Awareness level of fuzzy model attributes.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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This book is about the new role of IT in managing customer relationships. CRM (or customer relationship management) systems are well-known and widely implemented for a long time. On the edge IT technologies provide an opportunity for a breakthrough in customer relationship management and improve the role of IT in the process. In this book the respected authors represent their experience and provide meta-analysis of the IT technologies implementation in the subject area and the results gained.
In the current chapter, the author, who is the academic editor of the book, presents his vision and approach of the role of IT and especially on the edge technologies for customer relationship management.
The growth of the on the edge technologies such as big data [1], machine learning, and artificial intelligence [2] provides new opportunities for customer relationship management. In this chapter, the author makes a general overview of those new opportunities and tools.
Implementing artificial neural networks (ANN)
The ANN tools provide support for the vast area of business tasks in CRM area.
Define product groups or bundle offers for sales
Define customer groups by behavior
Search for customer individual preferences
Customer recognition technologies:
Face recognition to improve customer service
Voice recognition
Emotion recognition (voice and chat)
Graph models. Predict customer path and lifecycle in a company
Define probable customer journey (on the example of buying a ticket)
Define customer lifecycle in a company
Fuzzy logic models
Estimate customer satisfaction
Estimate customer demand and develop new products/services/offers
Estimate demand
Estimate internal capabilities and capacity
Estimate external (market/technology) capabilities and capacity
Develop a product/service/offer
Author analyses his own projects results.
Meta-analysis of existing publications and articles.
Analysis of request forms and queries.
In this section I deliver our team experience of applying emotion recognition of a customer for call (contact) center systems and leisure services (karaoke and similar). Well-known fact is that customer satisfaction is one of the most important indicators for business. Net present score (NPS) is often used as such indicator based on customer satisfaction surveys. But NPS is a kind of the lagging (or postmortem) indicator. An organization can implement also the so-called leading (or proactive) indicators. For example, contact centers and some other services can use speech emotion recognition services and manage customer satisfaction in a real-time mode.
Detecting customer reaction by emotion recognition is a task, which can be solved using both supervised and unsupervised ANN learning [2, 3]. In this chapter, the author represents the example of the emotion recognition based on voice speed and timbre analysis without speech recognition.
In this model we do not recognize speech, but emotions only. The recognition technique is based on the analysis of the so-called static and dynamic parameters. Static parameters are basic voice timbre: gender (if known). Dynamic parameters are the following: voice timbre change, speech speed and loudness, and surrounding noise level.
Static parameters are basic for a certain caller (or user). They help to adjust the dynamic parameters during the contact session. The ANN contains input layer, which consists of:
Gender (if known)
Age (if known)
Input audio are separated into eight components. Each component is equivalent to a certain frequency in the voice audio range: starting from 300 Hz and to 16 kHz.
The first hidden layer includes artificial neurons for:
Basic voice timbre
Gender (can be empty)
Age (can be empty)
Starting surrounding noise level
The second hidden layer proceeds the loudness parameter and contains, for example, artificial neurons for the following attributes:
Level of surrounding noise loudness
Level of total voice loudness
Level of high-frequency loudness
The third layer proceeds the timbre parameters, and contains the following:
Differences in timbre
Trend in timbre differences
The fourth layer proceeds speech tempo parameters, such as:
Faster
Slower
The same
Trend in speech tempo
The output layer contains the artificial neurons, which indicate user’s satisfaction:
Neutral
Better
Worse
Excited
Dissatisfied
The simplified example of the ANN is shown in Figure 1.
ANN Schema.
The training algorithm is based on backpropagation analysis with elements of convolutional artificial neural network [3, 4].
As the result, a contact center agent can receive an advice about customer emotions in a real-time visual mode. This helps an agent to correct his/her talk with a customer and gain customer satisfaction.
In this section I deliver the stochastic graph model [5], which controls a user—ticket buyer—behavior at the ticket-selling Website. The goal of the ticket selling organization is to maximize revenues with minimal expenditures and simultaneously serve as many clients as possible. The ticket system should be able to predict customer behavior at the Website, offer special prices in a dynamic mode, and change number of offered places (e.g., additional carriers in a train or additional flights).
The stochastic graph tools [6] are worth to solve this class of tasks, because user’s behavior at a Website is probabilistic at each step. Below we discuss step by step the model of a ticket-buying process.
The stochastic graph nodes indicate current state of a system, and edge reflects an action which leads to a new state. Each edge is weighted with a probability of a certain action—p(ij)—where i is a starting node and j is a target node (Figure 2).
Simple stochastic graph.
Usually a node will have a loop, which indicates that a user stays at a current state or leaves the system with a certain probability p(i,i) (Figure 3).
Stochastic loop.
The first node is “Start page open” V(1).
There are at least three probable next steps by a user (Figure 4):
Authorize in the ticket system (V2).
Select a route destination (V3).
Leave the ticket system (loop) (V1).
If V2 path is selected, the system makes estimate of the demand based on statistical data. Certain dynamically based tariff is generated for a user
If the user authorizes (V3 node), preferred routes and destinations can be offered. Let us examine the probabilities at the next step
From V2 node the user can:
Select the route, time, and carrier (move to V4 node). Note: each of the mentioned parameters implies on ticket cost. The ticket seller can make changes dynamically, for example:
Add or remove carriers.
Increase/decrease costs for certain routes/carrier types/time slots.
Move to a new selection page (V5 node).
Authorize (move to V3 node).
Leave the page (move to V1 node).
From the V3 node the user can:
Select a route and destination from a preferred list (V6 node).
Move to a new selection page (V5 node).
Leave to the start page (V1 node).
Stochastic routes at step 1.
Next, I show the main path on this graph, at which a user moves to buy a ticket procedure.
6. From V4 node a user can select a place (Figure 5). Note: each place has attribute of cost (e.g., more convenient according to general statistical information and more preferred by this certain user). The ticket system can have varying costs in order to gain more revenue from this certain order or by filling the carrier. In the first case the price increases and in the second decreases. See stochastic paths from V4 node in Figure 5.
a. Buy ticket—move to V(7) node.
b. Return to the selection page (V5 node).
Stochastic routes at the end of customer journey.
As a result, the stochastic graph model can adjust the ticket system behavior according to the user’s behavior based not only on previous statistical data but on the probability of his/her behavior in the current session. Those algorithms can improve both customer satisfaction and operator’s value.
Fuzzy (or linguistic) models usually implement subject matter (or common) terms, for example, “good,” “cheap,” “far,” etc. Fuzzy models, as described by Lotfi Zade and other researchers [7, 8, 9], provide formal models based on linguistic terms by simultaneous inclusion of an element into several sets. The author of the article provided a research [10], in which he shows the extended definition area of fuzzy element weight from interval [0; 1] to[− ∞; ∞]. The extended definition area provides the opportunity to apply fuzzy logic together with scoring models.
Let us consider a task of developing a new customer offer (either product, goods, or service) based on customer feedback analysis, company capabilities, technological opportunities, and other factors. The author previously published his experience in applying fuzzy logic to develop new service offers for lifelong learning business [10].
Learning organization efficiency on the other hand depends on student’s satisfaction and their willingness for continuous learning and capabilities to deliver learning services cheap and adapt them to changes in customer demand.
Based on customers’ surveys, and their analysis, the author defined students’ professional improvement and satisfaction as one of the critical success factors. The main satisfaction attributes are:
Price (mark this attribute with parameter named P)
Personalized approach (PA)
Mobility (MB)
Actual content (AC)
All attributes are represented as fuzzy linguistic variables in L. Zade terms [0;1] with step 0.2, which is like a common 5 grade model (1–5 grades). Based on those weights (and a lot of others as well), the learning organization developed a series of learning methods combining class-based, online, blended, synchronous, and asynchronous learning. Table 1 is the excerpt from the decision matrix.
Learning method/attributes | Class traditional | Webinar | Blended | Self-paced | Micro-learning |
---|---|---|---|---|---|
Price | 0.2 | 0.4 | 0.2 | 0.6 | 1 |
Mobility | 0.2 | 0.8 | 0.6 | 0.6 | 0.8 |
Personal approach | 0.6 | 0.2 | 0.8 | 0.4 | 0 |
Actual content | 0.8 | 0.6 | 0.4 | 0.4 | 0.6 |
Awareness level of fuzzy model attributes.
In this chapter, the general overview of this approach is given, including new data from the latest projects. The “big data” tools such as ANN, graph models, fuzzy sets, and others can help organizations in building strong customer relationship, based on real-time knowledge about customer demand, happiness, and trends.
In this book, the respected reader learns about concrete implementations of IT tools in CRM systems in different countries and businesses.
The human immune system has two major divisions: innate and acquired. We will talk about innate immunity. Innate immunity can be defined as the first line of defense against pathogens, which represents a great machinery to create an adequate and definitive systemic response to prevent infections and maintain homeostasis of the organism. The elements of innate immunity include external physical barriers, humoral and cellular effector mechanisms. This type of immunity recognizes pathogens such as bacteria and viruses. This works thanks to the phagocytosis of the pathogens with the consequent induction of inflammatory reactions. It also has a critical role in the activation and regulation of adaptive immunity. This immunity has the ability to develop an induced response during primoinfection. This response is specific due to the expression of cell surface pattern recognition (PRR) receptors, which are capable of recognizing complex polysaccharides, glycolipids, lipoproteins, and nucleic acids. We know that pathogens contain in their structure various components that act as substances strange (antigens) and this in turn will induce an innate immune response that will subsequently activate the adaptive response. It is imperative to recognize that the important exploration of these innate mechanisms is essential for the understanding of the complex events involved in human innate immunity and is also crucial for the discovery of new antimicrobials, antitumor drugs, and immunomodulators with therapeutic applications [1]. Innate immunity, which is considered a simple immune system, is essential for the onset of acquired immunity and has been found to play an important role in the pathogenesis of the disease age [2]. Among them, it recognizes nucleic acids derived from pathogens. The innate immune pattern recognition (PRR) receptor recognizes self-derived nucleic acids. Innate pattern recognition receptors regulate antigens for the presentation and subsequent responses of B cells and T cells, for example, physiological management of autoantigens, induction of immature dendritic cells to detect tolerant signals to T cells. The activation of toll-like receptors (TLR), NOD type receptors (NLR) or Helicases similar to RIG (RLH) by molecular agents associated with pathogens where the patterns will induce dendritic cell maturation, costimulation.
\nT cell activation and production of antibodies by B cells. Therefore, recognition of innate patterns is now being considered as a central element of immunity modulation. There are at least 80 different autoimmune diseases discovered so far, which in the US alone, affect 20 million people [3]. These pathologies are established systemically or in a specific organ, but require for their expression certain conditions that are the result of multifactorial processes that involve a deregulation of the innate immune system and therefore adaptive that lead the body to erroneous responses with the subsequent attack itself of their own tissues. The innate immune system as discussed above is the first line of immediate defense against invading microorganisms that links to the adaptive response. Specific cells of the innate immune system, which are dendritic cells (DC) (antigen presenting), which are cells with an important and critical role in promoting the responses of B and T cells. This type of immunity is critical to maintain homeostasis and prevent microbial invasion, eliminating a wide variety of pathogens and contributing to the activation of the adaptive immune response.
\nIt is the control point. A dendritic type receptor that bears the title of “access gate” for innate cellular immunity: this basically consists of a type of toll-like receptor. It has been found that it plays a fundamental role as a sensor in the recognition of pathogens in the innate immune system [4].
\nThis pattern recognition receptor acts on bacteria and viruses (PAMP) [5]. The innate immune response in immunological terms controls the infection and prevents its spread. And more recently it is known that to induce this series of reactions against pathogens, in addition to the existence of antigens, another series of molecules in the pathogens is required. These molecules are known as pathogen-associated molecular patterns (PAMPs). PAMPs play and interact with a series of receptors that are mainly present in phagocytic cells (macrophages), and these “gate” receptors have been called recognition patterns to pathogen-associated molecular patterns (PRRs). These receptors contain other subfamilies where we can find toll type receptors (TLRs), NOD type receptors (NLRs), RIG-1 type receptors (RLRs), and lectin C type (CLRs). This molecular pattern related to the associated damage known as DAMP comes to behave as a type of alert that recognizes signals and most importantly this does not involve pathogen detection. The main molecular recognition patterns (PRRs) include TLR and NLR receptors, also known as nucleotide binding oligomerization domains. TLR is the homologous receptor that has already been identified in the Drosophila genetic code, and that to date some TLRs have been found in humans mainly in the cell surface, membrane, and lipids [6]. Types 1, 2, 4, 5, and 6 are those that recognize proteins, nucleic acids located in the endoplasmic reticulum and those that are found in the endosomal membranes. 3, 7, 8, and 9 detect lipopolysaccharides in the outer membrane of gram-negative bacteria (endotoxins). The TLR4 type, which transmits inflammatory signals, is the best known in general and the most studied of the TLR. This receptor responds to MyD88, which becomes a station at the central point of the inflammation signal, and corresponds to the first phase of activation of the transcription factor NF-κB pathway (nuclear factor-kappa B), which a In turn, production begins and a kind of “chain reaction” of inflammatory cytokines to eliminate pathogens [7]. Meanwhile, these TLR receptors are incorporated into PAMPs, which by recognizing nucleic acids act as an inflammatory cytokine. Receptors that mediate innate immune responses, such as toll-like receptors (TLR) and specific C-type lectin receptors (CLR) that recognize associated molecular patterns (PAMP), have been implicated in autoimmune disease mechanisms, both directly through self-recognition ligands and indirectly through the regulation of immune homeostasis [8, 9].
\nIn intracellular infections, in addition to antigens and PAMPs, the participation of another series of molecules that participate in the activation of the immune response is necessary. Recently, some studies have shown that cells can die from a type of immunogenic “apoptosis” and thus expose their nuclear or cytoplasmic molecules to their membrane. These have a way of stimulating the immune response, thanks to their activity. They are also released during the process of necrosis and have been given the name of molecular patterns associated with damage or warning signs, the famous DAMPs. The NLR receptor is present in the cytoplasm. It has the particularity of recognizing not only PAMP but also several DAMP among them [uric acid, cholesterol, sterols crystals, extracellular ATP (adenosine triphosphate), silica] or even recognizing exogenous DAMP such as asbestos, origin of aseptic inflammation, such as gout, arteriosclerosis, and silicosis [10]. It is clear that it is a cause and attracts attention. The abnormalities in the immune system that are the basis and fertilizer for autoimmune diseases are mainly caused by an abnormal acquired immunity [11]. In recent years, in contrast to the concept that autoimmune or auto-inflammatory diseases are mainly due to abnormal innate immunity, it is attracting more attention.
\nDendritic cells, macrophages, and other myeloid cells also play an important role in the innate immune response, both as antigen presenting cells as effector cells that mediate the tissue damage [12, 13, 14]. Therefore, they are fundamental and will be as in conflicts, “the first line of defense” in the face of a bacterial or other stimulus. We will also take them into account in relation to autoimmune diseases, because of their responsiveness and because they are important mediators of innate immunity, an interest has arisen in this potential to contribute to the pathology of these diseases. Proinflammatory cytokines: mainly TNFa (tumor necrosis factor alpha), induce the activation of endothelial cells, resulting in an increase in the expression of different adhesion molecules (CD62E, CD62P, ICAM-1, and VCAM-1). This causes the leukocytes to roll over them, and during this bearing, they are activated by the intracellular signals that are generated through their adhesion molecules and different chemokine receptors, which interact with the ligands found on the surface of the cells endothelial. Subsequently, these activated leukocytes adhere firmly to the endothelium, change their morphology (cell polarization) and carry out their transendothelial migration, and then migrate to the inflammatory focus, guided by the gradient of chemotactic substances that are released. Macrophages are multifunctional antigen presenting cells, with an important role in innate immunity and, therefore, in the inflammation process [15]. Macrophages are found in almost all organs, and recent studies have demonstrated their multifunctionality and heterogeneous capacities established by their numerous subpopulations, adaptation in specific tissue microenvironments and different stages of maturation. For example, during a bacterial infection, classically activated macrophages show inflammatory functions (type 1 or M1 macrophages), while with alternative activation (by Th2 type cytokines, such as IL-4 or IL-13), macrophages acquire anti-inflammatory functions (type 2 macrophages or M2). In addition to depletion or inhibition of macrophage function, reprogramming of M2 has also been explored. Recently, it has been shown that paracoccin, a protein contained in a fungal human pathogen, induces the repolarization of M1 macrophages through interaction with toll as a receptor (TLR) 4, being a new possible immunotherapeutic agent for pathologies related to M2 macrophages. Macrophage-related therapies have been proposed for various autoimmune and inflammatory pathologies. In the case of PPARγ and PPARδ, which are nuclear receptors that control different genes associated with M2 macrophages, and their agonists have been proposed as a therapy directed at macrophages to induce M2 pathways. In addition, the demonstration that TLR9 receptor signaling can reverse the aberrant M2 macrophage phenotype.
\nDendritic cells (DC) are professional antigen presenting cells (APC), often referred to as “orchestra directors of the innate immune response” due to their ability to capture, process, and present antigens to T cells. Depending on the nature of the antigen may exhibit an immunogenic or tolerogenic effect, which will be defined by cytokine secretion. They are often considered tolerogenic, because they have autoantigens in the absence of costimulation and, together with anti-inflammatory stimuli, (TGF-β), can promote the induction of regulatory T cells and/or induce anergy of T cells [16]. After activation by proinflammatory stimuli, they mature and generate an expression of costimulatory molecules and the major histocompatibility complex (HCM) class II, which causes a potent response of specific T cells to the antigens. Therefore, they play a fundamental role in maintaining self-tolerance, and on the other hand, they initiate the response against foreign antigens for their subsequent elimination by effector immune cells. In a state of aberrant hyperreactivity, they could contribute to perpetuating immune responses, backed by evidence of a high frequency of immunogenic infiltration [17]. Due to their ability to modulate the cellular response, they have been considered a powerful target for immune modulation. Strategies such as pharmacological modulation to affect their maturation status and genetic engineering to improve their tolerance or immunogenic properties for the treatment of autoimmune diseases have been studied. In several murine models, they were transduced to express IL-4 and were able to prevent disease in 12-week NOD mice. In a murine model of collagen-induced arthritis (CIA), it was shown that the injection of dendritic cells with tolerogenic activity improves the clinical and the outcome of the disease. Although the treatment was found to be safe and feasible, other studies are needed to evaluate the efficacy of cellular treatment in autoimmunity.
\nThey are a growing family of immune cells that reflect the phenotypes and functions of T cells. Natural killer cells (NK) can be considered innate homologs of cytotoxic CD8 + T cells, while ILC1, ILC2, and ILC3 correspond to innate homologs of T cells CD4 + (TH1), TH2, and TH17. However, in contrast to T cells, they do not express antigen receptors or undergo clonal selection and expansion when stimulated [4]. The ILCs react and respond to the signs of tissue damage and produce a series of cytokines, which direct the immune response and this adapts to contain the lesion. Therefore, these cells can control or unleash the immune response. As with B cells and T cells, these also originate from the common lymphoid lineage but the specific transcription factors of these suppress and modify their development until the generation of the different types of ILC. The precursors of these can migrate from their primary production site in infected and injured tissues, where they complete their maturation, in a process very similar to the differentiation of virgin T cells into TH effectors. The cytokines produced by local cells, as well as some trauma and stress response ligands as well as bacterial and dietary compounds regulate the maturation and activation of ILC in effectors that play an important role in early immune responses to pathogens in particular has been found relationship with symbionts, helminths, and allergens. The cytokines they produce induce innate responses in stromal, epithelial, and myeloid cells that in turn will regulate the activity of dendritic cells and will also play a central role in the transfer of information between ILC and T cells. ILCs by activating DC found in tissues to migrate to the lymph nodes, where they cause specific T-type cellular responses. ILCs also regulate T cells directly through the presentation of peptide antigens through CMH type II. However, ILCs are also involved in autoimmunity, because their cytokine production can exacerbate and exaggerate the inflammatory process.
\nRecent research has revealed new knowledge about the respective roles of these cells in relation to cellular and humoral immunity as well as the extension to adaptive immunity [18]. There is talk of a recent study in which a genetically modified mouse prototype model was developed with an autoimmune disease similar to lupus that does not require to express the adaptive immune system machinery, but is triggered directly by the innate immune response [19]. For many autoimmune diseases, we largely know the roles that key cells (T cells and B cells) play and for example are evident in the success of existing therapies (anti-CD3 and anti-CD20). Then knowing this, each of the functions of myeloid cells, and in general of the innate immune response cells, can “autoimmune” disease occur in the absence of adaptive immunity and these cells act as effectors in disease progression? The answer to this could be yes [20]. The most recent example is the study of mice eaten by moths that have been genetically modified to have deficiencies in hematopoietic cells, and to express an autoimmune disease characterized by alopecia (giving a “peeled or eaten by moths”) and edema in their legs. These were also accompanied by high antibody titers, with renal and pulmonary functions being compromised due to immune complex deposits [21, 22]. However, in another study, mice with deficiency in hematopoietic cell phosphatase were crossed with mice that lack the recombinase-1 activator gene (RAG-1) that caused a subsequent deficiency in the production of T and B cells and found that the disease autoimmune had progressed normally in the absence of an adaptive immune response [22, 23] even though these mice lacked high antibody titers and immune complex deposits, and they exhibited all other symptoms of the disease. Subsequently, although the onset and progression of the disease could not be defined, it was concluded that the autoimmune disease of this type of mice was mediated by an aggressive response of macrophages and other myeloid cells. Now, a study with murine models is also described, with mice with a genetic alteration associated with the deficiency in the enzyme α-mannosidase type II (αM-II) where there is premature aging with the clinical expression and the characteristic symptoms of SLE and Lupus nephritis (high titers of anti-DNA antibodies, glomerulonephritis, and renal compromise due to deposition of immunoglobulins in the kidney) that seems to be driven by a mechanism that also seems to involve the innate immune system [12, 24, 25]. In the case of the murine model, evidence was provided that the abnormal presence of hybrid glycoprotein structures acts as a trigger for the induction of an innate immune response mediated by members of the C-type lectin family that is specific for mannose. Serum mannose-binding lectins (MBL-A and MBL-B) are soluble lectins that mediate innate immunity to pathogenic bacteria and fungi that express glucans (mannose). It is also believed that the macrophage of the mannose receptor cell surface (MMR) participates in innate immune responses, and its expression has been documented in mesangial renal cells [26, 27]. In mice with αM-II deficiency, MBL lectins are deposited in renal glomeruli which, when they express high levels of mannose glucans in mesangial cells, also express higher levels of MMR, which can bind mannose ligands in the serum. Monocyte chemoattractant protein 1 (MCP-1) levels, produced by activated mesangial cells, represent the entry of activated macrophages. By aberrantly expressing mannose-containing glucans in mice with αMII deficiency, they act as triggers for an innate immune response mediated by mannose-specific C-type lectins programmed to recognize mannose glucans as PAMP.
\nThe second point in importance is the role of antibodies in stimulating the innate immune response. How can this be to the production of autoantibodies in autoimmune diseases, such as our old friend, lupus? Systemic lupus erythematosus (SLE) is an autoimmune disease that translates inflammation and exaggerated immune responses and thus with a large generalized associated tissue damage. We are clear that innate immunity plays a great role in its development and sequentially its clinical expression, and it has been shown that defects found in any of the immune recognition pathways will promote autoimmunity. First, dendritic cells and macrophages activated by TLR receptors can regulate the differentiation of self-reactive B cells through the expression of CD40 and the action of IL-6. Second, by nucleic acids. These can activate and in a powerful and disorderly way certain TLR and RLH receptors; therefore, these are normally protected from immune recognition by multiple mechanisms (epigenetic modifications, nuclear compartmentalization, and the rapid elimination of cells that have entered apoptosis and extracellular compartments by a type of DNase and RNAse enzymes). These immune complexes containing chromatin or circulating RNA particles can avoid being “digested” by these enzymes in the extracellular space and facilitate the uptake of the complex in intracellular compartments through Fc receptor-mediated endocytosis (FcR) in dendritic cell-mediated uptake or by B cell receptor (BCR) in B cells. And it has also been confirmed by studies with lupus-prone mice deficient in TLR receptors and their respective signaling molecules. As an exception, mice with TLR-9 deficiency with a predisposition to lupus produce more autoantibodies against it, indicating that TLR-9s have additional functions in the regulation of systemic autoimmunity. Innate pattern recognition (PRR) receptors regulate the production of autoantibodies associated with lupus and self-reactive T cells by modulating the presentation of autoantigens and also contribute directly to the end result that is tissue or organ injury secondary to autoimmunity. In general, it is believed that this “injury” or tissue damage is generated from the deposition of the immune complex, complement activation, and subsequent release of cytokines and chemokines to trigger local inflammation. This concept has been redefined. For example in glomerulonephritis, in the glomerular immune complex, deposits are not always associated with innate and adaptive immune responses. These are traditionally seen as separated from each other, but emerging evidence suggests that they overlap and interact with each other. Recently discovered cell types, particularly innate lymphoid cells and myeloid cell-derived suppressors that are gaining increasing attention. It is a rapidly evolving field with molecular pathways and new types of discovered cells and multiple constantly changing paradigms. In general, it is believed that many autoimmune diseases are triggered by aggressive responses of adaptive immunity by an automatic antigen system, resulting in tissue damage and pathological sequelae.
\nThe third point is undoubtedly the role of infectious agents, which have the potential to trigger an exaggerated immune response, through molecular imitation, polyclonal activation or antigen release. For example, there are certain diseases that respond to certain infectious autoantigen peptides. This is the case of multiple sclerosis, where T cells are activated by Epstein-Barr virus peptides, type A flu, and human papilloma and that react with the myelin autoantigen peptide [28]. In this case, the viral infection could cause the activation of the lymphocytes, and the autoantigen could maintain this activation, even after the eradication of the infectious agent. Microbial infection can also cause polyclonal activation of lymphocytes, and this is the underlying mechanism in increasing the incidence of autoimmunity in murine models exposed to microbial pathogens [29]. Microbes (viruses or bacteria) that destroy cells also cause an inflammatory response and also the release of antigens that have been previously captured and this could also result in autoimmunity. There is another important point. Inflammation, even in the absence of infection, can trigger polyclonal activation and self-activity. This is that through the activation of annergic cells, by inflammatory mediators or the activation of new self-reactive cells in an inflammatory environment for example in the context of ischemia of any tissue, tissue autoreactivity could be caused and because not at a systematic level [3]. Within non-infectious detonators, we have those of the hormonal type that in many autoimmune diseases are more common in women than in men. Drugs can also alter the immune repertoire. One of the most common and studied procainamide induces antinuclear antibodies and sometimes induces a lupus-like syndrome. And even some substances produced by the same cells can act as haptens and make autoantigens immunogenic, for example, CD1 T cells, with receptors (gamma/delta), CD4+, CD25+, and cytokine-producing agents that monitor activity, reduce, and control self-reactive cells, and they can become pathogenic. As some must complete their maturation in the thymus, and others the activation of autoantigens in the periphery, in these processes alterations in the number and function of regulatory cells that can contribute to autoimmunization can be generated.
\nUpon contact with the stimulus, whether microbial or of any substance, the recruitment and activation of macrophages will begin. The macrophages will serve as the primary effector cells that cause tissue damage and loss. And it has been concluded that the vast majority of autoimmune diseases could be explained by an aberrant adaptation as an immune response to the antigens themselves. On the other hand, autoimmunity as a disease contrasts with innate immunity. The first in which the term autoinflammatory was used was the periodic fever syndrome related to the TNF receptor (tumor necrosis factor), whose causative gene is TRAPS 4 and which was directly related to the presence of genetic abnormalities associated with innate immunity autoinflammatory diseases that are generally considered as a group of diseases where we can find an active responsibility for aberrant innate immunity and in which T cells are not detected and include TRAPS, cryopyrine-associated syndrome (secondary to mutations in the NLRP3 gene in children) (CAPS), Familial Mediterranean Fever (FMF), Bechet’s disease, Still’s disease in adults, Crohn’s disease, Gout, Type 2 diabetes, and various metabolic disorders [30]. The mechanism of its many initiation is still unclear, but the symptoms and diseases themselves are caused by the collapse of immune tolerance. Thymus autoreactivity and subsequent and completely abnormal inactivation of receptive and regulatory (Th) T cells suppress the reaction to the foreign antigen. The other part of the aberrant response of the innate immune response is carried out in the recipients of recognition of autoimmune patterns and diseases recognized by nucleic acids (PRR). This recognition is transmembrane due to its location in the cell and is divided into two general and cytoplasmic phases. This receptor is found in the endoplasmic reticulum or endosome and is directly related to autoimmune diseases (SLE = TLR7/9). When comparing the sequence of own nucleic acids and pathogen derivatives by means of the TLR7/9TLR9 receptors, it is noted that it contains unmethylated CpG sequences, and these are derived from pathogens that in turn recognize a type of single stranded DNA. TLR7 on the other hand recognizes single stranded RNA derived from viruses and other types, as well as messenger RNA (mRNA). From this, TLR7/9 is self-sufficient, and this receptor can strictly distinguish between conventional nucleic acids and pathogen derivatives. Stimulates an immune response in response to auto-nucleic acid. In other words, viruses and infected cells are captured by endosomes, and these nucleic acids are recognized by TLR7/9. In the case of SLE, the TLR7/9 receptor, due to the genetic modification secondary to the aberrant response to the own nucleic acids that were released and transferred to the endosome and therefore increases the genetic expression of the type I IFN and is known as the “IFN signature.” This signature of IFN is directly related to SLE, rheumatoid arthritis (RA), and systemic sclerosis (SSc) and its effects, suggesting the importance of type I IFN in autoimmune disease [31]. It also activates and stimulates plasma cells that in turn produce large amounts of type I IFN.
\nThe TLR7/9 receptor also mediates the response of plasmacytoid cells and is considered an IFN type I producing cell, which through the TLR7/9 Fc receptor activates the signal to induce the production of non-protein IFN type I histone in the core (HMGB1), to subsequently activate DAMP. The balance between TLR7 and TLR9 is also considered important for inflammation and immune response. The other transmembrane PRR receptors TLR3 and TLR8 also recognize double stranded and single stranded RNA. On the other hand, the cytoplasmic PRR receptor, type RIG-I, and MDA-5 normally identifies a specific structure of single stranded RNA. By recognizing double-stranded RNA, the specific proteins of these DAI, IFI16, and DDX41 receptors induce the production of IFN and inflamasome and, in turn, the production of IL-1β and IL-18.
\n“The authors declare no conflict of interest.”
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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