Bond contributions to ZPE (in kcal/mol).
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"5485",leadTitle:null,fullTitle:"The Amygdala - Where Emotions Shape Perception, Learning and Memories",title:"The Amygdala",subtitle:"Where Emotions Shape Perception, Learning and Memories",reviewType:"peer-reviewed",abstract:"The amygdala is a central component of the limbic system, which is known to play a critical role in emotional processing of learning and memory. Over these last 20 years, major advances in techniques for examining brain activity greatly helped the scientific community to determine the nature of the contribution of the amygdala to these fundamental aspects of cognition. Combined with new conceptual breakthroughs, research data obtained in animals and humans have also provided major insights into our understanding of the processes by which amygdala dysfunction contributes to various brain disorders, such as autism or Alzheimer's disease. Although the primary goal of this book is to inform experts and newcomers of some of the latest data in the field of brain structures involved in the mechanisms underlying emotional learning and memory, we hope it will also help stimulate discussion on the functional role of the amygdala and connected brain structures in these mechanisms.",isbn:"978-953-51-3250-9",printIsbn:"978-953-51-3249-3",pdfIsbn:"978-953-51-4757-2",doi:"10.5772/63124",price:139,priceEur:155,priceUsd:179,slug:"the-amygdala-where-emotions-shape-perception-learning-and-memories",numberOfPages:336,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"5f5b1c2a89185e92e721290beabdc2e8",bookSignature:"Barbara Ferry",publishedDate:"July 5th 2017",coverURL:"https://cdn.intechopen.com/books/images_new/5485.jpg",numberOfDownloads:28014,numberOfWosCitations:36,numberOfCrossrefCitations:33,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:59,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:128,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 26th 2016",dateEndSecondStepPublish:"May 17th 2016",dateEndThirdStepPublish:"August 21st 2016",dateEndFourthStepPublish:"November 19th 2016",dateEndFifthStepPublish:"December 19th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"139945",title:"Dr.",name:"Barbara",middleName:null,surname:"Ferry",slug:"barbara-ferry",fullName:"Barbara Ferry",profilePictureURL:"https://mts.intechopen.com/storage/users/139945/images/3505_n.jpg",biography:"Dr. Barbara Ferry is a senior researcher at the National Center of Scientific Research CNRS, France. After obtaining her Ph.D. in Neuroscience at Louis Pasteur University, Strasbourg, France, in 1997, she joined the Center for the Neurobiology of Learning and Memory, in Irvine, California for her post-doctoral studies, from 1997 to 2000. During this time, she studied the role of the α- and β-noradrenergic systems in the basolateral amygdala in the consolidation processes of inhibitory avoidance learning in the rat. In 2000, she obtained a post in the CNRS (France) and joined the UMR 7521 unit, where she studied the role of the lateral entorhinal cortex and basolateral amygdala in the modulation of the olfactory memory trace during conditioned aversion learning in the rat, from 2000 to 2006. Then, she joined the UMR 5292 (Lyon, France) and focused her work on identifying the behavioral, pharmacological and molecular mechanisms that control olfactory memory formation during associative learning in the rat, with a particular emphasis on the basolateral amygdala. Recently, the expertise in the olfactory area she acquired during her career enabled her to develop a new research project to determine the processes underlying human scent identification by police dogs, through a worldwide collaboration.\nhttps://www.researchgate.net/profile/Barbara_Ferry",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Claude Bernard University Lyon 1",institutionURL:null,country:{name:"France"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1176",title:"Cellular Physiology",slug:"cellular-physiology"}],chapters:[{id:"55539",title:"Human Amygdala in Sensory and Attentional Unawareness: Neural Pathways and Behavioural Outcomes",doi:"10.5772/intechopen.69345",slug:"human-amygdala-in-sensory-and-attentional-unawareness-neural-pathways-and-behavioural-outcomes",totalDownloads:2044,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"One of the neural structures more often implicated in the processing of emotional signals in the absence of visual awareness is the amygdala. In this chapter, we review current evidence from human neuroscience in healthy and brain-damaged patients on the role of amygdala during non-conscious (visual) perception of emotional stimuli. Nevertheless, there is as of yet no consensus on the limits and conditions that affect the extent of amygdala’s response without focused attention or awareness. We propose to distinguish between attentional unawareness, a condition wherein the stimulus is potentially accessible to enter visual awareness but fails to do so because attention is diverted, and sensory unawareness, in which the stimulus fails to enter awareness because its normal processing in the visual cortex is suppressed. Within this conceptual framework, some of the apparently contradictory findings seem to gain new coherence and converge on the role of the amygdala in supporting different types of non-conscious emotion processing. Amygdala responses in the absence of awareness are linked to different functional mechanisms and are driven by more complex neural networks than commonly assumed. Acknowledging this complexity can be helpful to foster new studies on amygdala functions without awareness and their impact on human behaviour.",signatures:"Matteo Diano, Alessia Celeghin, Arianna Bagnis and Marco\nTamietto",downloadPdfUrl:"/chapter/pdf-download/55539",previewPdfUrl:"/chapter/pdf-preview/55539",authors:[{id:"193692",title:"Dr.",name:"Marco",surname:"Tamietto",slug:"marco-tamietto",fullName:"Marco Tamietto"},{id:"194265",title:"Dr.",name:"Matteo",surname:"Diano",slug:"matteo-diano",fullName:"Matteo Diano"},{id:"194266",title:"Dr.",name:"Alessia",surname:"Celeghin",slug:"alessia-celeghin",fullName:"Alessia Celeghin"},{id:"194267",title:"Ph.D. Student",name:"Arianna",surname:"Bagnis",slug:"arianna-bagnis",fullName:"Arianna Bagnis"}],corrections:null},{id:"54323",title:"Inner Design Technology: Improved Affect by Quadrato Motor Training",doi:"10.5772/67586",slug:"inner-design-technology-improved-affect-by-quadrato-motor-training",totalDownloads:1422,totalCrossrefCites:4,totalDimensionsCites:6,hasAltmetrics:0,abstract:"The relation between positive affect and negative affect is a predictor of emotional well-being. In addition, healthy neuronal synchronization is associated with higher emotional well-being and positive affect. Related to this, recent studies have consistently reported that Quadrato Motor Training (QMT), a sensorimotor-cognitive training, increases alpha synchronization and emotional well-being in healthy participants. QMT was further found to improve creativity, reflectivity, and mindfulness-related experiences in healthy participants. In the current research, we have examined the effect of QMT on emotional well-being using the Affect Balance Scale (ABS), comparing two 1-week training programs: (1) breathing meditation retreat with QMT training (QMT, n = 42) and (2) breathing meditation retreat without QMT (BM, n = 42). While both groups reported improved affect and self-efficacy following the training, the QMT group reported significantly higher ABS scores following the retreat. QMT can thus improve well-being and emotional regulation as measured by the ABS. The current results strengthen previous claims that different practices, such as BM and QMT, may improve emotional well-being. These results are discussed in the context of the possible mechanisms mediating training-induced improved affect, focusing on the amygdala and neuronal synchronization. In conclusion, incorporating specifically structured motor and mindful practices may serve as important tools to facilitate greater emotional well-being.",signatures:"Patrizio Paoletti, Joseph Glicksohn and Tal Dotan Ben-Soussan",downloadPdfUrl:"/chapter/pdf-download/54323",previewPdfUrl:"/chapter/pdf-preview/54323",authors:[{id:"191040",title:"Dr.",name:"Tal",surname:"Dotan Ben-Soussan",slug:"tal-dotan-ben-soussan",fullName:"Tal Dotan Ben-Soussan"},{id:"195042",title:"Prof.",name:"Joseph",surname:"Glicksohn",slug:"joseph-glicksohn",fullName:"Joseph Glicksohn"},{id:"195043",title:"Ms.",name:"Patrizio",surname:"Paoletti",slug:"patrizio-paoletti",fullName:"Patrizio Paoletti"}],corrections:null},{id:"54267",title:"Amygdala and Jaw Movements: A Hodological Review",doi:"10.5772/67581",slug:"amygdala-and-jaw-movements-a-hodological-review",totalDownloads:1517,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"The organization of emotional motor behavior including jaw movements is governed by neural circuits of the limbic system, such as the amygdala and hypothalamus. GABAergic neurons in the central amygdaloid nucleus (CeA) exert an inhibitory influence on premotor neurons for the trigeminal motor nucleus (Vm) in the parvicellular reticular formation (RFp) of the medulla oblongata. The CeA also influences glutamatergic posterior lateral hypothalamic neurons and non-dopaminergic neurons in the retrorubral field of the midbrain, both of which send their axons to Vm-premotor neurons in the RFp. In addition, the CeA may modulate the activity of Vm motoneurons via projections to the mesencephalic trigeminal nucleus whose neurons convey inputs from the masticatory muscle spindles and periodontal ligament receptors to jaw-closing motoneurons in the Vm. These pathways from the amygdala to the trigeminal motor system in the lower brainstem may underlie the regulation of emotional jaw movement.",signatures:"Yukihiko Yasui",downloadPdfUrl:"/chapter/pdf-download/54267",previewPdfUrl:"/chapter/pdf-preview/54267",authors:[{id:"191030",title:"Prof.",name:"Yukihiko",surname:"Yasui",slug:"yukihiko-yasui",fullName:"Yukihiko Yasui"}],corrections:null},{id:"54612",title:"Looping Circuits: Amygdalar Function and Interaction with Other Brain Regions",doi:"10.5772/67836",slug:"looping-circuits-amygdalar-function-and-interaction-with-other-brain-regions",totalDownloads:1403,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The ability to understand the relevance of environmental cues is necessary for animals to adapt and survive. How the brain interprets, understands, and reacts to stimuli is only partially understood. Such higher‐order brain processes occur within series of highly interconnected brain circuits that allow the brain to alter its response appropriately to an ever changing environment. The amygdala is one of the brain regions that determine the significance of incoming environmental stimuli. Once the significance of a stimulus or set of stimuli is determined, other circuits utilize this information to initiate physiological and behavioral responses (e.g., alter the attention of the animal to relevant sensory cues, change the emotional state, initiate fight or flight responses, etc.). Because circuits between the amygdala and other brain regions are highly interconnected, dysfunctions in one region of the brain can influence several other brain regions. Such alterations in normal activity can induce psychiatric, psychosocial, or attentional symptomatology. Therefore, identifying the role of individual circuits as well as the interconnected nature of these circuits is essential for understanding how a normal individual survives and adapts to its environment. It also provides the knowledge necessary to devise therapies for both the cause and symptoms of psychosis.",signatures:"Diana Peterson",downloadPdfUrl:"/chapter/pdf-download/54612",previewPdfUrl:"/chapter/pdf-preview/54612",authors:[{id:"191871",title:"Ph.D.",name:"Diana",surname:"Peterson",slug:"diana-peterson",fullName:"Diana Peterson"}],corrections:null},{id:"54422",title:"Brain Activity During Autobiographical Retrieval Is Modulated by Emotion and Vividness: Informing the Role of the Amygdala",doi:"10.5772/67583",slug:"brain-activity-during-autobiographical-retrieval-is-modulated-by-emotion-and-vividness-informing-the",totalDownloads:1494,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Growing evidence indicates that the amygdala contributes to processing both emotional stimuli and highly vivid episodic memories. The present research used event-related potentials (ERPs) to examine the individual and joint contributions of these dimensions on the neural responses to naturalistic stimuli, namely, autobiographical memories, which vary in terms of associated emotion and the vividness of recollection. In Experiment 1, participants recalled positive and negative personal memories, and memories for which no mention of emotion was made. Events recollected with high vividness showed no effect of emotion, whereas ERPs for events recollected with low vividness differed for both positive and negative memories versus non-emotional memories. The conjoint effects of emotion and vividness reflect the correlation of these variables in everyday life: more emotional memories are more vividly recalled. In Experiment 2, we pursued the interaction of emotion and vividness by asking participants to recall negative high-arousal, negative low-arousal, and emotionally neutral memories. Processing differed by vividness but not by emotion condition. The research implies that focus on the emotional valence associated with a memory, without conjoint consideration of how vividly it is recalled, neglects a critical determinant of neural processes that are modulated by the amygdala during recall of autobiographical memories.",signatures:"Patricia J. Bauer, Jennifer Strafford Stevens, Anaïs F. Stenson,\nTheodore Waters and Felicia L. Jackson",downloadPdfUrl:"/chapter/pdf-download/54422",previewPdfUrl:"/chapter/pdf-preview/54422",authors:[{id:"191551",title:"Dr.",name:"Patricia",surname:"Bauer",slug:"patricia-bauer",fullName:"Patricia Bauer"}],corrections:null},{id:"54301",title:"Revisiting the Role of the Amygdala in Posttraumatic Stress Disorder",doi:"10.5772/67585",slug:"revisiting-the-role-of-the-amygdala-in-posttraumatic-stress-disorder",totalDownloads:2201,totalCrossrefCites:2,totalDimensionsCites:8,hasAltmetrics:1,abstract:"Over the past 20 years, the reactivity of amygdala to emotive stimuli has been explored by emerging neuroimaging techniques in an effort to understand the role of amygdala in the pathophysiology of posttraumatic stress disorder (PTSD). A fear neurocircuitry model, whereby the amygdala is hyperactive due to poor top-down control from the anterior cingulate and ventromedial prefrontal cortices, has been supported by numerous experimental studies and meta-analyses. However, this model has not always been upheld by experimental data and clinical observations. In particular, many neuroimaging studies find that the amygdala fails to activate in response to negative stimuli in individuals with PTSD. Several technical and design issues may explain disparate results regarding amygdala reactivity in PTSD. However, biological and symptom-based factors emerge as possible mediators of amygdala function in PTSD, leading to the conclusion that symptoms of emotional disengagement and dissociation are associated with amygdala hyporeactivity, and symptoms of hypervigilance/hyperarousal and problems with fear conditioning and extinction are reflected by amygdala hyperactivity. Therefore, treatment of PTSD should take into account the nature of amygdala dysfunction in the individual to optimize treatment outcomes.",signatures:"Gina L. Forster, Raluca M. Simons and Lee A. Baugh",downloadPdfUrl:"/chapter/pdf-download/54301",previewPdfUrl:"/chapter/pdf-preview/54301",authors:[{id:"145620",title:"Dr.",name:"Gina",surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"195109",title:"Dr.",name:"Raluca",surname:"Simons",slug:"raluca-simons",fullName:"Raluca Simons"},{id:"195110",title:"Dr.",name:"Lee",surname:"Baugh",slug:"lee-baugh",fullName:"Lee Baugh"}],corrections:null},{id:"55211",title:"The Amygdala and Anxiety",doi:"10.5772/intechopen.68618",slug:"the-amygdala-and-anxiety",totalDownloads:3030,totalCrossrefCites:4,totalDimensionsCites:8,hasAltmetrics:0,abstract:"The amygdala has a central role in anxiety responses to stressful and arousing situations. Pharmacological and lesion studies of the basolateral, central, and medial subdivisions of the amygdala have shown that their activation induces anxiogenic effects, while their inactivation produces anxiolytic effects. Many neurotransmitters and stress mediators acting at these amygdalar nuclei can modulate the behavioral expression of anxiety. These mediators may be released from different brain regions in response to different types of stressors. The amygdala is in close relationship with several brain regions within the brain circuitry that orchestrates the expression of anxiety. Recent developments in optogenetics have begun to unveil details on how these areas interact.",signatures:"Sergio Linsambarth, Rodrigo Moraga-Amaro, Daisy Quintana-\nDonoso, Sebastian Rojas and Jimmy Stehberg",downloadPdfUrl:"/chapter/pdf-download/55211",previewPdfUrl:"/chapter/pdf-preview/55211",authors:[{id:"144923",title:"Dr.",name:"Jimmy",surname:"Stehberg",slug:"jimmy-stehberg",fullName:"Jimmy Stehberg"},{id:"194182",title:"Ph.D. Student",name:"Rodrigo",surname:"Moraga-Amaro",slug:"rodrigo-moraga-amaro",fullName:"Rodrigo Moraga-Amaro"},{id:"194183",title:"M.Sc.",name:"Sergio",surname:"Linsambarth",slug:"sergio-linsambarth",fullName:"Sergio Linsambarth"}],corrections:null},{id:"54565",title:"The Role of the Amygdala in Regulating the Hypothalamic-Pituitary-Adrenal Axis",doi:"10.5772/67828",slug:"the-role-of-the-amygdala-in-regulating-the-hypothalamic-pituitary-adrenal-axis",totalDownloads:3581,totalCrossrefCites:7,totalDimensionsCites:12,hasAltmetrics:0,abstract:"We investigated the regulatory role of the amygdala upon the function of the hypothalamic-pituitary-adrenal (HPA) axis as measured by median eminence corticotrophin releasing hormone (CRH) content and serum levels of adrenocorticotrophic hormone (ACTH) and corticosterone. Our findings showed that (1) lesions of the central amygdala inhibited the HPA axis responses to a variety of stressful stimuli. (2) Depletion of norepinephrine or serotonin in the amygdala and hypothalamus and local injections of norepinephrine and serotonin receptor antagonists into the central amygdala inhibited the HPA axis responses to neural stress. Norepinephrine and serotonin agonists injected into the amygdala caused an increase in HPA axis activity. The activation of the amygdala facilitated the in vivo release of serotonin from the paraventricular nucleus following electrical stimulation of the brainstem raphe nuclei. (3) Electrical stimulation of the amygdala impaired the glucocorticoid negative feedback action following neural stressful stimuli probably via a decrease in hippocampal corticosteroid receptors.",signatures:"Joseph Weidenfeld and Haim Ovadia",downloadPdfUrl:"/chapter/pdf-download/54565",previewPdfUrl:"/chapter/pdf-preview/54565",authors:[{id:"190851",title:"Ph.D.",name:"Haim",surname:"Ovadia",slug:"haim-ovadia",fullName:"Haim Ovadia"},{id:"192823",title:"Prof.",name:"Joseph",surname:"Weidenfeld",slug:"joseph-weidenfeld",fullName:"Joseph Weidenfeld"}],corrections:null},{id:"54675",title:"The Key Role of the Amygdala in Stress",doi:"10.5772/67826",slug:"the-key-role-of-the-amygdala-in-stress",totalDownloads:2967,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Several data highlighted that stress exposure is strongly associated with several psychiatric disorders. The amygdala, an area of the brain that contributes to emotional processing, has a pivotal role in psychiatric disorders and it has been demonstrated to be highly responsive to stressful events. Here we will review evidences indicating how the amygdala changes its functionality following exposure to stress and how this contributes to the onset of anxiety disorders.",signatures:"Diego Andolina and Antonella Borreca",downloadPdfUrl:"/chapter/pdf-download/54675",previewPdfUrl:"/chapter/pdf-preview/54675",authors:[{id:"190318",title:"Dr.",name:"Diego",surname:"Andolina",slug:"diego-andolina",fullName:"Diego Andolina"},{id:"192832",title:"Dr.",name:"Antonella",surname:"Borreca",slug:"antonella-borreca",fullName:"Antonella Borreca"}],corrections:null},{id:"55483",title:"Amygdala and Emotional Modulation of Multiple Memory Systems",doi:"10.5772/intechopen.69109",slug:"amygdala-and-emotional-modulation-of-multiple-memory-systems",totalDownloads:1694,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Stress and anxiety can either enhance or impair memory, and the direction of the effect partially depends on the type of memory being affected. Behavioral or pharmacological stressors typically impair cognitive memory mediated by the hippocampus, but enhance stimulus-response habit memory mediated by the dorsolateral striatum. Evidence also indicates that the effect of emotion on different kinds of memory critically depends on a modulatory role of the basolateral amygdala (BLA). BLA modulation of multiple memory systems may be achieved through its glutamatergic projections to other brain regions, which may enhance stress hormone activity, modulate competition between memory systems, and alter synaptic plasticity. The neurobiology underlying the emotional modulation of multiple memory systems may be relevant to understand the impact of emotional arousal on the development and expression of human psychopathologies characterized by maladaptive habitual behaviors (e.g., drug addiction and relapse).",signatures:"Jarid Goodman, Christa McIntyre and Mark G. Packard",downloadPdfUrl:"/chapter/pdf-download/55483",previewPdfUrl:"/chapter/pdf-preview/55483",authors:[{id:"190956",title:"Dr.",name:"Mark",surname:"Packard",slug:"mark-packard",fullName:"Mark Packard"},{id:"193276",title:"Dr.",name:"Jarid",surname:"Goodman",slug:"jarid-goodman",fullName:"Jarid Goodman"},{id:"193277",title:"Dr.",name:"Christa",surname:"McIntyre",slug:"christa-mcintyre",fullName:"Christa McIntyre"}],corrections:null},{id:"54746",title:"Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear",doi:"10.5772/67834",slug:"computational-models-of-the-amygdala-in-acquisition-and-extinction-of-conditioned-fear",totalDownloads:1588,totalCrossrefCites:4,totalDimensionsCites:4,hasAltmetrics:0,abstract:"The amygdala plays a central role in both acquisition and expression of conditioned fear associations and dysregulation of the amygdala leads to fear and anxiety disorders such as posttraumatic stress disorder (PTSD). Computational modeling has served as an important tool to understand the cellular and circuit mechanisms of fear acquisition and extinction. This review provides a critical appraisal of existing computational modeling studies of the amygdala and extended circuitry in acquisition and extinction of learned fear associations. It gives a broad overview of the computational techniques applied to amygdala modeling with an emphasis on how computational models could shed light on the neural mechanisms of fear learning, inform experimental design, and lead to specific, experimentally testable hypotheses. It covers different types of published models including rule‐based models, connectionist type models, phenomenological spiking neuronal models, and detailed biophysical conductance‐based models. Specific attention is given to the evolution of amygdala models from simple rule‐based and connectionist type models to more sophisticated and biologically realistic models. Future direction on computational modeling of the amygdala and associated networks in emotional learning is also discussed.",signatures:"Guoshi Li",downloadPdfUrl:"/chapter/pdf-download/54746",previewPdfUrl:"/chapter/pdf-preview/54746",authors:[{id:"191728",title:"Dr.",name:"Guoshi",surname:"Li",slug:"guoshi-li",fullName:"Guoshi Li"}],corrections:null},{id:"54963",title:"The Human Amygdaloid Complex: Cellular Architecture and Dopaminergic Innervation",doi:"10.5772/intechopen.68391",slug:"the-human-amygdaloid-complex-cellular-architecture-and-dopaminergic-innervation",totalDownloads:1486,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The human amygdaloid complex (AC) is associated with the perception of fear and consequent anxiety‐related behaviors, apart from other functions ranging from attention to memory and emotion. The AC is composed of several regions with specific cytoarchitectures, chemistry, and connections that encode different aspects of fear. Detailed understanding of AC cell composition is basic to determining whether cell number alterations coincide with neurological and psychiatric pathologies associated to anxiety imbalances, as well as with changes in brain functionality during aging. Here, we describe quantitative data gathered applying stereological methods to human AC tissue; the amounts of neurons, glial and endothelial cells, as well as of various interneuron subsets that populate the AC regions were noted and compared with those collected in the AC of non‐human primates and rodents. This chapter also addresses the dopaminergic innervation of the AC, which exerts a modulatory effect over the intrinsic AC network and is critical for reward‐related learning and fear conditioning. This innervation is twice as abundant in the main output nuclei as in the principal entry nuclei of the human AC, and this irregularity may indicate functional variations between these entry and output amygdaloid territories.",signatures:"María García‐Amado and Lucía Prensa",downloadPdfUrl:"/chapter/pdf-download/54963",previewPdfUrl:"/chapter/pdf-preview/54963",authors:[{id:"191571",title:"Ph.D.",name:"Maria",surname:"Garcia-Amado",slug:"maria-garcia-amado",fullName:"Maria Garcia-Amado"},{id:"195253",title:"Dr.",name:"Lucia",surname:"Prensa",slug:"lucia-prensa",fullName:"Lucia Prensa"}],corrections:null},{id:"54244",title:"The Key Amygdala-Hippocampal Dialogue for Adaptive Fear Memory",doi:"10.5772/67582",slug:"the-key-amygdala-hippocampal-dialogue-for-adaptive-fear-memory",totalDownloads:1830,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"For centuries, philosophical and clinical studies have emphasized a fundamental dichotomy between emotion and cognition, for instance between implicit/emotional memory and explicit/representative memory. However, in the last few decades, cognitive neuroscience has highlighted data indicating that emotion and cognition are in fact in close interaction and that reciprocal amygdalar-hippocampal influences underlie their mutual regulation. While supporting this view, the present chapter discusses experimental data indicating that the hippocampal and amygdalar systems not only regulate each other and their functional outcomes but also qualify specific emotional memory representations through specific activations and interactions. Specifically, we review consistent data unveiling a direct contribution of both the amygdala and septo-hippocampal system to the identification of the predictor of a threat in different situations of fear conditioning. Our suggestion is that these two brain systems and their interplay determine the selection of relevant emotional stimuli, thereby contributing to the adaptive value of emotional memory. Hence, beyond the mutual quantitative regulation of these two brain systems described so far, we propose that different configurations of the hippocampal-amygdalar network qualitatively impact the formation of memory representations, thereby producing either adaptive or maladaptive (e.g., PTSD-like) fear memories.",signatures:"Aline Desmedt",downloadPdfUrl:"/chapter/pdf-download/54244",previewPdfUrl:"/chapter/pdf-preview/54244",authors:[{id:"190128",title:"Dr.",name:"Aline",surname:"Desmedt",slug:"aline-desmedt",fullName:"Aline Desmedt"}],corrections:null},{id:"54509",title:"The Contribution of the Amygdala to Reward-Related Learning and Extinction",doi:"10.5772/67831",slug:"the-contribution-of-the-amygdala-to-reward-related-learning-and-extinction",totalDownloads:1761,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:1,abstract:"There has been substantial research into the role of the amygdala in fear conditioning and extinction of conditioned fear. The role of the amygdala in appetitive conditioning is relatively less explored. Here, we will review research into the role of the amygdala in reward‐related learning. Research to date suggests that the basolateral and central amygdala are responsible for learning about distinct aspects of a reinforcing event. For example, the basolateral amygdala is essential for distinguishing and choosing between specific rewards based on the specific‐sensory properties of those rewards as well as updating the relative value of specific rewarding events. In contrast, the central amygdala is involved in encoding reinforcement more generally and for regulating motivational influences on responding. 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ZPE (Figure 1) is a positive, additive, collective internal property and can be approximated by the harmonic formula:
The Morse potential and harmonic oscillator potential.
where the sum runs for the 3N-6 normal frequencies (νi) in the case of a nonlinear molecule of N atoms,
Thus, for a precise calculation of the enthalpies of formation, the use of a reliable method to determine vibrational zero-point energy appears necessary. The experimental determination of the ZPE requires the knowledge of all normal vibration mode frequencies by infrared and Raman method. In certain cases [1, 2], these methods involve experimental difficulties, and they are not feasible because of the existence of overtones and combination frequencies in the molecular spectra. Also, this experimental determination becomes even more difficult for the hazardous [3, 4, 5, 6] compounds which may be difficult to handle in air [7, 8, 9]. Otherwise, vibrational zero-point energy can be obtained by computing molecular vibrational frequencies with quantum chemistry methods [10, 11, 12, 13, 14]. However, such calculations can be very demanding in terms of computer time and disk space for large molecules and thus in daily practice; they are not usable by the chemist and technologist. Moreover, the theoretical vibrational frequencies are generally overestimated. The neglect of anharmonicity effects in the theoretical treatment constitutes the major source of this disagreement [15]. To solve this problem, we use an empirical correction factor which depends on the method, the basis set, the property to be determined (ZPE, Hvib(T), Svib (T), etc.), and sometimes the type of vibrations (low-frequency vibrations, high-frequency vibrations) [16]. For this reason and based on the fact that vibrational zero-point energy (ZPE) can be described with a good approximation by additivity rules [17, 18, 19], several researchers have sought to develop empirical rules to determine the vibrational ZPE.
Thus, to estimate ZPEs of molecules without using quantum chemistry methods, several empirical rules [20, 21, 22, 23, 24] have been developed in recent years. These additivity rules are classified into two categories: the first methods are based on the atomic contributions, while the second one are based on the contributions of bonds or groupings. The first category takes into account each of the atoms present in a given molecule. Thus, the studied property is evaluated as the sum of the atomic contributions. In the second approach, the molecule is divided into different fragments (bonds or groups). When a fragment is present in one molecule or another, the value of its contribution remains the same.
Using the first approach based on the additivity of atomic contributions, Flanigan et al. [20] have calculated the vibrational zero-point energy (ZPE) of hydrocarbons CnHm through the simple empirical relationship:
In 1985, using the least squares method, Schulman and Disch established a similar empirical relationship [25]. They determined the contributions of carbon and hydrogen atoms to estimate the vibrational zero-point energy of hydrocarbons. The relation thus obtained is written as follows:
Then, this last formula has been extended to polyatomic molecular systems containing nitrogen, oxygen, chlorine, fluorine [26], bromine, sulfur [27], and silicon [28]. More recently, in order to obtain the ZPE of organophosphorus compounds, we have determined the increment of the phosphorus atom [29].
Eq. (3) takes now the form
where N is the number of kinds of atom in the molecule; Ni is the number of atoms of type i; Xi is the increment of the atom i.
In this context, Grice and Politzer [1] have also developed a simple linear relationship between the ZPE and molecular stoichiometry for several organic compounds.
In 2003, Ruzsinszky et al. [30] examined the relationship between ZPE values and partial charges calculated at the functional density theory (DFT) level. The results show that atomic partial charges can be used to estimate ZPEs with high accuracy. However, this method still requires to quantum chemical calculations to estimate the ZPE of the molecules.
The sum ZPE + H(T) − H(0) was studied by Fliszar et al. [13]. They found that this quantity obeyed to certain additivity rules, and they proved its correlation with the structural characteristics of the molecule, more precisely the number of atoms and the degree of branching in the case of hydrocarbons.
For the approach based on the contributions of bonds or groupings, Pitzer [31] was interested in the computation of the thermodynamic functions for gaseous hydrocarbons; he attributed an empirical value to each mode of vibration. In an n-paraffin study, Cottrell [32] found that the ZPE increases gradually with successive additions of the methylene group (CH2). Later, Pitzer and Catalano [33] assigned the constant 17.7 kcal/mol per unit of CH2 to calculate vibrational zero-point energy of these compounds.
The empirical estimation of vibrational zero-point energies of halomethanes, ethylene, haloethylenes, methane isotopes, and benzene has been the subject of several studies by Bernstein [34, 35, 36]. He took into consideration the contributions of the internal coordinates and the interactions between them. A few years later, three empirical parameters were determined by Fujimoto and Shingu [37] to calculate the ZPEs of hydrocarbons with a precision similar to that of Bernstein, which are the contributions of C▬C bonds, C▬H bonds, and carbon chain.
In 1980, based on a system of harmonic oscillators, Oi and his collaborators published some papers [17, 18, 19] which describe the theoretical foundation showing the additivity of ZPE estimates.
Still in the framework of the estimates based on the contributions of the bonds or groupings, we established in 2001 an original empirical relation [38] which makes it possible to calculate the ZPE of the organic compounds. This rule was determined by linking the ZPE to the nature and type of bonds forming the molecular system. The established empirical formula is as follows:
where P is the number of bonds in the molecule; Ni is the number of bonds of type i; BCi is the contribution of the bond i to the ZPE.
This established empirical relationship also makes it possible to calculate the vibrational zero-point energies of the aromatic derivatives with accuracy, provided that the empirical values are adjusted by the following equation:
This adjustment can be explained by the fact that in aromatic compounds, the bonds in the aromatic nucleus are all identical because of the conjugation, whereas in our model it has been assumed that there are three C▬C single bonds and three C=C double bonds.
Eq. (5) was used to calculate the ZPEs of several organic compounds belonging to different classes of compounds (hydrocarbons, oxygen compounds, nitrogen compounds, chlorinated compounds, brominated compounds, fluorinated compounds, sulfur compounds, aromatic compounds, etc.). Thus, the contributions of the bonds C▬H, N▬H, O▬H, S▬H, C▬O, C▬C, C▬N, C▬S, N▬N, C▬F, C▬Cl, C=C, C=N, C=O, C=S, C☰C, and C☰N have been determined. To extend our model to brominated compounds, we have determined in 2004 the contribution of the C▬Br bond [39] and incorporated it into our empirical formula. The calculated vibrational zero-point energies for 38 compounds containing this bond (C▬Br) correlate well with experimental values. In addition, we have extended the field of application of this empirical model to organophosphorus compounds (III). The bond contributions of P▬F, P▬C, P▬H, P▬Cl, P▬S, P▬N, and P▬O were determined [29]. The results obtained for 101 chemical systems containing these bonds are in good agreement with experimental values. The estimated ZPEs were compared with the results obtained by application of quantum chemistry methods at the level ab initio (HF/6-31G*) and DFT(B3LYP/6-31G*), in all cases with satisfactory results.
More recently [40], to calculate vibrational zero-point energies (ZPEs) of organophosphorus compounds (V), we determined the contributions of the bonds P=O and P=S and incorporated them into our empirical formula. Comparison of the results obtained for more than 80 organophosphorus compounds (V) with the reported values and with those obtained by ab initio (HF/6-31G*) and DFT(B3LYP/6-31G*) shows the reliability of the empirical approach.
In this chapter, we describe the results obtained in the case of organosilicon compounds. We present the values obtained for the contributions of the Si▬H, Si▬C, Si▬Cl, Si▬O, and Si▬Si bonds which make it possible to calculate the vibrational zero-point energies of the silicon compounds. The results thus obtained are compared firstly with the available experimental values, secondly to the values obtained by the methods of the quantum chemistry at the semiempirical (AM1) and DFT(B3LYP/6-31G*) level, and finally to the results derived from a similar approach based on simple atom additivity.
The theoretical calculations were performed at the semiempirical [41] and density functional theory [42, 43] levels using, respectively, the AM1 method and B3LYP [44, 45, 46] with 6-31G* basis set which were implemented in the Gaussian03W program [47, 48]. The molecular geometries were optimized without any symmetry constraints, and the harmonic frequencies were calculated to ensure that the structures really corresponded to a true local minimum energy on the potential energy surface in the first time and to determine the vibrational zero-point energies in the second time.
Using the least squares method, we have determined the contributions of the Si▬H, Si▬C, Si▬Cl, Si▬O, and Si▬Si bond by correlating, for a population of molecules, the values of the vibrational zero-point energies obtained experimentally and those obtained by Eq. (5). The values of the contributions obtained for the studied bonds and those already established [29, 38, 39, 40, 49] are given in Table 1.
Bond | Bond contribution (BCi) | Ref. |
---|---|---|
C▬H | 7.5877 | [38] |
N▬H | 7.2013 | [38] |
O▬H | 7.2964 | [38] |
S▬H | 5.6921 | [38] |
C▬O | 2.6985 | [38] |
C▬C | 2.0751 | [38] |
C▬N | 4.1409 | [38] |
C▬S | 1.4403 | [38] |
N▬N | 6.8372 | [38] |
C▬F | 3.3078 | [38] |
C▬Cl | 2.2051 | [38] |
C=C | 2.6501 | [38] |
C=N | 3.8852 | [38] |
C=O | 3.9343 | [38] |
C=S | 2.7319 | [38] |
C☰C | 4.4125 | [38] |
C☰N | 4.8169 | [38] |
C▬Br | 1.9837 | [39] |
Si▬H | 5.8011 | [49] |
Si▬C | 0.3593 | [49] |
Si▬Cl | 1.7690 | [49] |
Si▬O | 1.3335 | [49] |
Si▬Si | −1.4548 | [49] |
P▬H | 6.6486 | [29] |
P▬C | 1.4190 | [29] |
P▬O | 1.8406 | [29] |
P▬Cl | 1.6717 | [29] |
P▬N | 0.9873 | [29] |
P▬F | 2.1507 | [29] |
P▬S | 1.5424 | [29] |
P=O | 2.4032 | [40] |
P=S | 0.6131 | [40] |
Bond contributions to ZPE (in kcal/mol).
To test the reliability of the extended empirical model, we applied it to 91 silicon compounds different from those used in the compilation of contributions. This group of molecules contains different classes such as silanes, siloxanes, chlorosilanes, silyl ethers, silanols, silyl chlorides, cyclic organosilicon, and aromatic organosilicon.
The ZPEs obtained are recorded in Table 2. These results show a very good agreement between the calculated and the experimental values. Indeed, the average error is of the order of 1.51 kcal/mol for the 91 molecular systems for which experimental or ab initio (HF/6-31G*) data are available. However, the ZPEs calculated for 3-phenyl-1,3-thiasilacyclohexane, 1-phenyl-1-silacyclohexane, 3-methyl-3-phenyl-1,3-thiasilacyclohexane, 1-methyl-1-phenyl-1-silacyclohexane, and (C6H5)3SiOH are underestimated.
Compound | ZPE (kcal/mol) | |||||
---|---|---|---|---|---|---|
Exp. | Eq. (5)a | AM1b | B3LYP/6-31G*c | Eq. (4)d | Ref. | |
SiHCl3 | 9.29 | 9.02 | 7.26 | 8.54 | 4.08 | [50] |
SiH3Cl | 17.01 | 17.08 | 14.50 | 15.88 | 13.88 | [51] |
SiH2Cl2 | 13.31 | 13.05 | 11.00 | 12.42 | 8.98 | [51] |
SiH4 | 19.90 | 21.11 | 17.71 | 18.87 | 18.78 | [52] |
C4H12Si (diethylsilane) | 90.68 | 90.26 | 87.36 | 89.65 | 91.26 | [52] |
C6H16Si (triethylsilane) | 125.98 | 124.83 | 121.78 | 124.68 | 127.50 | [52] |
C8H20Si (tetraethylsilane) | 161.23 | 159.40 | 156.08 | 159.52 | 163.74 | [52] |
CH4SiCl2 (dichloromethylsilane) | 27.09 | 27.67 | 25.27 | 26.67 | 27.10 | [53] |
CH5SiCl (chloromethylsilane) | 32.48 | 34.40 | 30.62 | 33.60 | 32.00 | [53] |
CH6Si (methyl silane) | 37.21 | 38.44 | 35.51 | 36.99 | 36.90 | [54] |
C3H6Si (1-silylpropyne) | 43.96 | 42.85 | 42.71 | 43.64 | 44.66 | [55] |
CH2Br(CH3)2SiH (bromomethyl dimethyl silane) | 66.33 | 67.47 | 64.75 | 66.89 | 67.62 | [56] |
H3SiSiH3 | 30.08 | 31.26 | 27.27 | 29.69 | 29.51 | [57] |
C11H16Si (1-phenyl-1-silacyclohexane) | 151.56 | 151.30e | 144.93 | 145.51 | 146.90 | [58] |
C10H14SiS (3-phenyl-1,3-thiasilacyclohexane) | 134.31 | 133.54e | 127.60 | 137.91 | 130.65 | [58] |
C12H18Si (1-methyl-1-phenyl-1-silacyclohexane) | 169.58 | 170.00e | 161.97 | 162.84 | 165.02 | [58] |
C11H16SiS (3-methyl-3-phenyl-1,3-thiasilacyclohexane) | 152.32 | 152.24e | 144.66 | 146.27 | 148.77 | [58] |
H2ClSiSiH3 | 27.48 | 27.23 | 23.89 | 26.34 | 24.61 | [59] |
HCl2SiSiH3 | 23.63 | 23.20 | 20.29 | 22.63 | 19.71 | [59] |
H2ClSiSiH2Cl | 23.94 | 23.20 | 20.64 | 22.93 | 19.71 | [59] |
Cl3SiSiH3 | 19.47 | 19.17 | 16.53 | 18.62 | 14.81 | [59] |
HCl2SiSiHCl2 | 16.09 | 15.13 | 13.29 | 15.43 | 9.91 | [59] |
Cl3SiSiH2Cl | 15.90 | 15.13 | 13.14 | 15.18 | 9.91 | [59] |
Cl3SiSiHCl2 | 11.95 | 11.10 | 9.56 | 11.37 | 5.01 | [59] |
Cl3SiSiCl3 | 7.77 | 7.07 | 5.85 | 7.31 | 0.11 | [59] |
Si4H10 (n-butasilane) | 50.50 | 51.56 | 46.54 | 50.82 | 50.97 | [60] |
Si5H12 (n-pentasilane) | 60.93 | 61.70 | 56.14 | 61.23 | 61.70 | [60] |
Si6H14 (n-hexasilane) | 71.39 | 71.85 | 65.80 | 71.85 | 72.43 | [60] |
Si7H16 (n-heptasilane) | 81.85 | 82.00 | 81.57 | 82.43 | 83.16 | [60] |
Si8H18 (n-octasilane) | 92.54 | 92.15 | 85.11 | 92.90 | 93.89 | [60] |
Si9H20 (n-nonasilane) | 102.75 | 102.29 | 94.71 | 103.47 | 104.62 | [60] |
Si10H22 (n-decasilane) | 113.33 | 112.44 | 104.26 | 114.00 | 115.35 | [60] |
C2H5SiCl (gauche vinyl silyl chloride) | 37.31 | 37.05 | 35.75 | 37.14 | 35.88 | [61] |
C2H5SiCl (cis vinyl silyl chloride) | 37.31 | 35.70 | 34.19 | 37.14 | 35.88 | [61] |
C2H6SiCl2 (2-chloroethylsilyl chloride C-gauche-Si-trans (Gt)) | 46.71 | 46.27 | 43.99 | 46.00 | 45.22 | [62] |
C2H6SiCl2 (2-chloroethylsilyl chloride C-trans-Si-trans (Tt)) | 46.62 | 46.27 | 44.01 | 46.03 | 45.22 | [62] |
C2H6SiCl2 (2-chloroethylsilyl chloride C-trans-Si-gauche (Tg)) | 46.46 | 46.27 | 44.30 | 46.07 | 45.22 | [61] |
C2H7SiCl (1-chloroethylsilane) | 49.70 | 50.30 | 47.41 | 49.27 | 50.12 | [63] |
C2H7SiCl (gauche ethyl chlorosilane) | 51.38 | 51.65 | 49.18 | 51.11 | 50.12 | [64] |
C2H7SiCl (trans ethyl chlorosilane) | 51.42 | 51.65 | 49.19 | 51.10 | 50.12 | [65] |
H3SiOH | 24.21 | 23.94 | 21.77 | 23.22 | 22.18 | [66] |
C3H6Cl2Si (1,1-dichlorosilacyclobutane) | 49.65 | 51.84 | 51.55 | 52.36 | 49.10 | [67] |
C2H8Si (ethylsilane) | 54.79 | 55.69 | 52.75 | 54.54 | 55.02 | [68] |
C3H9SiCl (chlorotrimethylsilane) | 68.16 | 69.05 | 66.13 | 67.91 | 68.24 | [69] |
C3H10Si (trimethylsilane) | 71.95 | 73.08 | 70.20 | 71.86 | 73.14 | [69] |
C4H12OSi (methoxytrimethylsilane) | 93.41 | 94.07 | 90.81 | 92.94 | 94.66 | [69] |
(CH3)3SiOH (trimethylsilanol) | 75.35 | 75.91 | 73.33 | 75.47 | 73.14 | [69] |
C3H10Si (gauche-n-propylsilane) | 72.29 | 72.94 | 70.00 | 71.89 | 73.14 | [70] |
C3H10Si (anti-n-propylsilane) | 72.07 | 72.94 | 70.00 | 71.78 | 73.14 | [70] |
C3H10Si (trans ethylmethylsilane) | 72.14 | 73.01 | 70.21 | 72.07 | 73.14 | [71] |
C3H10Si (gauche ethylmethylsilane) | 72.27 | 73.01 | 70.30 | 72.20 | 73.14 | [71] |
C4H10Si (cis methylsilylcyclopropane) | 76.81 | 77.09 | 74.07 | 75.51 | 77.02 | [72] |
C4H10Si (gauche methylsilylcyclopropane) | 76.44 | 77.09 | 74.10 | 75.45 | 77.02 | [72] |
C3H12Si2 (1,1,1-trimethyldisilane) | 82.56 | 83.23 | 79.69 | 82.38 | 83.87 | [73] |
C5H12Si (cyclopentylsilane) | 94.60 | 94.34 | 93.21 | 94.05 | 95.14 | [74] |
C6H14Si (cyclohexyl silane (chair-equatorial)) | 112.15 | 111.59 | 110.54 | 111.83 | 113.26 | [75] |
C6H14Si (cyclohexyl silane (chair-axial)) | 112.93 | 111.59 | 110.54 | 112.00 | 113.26 | [75] |
C3H8Si (allylsilane) | 57.75 | 58.34 | 56.22 | 57.51 | 58.90 | [76] |
C3H8SiCl2 (anti dichloromethyldimethyl silane) | 62.42 | 62.31 | 59.76 | 61.78 | 63.34 | [77] |
C3H8SiCl2 (gauche dichloromethyldimethyl silane) | 61.73 | 62.31 | 59.78 | 61.75 | 63.34 | [77] |
C3H7SiCl (methylvinyl silyl chloride) | 54.48 | 54.38 | 52.92 | 54.55 | 54.00 | [78] |
(H3Si)2CCH2 | 49.65 | 51.26 | 47.92 | 57.01 | 51.51 | ** |
((CH3)3Si)2CCH2 | 152.56 | 155.19 | 151.46 | 155.08 | 160.23 | ** |
C5H12Si (1,1-dimethyl-1-silacyclobutane) | 93.33 | 94.55 | 93.48 | 94.57 | 95.14 | ** |
C3H8Si (silacyclobutane) | 59.06 | 59.91 | 59.25 | 59.71 | 58.90 | ** |
C4H10Si (1-methyl-silacyclobutane) | 76.38 | 77.23 | 76.50 | 77.18 | 77.02 | ** |
Cl3SiCH3 | 25.44 | 26.34 | 24.28 | 25.83 | 22.20 | ** |
Cl2Si(CH3)2 | 46.36 | 47.69 | 45.17 | 47.17 | 45.22 | ** |
(CH3)3SiCN | 71.85 | 72.45 | 70.99 | 72.59 | 73.95 | ** |
SiH3CN | 19.27 | 20.49 | 19.36 | 19.98 | 19.59 | ** |
Si(OH)4 | 34.56 | 32.43 | 32.10 | 34.64 | 32.38 | ** |
HSi(OH)3 | 30.79 | 29.60 | 28.85 | 30.88 | 28.98 | ** |
H3SiOSiH3 | 32.90 | 35.38 | 30.93 | 33.16 | 32.91 | ** |
Si(OH)3-O-Si(OH)3 | 55.86 | 52.36 | 51.11 | 56.39 | 53.31 | ** |
C2Si2H8O (1-oxa-2,5-disilacyclopentane) | 56.90 | 56.93 | 55.41 | 57.43 | 54.91 | ** |
(H2SiO)3 (cyclotrisiloxane) | 41.40 | 40.72 | 38.12 | 41.43 | 36.20 | ** |
(H2SiO)4 (cyclotetrasiloxane) | 54.91 | 54.99 | 51.19 | 55.05 | 50.33 | ** |
(C2H5O)4Si | 171.18 | 174.09 | 170.16 | 172.95 | 177.34 | ** |
CH3SiH2SiH2CH3 | 63.83 | 65.90 | 62.65 | 65.31 | 65.75 | ** |
H3SiSiH2SiH3 | 39.94 | 41.41 | 36.92 | 40.19 | 40.24 | ** |
(Me3Si)2SiH2 | 143.61 | 145.34 | 141.84 | 145.47 | 148.96 | ** |
((CH3)3Si)3SiH | 205.33 | 207.45 | 204.16 | 208.86 | 214.05 | ** |
CH3OSi(CH3)3 | 91.48 | 94.07 | 90.79 | 92.94 | 94.66 | ** |
Et3SiOH | 126.18 | 127.66 | 125.19 | 128.10 | 130.90 | ** |
Ph3SiOH | 172.20 | 176.01e | 174.18 | 173.66 | 177.46 | ** |
nbutyl3SiOH | 228.34 | 231.16 | 228.10 | 231.39 | 239.62 | ** |
(CH3)3SiOSi(CH3)3 | 134.88 | 139.31 | 133.95 | 137.52 | 141.63 | ** |
(CH3)3SiOCOCH3 | 97.34 | 97.38 | 97.00 | 98.80 | 101.94 | ** |
C8H16SiO2 (4-trimethylsiloxy-3-penten-2-one) | 134.19 | 137.23 | 134.29 | 135.98 | 142.06 | ** |
(CH3)3SiOCOCF3 (trimethylsilyltrifluoroacetate) | 84.22 | 85.17 | 84.27 | 84.87 | 90.39 | ** |
C3H7SiCl (cis-cyclopropylchlorosilane) | 55.47 | 55.80 | 53.87 | 55.03 | 54.00 | [79] |
This is due to the presence of the aromatic ring in these organosilicon compounds [29, 38, 39, 40]. Nevertheless, the adjustment of empirical ZPE values by Eq. (6) leads to a decrease of the mean error which becomes 1.02 kcal/mol (i.e., 1.98%).
The curve of correlation between the experimental and empirical values (Figure 2) appears very satisfactory, the slope is close to unity (0.99), the correlation coefficient is equal to 0.9994, and standard deviation is 1.2. The statistical data concerning the regression curves ZPEexp = aZPEtheor + b and ZPEexp = aZPEtheor are summarized in Table 3. The use of this data for the adjustment of the empirical values of vibrational zero-point energies (ZPEs) reduces the mean error to 0.9 kcal/mol.
Correlation between experimental ZPEs and empirical values calculated using
Calculation method | Linear model | ||||
---|---|---|---|---|---|
ZPEexp = aZPEtheor | ZPEexp = b + aZPEtheor | ||||
a | R2 | a | b | R2 | |
Proposed empirical model (Eq. (5)) | 0.993 ± 0.002 | 0.9998 | 0.99 | 0.272 | 0.9994 |
AM1 | 1.025 ± 0.003 | 0.9992 | 0.95 | 2.212 | 0.9982 |
DFT (B3LYP/6-31G*) | 0.999 ± 0.002 | 0.9996 | 0.95 | 0.304 | 0.9986 |
Schulman-Disch extended model (Eq. (4)) | 0.984 ± 0.0038 | 0.9986 | 0.95 | 3.545 | 0.9972 |
Coefficients a, b, and R2 in equations ZPEexp = b + aZPEtheor in both cases b = 0 and b ≠ 0.
In order to be able to compare the results obtained by the application of the empirical formula based on bond contribution additivity (Eq. (5)) to those obtained by the approach based on atomic contribution additivity, we have grouped in Table 2 the values of vibrational zero-point energies computed with the extended rule of Schulman and Disch (Eq. (4)). The increment of the silicon atom was calculated by AbdulHussain and Fleifel [28]. The value of this increment is shown in Table 4, with those previously published [25, 26, 27, 28, 29] for the atoms H, C, O, N, Cl, F, Br, S, and P. Note that the formula of Schulman and Disch was established on the basis that the structural isomers of organic compounds have almost the same value of ZPE. However, the difference can reach 4 kcal/mol [9]. The results obtained by the method based on the additivity of the atomic contributions show, for the 91 silicon compounds, an average error of 2.53 (6.9%). The correlation between the experimental and calculated values by the Schulman-Disch extended formula is shown in Figure 3. The statistical parameters obtained in this case are a correlation coefficient of 0.9972, a slope of 0.95, and a standard deviation of 2.45. Using the regression curves (Table 3) for adjusting the calculated values permits to reduce the mean error to 1.68 (4.0%) if the intercept is different from 0 (b ≠ 0) to 2.28 (7.0%) if b = 0. These results are slightly less good than those obtained by our approach.
Atom | Increment | Ref. | Atom | Increment | Ref. |
---|---|---|---|---|---|
H | 7120 | [25] | Cl | 2220 | [26] |
C | 3880 | [25] | S | 1870 | [27] |
N | 4050 | [26] | Br | 1600 | [27] |
O | 3400 | [26] | Si | −3510 | [28] |
F | 3270 | [26] | P | 0.035 | [29] |
Atom contributions to ZPE (in kcal/mol).
Correlation between experimental ZPEs and empirical values calculated using
To compare the results obtained by our empirical relationship with those obtained by quantum chemistry methods, we have calculated vibrational zero-point energies for the same organosilicon compounds using semiempirical (AM1) and DFT(B3LYP/6-31G*) methods. Results are summarized in Table 2. To correct calculated ZPEs, the scaling factors of Scott and Radom [80] are used.
Based on these results, the values calculated at the DFT(B3LYP/6-31G*) level are closer to the experimental data than those obtained at the semiempirical (AM1) level. The mean error is 2.50 (5.45%) for AM1, compared to 1.08 (1.86%) for DFT(B3LYP/6-31G*). The correlations obtained between the experimental values and those calculated by using AM1 and DFT methods are represented, respectively, in Figure 4a and b. Examination of this correlations shows that these methods are able to calculate accurately the zero-point vibration energies of the organosilicon compounds with a small advantage for the DFT method. For the adjustment of calculated ZPEs, the use of the regression line of the form ZPEexp = aZPEtheor (Table 3) permits to reduce the mean error of 2.5–1.83 kcal/mol for AM1 and from 1.08 to 1.06 kcal/mol for DFT(B3LYP/6-31G*). When the intercept is different from zero, the average error decreases from 2.5 to 1.37 kcal/mol for AM1 and from 1.08 to 1.00 for DFT(B3LYP/6-31G*).
(a) Correlation between experimental and theoretical (AM1) ZPE and (b) correlation between experimental and theoretical (B3LYP/6-31G*) ZPE.
As a result, the four estimates of vibrational zero-point energy of the organosilicon compounds (our empirical model, Schulman-Disch extended empirical formula, AM1, and DFT) are correct. But the empirical approaches have the advantage of simplicity and speed. In addition, the approach based on bond contributions additivity has also the advantage of providing different values of ZPE for the function isomers. Furthermore, the adjustment of calculated values, empirically or using quantum methods, with the ZPEexp = b + aZPEtheor model makes the four estimates comparable.
In this chapter, we reported the extension of our empirical relationship established in 2011 for the computation of zero-point vibrational energies (ZPE) of organosilicon compounds. The bond contributions of Si▬H, Si▬C, Si▬Cl, Si▬O, and Si▬Si were determined. The application of the proposed empirical model to more than 90 organosilicon shows the reliability of this model. The results derived from this model are compared with those obtained by quantum chemistry methods (semiempirical (AM1) and DFT (B3LYP/6-31G*)) on the one hand and to those obtained by similar empirical approach on the other hand. As a result, the empirical model provides a simple, fast, and accurate way to estimate the vibrational zero-point energies of organosilicon compounds.
Establishing these empirical rules is becoming increasingly important. Indeed, thermodynamic data play an important role in the understanding and design of chemical processes. Experimental methods require appropriate equipment, sufficient product purity, time, and cost of experience. The use of such techniques becomes difficult for toxic compounds. In addition, the large difference between the number of synthesized compounds and the available experimental data continues to increase. In such situation, the practical approach is to use predictive models to estimate the properties of compounds from the molecular structures. In this outlook, we propose to extend the field of application of the proposed empirical model to other compounds such as organoborons, organomagnesians, etc., and to establish similar approaches to estimate other thermodynamic quantities such as enthalpy of formation (ΔH0f), entropy (S0), and heat capacity (C).
ZPE | zero-point vibrational energy |
AM1 | Austin model 1 (developed by Michael Dewar and collaborators in 1985) |
Hvib(T) | vibrational enthalpy of the molecule |
Svib(T) | entropy due to vibrational motion |
H(T)−H(0) | thermal correction |
HF/6-31G* | Hartree-Fock theory in combination with the 6-31G(d) basis set |
DFT | density functional theory |
B3LYP/6-31G* | Becke’s three parameter exchange function (B3) with Lee-Yang-Parr correlation function (LYP) functional coupled with 6-31G* basis set |
Eqn | equation |
Diabetes is a metabolic disease that affects over 300 million people worldwide [1]. Diabetes is characterized by high blood glucose levels due to inadequate amounts of insulin produced and secreted by pancreatic beta cells, plus the loss of sensitivity to insulin in peripheral tissues [2]. There are two major types of diabetes, type 1 diabetes (T1D) and type 2 diabetes (T2D) [3]. T1D constitutes about 5% of all diabetes and is known as a T-cell mediated autoimmune invasion and destruction of insulin-producing beta cells in the pancreas, characterized by a significantly reduced beta cell mass and a significantly reduced secretion of insulin [4]. T2D accounts for about 90% of diabetes, and results from the failure of beta cells to compensate for the insensitivity of cells in responsive to insulin, which is called insulin resistance [5].
Role of macrophages in wound healing.
Diabetic patients can develop severe complications due to impairment in cell proliferation, differentiation, migration, immune responses, angiogenesis, etc., under a sustained hyperglycemic status [6]. Non-healing wounds, or diabetic foot ulcers (DFUs), are one of the most severe diabetic complications, and represent the leading cause of amputations, with an associated greater than 50% 5-year mortality [7]. Correspondingly, the treatment of DFUs comprises the highest annual US medical expenditure for any diagnosis [8]. Therefore, great effort has been made to understand the pathological processes during diabetic wound healing and to create novel therapies.
During the study of the mechanisms underlying diabetes-related impaired wound healing, accumulating evidence suggests that macrophages play a pivotal role in orchestrating proper wound healing [9]. In the early stages of normal wound healing, macrophages polarize to an M1-like phenotype, whereby they remove pathogens, dead cells and debris, and promote inflammation through secreting pro-inflammatory cytokines [10]. Later in the repair process, macrophages transition to more of an M2-like phenotype to resolve the inflammation and secrete trophic factors that promote the proliferation, differentiation and migration of fibroblasts, keratinocytes, mesenchymal cells and vascular endothelial cells, leading to tissue regeneration, neovascularization and wound repair [10]. These wound macrophages originate from different sources and interact with several other cell types through which they develop diverse functions to properly and efficiently assist with the repair process [11]. It is noteworthy that in diabetes there are alterations in the baseline function of macrophages, as well as the corresponding phenotypic changes in macrophages during the wound healing process [10]. Early in wound healing macrophages are responsible for the initiation and progression of inflammation and removal of pathogens, dead cells and debris. However, at later stages, macrophages instead contribute to the resolution of inflammation, re-organization of extracellular matrices (ECM), re-epithelialization, angiogenesis, cell and tissue regeneration and tissue remodeling through secreting a number of factors at late stages [12]. In a normal healthy situation, macrophages initially polarize to a pro-inflammatory M1 subtype to assist in the early stages of wound healing but then re-polarize to an alternative anti-inflammatory M2 subtype to assist in the later stages of wound healing [13]. Interestingly, diabetes leads to impairment of the M1-to-M2 transition in the later stages of wound healing, resulting in sustained inflammation and compromised cell proliferation, differentiation and migration, abnormal immune responses and inadequate angiogenesis [14]. Therefore, different strategies have been generated to target macrophages in order to reverse this pathologic inflammation during diabetic wound healing through modulating macrophage polarization [15]. In this book chapter, we discuss the role of macrophages in normal wound healing and their impairments during diabetic wound healing. We also discuss the present approaches to enhancing the repair of DFUs through targeting macrophages.
Neutrophils, macrophages and other cells involved in the innate immune system constitute a first line of defense against microorganisms and are critical for the control and resolution of common infections [16]. However, not all infectious organisms can be recognized by macrophages, for which lymphocytes of the adaptive immune system are present to create a more versatile defense system [17]. The innate and adaptive immune systems cooperate through many interactions among different cell types. For example, cells of the innate immune system such as macrophages, dendritic cells and natural killer (NK) cells orchestrate the initiation and the subsequent progression of lymphocyte-mediated adaptive immune responses, and then receive feedback signaling from lymphocytes to adapt their phenotypes and functions for the direct involvement in the removal of pathogens targeted by adaptive immune responses [18]. Moreover, the innate immune response by macrophages is the critical response to control infections before the adaptive immune response takes effect a few days after the infection [19]. Furthermore, macrophages also contribute to directing the adaptive immune response through antigen presentation and production and secretion of cytokines and chemokines [20].
Classical macrophages display a pro-inflammatory phenotype, which has been designated as “M1” macrophages, while another subtype of macrophages that are alternatively differentiated and exhibit anti-inflammatory properties or contribute to resolution of inflammation, tissue regeneration and remodeling, have been designated as “M2” macrophages [21]. The overall M1 or M2 characteristics in a given macrophage is called its “polarization” [22]. When a macrophage changes its expression pattern to fit a more M1 or M2 phenotype, it is called a polarized macrophage [22]. Macrophage polarization can either occur in undifferentiated macrophages (naïve macrophages) or occur in polarized macrophages, which is then called “re-polarization” [22]. It is now known that polarization of macrophages into the precise definition of “M1” or “M2” macrophages rarely occurs. Instead, macrophage typically polarize into a wide spectrum of phenotypes that exhibit distinct gene and protein expression patterns [23]. This broad range of differentiation pattern allows macrophages to perform diverse tasks throughout the body [23]. M1 macrophages are characterized by high levels of proinflammatory markers such as reactive oxygen species (ROS), inducible nitric oxide synthase (iNOS), nitric oxide (NO), CD11c, tumor necrosis factor (TNF)α, interleukin (IL)-6, IL-1β and major histocompatibility complex (MHC)-II [24]. In contrast, M2 macrophages express markers associated with healing and inhibition of inflammation such as high levels of arginase 1, CD163, CD206, CD301, IL-10, resistin-like molecule alpha1 (Fizz1) and chitinase-like protein 3 (Ym1) [25]. Microenvironmental autocrine and paracrine signals, together with epigenetic changes, seem to influence macrophage activation and polarization [26].
Wound healing is a very complex process encompassing 4 specific phases: hemostasis, inflammation, proliferation and remodeling. A successful wound closure requires the process to be well orchestrated by multiple cell types including keratinocytes, fibroblasts, endothelial cells, mesenchymal cells and inflammatory cells (macrophages, lymphocytes, neutrophils, NK cells, etc.) in a dynamic interactive way [10]. These 4 phases occur in a coordinated, linear and partially overlapping manner, in which one earlier phase is required for the completion of later phases [11]. The hemostasis phase is initiated immediately after injury, which involves vasoconstriction, platelet aggregation and activation of the clotting cascade resulting in clot formation and degranulation of platelets to convert soluble fibrinogen to insoluble fibrin and to release factors like P-selectin to recruit neutrophils to initiate the inflammatory phase [27, 28]. Next, the recruited neutrophils (peaking at 2 days after injury) release ROS, antimicrobial peptides and neutrophil extracellular traps in addition to chemokines to attract monocytes/macrophages [27]. Meanwhile, tissue-resident macrophages and other antigen-presenting cells like dendritic cells are activated and release factors [11]. The monocytes/macrophages from either the tissue or the circulation thus become the cell type dominating the inflammatory phase and are the central regulators of this inflammatory phase [29]. Here, the macrophages interact with many cell types, activate a number of signaling pathways, and release many soluble mediators, such as growth factors, chemokines, cytokines and metabolites that signal to other cell types. Thus, the macrophages orchestrate the complex tasks and biological activities to enhance wound healing [30]. At this phase of wound healing, which typically lasts 3 days, macrophages are mainly pro-inflammatory, or M1-like, and produce cytokines such as IL-1β, IL-6, IL-12 and TNFα [10]. However, as early as day 1 after initiation of wound healing, anti-inflammatory or M2-like macrophages start to appear and their numbers increase little by little until they become dominant by about 4 days after wounding when the proliferative phase starts [14]. During the proliferative phase, M2-like macrophages (peaking at 7 days after wound) produce and secrete factors to activate fibroblasts and keratinocytes to proliferate, differentiate and migrate to the wound and deposit collagen and other ECM proteins, though keratinocytes start to proliferate immediately after wound due to loss of contact inhibition [13]. Moreover, M2-like macrophages also promote angiogenesis through interactions with vascular endothelial cells [13]. A requirement for macrophages in the activation and functions of fibroblasts at this phase was proven by a study applying macrophage-depletion [31]. Interestingly, a very recent report showed a myeloid origin of about 10% of wound fibroblasts in a mouse skin wounding model, further supporting the important role and plasticity of macrophages during wound healing [32]. The final remodeling phase is the longest phase of wound healing, during which type I collagen gradually replaces type III collagen to increase tensile strength [33].
Macrophages are key regulators for the overall wound repair process [34]. The wound monocytes/macrophages have different origins. Before injury occurs, skin monocytes/macrophages consist of the tissue-resident macrophages as predominantly Langerhans cells in the epidermal layer, and also as dermal macrophages in the dermis [35]. Wounds without macrophages have less cell proliferation, inadequate differentiation, compromised migration, delayed re-epithelialization, impaired angiogenesis and reduced collagen deposition [27]. Moreover, reduced secretion of vascular endothelial growth factor (VEGF)-A and transforming growth factor (TGF)β1 renders the wounds less conducive to angiogenesis and cell proliferation, which are critical for a proper completion of wound repair [36]. The plasticity of macrophages allows them to be first polarized to a pro-inflammatory M1-like phenotype, and then transition or repolarize to an anti-inflammatory M2-like phenotype [29]. Indeed, M1 macrophages are primarily responsible for destruction of pathogens and production and release of inflammatory cytokines in the wound. Meanwhile, M2 macrophages are associated with the late repair and regeneration phases of wound healing, and they are critical for proper angiogenesis, regeneration and remodeling of ECM, cell growth and replacement, production of anti-inflammatory and trophic cytokines and resolution of the inflammation [37]. Actually, as stated above, macrophages are not all polarized uniformly throughout wound healing [23]. At any given time point they may be in a wide spectrum of phenotypes, and only the predominant phenotype presented as the overall macrophage phenotype at any given time in the repair process (Figure 1) [10, 23, 29, 38].
Dermal wound healing is a very complex process encompassing 4 specific phases: hemostasis, inflammation, proliferation and remodeling. These 4 phases occur in a coordinated, linear and partially overlapping manner, in which one earlier phase is required for the completion of later phases. The hemostasis phase is initiated immediately after injury, and involves vasoconstriction, platelet aggregation and activation of the clotting cascade, resulting in clot formation and degranulation of platelets to convert soluble fibrinogen into insoluble fibrin and to release factors like P-selectin to recruit neutrophils to initiate the inflammatory phase. Next, the recruited neutrophils (peaking at 2 days after injury) release chemokines to attract monocytes/macrophages. Meanwhile, tissue-resident macrophages and other antigen-presenting cells like dendritic cells are activated and release factors. The monocytes/macrophages from either the local tissue or the circulation thus dominate this phase of wound healing, which typically lasts 3 days. At this time, macrophages are mainly pro-inflammatory or M1-like, and produce cytokines such as IL-1β, IL-6, IL-12 and TNFα. However, as early as day 1 after the initiation of wound healing, anti-inflammatory or M2-like macrophages start to appear and their numbers increase little by little until they become dominant by about 4 days after wounding, the point when the proliferative phase starts. In the proliferative phase, M2-like macrophages (peaking at 7 days after wounding) produce and secrete factors to activate fibroblasts and keratinocytes to proliferate, differentiate and migrate to the wound and deposit of collagen and other ECM proteins. Moreover, M2-like macrophages also regulate angiogenesis through interactions with vascular endothelial cells. The final remodeling phase is the longest phase of wound healing, during which type III collagen is gradually replaced with type I collagen to increase tensile strength.
Innate immune cells including macrophages have been shown to exhibit a pro-inflammatory phenotype with production and secretion of inflammatory cytokines, factors and chemokines. In diabetic wounds these processes are pathologically exaggerated as a possible contributor to the poorly healing DFU [39]. High susceptibility of diabetic patients to bacterial infections and impaired wound repair is well known [11]. The molecular mechanisms underlying this weakness are not fully understood but have been extensively studied in the past. Now it is believed that disorders in glucose metabolism and related alteration in metabolic pathways may be the reason for this susceptibility [40, 41, 42]. Interestingly, for unclear reasons the number of Langerhans and dermal macrophages significantly increases in uninjured diabetic skin [43]. Moreover, the effects of diabetes on macrophages and the related wound healing process are profound, and likely stem from changes in many aspects of the diabetic environment [13, 15, 44, 45, 46, 47, 48]. Macrophages that are generated in a high glucose culture system exhibit a reduction in their phagocytic potential and are less capable of clearing an infection [49, 50]. Furthermore, macrophages derived from diabetic mice and human patients appear to have increased responsiveness to inflammatory stimulants and secrete more proinflammatory cytokines than normal, which seems to prevent their later transition into the more reparative M2-like phenotype [51, 52, 53, 54, 55]. Indeed, at the initiation of wound healing, the phagocytotic capacity of diabetic M1 macrophages is reduced due to suboptimal differentiation, which happens before the impaired transition of M1 macrophages to M2-like macrophages later on [49, 50]. The failure of their transition or repolarization in the later stages of wound healing prevents regeneration and the repair process, resulting in a delay or even failure to heal [12].
The mechanisms underlying these alterations in the diabetic macrophage phenotype have been extensively studied. Hyperglycemia affects macrophage polarization in vitro and in vivo [46, 56, 57, 58, 59, 60, 61, 62]. For example, it has been shown that diabetic mice or human patients have an increased ratio of chemokine (C-C motif) receptor (CCR7) to CD48 3 days after wound formation [46]. CCR7 is an M1 macrophage marker whereas CD48 is an M2 macrophage marker [24]. Moreover, M1 macrophages in diabetes express less matrix metalloproteinases 1 (MMP1) and more pro-inflammatory cytokines like TNFα, resulting in an impairment in keratinocyte migration and subsequent delay of wound repair [46]. Furthermore, a hyperglycemic environment has been shown to lead to an increase in many pro-inflammatory cytokines, including TNFα, IL-1β, IL-12 and IL-6 [63], rendering these M1 macrophages more metabolically active and pro-inflammatory, but less phagocytic [63, 64]. This specific alteration in the phenotype of M1 macrophages under hyperglycemic conditions further increases the sensitivity of macrophages to cytokine stimulation and starts a vicious cycle that maintains M1 macrophage polarization and leads to a prolonged inflammation during the wound healing process [64, 65].
The role of interleukins in macrophage differentiation and polarization has been recently studied [66, 67, 68, 69, 70, 71, 72, 73]. Some interleukins have been targeted in a therapeutic modality, exhibiting a significant impact on treatment outcomes. For example, depletion of a pro-inflammatory cytokine, IL-23, causes a significant increase in M2 macrophage polarization through loss of IL-17, which leads to improvements in diabetic wound healing [74]. Similar results have been obtained using IL-17-knockout mice or using antisera against IL-17 [74]. IL-1β is highly expressed in activated M1 macrophages in a hyperglycemic environment [63, 64]. Interestingly, experiments have shown that IL-1β expression is regulated by a protein complex called NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3), which is an inflammasome [75] that controls the dimerization and activation of caspase-1, leading to the subsequent transformation of the IL-1β precursor (pro-IL-1β) into its activated form IL-1β to be secreted [76]. Knockdown of NLRP3 with siRNA-mediated gene silencing reduced the production and secretion of IL-1β, which is beneficial to diabetic wound healing [77].
Recent research has also shed light on epigenetic alterations in macrophages in a hyperglycemic environment. These epigenetic changes can induce enhanced expression of proinflammatory cytokines to promote and sustain M1 macrophage polarization [78]. Now it is believed that epigenetic modifications are the main cause of the alterations in macrophage phenotype in diabetes [79]. The epigenetic modifications include histone modifications, DNA modifications and other post-transcriptional controls like microRNAs [10]. Moreover, regulation of macrophage polarization requires interactions with other cell types such as adipocytes, keratinocytes, fibroblasts and other immune cells (Neutrophils, T-cells, dendritic cells, etc.) that secrete factors to modulate macrophage polarization [14]. In the setting of diabetes, these cell-cell interactions are altered, leading to a suboptimal polarization of macrophages [46, 80, 81, 82].
A specific role for histone modification in the control of macrophage polarization has been recently highlighted. In eukaryotes, DNA and histones gather together to generate units called nucleosomes [83]. When histones are tightly wrapped with DNA, the access to transcriptional machinery is blocked to prevent transcription [84]. However, when DNA-histone machinery is disassembled, transcriptional binding is allowed. An N-terminal “tail” with lysine (K) residues on histones can be modified by some enzymes through catalyzing methylation and acetylation [84]. Histone methylation and demethylation are controlled by histone methyltransferases (HMTs) and histone demethylases (HDMs), respectively, which regulate macrophage differentiation and polarization [84] and are responsible for the M1 to M2 repolarization during wound healing [85]. For example, mixed-lineage leukemia 1 (MLL1) is a methyltransferase that catalyzes H3K4me3 deposition to affect macrophage polarization and the induction of expression of proinflammatory genes in macrophages [86]. Mechanistically, MLL1 is found to regulate changes in macrophages partially via Toll-like receptor 4 (TLR4) in both diabetic humans and diabetic mice [87, 88]. On the other hand, Suppressor of variegation, Enhancer of Zeste, Trithorax and myeloid-Nervy-DEAF-1 domain-containing protein 3 (SMYD3), which is another H3K4me3 methyltransferase, has been shown to regulate M2-like polarization of macrophages [89]. Besides HMTs, HDMs also play a critical role in macrophage polarization during wound healing. For example, Jumonji domain-containing protein 3 (JMJD3) is a H3K27 demethylase that regulates a context-dependent polarization of macrophages towards either a proinflammatory M1-like or an anti-inflammatory M2-like macrophage phenotype [90, 91, 92, 93, 94, 95]. JMJD3-mediated release of H3K27me3 is compromised in diabetic wound macrophages, resulting in enhanced and sustained expression of genes associated with inflammation [96, 97]. However, lipopolysaccharides (LPS) and IL-4 have been shown to induce JMJD3 for directing M2-like macrophage polarization [96]. Together, a lot of data have demonstrated the importance of histone methylation and demethylation by HMTs and HDMs in controlling macrophage polarization during wound repair [98, 99]. Transcriptional repression is often regulated by DNA methylation, which is catalyzed by DNA methyltransferases (DNMTs) to transfer a methyl group to the cytosine ring of DNA at clusters of CpG islands [100]. The potential binding of transcription factors to a promoter region is significantly altered via methylation of CpG islands on the promoter [101]. For example, DNMT1 has been shown to induce M1-like polarization of macrophages [102]. Moreover, genetic depletion of DNMT1 or chemical suppression of DNMT1 by 5-aza-2′-deoxycytidine promotes M2-like macrophage polarization [103] and improves wound healing in diabetic mice [104]. Macrophage polarization during wound healing has also been shown to be affected by histone acetylation and deacetylation. Transcriptional activation is enhanced by acetylation of the lysine residue on the histone tail, for which an acetyl group is transferred from acetyl CoA to the lysine residue catalyzed by histone acetyltransferases (HATs) [105]. In diabetes, it has been shown that histone deacetylase 6 (HDAC6) alters the phenotype of macrophages through IL-1β but not IL-10 [106].
Post-transcriptional control is also an important regulator of macrophage polarization in diabetes. For example, microRNAs have been shown to be important regulators of gene expression during macrophage polarization [9]. MicroRNAs (miRNAs) are non-coding small RNAs about 20 base pairs in length. miRNAs control protein levels of an expressed gene through Watson-Crick pairing to the 3′-untranslated region (3′-UTR) of the mRNA of a specific gene, resulting in altered protein translation [107]. It has been reported that M2 macrophages express high levels of miRNA-146, while M1 macrophages express low levels of miRNA-146 [108, 109], and the levels of miR-146 appear to alter macrophage polarization and their production and secretion of proinflammatory and anti-inflammatory cytokines [108, 109]. It has also been shown that miR-155 can induce an M1-like macrophage polarization through suppressing antagonists of proinflammatory cytokines [110]. Moreover, miR-33 was shown to favor M2 macrophage polarization through suppressing NLRP3 [111], a key inducer of IL-1β and inflammation [76, 77]. Similarly, long noncoding RNAs (lncRNAs) have an emerging role in regulating macrophage polarization [112, 113, 114, 115, 116]. Besides miRNAs, long non-coding RNAs also play essential roles in macrophage phenotypic determination and control of inflammation [117]. The role of lncRNA GASS in macrophage polarization and associated wound healing has been reported recently [113].
DFUs will likely require multimodal therapies for optimal treatment. Novel therapeutics are being generated, including specific targeting of macrophages. Since the initial trigger of all the macrophage defects in diabetes appears to be sustained high blood glucose, correction of the primary problem, the hyperglycemia, would appear to be the first approach to treat problems related to diabetic wound healing [7]. However, only half of diabetics can reach the recommend standard hemoglobin A1c (HbA1c) level of <7.0% (issued by the American Diabetes Association (ADA)) [118]. Thus, it is important to search for novel therapies beyond control of blood glucose [7].
Insulin administration is a regular and effective therapy for those unresponsive to diet changes or non-insulin medications. Insulin has been found to reduce the number of M1 macrophages. Moreover, insulin has been shown to induce M1 to M2 macrophage polarization through peroxisome proliferator-activated receptor-gamma (PPAR-γ) and phosphatidylinositol-3 kinase (PI3K)/Protein kinase B (Akt)/Ras-related C3 botulinum toxin substrate 1 (Rac-1) signaling pathways [47]. Interestingly, the PPAR-γ pathway that reduces proinflammatory cytokine expression and enhances M2 macrophage polarization in normal wound healing is impaired in diabetes and could be partially recovered by insulin [119]. Metformin is a commonly used medication for diabetes. Metformin treatment has been shown to increase M2 macrophages and decrease M1 macrophages [120, 121, 122, 123, 124, 125], likely through NLRP3 inflammasome suppression, which was discussed above [123]. Melatonin is a medication not for diabetes. However, it was found to affect macrophage polarization in diabetic wound, likely through effects on insulin [47, 126].
Treatment of chronic wounds benefits largely from localized therapies, which have the advantage of avoiding systemic effects and allowing for local and direct treatment [127]. A hydrocolloid dressing to provide moisture to the wound has been shown to improve M1-to-M2 macrophage transition to favor wound healing, especially in diabetes [128]. In another study, use of a modified dressing in diabetic mice led to an earlier appearance of M2 macrophages at the wound [129]. Thus, certain dressings to induce an M2 macrophage polarization appear to be an attractive strategy for treating chronic diabetic wounds. These dressings help the wounds heal through moisture provision, prevention of infection, induction of anti-inflammatory effects and generation of trophic factors. Along with these dressing benefits, Collagenase Santyl Ointment (CSO), with an important component called Clostridial collagenase, has been shown to increase local expression of IL-10 and arginase 1 that are both critical for M2-like macrophage polarization and functionality [130]. Another modified collagen gel has also been shown to increase IL-10 expression and macrophage migration, resulting in a substantial increase in M2 macrophages in the wound at different time points [128]. In addition, increases in IL-10 expression have also been detected after use of docosahexaenoic acid to treat diabetic wounds, with the therapeutic outcome correlated to the degree of M2 macrophage polarization [45, 131, 132].
Multipotent stem cells (MSCs) have been used in the treatment of DFUs, taking advantage of the capacity of MSCs to differentiate into different cell types such as endothelial cells, fibroblasts and smooth muscle cells that are critical for wound healing. These newly formed cells can produce and secrete trophic factors like VEGF-A, fibroblast growth factor (FGF) and platelet-derived growth factor (PDGF) [133, 134, 135]. Recently, we have reported that human MSCs express high levels of miR-205-5p, which inhibits protein translation of VEGF-A through 3’-UTR interactions [136]. Expressing antisense of miR-205-5p (as-miR-205-5p) in human MSCs significantly improved the therapeutic effects of human MSCs on diabetic wound healing in rodent models [136]. Next, we reported that MALAT1 is a lncRNA acting as a competing endogenous RNA (ceRNA) for miR-205-5p and is absent in human MSCs [137]. Expression of MALAT1 significantly attenuated the high expression of miR-205-5p in human MSCs, resulting in upregulation of VEGF-A production and improved therapeutic effects of human MSCs on diabetic wound repair [137]. Of note, these improvement in treating diabetic wounds through increasing VEGF-A levels in human MSCs was shown to be associated with increased vascularization of the wounds [136, 137]. Macrophages express high levels of VEGF receptor 1 (VEGFR1), which is one of the major receptors on macrophages to respond to chemoattractants. Thus, it is possible that the therapeutic effects of increased VEGF-A levels in human MSCs may be due, at least partially, to alterations in macrophage proliferation, differentiation and polarization [21]. Further studies of the exact alterations in macrophages caused by VEGF-A are needed. We have recently shown that another VEGF family member, placental growth factor (PlGF), is capable of altering macrophage migratory capacity and polarization [138]. Moreover, we and others have recently shown that PlGF is decreased in DFUs [139, 140]. PlGF injection significantly improved angiogenesis and diabetic wound healing, with both positive effects being abolished by macrophage-specific depletion of VEGF1R to block the effects of PlGF on macrophages [139]. Together, these approaches showed promise as strategies for treating diabetic wound through targeting macrophages.
The treatment of diabetic wounds will benefit from a multi-modal approach including control of hyperglycemia, topical treatment, prevention of secondary infection and inflammation and cellular therapy targeting macrophages.
We appreciate the help from Dr. Xiangwei Xiao from Children’s Hospital of Pittsburgh, USA, for his critical advice and proofreading during the preparation of this book chapter. This work was supported by grants from the National Natural Science Foundation of China (NO: 81860153 and 81500589), Key Project of Natural Science Foundation of Jiangxi Province (NO: 20202ACBL216007), Key project of Jiangxi Provincial Department of Education (NO: GJJ180021), all awarded to Dr. Lingyan Zhu.
The authors declare no conflict of interest.
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Subtle changes that occur over time in periodontal tissues that are below the detection limit of visual examination or periodontal probing can be found and tracked accurately over time using 3D imaging, fluorescence spectroscopy, and optical coherence tomography. During debridement of teeth and dental implants, the effective removal of subgingival microbial biofilms and dental calculus deposits can be enhanced using magnifying loupes and operating microscopes and by novel methods based on the interactions of light with bacterial deposits, such as differential reflectometry and light-induced fluorescence. While such techniques can also be used using initial case assessment, their primary purpose is for checking debridement procedures, since the point when bacterial deposits are no longer present represents an endpoint for treatment. The concept of real-time feedback has been developed, using fluorescence readings to control the removal of deposits. Overall, optical methods can support traditional periodontal diagnosis and improve treatment planning and clinical periodontal care.",book:{id:"7244",slug:"periodontology-and-dental-implantology",title:"Periodontology and Dental Implantology",fullTitle:"Periodontology and Dental Implantology"},signatures:"Fardad Shakibaie and Laurence Walsh",authors:[{id:"179467",title:"Prof.",name:"Laurence",middleName:null,surname:"Walsh",slug:"laurence-walsh",fullName:"Laurence Walsh"},{id:"235443",title:"Dr.",name:"Fardad",middleName:null,surname:"Shakibaie",slug:"fardad-shakibaie",fullName:"Fardad Shakibaie"}]},{id:"24363",title:"Biomechanics of Tooth-Movement: Current Look at Orthodontic Fundamental",slug:"biomechanics-of-tooth-movement-current-look-at-orthodontic-fundamental",totalDownloads:26816,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"277",slug:"principles-in-contemporary-orthodontics",title:"Principles in Contemporary Orthodontics",fullTitle:"Principles in Contemporary Orthodontics"},signatures:"Joanna Antoszewska and Nazan Küçükkeles",authors:[{id:"50158",title:"Prof.",name:"Joanna",middleName:null,surname:"Antoszewska",slug:"joanna-antoszewska",fullName:"Joanna Antoszewska"}]},{id:"71271",title:"Flap Techniques in Dentoalveolar Surgery",slug:"flap-techniques-in-dentoalveolar-surgery",totalDownloads:2628,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Most dentoalveolar procedures involve the reflection of mucosal flaps. 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Yet the common flap’s principles are fundamental for all types of flaps regardless of their application, namely, it should provide wide exposure, clear vision, good access, and assure rich vascularity and good final aesthetic outcome.",book:{id:"9387",slug:"oral-diseases",title:"Oral Diseases",fullTitle:"Oral Diseases"},signatures:"Randa Abdulmoein AlFotawi",authors:[{id:"308701",title:"Dr.",name:"Randa",middleName:"Abdulmoein",surname:"Alfotawi",slug:"randa-alfotawi",fullName:"Randa Alfotawi"}]},{id:"65088",title:"Evaluation and Management of Mandibular Fracture",slug:"evaluation-and-management-of-mandibular-fracture",totalDownloads:2903,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The mandibular bone is an important component of the facial bone, which has a unique role in digestive system, speech, and facial esthetics. For these important functions of mandibular bone, it is vital that surgeons should not only treat function but also consider the esthetics together. Mandibular fractures are among the most common traumatic injuries of the maxillofacial region. Even though treatment modalities are well established and being practiced for a long time, untreated and postoperative complications still decrease the patient’s quality of life. This chapter aims to describe the cause, clinical presentations, diagnoses, and current treatment methods on the basis of resent literature.",book:{id:"7572",slug:"trauma-in-dentistry",title:"Trauma in Dentistry",fullTitle:"Trauma in Dentistry"},signatures:"Guhan Dergin, Yusuf Emes and Buket Aybar",authors:[{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin"},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes"},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar"}]},{id:"56461",title:"Permanent Maxillary and Mandibular Incisors",slug:"permanent-maxillary-and-mandibular-incisors",totalDownloads:2715,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The permanent incisors are the front teeth that erupt between 6 and 8 years of age. They are eight in number, four upper and four lower, two centrals and two laterals. They have sharp biting surfaces designed for shearing and cutting of food materials into small chewable pieces. They are the teeth most visible to the others during eating, smiling and talking, and thus, they have high aesthetic value for the individuals. The unique characteristics, arch position, function, development and chronological age of each tooth will be highlighted. In addition, the different aspects with their geometric outlines, outlines and surface anatomy of these teeth will be described. A brief explanation about the pulp cavity, tooth socket and normal occlusion for each tooth will be included.",book:{id:"5814",slug:"dental-anatomy",title:"Dental Anatomy",fullTitle:"Dental Anatomy"},signatures:"Mohammed E. Grawish, Lamyaa M. Grawish and Hala M. Grawish",authors:[{id:"82989",title:"Prof.",name:"Mohammed",middleName:"E",surname:"Grawish",slug:"mohammed-grawish",fullName:"Mohammed Grawish"}]}],onlineFirstChaptersFilter:{topicId:"174",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"80964",title:"Upper Airway Expansion in Disabled Children",slug:"upper-airway-expansion-in-disabled-children",totalDownloads:44,totalDimensionsCites:0,doi:"10.5772/intechopen.102830",abstract:"Breathing is essential for life in all of its stages. Cellular, mitochondrial respiration requires an adequate supply of oxygen, provided by the air we breathe, after airway conduction, treatment by the lungs, and transport to tissues. At different stages of life, pediatric dentists and orthodontists can intervene in the upper airway, expanding it, which helps with ventilation. The greater airway space, if used, contributes in different ways to the child’s development and the recovery of respiratory problems and should always be present as a weapon that physicians and the population should know. The value of the techniques becomes even more important when applied to children and young people with disabilities who can significantly improve their development and performance. Rapid Maxillary Expansion and Extraoral Traction Appliances are two important pediatric resources to treat these children. Clinical practice of the authors, is discussed, emphasizing the importance of early intervention and the need for multi and interdisciplinary collaboration in the follow-up of disabled people.",book:{id:"10827",title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg"},signatures:"David Andrade, Joana Andrade, Maria-João Palha, Cristina Areias, Paula Macedo, Ana Norton, Miguel Palha, Lurdes Morais, Dóris Rocha Ruiz and Sônia Groisman"},{id:"80963",title:"Pain Perception in Patients Treated with Ligating/Self-Ligating Brackets versus Patients Treated with Aligners",slug:"pain-perception-in-patients-treated-with-ligating-self-ligating-brackets-versus-patients-treated-wit",totalDownloads:33,totalDimensionsCites:0,doi:"10.5772/intechopen.102796",abstract:"This study compared the perception of pain experienced by patients undergoing orthodontic treatment with conventional, self-ligating brackets and aligners, and investigated the impact that pain had on their daily lives. 346 consecutive patients were included in the study: 115 patients treated with conventional brackets, 112 Patients treated with self-ligating brackets, and 119 patients treated with aligners. The quantitative aspect of pain was assessed using the Visual Analogue Scale, while the qualitative aspect of pain was evaluated using the Moroccan Short Form of McGILL Pain questionnaire. In all three groups experienced pain after activation tended to decrease in the following week. This pain was greater in patients with conventional braces and less in patients with aligners. Using the M-SF-MPQ to describe the qualitative aspect of the pain revealed that the “cramping مزير,” “aching تيألم ” aspect was most accentuated in the 3 groups. Medication intake was correlated with the intensity of pain experienced in all 3 systems. As for the impact of pain on daily activities, patients in groups of conventional and self-ligating braces showed more pain than those in the aligners group. Overall, aligners were less painful than conventional and self-ligating appliances. Patients did not suffer from an alteration in their quality of life due to orthodontic treatment.",book:{id:"10780",title:"Current Trends in Orthodontics",coverURL:"https://cdn.intechopen.com/books/images_new/10780.jpg"},signatures:"Farid Bourzgui, Rania Fastani, Salwa Khairat, Samir Diouny, Mohamed El Had, Zineb Serhier and Mohamed Bennani Othmani"},{id:"80839",title:"Herbs and Oral Health",slug:"herbs-and-oral-health",totalDownloads:69,totalDimensionsCites:0,doi:"10.5772/intechopen.103715",abstract:"Herbal medicine has long been used to prevent and control disease, and it can minimize the potential side effects of chemical products. However, side effects from herbs do exist. Most of the challenges with herbal medicine revolves around inadequate information about the effect of herbs in the oral cavity, the mechanism of action, and potential side effects. There are several herbs described in this chapter have anti-inflammatory, anti-bacterial, anti-viral, anti-fungal in oral micro-organisms. It includes aloe vera, ginger, clove, cinnamon, garlic, neem, miswak, turmeric, tulsi, green tea, chamomile, fenugreek, anise plant, peppermint, bloodroot, caraway, eucalyptus, phyllanthus emblica, black seed, myrrh, rosemary, sage, and thyme; some may act as an alternative management option to current treatments for oral conditions such as caries prevention, gingivitis, periodontitis, oral burn, ulcers and inflammation, after extraction, dry mouth, pain reduction, anesthesia, intracanal medications, ill-fitting dentures, peri-implant mucositis and peri-implantitis. It can be used in several forms such as mouthwashes, toothpastes, topical agents or local drug delivery devices. However, more research is needed to understand their mechanisms and potential side effects.",book:{id:"10827",title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg"},signatures:"Zuhair S. Natto"},{id:"80441",title:"Periodontitis and Heart Disease: Current Perspectives on the Associative Relationships and Preventive Impact",slug:"periodontitis-and-heart-disease-current-perspectives-on-the-associative-relationships-and-preventive",totalDownloads:66,totalDimensionsCites:0,doi:"10.5772/intechopen.102669",abstract:"Due to the important advancement and the accumulation of new evidence on the periodontitis-cardiovascular disease (CVD) relationship as well as the major medical, economic and social burden caused by both diseases this chapter aims to review existing epidemiological and pathogenetic links related to this topic. Also, this chapter aims to highlight the impact of the periodontitis-CVD relationships on clinical practice and on the preventive approaches targeting to decrease the impact of periodontitis on CVD. Periodontitis is an infectious disease eliciting local and general inflammation, which leads to periodontal destruction and systemic involvement. Several pathways could explain the link between periodontitis and CVD such as bacteraemia, chronic persistent systemic inflammation and oxidative stress. The first step in the treatment of periodontitis addresses the elimination of microbial components, which lead to a decrease in local and systemic inflammation. Periodontal therapy seems to positively impact CVD. Specialists should inform patients with CVD on the negative impact of periodontitis on their systemic status and refer patients to the periodontist for an extensive examination as routine management of CVD. Some possible risks of periodontal therapy should be considered in patients undergoing antithrombotic medication.",book:{id:"10827",title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg"},signatures:"Alexandra Roman, Andrada Soancă, Bogdan Caloian, Alexandru Bucur, Gabriela Valentina Caracostea, Andreia Paraschiva Preda, Dora Maria Popescu, Iulia Cristina Micu, Petra Șurlin, Andreea Ciurea, Diana Oneț, Mircea Viorel Ciurea, Dragoș Alexandru Țermure and Marius Negucioiu"},{id:"79498",title:"Oral Aspects and Dental Management of Special Needs Patient",slug:"oral-aspects-and-dental-management-of-special-needs-patient",totalDownloads:113,totalDimensionsCites:0,doi:"10.5772/intechopen.101067",abstract:"Individuals with special needs are the most underserved regarding healthcare needs in almost all populations. Special needs patients with intellectual disability have muscle coordination disorder, impaired oral motor function, drooling, weak muscles that cause chewing and swallowing problems. Also, soft diet consumption makes this population more prone to dental disease. They have more caries, missing teeth, orthodontic and periodontal problems. Besides more difficulties obtaining professional dental care than other segments of the population. Though many countries developed community-based systems to improve oral health for people with special needs, providing good oral health mainly depends on the effort of the families. Therefore the education of the caregiver about oral hygiene provision is also critical for the special needs patient to enjoy a lifetime of oral health the same as other members of the society.",book:{id:"10827",title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg"},signatures:"Pinar Kiymet Karataban"},{id:"79699",title:"Metabolomics Distinction of Cigarette Smokers from Non-Smokers Using Non-Stationary Benchtop Nuclear Magnetic Resonance (NMR) Analysis of Human Saliva",slug:"metabolomics-distinction-of-cigarette-smokers-from-non-smokers-using-non-stationary-benchtop-nuclear",totalDownloads:56,totalDimensionsCites:0,doi:"10.5772/intechopen.101414",abstract:"Implementations of high-field nuclear magnetic resonance (NMR) facilities into metabolomics studies are unfortunately restricted by their large dimensions, high costings, and specialist technical staff requirements. Therefore, here the application and practical advantages offered by low-field (60 MHz), compact NMR spectrometers for probing the metabolic profiles of human saliva was explored, as was their value in salivary metabolomics studies. Saliva samples were collected from cigarette smoking (n = 11) and non-smoking (n = 31) human participants. 1H NMR spectra were acquired on both low-field (60 MHz) and medium-field (400 MHz) spectrometers. Metabolomics analyses were employed to evaluate the consistencies of salivary metabolite levels determined, and their abilities to distinguish between smokers and non-smokers. Low-field 1H NMR analysis detected up to 15, albeit permitted the reliable quantification of 5, potentially key diagnostic biomolecules simultaneously (LLOQ values 250–400 μmol/L), although these were limited to those with the most prominent resonances. Such low-field profiles were also found to be suitable for salivary metabolomics investigations, which confirmed the successful discrimination between smoking and non-smoking participant sample donors. Differences observed between these groups were largely ascribable to upregulated salivary levels of methanol, and its metabolite formate, in the smoking group, but higher smoking-mediated concentrations of acetate, propionate and glycine may arise from a diminished salivary flow-rate in these participants. In conclusion, determination of salivary biomolecules using low-field, benchtop 1H NMR analysis techniques were found to be valuable for bioanalytical and metabolomics investigations. Future perspectives for the applications of this non-stationary NMR technique, for example for the on-site ‘point-of-care’ testing of saliva samples for diagnostic oral disease screening purposes at dental surgeries and community pharmacies, are considered.",book:{id:"10827",title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg"},signatures:"Benita C. 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",coverUrl:"https://cdn.intechopen.com/series/covers/23.jpg",latestPublicationDate:"August 12th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"280770",title:"Dr.",name:"Katherine K.M.",middleName:null,surname:"Stavropoulos",slug:"katherine-k.m.-stavropoulos",fullName:"Katherine K.M. Stavropoulos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRdFuQAK/Profile_Picture_2022-05-24T09:03:48.jpg",biography:"Katherine Stavropoulos received her BA in Psychology from Trinity College, in Connecticut, USA and her Ph.D. in Experimental Psychology from the University of California, San Diego. She completed her postdoctoral work at the Yale Child Study Center with Dr. James McPartland. Dr. Stavropoulos’ doctoral dissertation explored neural correlates of reward anticipation to social versus nonsocial stimuli in children with and without autism spectrum disorders (ASD). She has been a faculty member at the University of California, Riverside in the School of Education since 2016. Her research focuses on translational studies to explore the reward system in ASD, as well as how anxiety contributes to social challenges in ASD. She also investigates how behavioral interventions affect neural activity, behavior, and school performance in children with ASD. She is also involved in the diagnosis of children with ASD and is a licensed clinical psychologist in California. She is the Assistant Director of the SEARCH Center at UCR and is a faculty member in the Graduate Program in Neuroscience.",institutionString:null,institution:{name:"University of California, Riverside",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:2,paginationItems:[{id:"89",title:"Education",coverUrl:"https://cdn.intechopen.com/series_topics/covers/89.jpg",isOpenForSubmission:!1,editor:{id:"260066",title:"Associate Prof.",name:"Michail",middleName:null,surname:"Kalogiannakis",slug:"michail-kalogiannakis",fullName:"Michail Kalogiannakis",profilePictureURL:"https://mts.intechopen.com/storage/users/260066/images/system/260066.jpg",biography:"Michail Kalogiannakis is an Associate Professor of the Department of Preschool Education, University of Crete, and an Associate Tutor at School of Humanities at the Hellenic Open University. He graduated from the Physics Department of the University of Crete and continued his post-graduate studies at the University Paris 7-Denis Diderot (D.E.A. in Didactic of Physics), University Paris 5-René Descartes-Sorbonne (D.E.A. in Science Education) and received his Ph.D. degree at the University Paris 5-René Descartes-Sorbonne (PhD in Science Education). His research interests include science education in early childhood, science teaching and learning, e-learning, the use of ICT in science education, games simulations, and mobile learning. He has published over 120 articles in international conferences and journals and has served on the program committees of numerous international conferences.",institutionString:"University of Crete",institution:{name:"University of Crete",institutionURL:null,country:{name:"Greece"}}},editorTwo:{id:"422488",title:"Dr.",name:"Maria",middleName:null,surname:"Ampartzaki",slug:"maria-ampartzaki",fullName:"Maria Ampartzaki",profilePictureURL:"https://mts.intechopen.com/storage/users/422488/images/system/422488.jpg",biography:"Dr Maria Ampartzaki is an Assistant Professor in Early Childhood Education in the Department of Preschool Education at the University of Crete. Her research interests include ICT in education, science education in the early years, inquiry-based and art-based learning, teachers’ professional development, action research, and the Pedagogy of Multiliteracies, among others. She has run and participated in several funded and non-funded projects on the teaching of Science, Social Sciences, and ICT in education. She also has the experience of participating in five Erasmus+ projects.",institutionString:"University of Crete",institution:{name:"University of Crete",institutionURL:null,country:{name:"Greece"}}},editorThree:null},{id:"90",title:"Human Development",coverUrl:"https://cdn.intechopen.com/series_topics/covers/90.jpg",isOpenForSubmission:!0,editor:{id:"191040",title:"Dr.",name:"Tal",middleName:null,surname:"Dotan Ben-Soussan",slug:"tal-dotan-ben-soussan",fullName:"Tal Dotan Ben-Soussan",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBf1QAG/Profile_Picture_2022-03-18T07:56:11.jpg",biography:"Tal Dotan Ben-Soussan, Ph.D., is the director of the Research Institute for Neuroscience, Education and Didactics (RINED) – Paoletti Foundation. Ben-Soussan leads international studies on training and neuroplasticity from neurophysiological and psychobiological perspectives. As a neuroscientist and bio-psychologist, she has published numerous articles on neuroplasticity, movement and meditation. She acts as an editor and reviewer in several renowned journals and coordinates international conferences integrating theoretical, methodological and practical approaches on various topics, such as silence, logics and neuro-education. She lives in Assisi, Italy.",institutionString:"Research Institute for Neuroscience, Education and Didactics, Patrizio Paoletti Foundation",institution:null},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:11,paginationItems:[{id:"83053",title:"Apologies in L2 French in Canadian Context",doi:"10.5772/intechopen.106557",signatures:"Bernard Mulo Farenkia",slug:"apologies-in-l2-french-in-canadian-context",totalDownloads:0,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Bernard",surname:"Mulo Farenkia"}],book:{title:"Second Language Acquisition - Learning Theories and Recent Approaches",coverURL:"https://cdn.intechopen.com/books/images_new/11480.jpg",subseries:{id:"89",title:"Education"}}},{id:"82903",title:"Walking Accessibility to Primary Healthcare Services: An Inequity Factor for Olders in the Lisbon Metropolitan Area (Portugal)",doi:"10.5772/intechopen.106265",signatures:"Eduarda Marques da Costa, Ana Louro, Nuno Marques da Costa, Mariana Dias and Marcela Barata",slug:"walking-accessibility-to-primary-healthcare-services-an-inequity-factor-for-olders-in-the-lisbon-met",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Social Aspects of Ageing - 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Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"August 3rd, 2022",hasOnlineFirst:!0,numberOfOpenTopics:3,numberOfPublishedChapters:107,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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