \n40 | \n98.93 | \n1.86 | \n50 | \n95.40 | \n5.16 | \n<0.001 | \n
L | \n40 | \n97.93 | \n4.15 | \n50 | \n92.55 | \n7.95 | \n<0.001 | \n|
FPT | \n\n | \n | \n | \n | \n | \n | \n | \n |
Humming | \nB | \n80* | \n73.50 | \n21.2 | \n100* | \n42.7 | \n22.2 | \n<0.001 | \n
Verbalizing | \nB | \n80* | \n73.50 | \n21.2 | \n100* | \n42.7 | \n22.2 | \n<0.001 | \n
SSI | \nB | \n80* | \n67.5 | \n13.9 | \n100* | \n59.8 | \n16.9 | \n0.020 | \n
GIN | \nR | \n40 | \n4.65 | \n1.00 | \n50 | \n6.22 | \n1.40 | \n<0.001 | \n
L | \n40 | \n4.72 | \n1.06 | \n50 | \n6.56 | \n1.52 | \n<0.001 | \n
Behavioral evaluation values of central auditory processing between control and experimental groups.
n = number, * = number of ears, B = both, R = right, L = left; ∑ = mean, SD = standard deviation, DD = dichotic digits, SSI = synthetic sentence identification, FPT = frequency pattern test, GIN = gaps-in-noise.
Recent research has demonstrated associations similar to those found in the present study. Borges et al. [11] studied the effect of OM in 69 children of different socioeconomic levels who underwent surgical intervention (insertion of ventilation tubes) and observed worse performance in both the DD and GIN tests. The authors concluded that a history of OM can lead to changes in central auditory functioning, regardless of socioeconomic status.
\nKhavarghazalani et al. [24] evaluated 12 children with a history of OM who had undergone surgical intervention for insertion of ventilation tubes and found worse performance in the DD and GIN responses than in normals.
\nGravel and Wallace [25] also found a significant increase in signal-to-noise ratio in a prospective study of children with a history of OM. There was worse performance on the SSI test (responsible for the figure-ground ability) in the OM group.
\nTomlin and Rance [26] recommend that children with a history of OM undergo an evaluation of spatial processing upon entering school. They studied 35 children with a history of chronic OM and found a statistically worse performance compared to the control group in the listening in spatialized noise-sentences test (LISN-S). They concluded that these children have altered spatial processing, difficulty in focusing attention on the relevant stimulus, and difficulty in simultaneously suppressing competing stimuli coming from other directions. It is hypothesized that fluctuating access to binaural cues, caused by OM, may negatively affect the development of spatial processing in the CANS.
\nAuditory evoked potentials are an extremely useful instrument for the study of auditory perception and its disorders, especially when a range of stimuli are used [27].
\nIn the literature, there are contradictory results in Click ABR responses in individuals with a history of OM. Chambers et al. [28] and Folsom et al. [29] identified an increase in the latency of waves III and V in a group of children with a history of OM, whereas Shaffer [30] did not find a statistically significant difference in Click ABR responses in individuals with and without a history of OM. The majority of studies relating Click ABR results with OM history have investigated latency values; however, Maruthy and Mannarukrishnaiah [31] found a reduction in the amplitude of waves I and III. Sanfins et al. [32] observed statistically significant differences in the absolute latencies of waves I and V as well as in the amplitude of waves III and V from children with a history of bilateral OME compared to their healthy peers. Colella-Santos et al. [11] reported a significant increase in the absolute latency of wave III associated with a decrease in amplitude in children with bilateral OME. Finally, Sanfins [33] reported alterations in the values of waves III and V for both groups of children with a history of OME, seeing both bilateral and unilateral alterations Figure 3.
\nClick ABR. Personal collection.
In animals, the effect of conductive hearing loss on CANS was studied by unilaterally removing the malleus and applying a fluid to simulate OM [34], finding a decrease in neuronal activity due to changes in various structures (wave III), upper olivary complex (wave IV), and lateral lemniscus (wave V). At the same time, based on the results of Maruthy and Mannarukrishnaiah [31], it has been suggested that the auditory nerve and cochlear nuclei are more susceptible to modifications after OM infection.
\nSanfins et al. [32] suggest that different modifications may occur in CANS structures depending on the unilaterality or bilaterality of the infection. In episodes of bilateral OME, the latency values indicated that the auditory nerve (wave I, wave III) and the lateral lemniscus (wave V) were affected, whereas in unilateral OME, the cochlear nuclei (wave III) was affected. However, when the amplitudes were analyzed, the structures involved were the cochlear nuclei (wave III) and the lateral lemniscus (wave V), both for children with unilateral and bilateral involvement. It should be noted that when evaluating click ABR, the amplitude values show greater variability than the latencies. It is important to emphasize that a unilateral OM may not provide a better performance in the processing of auditory information than bilateral OM. The use of only one ear can lead to damage to the functionality of the CANS and, over time, activities that depend on binaural auditory processing (binaural interaction and binaural integration, among others) can be compromised due to the auditory imbalance arising from OM.
\nFew studies have investigated the frequency following response (FFR) in cases of otitis media. A study of two groups of children with a history of bilateral OM (recent onset and long-term) showed that FFR responses were affected in a statistically significant way in the onset portions (waves V and A) and offset portion (wave O), along with reduced values of the VA complex (more specifically VA slope) when responses between the groups were compared. The findings suggest that long-term OM in children is associated with a reduced neural conduction velocity relative to the processing time of speech stimuli, either at the beginning (onset) or final portion (offset), resulting in a decrease in the coding of speech in the brainstem [35] Figure 4.
\nFFR. Personal collection.
Sanfins et al. [32] reported that children with a history of SOM present an increase in the absolute latency of all FFR waves compared to children with no history of otological problems. In addition, children without hearing loss have more coherent responses in both ears, whereas the group of children with a history of OME has a greater dispersion of latencies in all FFR components (Figure 5). Colella-Santos et al. [11] also reported a decrease in VA slope in girls with OME.
\nComparison (left vs. right ear) of absolute latency values of FFR components in children with a history of otitis media (right panel) and children with no history of otitis media (left panel), from Sanfins et al. [32].
The literature reports alterations in the components of the LLAEP in children with language disorders and also in those with phonological disorders [36] changes that are frequently associated with problems arising from OM. Researchers note that OM can lead to changes in central auditory pathways [30, 37, 38]. However, there are few studies that have associated the LLAEP responses in children with a history of OM Figure 6.
\nLLAEP. Personal collection.
Maruthy and Mannarukrishnaiah [31], Shaffer [30], Sanfins [33], and Colella-Santos [11] reported similar results, i.e., the presence of LLAEP changes in children with a history of OME. In the studies by Maruthy and Mannarukrishnaiah [32], all components of the LLAEP (P1, N1, P2, and N2) were significantly longer in children with an SOM history. Shaffer [31] showed an increase in the latencies of N1 and P2, associated with the absence of the P300, in the majority of children evaluated. Sanfins [34] found prolongation of latencies only for P2 and N2 (for females), in comparison with the responses of children without otological alterations. Colella-Santos [11] observed an increase in P2, N2, and P300 latencies in children with a history of OME.
\nThe LLAEP with verbal stimuli provides additional information about the biological processes involved in speech processing, enabling the collection of information complementary to those obtained by standard behavioral evaluations [30, 39, 40].
\nIn the studies of Sanfins [33], children with bilateral OME presented prolonged latencies for N1, P2, N2 (female), and P300, in comparison with responses of children without auditory changes. Children with unilateral OME had prolonged latencies for P2 and P300 in comparison to the responses from healthy children.
\nThe evaluation of the LLAEP using both nonverbal and verbal stimuli seems to be able to identify neurophysiological changes resulting from OM. However, it is important to note that, in unilateral OM episodes, only verbal sound stimuli (speech LLAEP) seem to be able to differentiate groups on the basis of latency. OM impairs speech perception as a result of a failure to recognize sound signals (discrimination, storage, memory). Therefore, the more accurate identification of LLAEP changes with verbal and non-verbal stimuli may relate to underlying OM.
\nIt is known that hearing loss due to OM during childhood development may result in long-term changes in neural function, structure, and connectivity. The changes are associated with a series of sensory, cognitive, and social difficulties suggestive of impaired brain function [41, 42] which may culminate in central auditory processing disorder (CAPD) [11].
\nIntervention for CAPD should be initiated as soon as the diagnosis, made through a series of behavioral and electrophysiological procedures, demonstrates the involvement of the CANS. Early identification, followed by intensive intervention, makes best use of the brain’s inherent plasticity. Successful treatment outcomes depend on stimulation and repeated practice that induce cortical reorganization (and possibly reorganization of the brainstem), which is reflected in behavioral change [43, 44, 45].
\nNeuroplasticity is the key to the effectiveness of repeated auditory stimulation. Through experience and stimulation it induces reorganization of the cortex and brainstem, improving synaptic efficiency and neural density, giving rise to associated cognitive and behavioral changes [46, 47, 48]. The ability of the CANS to adapt to internal and external changes has important implications for learning [49].
\nAuditory training (AT) is defined as a set of (acoustic) conditions and/or tasks designed to activate the auditory system and related structures in such a way that their underlying neural processes and associated auditory behavior is altered in a positive way [8]. Both formal and informal AT procedures are conducted by audiologists in clinics; the difference between them is that formal training is acoustically controlled, meaning control over stimulus generation and presentation. Combined formal and informal AT offers an approach that provides more intensive practice and leads to better treatment efficacy [8]. AT performed in an individual with CAPD should include activities that aim to improve auditory skills such as sound localization and lateralization tasks, auditory discrimination, auditory pattern recognition, temporal aspects of audition, and auditory discrimination among competing acoustic signals [4].
\nDonadon and colleagues [50] have studied the efficacy of AT through behavioral CAP tests in children with a history of OM who had undergone bilateral tympanotomy for insertion of ventilation tubes. The sample consisted of 34 subjects who were divided into two groups: an auditory training group (ATG) formed by 20 children and adolescents, aged 8–13 years, diagnosed with CAPD, who were given an auditory training program; and a visual training group (VTG) formed of 14 children and adolescents, aged 9–13 years, diagnosed with CAPD who were given a visual training program. All subjects underwent peripheral auditory evaluation and behavioral evaluation of their CAP (using the dichotic digit test, sentence identification test with ipsilateral competing message, gaps-in-noise test, frequency pattern test, and dichotic vowel test). Auditory training was given through repeated verbal and non-verbal stimuli and associated tasks (available at the website
binaural integration-through dichotic listening exercises;
temporal resolution-by means of minimum time interval perception exercises;
temporal ordering-using nonverbal tasks related to frequency, intensity, and duration; and
figure-background exercises with competing noise.
The visual stimulation protocol was elaborated using varied stimuli and tasks from the website via a 15″ notebook positioned in front of the subject on a table arranged in a sound booth. The stimulation protocol was designed with the purpose of stimulating the visual abilities of:
visual background;
visual closure;
perception and discrimination of sizes and formats; and
visual memory.
All subjects were reevaluated after 8 weeks with the same battery of behavioral tests as performed at the initial evaluation. In the ATG the results showed a statistically significant difference in the abilities of binaural integration (p = 0.001), temporal ordering (p < 0.0001), temporal resolution (p < 0.0001), and bottom figure (<0.0001) in a comparison of before and after AT. These results suggest that the auditory stimulation performed during AT induced changes in the central auditory nervous system, as demonstrated by the better values recorded in the behavioral tests after intervention. Behavioral changes observed after AT in this population with a history of OM point to evidence of neuroplasticity, since auditory stimulation brought about improvements to the identified impaired hearing abilities.
\nFor the visual training group, however, there was no significant difference in performance for any CAP behavioral tests when comparing pre and post interventions. Thus, auditory training appears to be effective as an intervention strategy for re-adjusting the auditory skills in subjects with a history of OM. Auditory stimulation brought about improvements in impaired hearing skills. AT was able to reorganize the neural substrate, providing appropriate experiences, shaping existing circuits in the CANS, and increasing neural density, reflected by an improvement in the behavioral evaluation Figure 7.
\nComparison of performance in behavioral evaluation pre and post intervention by groups. ATG Pre = auditory training pre intervention; ATG Post = auditory training post intervention; VTG Pre = visual training pre intervention; VTG Post = visual training post intervention; DD = dichotic digits; FPT V = frequency pattern test verbalizing; FPT H = frequency pattern test humming; SSI = synthetic sentence identification.
Modifications to a child’s environment are also important aspects for teachers and parents to address in order to help individuals with CAPD improve access to auditory information outside the therapy room. Some simple changes may bring many benefits to learning. Common recommendations for individuals with auditory disorders include the following:
Preferred seating arrangements
Addition of visual cues
Clear language
Making frequent checks for understanding
Repetition or rephrasing
Multimodality cues and hands-on demonstrations
Preteaching of new information and new vocabulary
Provision of a notetaker
Recording information pictorially
Gaining attention prior to speaking
Positive reinforcement
Reducing environmental noise
FM systems
\n
The negative effects of otitis media on the development of auditory abilities in children and the maturation of their central auditory pathways is undeniable;
Early medical intervention in OM and family counseling is extremely important;
The aim should be to avoid prolonged auditory fluctuation caused by OM, thereby minimizing the effects generated by fluid in the middle ear in the development of auditory abilities;
The overall recommendation is that audiological diagnosis should include both behavioral evaluations and electrophysiological testing of auditory processing;
In cases of auditory processing disorder, research shows that auditory training is the most effective procedure to re-adjust auditory skills.
Leprosy is a chronic infectious granulomatous disease caused by the obligate intracellular bacillus Mycobacterium leprae (M. Leprae). Dermatoneurological signs and symptoms, such as skin and peripheral nerve lesions, are common manifestations of the disease and occasionally, it may affect respiratory tract, eyes, lymph nodes, nasal structures, testicles and internal organs [1, 2].
Leprosy is an important endemic disease, considered as a serious public health and social problem worldwide, as it leads to neural impairment or physical disability. Thus, special attention is needed, due to the consequences in the socioeconomic life of the patients or even their possible sequels in those who are cured. Worldwide, leprosy cases spread across more than 140 countries, with 22 countries accounting for 95% of global leprosy. These countries such as India, Brazil, Indonesia, Democratic Republic of Congo, Ethiopia, Nepal, Bangladesh and others have a high detection rate [3].
Bacillus has a high infectivity and low pathogenicity, that is, it infects many people, but only few become ill [1]. Leprosy is influenced by host genetic and mycobacterial factors, and environmental factors such as nutritional status and rate of exposure to bacillus. The immune response is of fundamental importance for the body’s defense against exposure to the bacillus, but in some individuals, leprosy can lead to changes in the immune response and to the development of distinct clinical forms. Among those who fall ill, the degree of immunity varies by determining the clinical form and course of the disease [4].
The immune response to the M. leprae is a task of the T lymphocytes responsible for adaptive immunity. CD4+ T lymphocytes can be divided into two subpopulations, which exert different functions in the defense of the organism mainly against intracellular bacterial infections, such as leprosy. These lymphocytes have the ability to induce the cellular or humoral immune response that is related to the types of secreted cytokines and the development of Th1 or Th2 responses [5, 6].
The predominance of cellular or humoral immune response may influence the evolution of the leprosy and the clinical characteristics observed in the tuberculoid (TT) and lepromatous (LL) clinical forms. The patients with the TT form have a strong cellular immune response, with a predominance of Th1 cells, activation of macrophages and Th1 cytokines secretion, such as interleukin (IL)-2, IL-6, IL-12, IL-15, IL-18, tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ), limiting the disease to few localized lesions of the skin and peripheral nerves. Patients with the LL form present a humoral response and lack of cellular response, with a predominance of CD8+ suppressor T cells and Th2 standard cytokines, such as IL-4, IL-5, IL-10 and IL-13, which inhibit the activation of macrophages. Here there is a proliferation of bacillus and presence of many lesions in the skin and peripheral nerves [5–7].
The disease can be classified into three forms: (i) Madrid (1953) classification, based on clinical and bacteriological criteria [8]; (ii) Classification of Ridley and Jopling (1966) that emphasizes clinical, bacteriological, immunological and histological aspects [9] and (iii) World Health Organization (WHO) (1982) operational classification with therapeutic purpose, based on the bacilloscopic index, which is related to the clinical forms [10]. In 1988, this operational classification was updated and clinical criteria were also established, considering paucibacillary (PB) patients such as those with less than five cutaneous lesions and/or one affected nerve trunk and multibacillary (MB) such as those with more than six lesions and/or more than one affected nerve trunk. It is still considered MB when the bacilloscopy is positive, regardless the number of lesions [11]. The classifications adopted for clinical forms of leprosy such as Madrid, Ridley and Jopling and WHO are summarized and listed in Table 1.
WHO | Paucibacillary (PB) | Multibacillary (MB) | ||
---|---|---|---|---|
MADRID | Indetermined (I) | Tuberculoid (T) | Dimorph (D) | Virchowian (V) |
Ridley and Jopling | TT | BT BB BL | LL |
Correlation between the classifications of Madrid [8], Ridley and Jopling [9] and WHO [10, 11] adopted for leprosy.
TT: tuberculoid-tuberculoid, BT: borderline tuberculoid, although presenting characteristics of the paucibacillar form, it has been operationally classified as multibacillary, BB: borderline borderline, BL: borderline lepromatous,LL: lepromatous-lepromatous.
At present, it is known that there are several factors influencing the control and appearance of the disease, such as immune response, time of exposure to bacillus, virulence of the pathogen, environmental factors, genetic variation of the bacillus and, mainly, the immunogenetic variability of the host leading to susceptibility or resistance to leprosy per se [12–17], clinical forms [18–20] of the disease and leprosy reactions [21, 22] (Figure 1).
Schematic representation of the clinical spectrum of leprosy suggesting the participation of different genes (HLA, MICA and KIR) in the control of the pathogenesis of the disease. Susceptibility or resistance to leprosy per se, clinical forms and leprosy reactions were showed. After exposure, most individuals are resistant to leprosy. Susceptible individuals may present the infection per se or develop one of the clinical forms and reactional types of leprosy, which are dependent on the host’s immune response pattern. MB: multibacillary, BB: borderline borderline, BL: borderline lepromatous, BT: borderline tuberculoid, LL: lepromatous leprosy, TT: tuberculoid leprosy; per se: leprosy independent of specific clinical manifestation. RR: type 1 or reversal reaction. ENL: type 2 reaction or erythema nodosum leprosum.
The selection of candidate genes in disease pathogenesis is usually based on two criteria: functional genes with a critical role in the pathogenesis of the disease and the location in the genomic region that may be involved in disease control; and yet a combination of the both. These genes are generally those that participate in the immune response in leprosy, such as cytokine genes, HLA (human leukocyte antigen) genes, MICA (major histocompatibility complex class I chain-related protein A) and KIR (killer cell immunoglobulin-like genes receptors), among others.
The two types of studies with molecular genetic markers are those of binding and association. The binding studies are related to the genetic mapping that allows the tracking of chromosomal regions linked to the disease. Gene-susceptibility/disease resistance studies are based on the comparison of the allelic frequencies of a genetic marker in populations (affected and unaffected individuals) [23].
Recently, a new approach to identify genes involved in human diseases is being carried out; it is the so-called genome-wide association study (GWAS). This is an association study of the entire genome in which many single nucleotide polymorphisms (SNPs) are tested in healthy controls and patients, allowing the analysis of hundreds or thousands of these polymorphisms at the same time. Genetic markers are considered to be associated with disease phenotypes when there is a significant difference in the frequencies observed between these two groups [24]. These works with genetic markers are performed aiming to contribute to the early diagnosis, prognosis, understanding of pathophysiology and improvement in the treatment of the disease.
Thus, the proposal of this chapter is to evidence the participation of some innate immune response genes, specifically, HLA, MIC and KIR genes, on overall leprosy and on evolution to the various clinic forms of disease.
The major histocompatibility complex (MHC) is composed of several genes, some of which are capable of encoding molecules that will display antigenic peptides on the cell surface for recognition by T cells. Other genes encode heat shock proteins, some cytokines and complement factors and approximately 40% of them have some function in the immune system [25, 26].
In relation to antigen presentation on the cell surface, the antigenic peptides originate from several sources, such as intracellular bacteria and viruses, products of cellular metabolism or proteins and lipids own or foreign to the organism [26].
In humans, a MHC sub region, called human leukocyte antigen (HLA), is located on the short arm of chromosome 6 and gives rise to HLA class I and II molecules. The HLA is polymorphic and each locus has many alleles contributing to human diversity as well as meeting the need for presentation of a wide range of antigens. The set of HLA alleles present on each chromosome is called haplotype, so all heterozygous individuals have two codominant HLA haplotypes [25, 27].
Understanding the mechanism of the presentation of antigens is of great importance for immunology, since it is able to explain events such as transplant rejection, autoimmune diseases, tumor immunity and response to infection, such as leprosy [28].
Each HLA molecule consists of a peptide-binding cleft, immunoglobulin (Ig)-like domains and transmembrane and cytoplasmic domains. Class I HLA has the α-chain encoded by MHC genes and the β2-microglobulin chain encoded by a non-MHC region. Class II HLA has both the α- and β-chain encoded in the MHC (Figure 2). The cleavage site is the site where the peptides are established during their presentation to the T lymphocytes. In addition, cleft are the polymorphic residues, that the amino acids responsible for differentiating the HLA from each other, as well as making the presentations more antigenic specific. The Ig domains are non-polymorphic and are responsible for binding between HLA and T cell: class I HLA molecules bind to CD8+ T cells and HLA class II molecules bind to the helper T cells CD4+ T cells [29, 30].
Structure of the class I and II MHC molecules.
The convention for the use of a four-digit code to name HLA alleles and distinguish them from the nomenclature given to coded proteins was introduced by the Nomenclature Report 1987. Currently, an allele name can be composed of four, six or eight digits, depending on its sequence. The first two digits describe the allele family. The third and fourth digits refer to the way in which DNA sequences were discovered.
Alleles that are different in the initial four digits have differences in nucleotide substitutions, which alter in protein coding. The fifth and sixth digits are used to distinguish alleles that differ by the synonymous substitutions of nucleotides in the coded sequence. The seventh and eighth digits are used when the alleles differ by sequence polymorphisms in introns or in 5′ and 3′ untranslated regions.
Each HLA allele name has a unique number, corresponding to up to four sets of digits, separated by a colon. The first two sets of digits are assigned to all alleles and the other two only for longer names and when needed (Figure 3) [31].
Schematic example of the meanings for each code in the HLA nomenclature [31].
There are three classical loci belonging to MHC class I: HLA-A, HLA-B and HLA-C. They encode molecules that have the same name as their respective genes. HLA class I molecules are expressed in all nucleated cells and platelets, as these molecules present the antigenic peptides for CD8+ T lymphocytes, which kill infected cells or with tumor antigens. The HLA-E, HLA-F and HLA-G loci also belong to HLA class I, but are considered non-classical (Figure 3) [32]. They are expressed at low levels when compared to classical HLA class I as well as do not have as many polymorphisms, and their functions in the immune system are limited [29, 30].
HLA class II molecules are expressed in dendritic cells, B lymphocytes, macrophages and other cell types, and present the antigenic peptides to the virulent CD4+ helper T lymphocytes, which recognize the antigens in the secondary lymphoid organs. Differentiated CD4+ helper T cells activate other cells, together with B lymphocytes, so that the extracellular microorganisms are eliminated. The three HLA class II loci are called HLA-DP, HLA-DQ and HLA-DR. The two chains of each molecule of class II are encoded by two different MHC genes. Thus, the extracellular parts of α and β chains are subdivided into two segments, A1 and A2, or B1 and B2, both of which are polymorphic chains, that is, each of the DP, DQ and DR loci contain separate genes designated as A or B, which encode α and β chains, respectively, in each copy of chromosome 6. Each individual has one HLA-DRA (DRA1), one to three DRB (DRB1 and DRB3, 4 and/or 5), one DQA (DQA1), one DQB (DQB1), one DPA (DPA1) and one DPB (DPB1) [25, 29, 30].
The human MHC class I chain-related genes (MICA and MICB) are located in the HLA class I region in chromosome 6, but are not part of the classical HLA (Figure 4). These genes show about 30% of homology to HLA class I, but the transcribed molecules do not present antigenic peptides on the cell surface. These genes are mainly transcribed into fibroblasts and epithelial cells. The MIC molecules bind to NKG2 receptors, activating NK cells and also modulate the function of CD8+ T cells. Studies have related associations of polymorphisms in MICA and MICB genes with several diseases (ankylosing spondylitis, psoriasis, dengue and tuberculosis) [32–36], one of them being leprosy, which will be discussed in a next topic in this chapter.
Schematic map of the human MHC gene [32].
The immune system has the complex task of responding to different types of pathogens that come in contact with the human organism. Adaptation that ensures antigen protection and increased immune system efficiency can occur through life-long genetic recombination, such as antibody formation, or the different HLA molecules in the population. HLA molecules are responsible for presenting a fraction of the antigenic peptide (epitope) for T cells; however, the choice to determine which epitope will be presented according to the HLA genes and their alleles in each individual. Thus, the regions responsible for the antigenic presentation in the HLA molecules present high polymorphism rates. This means that with the advancement of diagnostic methodologies, the discovery of allelic variations of HLA has increased exponentially (Figure 5) [27, 29].
Advances in the findings of allelic variations in HLA class I and II loci over the past 30 years. Class I HLA alleles are represented in green and class II HLA alleles in black [32].
The evolutionary success in the amplification of the HLA repertoire may explain why it is difficult to associate a specific HLA phenotype with the susceptibility or protection against a particular disease, since the change of a single amino acid in the sequence of the HLA molecule can affect the adaptive immune response of the individual [32]. Despite this difficulty, studies have shown associations among several HLA and autoimmune and infectious diseases [27, 29].
The role of HLA molecules in leprosy is to present epitopes of the bacillus to T lymphocytes. However, polymorphisms in HLA genes or incorrect presentation of the antigenic peptide may interfere or contribute to the success of the response of the host against the pathogen. In view of this, several studies have indicated genes associated with susceptibility or protection against leprosy in different populations (Tables 2 and 3).
Allele, haplotype | Population | Population size | Phenotype | Association |
---|---|---|---|---|
A*02:06 | Southern Indian | 32 leprosy patients and 67 healthy individuals | Leprosy per se | Susceptibility [37] |
A*02:06 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | Leprosy per se | Susceptibility [14] |
A*11 | Brazilian | 225 leprosy patients and 450 healthy individuals | Leprosy per se | Susceptibility [38] |
A*11:02 | Southern Indian | 32 leprosy patients and 67 healthy individuals | Leprosy per se | Susceptibility [37] |
A*11:02 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | Leprosy per se | Susceptibility [14] |
B*15 | Brazilian | 202 leprosy patients and 478 healthy individuals | RR | Susceptibility [22] |
B*18:01 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | Leprosy per se | Susceptibility [14] |
B*18:01 | Southern Indian | 32 leprosy patients and 67 healthy individuals | Leprosy per se | Susceptibility [37] |
B*35 | Brazilian | 225 leprosy patients and 450 healthy individuals | LL | Protection [38] |
B*38 | Brazilian | 225 leprosy patients and 450 healthy individuals | Leprosy per se | Susceptibility [38] |
B*51:10 | Southern Indian | 32 leprosy patients and 67 healthy individuals | Leprosy per se | Susceptibility [37] |
B*51:10 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | MB | Susceptibility [14] |
C*04 | Brazilian | 225 leprosy patients and 450 healthy individuals | LL | Protection [38] |
C*04:07 | Southern Indian | 32 leprosy patients and 67 healthy individuals | Leprosy per se | Susceptibility [37] |
C*04:07 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | MB | Susceptibility [14] |
C*04:11 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | MB | Protection [14] |
C*04:11 | Southern Indian | 32 leprosy patients and 67 healthy individuals | Leprosy per se | Protection [37] |
C*05 | Brazilian | 202 leprosy patients and 478 healthy individuals | B | Protection [22] |
C*07 | Brazilian | 225 leprosy patients and 450 healthy individuals | Leprosy per se | Susceptibility [38] |
C*07:03 | Southern Indian | 32 leprosy patients and 67 healthy individuals | Leprosy per se | Susceptibility [37] |
C*07:03 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | MB | Susceptibility [14] |
C*12 | Brazilian | 225 leprosy patients and 450 healthy individuals | Leprosy per se | Susceptibility [38] |
C*16 | Brazilian | 225 leprosy patients and 450 healthy individuals | Leprosy per se | Protection [38] |
C*15:05 | Indian | 364 leprosy patients and 371 healthy individuals | Leprosy per se | Susceptibility [15] |
C*15:05 | Vietnamese | 198 families | Leprosy per se | Susceptibility [15] |
C*15:05 | Vietnamese | 292 families | Leprosy per se | Susceptibility [15] |
A*11-B*40 | Mumbai/Indian | 103 leprosy patients and 101 healthy individuals | ML | Susceptibility [14] |
Associations between HLA class I and leprosy.
MB: multibacillary, PB: paucibacillary; B: borderline leprosy, BB: borderline borderline, BL: borderline lepromatous, BT: borderline tuberculoide, LL: lepromatous leprosy; TT: tuberculoid leprosy, per se: Leprosy independent of specific clinical manifestations, ENL: type 2 reactions or erythema nodosum leprosum, RR: Type I or reversal reaction.
Allele, haplotype | Population | Population size | Phenotype | Association |
---|---|---|---|---|
DQA1*01:02 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DQA1*01:03 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DQA1*02:01 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DQA1*03 | Japanese | 93 leprosy patients and 114 healthy individuals | Leprosy per se | Protection [40] |
DQB1*02 | Brazilian | 202 leprosy patients and 478 healthy individuals | B | Protection [22] |
DQB1*02:01 | Brazilian | 76 families (1166 individuals) | TT | Protection [41] |
DQB1*02:01 | Brazilian | 76 families (1166 individuals) | Leprosy per se | Protection [41] |
DQB1*02:01 | Argentinean | 89 leprosy patients and 112 healthy individuals | LL | Protection [42] |
DQB1*02:02 | Argentinean | 89 leprosy patients and 112 healthy individuals | LL | Protection [42] |
DQB1*02:03 | Argentinean | 89 leprosy patients and 112 healthy individuals | LL | Protection [42] |
DQB1*04:01 | Japanese | 93 leprosy patients and 114 healthy individuals | Leprosy per se | Protection [40] |
DQB1*05:01 | Brazilian | 76 families (1166 individuals) | TT | Susceptibility [41] |
DQB1*05:01 | Brazilian | 76 families (1166 individuals) | Leprosy per se | Susceptibility [41] |
DQB1*05:03 | Indian | 93 leprosy patients and 47 healthy individuals | TT | Protection [39] |
DQB1*06:01 | Indian | 93 leprosy patients and 47 healthy individuals | TT | Susceptibility [39] |
DQB1*06:01 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DRB1*02 | Japanese | 79 leprosy patients and 50 healthy individuals | BL/LL | Susceptibility [43] |
DRB1*04 | Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Protection [44] |
DRB1*04 | Euro-Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Protection [44] |
DRB1*04 | Vietnam | 194 single-case families | Leprosy per se | Protection [44] |
DRB1*04 | Argentinean | 89 leprosy patients and 112 healthy individuals | TT | Protection [42] |
DRB1*04:05 | Japanese | 93 leprosy patients and 114 healthy individuals | Leprosy per se | Protection [40] |
DRB1*04:05 | Taiwanese | 65 leprosy patients and 190 healthy individuals | MB | Protection [45] |
DRB1*07 | Brazilian | 76 families (1166 individuals) | Leprosy per se | Protection [41] |
DRB1*07 | Euro-Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Protection [44] |
DRB1*07 | Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Protection [44] |
DRB1*07 | Brazilian | 202 leprosy patients and 478 healthy individuals | B | Protection [22] |
DRB1*07:01 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DRB1*08 | Brazilian | 169 leprosy patients and 217 healthy individuals | LL | Susceptibility [46] |
DRB1*08:08 | Argentinean | 71 leprosy patients and 81 healthy individuals | Leprosy per se | Protection [47] |
DRB1*09 | Southern Indian | 230 leprosy-affected sib-pair | TT | Protection [48] |
DRB1*09 | Chinese | 305 leprosy patients and 527 healthy individuals | Leprosy per se | Protection [49] |
DRB1*10 | Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Susceptibility [44] |
DRB1*10 | Afro-Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Susceptibility [44] |
DRB1*10 | Vietnam | 194 single-case families | Leprosy per se | Susceptibility [44] |
DRB1*11 | Brazilian | 70 leprosy patients and 77 healthy individuals | LL | Protection [50] |
DRB1*11:03 | Argentinean | 71 leprosy patients and 81 healthy individuals | Leprosy per se | Protection [47] |
DRB1*12 | Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Protection [44] |
DRB1*12 | Japanese | 79 leprosy patients and 50 healthy individuals | Leprosy per se | Protection [43] |
DRB1*14 | Brazilian | 85 leprosy patients and 85 healthy individuals | TT | Susceptibility [20] |
DRB1*14:01 | Argentinean | 71 leprosy patients and 81 healthy individuals | Leprosy per se | Susceptibility [47] |
DRB1*14:06 | Argentinean | 71 leprosy patients and 81 healthy individuals | Leprosy per se | Susceptibility [47] |
DRB1*15 | Afro-Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Susceptibility [44] |
DRB1*15 | Brazilian | 578 leprosy patients and 691 healthy individuals | Leprosy per se | Susceptibility [44] |
DRB1*15 | Chinese | 305 leprosy patients and 527 healthy individuals | Leprosy per se | Susceptibility [49] |
DRB1*15 | Indian | 93 leprosy patients and 47 healthy individuals | TT | Susceptibility [39] |
DRB1*15 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DRB1*15 | Indian | 54 leprosy patients and 44 healthy individuals | TT | Susceptibility [51] |
DRB1*15:01 | North Indian | 113 leprosy patients and 111 healthy individuals | BL/LL | Susceptibility [52] |
DRB1*15:01 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DRB1*15:02 | Southern Indian | 230 leprosy-affected sib-pair | TT | Susceptibility [48] |
DRB1*15:02 | Indian | 93 leprosy patients and 47 healthy individuals | TT | Susceptibility [39] |
DRB1*15:02 | Indian | 85 leprosy patients and 104 healthy individuals | TT | Susceptibility [53] |
DRB1*15:02 | Asian Indian | 27 leprosy patients and 19 healthy individuals | TT | Susceptibility [54] |
DRB1*16 | Brazilian | 85 leprosy patients and 85 healthy individuals | LL | Susceptibility [20] |
DRB1*16:01 | Brazilian | 169 leprosy patients and 217 healthy individuals | BL | Susceptibility [46] |
DRB5*01:01 | Indian | 93 leprosy patients and 47 healthy individuals | LL | Susceptibility [39] |
DRB1*15:01-DRB5*01:01-DQA1*01:02-DQB1*05:02 | Indian | 85 leprosy patients and 104 healthy individuals | TT | Protection [53] |
Associations between HLA class II and leprosy.
MB: multibacillary, PB: paucibacillary; B: borderline leprosy, BB: borderline borderline, BL: borderline lepromatous, BT: borderline tuberculoide, LL: lepromatous leprosy; TT: tuberculoid leprosy, per se: Leprosy independent of specific clinical manifestations, ENL: type 2 reactions or erythema nodosum leprosum, RR: type 1 or reversal reaction.
The findings of new immune response genes are occurring in order to clarify their possible participation in the occurrence or severity of a disease. Among them, we can highlight MIC (MHC class I chain-related genes) that were discovered during a search for new coding sequences, located near the HLA-B gene [55].
MIC constitutes a second lineage of non-classical MHC class I genes and correspond to the MICA, MICB, MICC, MICD, MICE, MICF and MICG loci (Figure 6). MICA genes are located on the short arm of chromosome 6 (6p21.3), about 46.5 kb from HLA-B toward the centromere. Only MICA and MICB are expressed in proteins that belong to the immunoglobulin superfamily (IgSF) [56–58].
Genes and pseudogenes of the MIC family on region class I of the human MHC. Functional genes are represented in blue and the pseudogenes in yellow.
Like classical HLA genes, MICA also shows a high polymorphism in humans, whereas MICB appears to be less polymorphic, although it has been little explored. Since the discovery and characterization of NKG2D as its corresponding receptor in NK cells and in subsets of T cells, these genes have received increasing attention in the context of organs and stem cell transplantation. MICA and MICB encode glycoproteins, which are stress induced and can be recognized by receptors such as NKG2D (C-type lectin-like activating immunoreceptor). They are capable of inducing immune responses involving Tγδ cells and NK cells, independently of the processing of conventional class I MHC antigens [57, 59, 60].
MICA molecules are codominantly expressed and are polypeptides of 383–389 amino acids with a size of 43 kDa in length [56, 57] and the MICB molecules are also polypeptides with a similarity of 83% amino acids with MICA. The structure of the MICA molecule is similar to HLA class I antigens, with three extracellular domains (α1, α2 and α3), a transmembrane domain and a cytoplasmic tail. MICA molecules have an extremely flexible rod connected to the platform formed by the α1/α2 domains and the α3 domain. Four α-helices are arranged under eight pleated β-strands forming a reduced slit that it would not be possible to attach a peptide composed of more than three or four amino acid residues (Figure 7) [61].
The structure of the MICA. Exon 2 encodes a leader peptide, exons 2–4 encode three extracellular domains, exon 5 a transmembrane domain and exon 6 a cytoplasmic tail [61].
In exon 5, there is a short tandem repeat sequence (STR) at position 304 consisting of GCT nucleotide breaks, which encode the amino acid alanine in the transmembrane region (TM). STR is absent in MICB. Based on the number of GCT, the alleles are named as A4, A5, A5.1, A6, A7, A8, A9 and A10. A5.1 differs from A5 by the insertion of a guanine nucleotide in the GCT (GGCT) [62], leading to a change in the reading matrix causing a terminus premature codon within the exon that encodes the transmembrane domain [33, 63, 64]. Thus, A5.1 is a 35–40 kDa truncated glycoprotein that eventually reaches the cell surface, but not at its physiological site. This is another characteristic of the MICA polymorphism: several alleles have identical extracellular domains but differ in the TM region. The identification of the polymorphism in the TM region is essential to avoid ambiguities [65].
The expression of the MICA gene was recognized in gastrointestinal and thymic epithelial cells in isolated endothelial cells, fibroblasts and keratinocytes. MICA molecules are ligands of the NKG2D receptors and Tγδ cell receptors (TCRγδ). The recognition of the MICA molecules by Tγδ Vδ1 cells through the interaction with the α1 and α2 domains was confirmed later in another study [66].
Tγδ cells constitute a small population of T cells expressing antigenic receptor proteins that resemble those of CD4+ and CD8+ T cells, but are not identical. Tγδ cells recognize many different types of antigens, including some proteins and lipids, as well as small phosphorylated molecules and alkyl amines. These antigens are not presented by MHC molecules [25]. It is not known whether there is a need for a particular cell type or distinct antigen presentation system for the presentation of antigens to these cells. MICA molecules are also recognized by their NKG2D receptors present on the surfaces of NK cells, associated with DAP10 molecule. This NKG2D-MICA complex activates phosphorylation of the tyrosine residues of the DAP10 molecule, triggering a cascade of cell signaling that enhances the cytotoxicity of NK cells. This complex also enhances the production of IFN-γ by NK cells, participating as a co-stimulator factor in the immune response against Mycobacterium [67].
Therefore, MICA is a stress-induced MHC class I molecule that binds to NKG2D receptors, primarily NK cells, stimulating NK cells, T CD8+ cells and some Tγδ cells [68]. Previous studies have suggested that HLA-B loci alleles were associated with some diseases caused by pathogens and, as there is strong linkage disequilibrium between the two genes due to the proximity of MICA, this could indirectly contribute to this response.
Some infectious and noninfectious diseases such Behçet’s disease, ankylosing spondylitis, Reiter’s syndrome, Kawasaki disease, psoriasis vulgaris and Chagas disease have been associated to MICA genes. These studies suggest that allelic variants of MICA may be directly related to NKG2D receptor binding of Tγδ and NK cells affecting the effects of cells activation [35, 69–74].
In the first study of association between the MICA gene and leprosy, the MICA*A5 allele was found associated with protection against MB form in Chinese patients [19]. In India, the MICA*5A5.1, MICB*CA16 and MICB*CA19 alleles were associated with susceptibility to leprosy per se and MICB*CA21 allele with protection [48]. Recently, in a study in Brazil, the MICA*010 and MICA*027 alleles were associated with protection against the MB form and MICA*027 was associated with protection to leprosy per se [16].
Natural killer (NK) cells make up about 10–15% of the lymphocytes in human peripheral blood, with an important participation on the innate immune response. In addition, they are sources of type I cytokines, IFN-γ, as well as TNF-α, granulocyte macrophage colony-stimulating factor (GM-CSF) and other cytokines and chemokines [75]. In their original lineage, repertoire of receptors and effector functions, the NK cells appear to be a transitional cell type, which would be a bridge between the innate and adaptive immune system. The name is derived from two aspects: (i) NK cells are able to mediate their effector function (lysis of target cells) spontaneously in the absence of prior sensitization and are then called “killer” and “natural” and (ii) another aspect is that they perform their function with a very limited repertoire of receptors encoded in progenitor lines that do not undergo somatic recombination. The absence of previous sensitization and the absence of gene rearrangement for the formation of receptors for target cells indicate that NK cells are part of the innate immune system [76]. The major surface markers associated with NK cells are CD16 and CD56, while the T cell receptor (TCR) is absent [77].
The function of NK cells is to remove abnormal cells from the host, as infected cells or tumor cells, by exocytosis of lytic proteins (perforin/granzyme pathway) and by FasL or TRAIL (factor-apoptosis inducing linker of tumor necrosis) expression. Chemokines secreted by NK cells, such as IFN-γ and TNF-α, can mediate cytotoxic effects, activate dendritic and T cells, and influence the individual’s immune response [78].
NK cells perform their task using two sets of receptors: activators and inhibitors present on their surface that interact with binding molecules on the surface of the target cell. The balance of these interactions determines whether or not the NK cell will be activated [9]. The major activation receptors expressed on NK cells include FcγRIIIA (CD16), DNAM-1 (CD226), NKG2C (KLRC2: killer cell lectin-like C2 receptor), NKG2E (KLRC3: killer cell lectin-like C3 receptor), NKG2D (KLRK1: killer cell lectin-like receptor K1), KIR-activating forms (KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5 and KIR3DS1), natural cytotoxicity receptors (NCRs) called NKp30 (natural cytotoxicity triggering receptor 3), NKp46 (NCR1: natural cytotoxicity triggering receptor 1), NKp65 (KLRF2: killer cell lectin-like F2 receptor) and NKp80 (KLRF1: killer cell lectin-like F1 receptor). The inhibitory receptors are KIR2DL1, KIR2DL2, KIR2DL3, KIR2DL5, KIR3DL1, KIR3DL2, KIR3DL3, NKG2A (KLRC1: killer cell lectin-like C1 receptor), LILRB1 (leukocyte immunoglobulin-like B1 receptor), KLRG1 (NKR2B4: natural killer cell receptor 2B4), NKp44 (NCR2: natural cytotoxicity triggering receptor 2) and KIR2DL4 (NKR2B4: natural killer cell receptor 2B4) [75].
KIRs are members of a group of regulatory molecules found on the surface of NK cells and T cell subpopulations. They were first identified for their ability to confer some specificity in cytolysis mediated by NK cells [79, 80]. This specificity occurs through the interaction of isotypes of KIR with HLA class I molecules, protecting unaltered cells from the destruction caused by NK cells. Different types of KIRs can be expressed on the surface of NK cells, which may be activators or inhibitors [79], with a combinatorial selection of receptors to be expressed by the cell.
Thus, in an individual, NK cells can randomly express a different set of activating and inhibitory receptors, and not all NK cells in an individual have the same receptors. This differential expression between NK cells and certain KIR/HLA interactions may contribute to heterogeneity in NK cell activation levels, observed both among different individuals and among distinct NK cell subpopulations of the same individual [81].
NK cells become responsible for tolerance when their inhibitory KIRs identify class I HLA surface molecules as self-antigens, and trigger inhibitory signaling through the tyrosine kinase phosphorylation of intracytoplasmic inhibition motifs based on tyrosine immunosorbent (ITIM) [82]. Even with the presence of activating receptors, the inhibitory signal is translated into tolerance, absence of cytotoxicity and cytokine production by NK cells when the target cell is normal. When the cell is infected with a virus or transformed into a tumor cell, this tolerance environment is altered, especially by the low or no expression of HLA class I molecules, which is known as part of the escape mechanism of tumor cells to the adaptive immunity [83].
NK cells are activated to produce cytotoxicity and cytokines, precisely due to the escape mechanism of altered ITIM cells; but alternatively there are positively charged transmembrane residues, which facilitate the physical association with DAP12 accessory proteins, releasing the activating signal via immunoreceptor tyrosine-based activation motifs (ITAM) [75].
The KIR genes are located on chromosome 19 (19q13.4) in a 1 Mb gene complex called the leukocyte receptor complex (LRC) which is shown in Figure 8. There are several gene families in the LRC region, among them leukocyte Ig-like receptors (LILRs); Ig-like transcripts (ILTs); killer cell Ig-like receptors (KIRs); platelet collagen receptor glycoprotein VI (GPVI); Fc IgA receptors, FcGammaR; natural cytotoxicity triggering receptor 1 (NRC1); leukocyte-associated Ig-like receptors (LAIRs); sialic acid-binding immunoglobulin-like lectins (SIGLECs); members of the CD66 family, such as the carcinoembryonic antigen (CEA) genes and the genes encoding the transmembrane adapter molecules DAP10 and DAP12 [84, 85].
Diagram showing the cluster genes of the extended leukocyte receptor complex located (LRC) on chromosome 19 with highlight to KIR A haplotype at position 19q13.4 (in red). Among the molecules encoded by the extended LRC set of genes are the DAP adaptor proteins, CD66 antigens, SIGLEC, FcGRT, LILR, LAIR, FcAlphaR and NCR1 receptors. Within the KIR A haplotype are the framework genes (blue boxes), pseudogenes (purple box), inhibitory KIR (red boxes) and activating KIR genes (green box). KIR2DL4 can be an inhibitory or an activating gene and KIR3DP1 is also considered as framework gene [86].
The KIR gene family has 15 genes (KIR2DL1, KIR2DL3, KIR2DL4, KIR2DL5A, KIR2DL5B, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2D5, KIR3DL1, KIR3DL2, KIR3DL3 and KIR3DS1) and 2 pseudogenes (KIR2DP1 and KIR3DP1). They are divided into two functional groups: inhibitors that prevent lysis of the target cell and the activators that cause lysis of the target cell. The inhibitory group has eight genes that are KIR2DL1, KIR2DL2, KIR2DL3, KIR2DL5A, KIR2DL5B, KIR3DL1, KIR3DL2 and KIR3DL3; the activator group has genes such as KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5 and KIR3DS1; while KIR2DL4 may be an activator or inhibitor. Between them, there are four KIR genes that are called structural (framework) genes, since they are present in almost all individuals: KIR3DL3, KIR3DP1, KIR2DL4 and KIR3DL2 [85, 86].
The naming of KIR genes is responsibility of the HUGO Genome Nomenclature Committee (HGNC) [87]. The designation of the KIR gene system considers the structure of the KIR protein. They are classified based on two characteristics: number of extracellular Ig domains (2D or 3D) and characteristics of the cytoplasmic tail of the KIR protein, being S for short tail and L for long tail [88]. KIR3D is formed by the domains D0, D1 and D2, while KIR2DL1, KIR2DL2, KIR2DL3 and all KIR2DS have the D1 and D2 (Type I) domains; and KIR2DL4 and KIR2DL5 have the domains D0 and D2 (Type II) [89]. The long cytoplasmic tail (L) is associated with ITIM motifs that release a signal of inhibition to the cell. This signal of inhibition is due to the phosphorylation of a tyrosine residue that promotes the recruitment of (SHP-1 and SHP-2), which promote the dephosphorylation of protein substrates of tyrosine kinases related to the activation of NK cells. On the other hand, short tail (S) activation receptors have ITAM motifs in their transmembrane domain that associate with the adapter molecule DAP-12. The interaction of these receptors with their ligands results in the recruitment of SyK and ZAP-70 tyrosine kinases by ITAMs, resulting in the reorganization of the cytoskeleton to release granules and also in the transcription of cytokine and chemokine genes [90]. The structural characteristics of KIR that define their nomenclature are represented in Figure 9.
Domain structure of the KIR molecules. The structural characteristics of two and three Ig-like domain KIR proteins are shown. The association of activating KIR to adaptor molecules is shown in green, whereas the ITIM of inhibitory KIR are shown as red boxes. KIR2DL4 contains signature sequences of both activating and inhibitory receptors [86].
The KIR pseudogenes are identified by the letter “P” just after the digit corresponding to the domain type, as in the pseudogenes: KIR2DP and KIR3DP.
KIR genes follow a basic organization structure with 4–9 exons. Exons 1 and 2 encode the protein leader sequence; exons 3, 4 and 5 encode extracellular domains (D0, D1 and D2, respectively); exon 6 encodes the tail, which lies between the extracellular domain and the membrane; exon 7, the transmembrane portion; and exons 8 and 9 encode the cytoplasmic tail [91].
The KIR genes in the LRC form haplotypes on the same chromosome passed in blocks from generation to generation. There are two groups of KIR haplotypes: A and B, differentiated mainly by the number of activator KIR genes [92].
The A haplotype has seven KIR genes, predominantly the genes that encode the inhibitor receptors, such as KIR2DL1, KIR2DL3, KIR2DL4, KIR3DL1, KIR3DL2 and KIR3DL3, with only one activator gene, KIR2DS4. The B haplotype has a greater diversity of genes: KIR2DL5, KIR2DL2, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS5 and KIR3DS1, with the activation signals predominating. A and B haplotypes have the frameworks genes [86, 93].
The KIR Nomenclature Committee considered that the distinction between A and B haplotypes is useful in biological and clinical terms, and thus developed a consistent and logical set of criteria to distinguish them. Therefore, a haplotype can, for example, be called KH-001A or KH-022B [86]. The haplotypic diversity of KIR genes varies in different populations, suggesting that there may be variable effects of the receptors on several diseases, offering protection against one particular pathology or predisposition to the other.
NK cells perform the recognition of foreign cells in the body through the interaction of KIRs on own cell surface with ligands on target cells surface: classical class I HLA-specific molecules (HLA-A, HLA-B and HLA-C) and non-classical (HLA-E and HLA-G) [94]. The activity of NK cells requires the interaction between a given class I HLA antigen expressed on the surface of the cells and a specific KIR, inhibitor or activator.
HLA-C molecules are the major ligands of KIR and can be distinguished in two groups of ligands (C1 and C2). All HLA-C carry a valine (V) at position 76 and a dimorphism in the position 80, which may be asparagine (N) or lysine (K). The alleles that have asparagine at position 80 are called C1 group (codifying by C*01, C*03, C*07, C*08, C*12, C*13, C*14, C*16:01, C*16:03 and C*16:04) and are the ligands of KIR2DL2/KIR2DL3 and KIR2DS2. On the other hand, the molecules that possess lysine at position 80 (K80) belong to the C2 group (codifying by C*02, C*04, C*05, C*06, C*15, C*16:02, C*17 and C*18 genes) and bind to KIR2DL1 and KIR2DS1 [95].
Some HLA-B molecules express Bw4 epitopes that are also present in some HLA-A molecules encoded by HLA-A*09, HLA-A*23, HLA-A*24, HLA-A*24:03, HLA-A*25 and HLA-A*32. The KIR3DL1 and the KIR3DS1 interact with HLA-Bw4, which differs from Bw6 due to a polymorphism at position 77 and 80. Bw4 molecules may have multiple amino acids at the position 77, either asparagine or aspartic acid or serine, and a dimorphism at the position 80, which may be isoleucine or threonine. The allotypes containing Bw4 with Isoleucine (Bw4-80I) generally exhibit strong inhibition, while Bw4 alleles with Threonine (Bw4-80 T), such as those encoded by HLA-B*13, HLA-B*27, HLA-B*37:01 and HLA-B*44, appear to be better ligands for certain KIR3DL1 subtypes. Other KIRs have less defined specificities, such as KIR3DL2, which recognizes HLA-A variants (A3 and A11), KIR2DL4 recognizing HLA-G and KIR2DS4 recognizing C*04. The ligands for KIR2DL5, KIR2DS3, KIR2DS5, KIR3DS1 and KIR3DL3 have not been identified to date [95, 96].
Although KIR activators exhibit a ligand recognition structure very similar to inhibitory receptors, as in the 2DL1/2DS1-C2 group pair and the triad of 2DL2/2DL3/2DS2-C1 group, the binding affinity of the activating variants is strongly reduced in comparison to the inhibitory variants. Therefore, when there are binding of inhibitory and activating receptors at the same time, it is believed that the inhibitory signal prevails [96].
It is known that the interaction of KIRs and their HLA ligands can result in activation or inhibition of NK cells and the occurrence of different immunological and clinical responses to various types of diseases, such as infectious diseases (AIDS, malaria, tuberculosis, Chagas disease, dengue fever and leprosy) [97–101], autoimmune and inflammatory diseases (psoriasis, rheumatoid vasculitis and Crohn’s disease) [102–104] in different populations and ethnicities.
The pioneering studies of KIR genes in leprosy were carried out in Brazil. The first study was performed in the southern region of Brazil, where the KIR2DL1 inhibitor gene with its C2 group ligand was shown to be protective for BB and its homozygous ligand (KIR2DL1-C2/C2) was associated with the clinical form TT. Another inhibitory gene and its ligands (KIR3DL2-A*03/A*11) were associated with susceptibility to borderline leprosy. The activating genes KIR2DS2 and KIR2DS3 were shown to be a risk factor for TT form, compared to the more widespread form LL. Thus, TT patients with both activating genes (KIR2DS2 and KIR2DS3) may develop better activation of NK cells and a competent cellular immune response with a more localized manifestation of the disease. The inhibitory KIR2DL3-C1 and KIR2DL3-C1/C1 were associated to protection against TT form, when compared to the control group and other clinical forms [105].
The second study of KIR genes with leprosy was performed in a hyperendemic region of Brazil, and the KIR2DL1 inhibitory gene was a protective factor for leprosy per se and its BB form. The frequency of the homozygous KIR2DL2 gene in the presence of the C1 group (KIR2DL2/KIR2DL2-C1) was higher in leprosy patients per se and in clinical forms TT and LL, when compared to the control group. The KIR2DL2/KIR2DL3 haplotype with its homozygous C1 ligand (C1/C1) was associated with protection for leprosy per se and TT and LL forms [17].
The inhibitory effect of KIR2DL2/2DL2-C1 may contribute to the development of leprosy, mainly to a worse prognosis in M. leprae infections. The activating KIR2DS2 gene with its C1 ligand was a risk factor for leprosy per se and the clinical form TT. In this same study, it was observed that higher frequency of inhibitory genes may favor the susceptibility of the development of the disease [17]. Thus, this study confirmed the influence of KIR genes and their HLA ligands on the immunopathology of leprosy.
Activating and inhibitory KIR genes in the presence of their HLA ligands may have an impact on the development of leprosy and its clinical forms. The balance between these genes may interfere with the progression of the disease to a more localized (TT) or disseminated (LL), or to maintain an intermediate pattern between the two poles (BB), thus highlighting the role of NK cells and the production of cytokines.
This chapter outlined the contribution of the innate and adaptive immune genes to leprosy pathogenesis, highlighting the HLA, KIR and MIC polymorphism genes contribution for clinical forms and reactions of leprosy. Immune responses against the M. leprae vary considerably between populations, which can be partly attributed to the genetic variation of the immune response to ensure the survival of populations. HLA and non-HLA genes should act together affecting the susceptibility to leprosy, resulting in different clinical manifestations or reactions. Hence, for a complete understanding of the genetic mechanisms of leprosy susceptibility, it will be necessary to join efforts to present a pattern of genes that would in fact be important to predict a clinical form or more severe reaction of the disease.
This study was supported by Laboratory of Immunogenetics – UEM (Proc. No. 00639/99-DEG-UEM), Fundação Araucária (State of Parana Research Foundation), CNPq (National Council for Scientific and Technological Development) and CAPES Foundation (Coordination for the Improvement of Higher Education Personnel). The authors are grateful to Prof Steven GE Marsh, Anthony Nolan Research Institute, London, UK for permission to reproduce this graph authors.
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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. From 2004 to 2011, he was a Research Assistant with the Communications Engineering Department at the University of Málaga. In 2011, he became an Assistant Professor in the same department. From 2012 to 2015, he was with Ericsson Spain, where he was working on geo-location\ntools for third generation mobile networks. Since 2015, he is a Marie-Curie fellow at the Denmark Technical University. 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