\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{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"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"10072",leadTitle:null,fullTitle:"Nanotechnology and the Environment",title:"Nanotechnology and the Environment",subtitle:null,reviewType:"peer-reviewed",abstract:"Nanotechnology is a vibrant area of research and a growing industry. The core scientific principles and applications of this interdisciplinary field bring together chemists, physicists, materials scientists, and engineers to meet the potential future challenges for sustainable development through new technologies and preparation of advanced materials with sustainable environmental protection. This book on Nanotechnology and the Environment includes the design and the sophisticated fabrication of nanomaterials along with their potential energy and environmental applications. This book is a significant contribution towards the development of the knowledge for all advanced undergraduate, graduate level students, researchers, and professional engineers leading in the fields of nanotechnology, nanochemistry, macromolecular science and those who have interest in energy and environmental science.",isbn:"978-1-78985-671-2",printIsbn:"978-1-78985-228-8",pdfIsbn:"978-1-78985-672-9",doi:"10.5772/intechopen.87903",price:119,priceEur:129,priceUsd:155,slug:"nanotechnology-and-the-environment",numberOfPages:170,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"f68ba7ccb7700868a54c347421f572fb",bookSignature:"Mousumi Sen",publishedDate:"December 2nd 2020",coverURL:"https://cdn.intechopen.com/books/images_new/10072.jpg",numberOfDownloads:8097,numberOfWosCitations:13,numberOfCrossrefCitations:17,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:51,numberOfDimensionsCitationsByBook:3,hasAltmetrics:1,numberOfTotalCitations:81,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 3rd 2019",dateEndSecondStepPublish:"March 3rd 2020",dateEndThirdStepPublish:"May 2nd 2020",dateEndFourthStepPublish:"July 21st 2020",dateEndFifthStepPublish:"September 19th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"310218",title:"Dr.",name:"Mousumi",middleName:null,surname:"Sen",slug:"mousumi-sen",fullName:"Mousumi Sen",profilePictureURL:"https://mts.intechopen.com/storage/users/310218/images/system/310218.jpg",biography:"Mousumi Sen has a doctorate degree from the Indian Institute of Technology, Delhi, India. She is currently an Assistant Professor in the Department of Applied Chemistry, Amity University. Her research focuses on the prediction of pollutant dispersion from industrial areas, development of effective and sustainable methods for the removal of inorganic and organic pollutants from polluted water, food chemistry, heavy metal detoxification, composites/nanocomposites, water research, bio-inorganic chemistry, and nano chemistry. She has published numerous peer-reviewed research articles in journals of high repute as well as edited book chapters, authored a book, edited a book, and conference proceeding papers in her credit.",institutionString:"Amity University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Amity University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"208",title:"Material Science",slug:"nanotechnology-and-nanomaterials-material-science"}],chapters:[{id:"72931",title:"Modern Trends in Uses of Different Wastes to Produce Nanoparticles and Their Environmental Applications",doi:"10.5772/intechopen.93315",slug:"modern-trends-in-uses-of-different-wastes-to-produce-nanoparticles-and-their-environmental-applicati",totalDownloads:464,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Wastes are produced at large amounts all over the world. These wastes cause a variety of problems to the ecosystem, plants, animals, and humans. In this chapter, we discuss the wastes, types of wastes, sources of wastes, and problems related to wastes, especially health-related problems. Then we discuss agricultural wastes and how we can synthesize different nanoparticles from them. Also, we discuss industrial wastes and different nanoparticles synthesized from them. Additionally, we discuss fruit wastes and production of different nanoparticles and also food wastes and their uses in nanoparticle syntheses. Also, we can use other wastes to produce nanoparticles. In applications section, we discuss the use of different nanoparticles produced in agriculture, removal of heavy metals and pollutants from environment, industry and finally medical applications. We will finish our chapter with the topic of healthy and safe synthesis of nanoparticles produced by different wastes and then conclusion.",signatures:"Salah Abdelbary and Hadeer Abdelfattah",downloadPdfUrl:"/chapter/pdf-download/72931",previewPdfUrl:"/chapter/pdf-preview/72931",authors:[{id:"272562",title:"Dr.",name:"Salah",surname:"Abdelbary",slug:"salah-abdelbary",fullName:"Salah Abdelbary"}],corrections:null},{id:"71346",title:"Application of Nanomaterials in Environmental Improvement",doi:"10.5772/intechopen.91438",slug:"application-of-nanomaterials-in-environmental-improvement",totalDownloads:1757,totalCrossrefCites:0,totalDimensionsCites:13,hasAltmetrics:0,abstract:"In recent years, researchers used many scientific studies to improve modern technologies in the field of reducing the phenomenon of pollution resulting from them. In this chapter, methods to prepare nanomaterials are described, and the main properties such as mechanical, electrical, and optical properties and their relations are determined. The investigation of nanomaterials needed high technologies that depend on a range of nanomaterials from 1 to 100 nm; these are scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffractions (XRD). The applications of nanomaterials in environmental improvement are different from one another depending on the type of devices used, for example, solar cells for producing clean energy, nanotechnologies in coatings for building exterior surfaces, and sonochemical decolorization of dyes by the effect of nanocomposite.",signatures:"Ali Salman Ali",downloadPdfUrl:"/chapter/pdf-download/71346",previewPdfUrl:"/chapter/pdf-preview/71346",authors:[{id:"313275",title:"Associate Prof.",name:"Ali",surname:"Salman",slug:"ali-salman",fullName:"Ali Salman"}],corrections:null},{id:"73430",title:"Biological Synthesis of Nanoparticles Using Endophytic Microorganisms: Current Development",doi:"10.5772/intechopen.93734",slug:"biological-synthesis-of-nanoparticles-using-endophytic-microorganisms-current-development",totalDownloads:687,totalCrossrefCites:5,totalDimensionsCites:9,hasAltmetrics:0,abstract:"Nanotechnology is a new emerging interdisciplinary approach created by pairing of engineering, chemical, and biological approaches. This technology produces nanoparticles using different methods of traditional physical and chemical processes; however, the outlook in this field of research is to use ecofriendly, nontoxic, and clean methods for the synthesis of nanoparticles. Biological entities, such as plants, bacteria, fungi, algae, yeast, and actinomycetes, are the best candidate to achieve this goal. Among the biological route, those involve endophtic microorganisms to reduce metallic ions into nanoparticles. This method is considered as an attractive option and can open a new horizon on the interface of biology and nanotechnology. The present chapter highlights the latest research about endophytic microorganisms and their application in the synthesis of nanoparticles, as well as the mechanisms involved in the formation of nanoparticles.",signatures:"Omar Messaoudi and Mourad Bendahou",downloadPdfUrl:"/chapter/pdf-download/73430",previewPdfUrl:"/chapter/pdf-preview/73430",authors:[{id:"318629",title:"Dr.",name:"Omar",surname:"Messaoudi",slug:"omar-messaoudi",fullName:"Omar Messaoudi"},{id:"329043",title:"Prof.",name:"Mourad",surname:"Bendahou",slug:"mourad-bendahou",fullName:"Mourad Bendahou"}],corrections:null},{id:"73145",title:"Nanotechnology in the Service of Solar Energy Systems",doi:"10.5772/intechopen.93014",slug:"nanotechnology-in-the-service-of-solar-energy-systems",totalDownloads:892,totalCrossrefCites:4,totalDimensionsCites:6,hasAltmetrics:0,abstract:"Nanotechnology can help to address the existing efficiency hurdles and greatly increase the generation and storage of solar energy. A variety of physical processes have been established at the nanoscale that can improve the processing and transmission of solar energy. The application of nanotechnology in solar cells has opened the path to the development of a new generation of high-performance products. When competition for clean energy options is growing, a variety of potential approaches have been discussed in order to expand the prospects. New principles have been explored in the area of solar cell generation, multi-generation, spectrum modulation, thermo-photoelectric cells, hot carrier, the middle band, and many other techniques. Nanoparticles and nanostructures have been shown to enhance the absorption of light, increase the conversion of light to energy, and have improved thermal storage and transport.",signatures:"Farzaneh Ghasemzadeh and Mostafa Esmaeili Shayan",downloadPdfUrl:"/chapter/pdf-download/73145",previewPdfUrl:"/chapter/pdf-preview/73145",authors:[{id:"317852",title:"Ph.D.",name:"Mostafa",surname:"Esmaeili Shayan",slug:"mostafa-esmaeili-shayan",fullName:"Mostafa Esmaeili Shayan"},{id:"319145",title:"Prof.",name:"Farzaneh",surname:"Ghasemzadeh",slug:"farzaneh-ghasemzadeh",fullName:"Farzaneh Ghasemzadeh"}],corrections:null},{id:"72801",title:"Ultrasound-Assisted Preparation Methods of Nanoparticles for Energy-Related Applications",doi:"10.5772/intechopen.92802",slug:"ultrasound-assisted-preparation-methods-of-nanoparticles-for-energy-related-applications",totalDownloads:817,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Ultrasound (US) technology is already into the research field providing a powerful tool of producing nanomaterials or being implicated in decoration procedures of catalyst supports for energy applications and material production. Toward this concept, low or/and high-frequency USs are used for the production of nanoparticles, the decoration of catalytic supported powders (carbon-based, titania, and alumina) with nanoparticles, and the production of metal-organic frameworks (MOFs). MOFs are porous, crystalline materials, which consist of metal centers and organic linkers. Those structures demonstrate high surface area, open metal sites, and large void space. All the above produced materials are used in heterogeneous catalysis, electrocatalysis, photocatalysis, and energy storage. Batteries and fuel cells are popular systems for electrochemical energy storage, and significant progress has been made in nanostructured energy materials in order to improve these storage devices. Nanomaterials have shown favorable properties, such as enhanced kinetics and better efficiency as catalysts for the oxygen reduction reaction (ORR).",signatures:"Christos Vaitsis, Maria Mechili, Nikolaos Argirusis, Eirini Kanellou, Pavlos K. Pandis, Georgia Sourkouni, Antonis Zorpas and Christos Argirusis",downloadPdfUrl:"/chapter/pdf-download/72801",previewPdfUrl:"/chapter/pdf-preview/72801",authors:[{id:"246300",title:"Dr.",name:"Antonis",surname:"Zorpas",slug:"antonis-zorpas",fullName:"Antonis Zorpas"},{id:"319540",title:"Dr.",name:"Christos",surname:"Argirusis",slug:"christos-argirusis",fullName:"Christos Argirusis"},{id:"319541",title:"Dr.",name:"Pavlos",surname:"Pandis",slug:"pavlos-pandis",fullName:"Pavlos Pandis"},{id:"319542",title:"Dr.",name:"Georgia",surname:"Sourkouni",slug:"georgia-sourkouni",fullName:"Georgia Sourkouni"},{id:"319543",title:"Mr.",name:"Christos",surname:"Vaitsis",slug:"christos-vaitsis",fullName:"Christos Vaitsis"},{id:"319544",title:"Mrs.",name:"Eirini",surname:"Kanellou",slug:"eirini-kanellou",fullName:"Eirini Kanellou"},{id:"320950",title:"Mr.",name:"Nikolaos",surname:"Argirusis",slug:"nikolaos-argirusis",fullName:"Nikolaos Argirusis"},{id:"320952",title:"Dr.",name:"Maria",surname:"Mechili",slug:"maria-mechili",fullName:"Maria Mechili"}],corrections:null},{id:"72636",title:"Nanocomposite Materials",doi:"10.5772/intechopen.93047",slug:"nanocomposite-materials",totalDownloads:2200,totalCrossrefCites:5,totalDimensionsCites:13,hasAltmetrics:1,abstract:"Nanocomposites are the heterogeneous/hybrid materials that are produced by the mixtures of polymers with inorganic solids (clays to oxides) at the nanometric scale. Their structures are found to be more complicated than that of microcomposites. They are highly influenced by the structure, composition, interfacial interactions, and components of individual property. Most popularly, nanocomposites are prepared by the process within in situ growth and polymerization of biopolymer and inorganic matrix. With the rapid estimated demand of these striking potentially advanced materials, make them very much useful in various industries ranging from small scale to large to very large manufacturing units. With a great deal to mankind with environmental friendly, these offer advanced technologies in addition to the enhanced business opportunities to several industrial sectors like automobile, construction, electronics and electrical, food packaging, and technology transfer.",signatures:"Mousumi Sen",downloadPdfUrl:"/chapter/pdf-download/72636",previewPdfUrl:"/chapter/pdf-preview/72636",authors:[{id:"310218",title:"Dr.",name:"Mousumi",surname:"Sen",slug:"mousumi-sen",fullName:"Mousumi Sen"}],corrections:null},{id:"72865",title:"Novel Slow Release Nanocomposite Fertilizers",doi:"10.5772/intechopen.93267",slug:"novel-slow-release-nanocomposite-fertilizers",totalDownloads:556,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Nanotechnology deals with atom-by-atom manipulation and the strategies and products developed are quite precise. Despite the fact that the nanotechnology is noticeably exploited in the subject of energy, environment and health, the research is agricultural sciences had just scratched the surface. However, the potentials of nanotechnology in agricultural sciences had been reviewed. Among the applications, nanofertilizers technology is very revolutionary and known to exhibit economic advantage if the products advanced are economically feasible and socially sustainable. These nano fertilizers are pronounced to reduce nutrient loss due to leaching, emissions, and long-term incorporation by soil microorganisms.",signatures:"Muthuraman Yuvaraj and Kizhaeral Sevathapandian Subramanian",downloadPdfUrl:"/chapter/pdf-download/72865",previewPdfUrl:"/chapter/pdf-preview/72865",authors:[{id:"280193",title:"Dr.",name:"Muthuraman",surname:"Yuvaraj",slug:"muthuraman-yuvaraj",fullName:"Muthuraman Yuvaraj"}],corrections:null},{id:"73068",title:"Graphene Oxide-Based Nanohybrids as Pesticide Biosensors: Latest Developments",doi:"10.5772/intechopen.93538",slug:"graphene-oxide-based-nanohybrids-as-pesticide-biosensors-latest-developments",totalDownloads:731,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Graphene is the most significant two-dimensional nanomaterial with sp2 hybridized carbon atoms in a honeycomb arrangement with an extremely high surface area, excellent electrical properties, high mechanical strength, and advantageous optical properties and is relatively easy to functionalize and mass produce. Various inorganic nanoparticles incorporated with graphene, such as gold, silver, and palladium nanoparticles are brought into sharp focus due to their catalytic, optical, electronic, and quantized charging/discharging properties. Graphene oxide-based nanohybrids are particularly well suited for biosensing applications and catalysis. Consequently, this area of research has grown to represent one of the largest classes within the scope of materials science and is rapidly becoming a key area in nanoscience and nanotechnology offering significant potential in the development of advanced materials in multiple and diverse applications. Here in this present chapter, synthesis, characterization of graphene oxide, and their nanohybrids are discussed thoroughly with their application in the field of pesticide biosensors. This chapter will help in a further understanding of graphene-based nanohybrids as a biosensing platform for their future applications in a sustainable environment.",signatures:"Navin Kumar Mogha",downloadPdfUrl:"/chapter/pdf-download/73068",previewPdfUrl:"/chapter/pdf-preview/73068",authors:[{id:"320287",title:"Dr.",name:"Navin",surname:"Mogha",slug:"navin-mogha",fullName:"Navin Mogha"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6408",title:"Novel Nanomaterials",subtitle:"Synthesis and Applications",isOpenForSubmission:!1,hash:"f3585d338d78e4d31c200d9991b03692",slug:"novel-nanomaterials-synthesis-and-applications",bookSignature:"George Z. Kyzas and Athanasios C. 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Accordingly, the final product of information processing taking place in the brain, such as perception, action, or interaction with external environment, is dependent on the accurate representation of time-dimension in neural circuits. The physical surroundings with which humans interact is four dimensional, three geometric dimensions, and the time-dimension. Psychological time has been a subject of intellectual curiosity for most of the known history, but time-dimension has been studied as the fourth physical dimension only during the last century [2, 3]. Time-dimension, unlike other physical qualities, is never perceived as a novelty but only reported as the flow of time [1], and therefore, it is not easy to study by observation alone.
\nTime-dimension plays a key role in many aspects of information processing in the brain. Temporal coupling of two or more events, neural or physical, occurs when they share the same coordinate on time-axis given that time-axis is represented accurately for all events. This allows the binding of events at the level of neural circuits or external physical surroundings. External events that are temporally coupled after processing in neural circuits lead to the subjective experience of entire repertoire of sensory inputs.
\nIn coincidence detection, coincident activation of a third neuron by two oscillator circuits is proposed to play a role in analysis of frequency of auditory tones in the brain stem [4, 5]. The mental time travel is a fundamental ability of the human brain to “mentally” relocate oneself to a time point in the past or future [6]. The mental time travel allows the projection of self in past or future by referring a temporal order of events, which is processed by the hippocampus [7].
\nInterval timing functions of the brain have arguably played a key role in the survival of the human species during most of their existence. Humans have been sustained by hunting and foraging [8], which requires interaction with external objects with a variety of physical characteristics, such speed, hardness, elasticity, and matter state—fluid or solid. These interactions are both (a) feedforward motor and (b) sensory—scene searching and sensory input. The feedforward motor control of external objects will be processed by sensory inputs resulting from reactional forces and impedance control (Figure 3). Mental time travel, by virtue of the ability to store the temporal order of events, helps to provide priors for the control of interaction with the environment as events unfold.
\nDepicts modular neural clock mechanism. Modular clock mechanism shown here is a prototype for the modular connections that form networks, processing information during interactions with external physical environment. The neural clock mechanism (C) can be synchronized with motor circuits (A) or sensory circuits (B) by low-frequency oscillations (broken rectangular lines).
In addition, these activities require multiple temporal couplings of sensory inputs with motor responses at the level of a single individual. The use of limited energy stores by muscles must be optimized, which will constrain the central nervous system to recruit only certain muscle activation patterns.
\nAbility of individuals to communicate in groups, which is important for foraging, depends upon the temporal coupling of same brain circuits of different individuals to same stimuli, which may be hand gestures. Such brain circuits, found in the posterior parietal and motor areas, form mirror mechanisms in monkeys [9, 10, 11]. This is supported by imaging studies done in humans, which showed that the posterior parietal areas and premotor areas became active during action-observation and imitation [12, 13]. Moreover, rich reciprocal connections are present between different areas of the posterior parietal cortex and the premotor cortex in monkeys, which provide the anatomical basis of the mirror neuron mechanism [14, 15].
\nRepresentation of time unit by regular events is inherent in the definition of a regular event—that repeats itself after the same interval every time. Time units, such as seconds, measured by swings of pendulum in a mechanical clock, can help in the measurement of a duration by counting the number of seconds or swings of a pendulum. Using this analogy, a neural temporal unit is defined as the interval between two adjacent regular spikes, spike bursts, and is proposed to represent time units in neural circuits [16].
\nAccording to the pacemaker-accumulator model, when neural temporal units are added (or counted) by the accumulator, it processes neural time intervals for various subjective or motor tasks. According to this model, if the neural temporal units represented by neural oscillators in the brain’s timing circuits are smaller on the physical time scale, then subjective time reported in the task will be greater than the elapsed physical time. This will be the result of a greater number of neural temporal units present within a given external time duration. As predicted by the pacemaker-accumulator model, a greater number of neural temporal units within a timed interval will lead to subjective overestimation of intervals. This is supported by a study in which entrainment using visual flickers with faster frequency increased time measurement in a time reproduction task [17]. Entrainment by faster flickers increases the frequency of neural oscillators in the brain, which leads to a smaller temporal unit—a result of entrainment by faster rate of oscillations. Another study used auditory click trains to increase the speed of neural clocks, and its effect on pair-wise duration comparison and verbal time estimation task, and had arrived at similar conclusions [18]. However, not all entrainment studies agree with this conclusion [19]. Thus, a different role of neural oscillator is suggested within the modular clock model [16]. According to this formulation, the role of rhythmic activity is to only represent a physical property of the time-dimension in various neural clock mechanisms. Rhythmic activities are shown to be important for cognitive functions and various forms of behavior as reviewed by Herbst and Landau [20], but its precise role is yet to be understood.
Modular model of distributed neural clocks is proposed by Gupta [16] for interval timing functions of the brain, such as timed motor movements, time reproduction, and time estimation. As depicted in the schematic in Figure 1, the proposed neural clock mechanism has three main modular components [16]: (1) calibration module, which is sensory and motor circuits of the brain that are involved in feedback interaction with the external four-dimensional surrounding; (2) endogenous neural oscillator to represent physical time in neural circuits; and (3) a clock mechanism for timing the behavioral response.
\nThe functional role of the calibration module in the neural clock mechanism is to transfer information about the physical time from external surroundings into neural circuits. Physical time information is transferred into neural circuits when motor and sensory information is processed during an interaction of the brain with external surroundings. During the feedback process resulting from the interaction with physical surroundings, circuits associated with motor and sensory functions produce neuronal activities that parallel the interactions between effector organs, muscles, sensory organs, and external physical objects. A comparison of the intervals between changes external to the body and the intervals between corresponding feedback changes in neural activities in the brain is proposed to serve as a basis for the calibration of neural clocks.
\nEndogenous neural oscillator is the second component of the proposed modular neural clock mechanism. Neural oscillators are the rhythmic neural activities within the brain, such as neural oscillations, periodic bursts, or rhythmic circuits. The idea of neural oscillator to represent time-dimension is very old, which is based on the intuitive role of the pendulum in mechanical clocks. Treisman [21] had originally proposed pacemaker-accumulator model. According to this model, a neural oscillator generates pulses, which are accumulated by a counter to encode time intervals in neural circuits [22].
\nInstead of serving as the source for temporal units for pulse accumulation in the Treisman model, the endogenous neural oscillator in the modular clock mechanism only represents a property of physical time. Further, note that the periodicity of the endogenous oscillator does not simply represent a number that is added numerically to process time intervals for neural or psychological processes. However, as mentioned later, the numerical quantification of time intervals in neural processes is likely encoded by spike patterns and their temporal relationship. Neural oscillators, representing physical time, along with the calibration module and various task-specific circuits, synchronously generate information in networks, forming modular clock mechanism (Figure 1) to encode timed behavior.
\nTask-relevant neural clock is the third module, which is generally a part of local circuits, distributed across the central nervous system. For example, the neural timers for visual time reproduction tasks in seconds range are present in the right dorsolateral prefrontal cortex [23, 24, 25].
\nAt present, it is not clear how neural patterns, representing information, are coded and decoded to result in behavior, such as timing movements or time estimation. It is likely that a combination of different patterns, such as spike patterns, logic states of neural circuits, ramping activity of neurons, are important for coding and decoding information, leading to timed behavioral responses [16].
\nQuantitative measurements, such as time intervals, are likely represented in neural circuits in numerical representations [26], such as spike patterns, which can be read as the binary numbers [1, 27]. Studies suggest that the information about behaviorally relevant quantities such as timing behavior is not represented by the rates of spikes but rather by the intervals between their arrivals at synapses [26]. Coincidental activation of neurons by two different sources in a periodicity analysis model is proposed by Langner and Bahmer [28] for analysis of auditory signals in the brain stem.
\nAlthough the neurobiological basis of information processing, underlying the timing of behavior, remains far from clear [26], some consensus is present, such as, neurons encode sensory information using a small number of active neurons, called sparse coding [29]. Independent activation of a small number of neurons is consistent with the cytoarchitectonic data that show a low level of connectivity among the neurons of the cortex.
\nModular connections of local circuits forming dynamic networks are supported by cytoarchitectural and electrophysiological data from the study of the cortex. The cerebral cortex is divided into tiny computational units of mm range size, called the canonical microcircuits [30]. The neurons forming the canonical microcircuits have limited but conserved patterns of inputs and outputs [31]. Although the neurons within a canonical microcircuit are interconnected in specific patterns, the connectivity rates between most neuron pairs in the cortex are very low, which are less than 10–20% in most cases [31]. Due to a low level of connectivity among neurons, different combinations of multiple canonical microcircuits can be configured into a large variety of neuronal circuits and, therefore, provide the ability to perform a wide variety of computations. This feature is particularly useful for the role of small areas of the cortex to act as relatively independent modules in neural networks.
\nMoreover, inputs relayed to the cortex are organized in spatial patterns. This, combined with a little direct interaction between the canonical circuits in horizontal direction, results in independence of small cortical areas, which allows small cortical areas to act as relatively independent local circuits or modules. Local circuits are interconnected by synchronization, processing information to allow the brain to interact with the external physical world. The synchronization of local circuits is due to the oscillating states of excitability and inhibition, which allows neurons to fire during a specific phase of a long-range oscillation when neurons are excitable—coupling the modules of a neural network [27, 32, 33]. Periodic excitability of neurons during synchronization, due to pacing by inhibitory neurons, produces oscillating extracellular currents that are recorded as neural oscillations [34], which show different patterns during different behaviors. The behavioral significance of synchronization is due to the temporal coupling of neural events that underlie action and perception.
\nSince the discovery of neural oscillations in electroencephalography (EEG) by Hans Berger at the University of Jena, our understanding about its importance in cognitive functions has grown exponentially. Theoretical consideration as well as experimental evidence suggests that time-dimension is represented in the central nervous system by rhythmic activity of neural oscillations [1, 16, 27]. The importance of neural oscillations in cognitive functions became known when synchronized neuronal firing pattern, which was tightly correlated with the phase and amplitude of an oscillatory local field potential in the cat visual cortex, was reported in 1989 by Gray and Singer [35]. Furthermore, the stimuli were correlated to the amplitude of the oscillatory field in specific neuron clusters [35]. Neural oscillations represent a common unit of physical time-dimension in information processing when they synchronize different parts of the brain into networks [27].
\nAccumulating body of evidence now suggests that beta-range neural oscillations represent physical time information in the brain [16, 19, 27, 36–40]. A recent study has concluded that beta oscillations play an important role in the retention and manipulation of time information held in working memory [37]. A causal relationship between beta oscillations and the control of movements [41] has been shown, which further suggests that beta oscillations are responsible for coupling the neural-timer mechanism with the motor circuits for the control of movements.
\nCentral pattern generators (CPG) are networks of neurons in the spinal cord-forming oscillators that play a role in hierarchical control, generating rhythmic motor activities in animals, such as walking and chewing [42]. The rhythmic activity of CPG networks, according to the formulations of distributed modular clock mechanism, represents time-dimension in spinal cord motor circuits that help to control the temporal characteristics of locomotion. Although, CPG activity is observed after deafferentation or spinal cord injury, the sensory inputs, especially proprioceptive signals, are crucial for its role in locomotion [43]. The function of proprioceptive signals is likely the calibration of neural temporal units represented by rhythmic activity of the CPG [16].
\nThe evidence for the direct role of spinal cord CPG networks in human locomotion is scant and is mostly indirect [44, 45]. Some beneficial effects are seen in spinal cord injury patients following locomotor training [46], which can be attributed in part to the plastic changes in spinal cord CPG networks following the training, which updates physical time-dimension information from sensory, especially proprioceptive inputs during training sessions [16].
\nRepresentation of time-dimension in the brain is important for human and nonhuman primates’ ability to survive. As argued earlier, the representation of time-dimension in neural circuits plays a key role in the information processing underlying complex cognitive functions of the primate brain. Survival in many circumstances depends on the temporal coupling of action with the perception during the interaction with the external physical surroundings.
\nDepending upon the demands of a task, such as speed, the cost of failure, and the degree of coupling between action and perception, may vary. To couple feedforward motor output with sensory inputs on a small temporal scale, a more accurate representation of time information in neural circuits is required.
\nCoincidence detection refers to occurrence of an event only when two or more events take place synchronously. Oscillations are hypothesized to play a role in decoding the temporal information in ramping neuronal activities [16] that are commonly observed in the cortex [47, 48, 49, 50]. Coincidence detection would play a role in generating the information that produces timed behavior. This information is processed when coincidence detector neuron is stimulated by both excitatory presynaptic terminals controlled by gamma oscillations [51] and an increasing excitatory input coming from a ramping neuronal activity (Figure 2). This coincidence detection model is based on the periodicity analyzing model for auditory signals in the brain stem proposed by Langner and Bahmer [28].
\nThis figure shows how coincidence activation can result in the analysis of temporal information represented by neuronal ramping activities, commonly observed in the cortex. The ramping neuronal activity provides an increasing input to neuron that is synchronized by a high frequency nested within a low frequency (C). The neuron synchronized by (C) is excited by high frequency (periodicity is τhigh) gamma oscillations, within a specific phase of low frequency oscillation (periodicity is τlow). The excitation of neuron (B) will result in an activity (coincidence detection), if there are three simultaneous events (1) input from neuron with climbing output (A) after reaching a particular level of activity (2) phase of long-range oscillations that allows gamma cycles (3) excitatory phase of gamma cycles. Thus, the integration period resulting from coincidence analysis will be represented by the formula shown within the figure, where m and n are integers.
Psychoacoustical studies have indicated that perception of speech is not adequately accounted by place frequency mechanisms [52]. But the temporal information represented in sounds is also important in the perception of speech [52]. Therefore, it noteworthy that recent theoretical work and a growing number of experimental studies indicate that time-dimension is an integral part of information processing underlying perceptual functions of the cortex [16, 27].
\nMost natural sounds are modulated in amplitude, which can be explained by a mixture of sound waves of slightly different frequencies producing destructive interference near the tails and summation near the peak in the center [53, 54]. Thus, time-dimension is represented by the modulation frequency in addition to fine oscillations of air pressures causing sound waves. The oscillations of both frequencies, forming the structure of natural sounds, represent physical time-dimension [16]. The processing of sound waves by the cochlea produces amplitude modulation (AM) signals in the brain stem. The spike structure of AM signals is phase locked to changes in pressure produced by amplitude-modulated sound waves during the transduction by the cochlea. Studies also suggest the presence of tonotopic organization of subpopulations of neurons tuned to modulation frequencies [53], consistent with the transduction of time information in modulated sound waves, which is later processed in the auditory cortical areas contributing to perceptual qualities of sound.
\nMuscle synergies represent the central nervous system’s response to the redundancy problem in motor movements. There are many more degrees of movements possible than are the number of muscle activation patterns that can produce movements [55, 56]. Several studies in vertebrates and non-vertebrates demonstrate the presence of elements or muscle synergies, from which complex patterns of motor movements can be constructed [56, 57, 58].
\nAccording to the computational models of muscle synergy, a synergy can be described as a D-dimensional vector field, where D is the number of muscles involved in movements [59, 60]. The level of contraction of each muscle, represented by weighting coefficient (Wi) multiplied by a coefficient (Ci) to yield (Ci(t)Wi), represents one of the dimensions. The level of contraction (Coefficient*W, W is the weight of contraction) is referred to as synergy in the following computational models of synergy. The synergies are extracted using non-negative matrix factorization of EMG patterns. Prior to the analysis, EMG data is adjusted by subtracting the tonic component of EMG activity responsible for postural activity and balance [60] or by normalizing EMG data as discussed earlier [61].
\nThe maximum number of dimensions (D) in muscle synergies after extraction is the number (D) of all muscles that could play a role in the movement. After the extraction of muscle synergies from EMG patterns, the minimum number (N) of synergies is computed, such that they account for 80–90% of variability of the movements by choosing either coefficient of determination (CD) or variability accounted for (VAF) criteria. It has been observed that a small number of extracted muscle synergies can account for most of the variability of movements, while others account for 10–20% of the variability [59]. This provides a solution to the redundancy problem: few patterns of movements are preferred over far larger number of possible movements.
\nIn time-invariant synergy, there is a fixed level of contractions (weights) of muscles (M1, M2…Mn)—Wi for ith synergy. Each time-invariant ith synergy is activated by the nervous system temporally, represented by a time-varying coefficient (Ci(t) for ith synergy at time t). In this muscle (ith) synergy, fixed weights of contraction of muscles are multiplied by a time-varying non-negative coefficient Ci to obtain muscle activation contributed by each synergy (Wi) [58]. Time invariant or spatial synergies can be described at a more abstract level as the uniform modulation of a D-dimensional vector field wherein the amplitude of the vector is the fixed levels of contraction of individual muscles.
\nIn time-varying synergy, there are multiple waveforms corresponding to muscle contractions or vector amplitudes of individual synergies. Synchronous contraction waveforms of different muscles are multiplied by the same coefficient (Ci) for the ith synergy and shifted in time by a delay ti and added to generate muscle activity pattern [59].
\nThe computational models of muscle synergies reveal the modular control of muscles during movements. The muscle synergy recruitment in computational model can be explained by two orthogonal components. The spatial synergy model (Eq. 1) suggests that signals originating from a cortical circuit (from convergent to divergent) (defined as type I) control multiple muscles by sending sending signals of different strengths to different muscle with a fixed amplitude relationship between them, while another circuit (defined as type II) modulates the activity of type I circuits, producing a time-varying modulation of fixed levels of muscle contractions constrained by the amplitude relationship, reflecting the spatial demands of the task. During a movement, synergies are recruited by sending signals from type II circuits to type I circuits. When several synergies (Wi) (type I circuit) are recruited by several corresponding sources of time-varying signals (type II circuit), it leads to the movement to be constructed by a computational mechanism. The orthogonal components of spatial synergies are detected by decomposing EMG patterns.
\nThe type II circuits, which produce time-varying signals, represent time-dimension in the information processing that underlies the control of movements. Due to the representation of time-dimension, type II circuits are likely to be controlled by dynamic inputs, such as speed and temporal coupling. Type I circuits are responsible for representing the spatial directions of movements. Muscle contractions represented by type I circuit will determine the set of conditions, representing limb and joint positions for specific directions required for external task conditions. Furthermore, types I and II circuits are orthogonal or statistically independent, which is suggested by the decomposition of EMG patterns by the matrix factorization.
\nIt is noteworthy that movements in humans are smooth. This suggests that muscles are not controlled individually by independent feedback processes, which would increase variations between contraction states of individual muscles, thereby decreasing the smoothness of movements. Instead, the muscles in each spatial synergy is controlled by a single signal (represented by time-varying coefficient in Eq. 1), which helps to reduce the number of variables being controlled, leading to an overall reduction in variations within movements. Thus, muscle synergies represent simpler computational solutions implemented by the central nervous system for controlling movements. Moreover, only a limited number of synergies can be recruited within the constraints of musculoskeletal system.
\nAccording to the classical view of the control of reaching movements to catch a ball, there is a creation of a neural representation of endpoint of the task, such as the hand meeting the ball in the physical space to execute a catch [62, 63]. This neural representation is formed by an initial approximation, which then evolves temporally [63]. The motor movements are produced by recruiting a limited number of synergies by the central nervous system, which can be directed by the neural representation of the prospective endpoint of the task. State estimators, which are discussed later, play a key role in optimum feedback control as it would predict the neural representation of the endpoint. The time-varying coefficient represents the time-dimension in the information processing movements, as it is modulated with time. Thus, time-varying signal (Ci) helps to determine the speed of movement, while the other orthogonal component, muscle synergies, is a response to spatial and musculoskeletal constraints.
\nTemporo-parietal cortical areas are believed to play a significant role in the feedback processes that help to represent the musculoskeletal system in the external four-dimensional environment [64, 65]. Studies of the computational models of muscle synergies indicate that the nervous system recruits a limited number of synergies that optimizes according to a temporally evolving map of the neural representation of the hand meeting the ball. Recruitment of muscle synergies is also due to the numerous specific, reciprocal connections between the regions of the parietal cortical areas and frontal motor areas [66]. These specific multiple, reciprocal connections may form the basis for the recruitment of muscle synergies as well as the temporally evolving map of the neural representation of the map of the endpoint of the task. Furthermore, it is likely that the direct effect of state estimator module of the optimum feedback control is on the neural representation of the endpoint of the task rather than on muscle activities. The muscles are controlled from frontal motor areas, which are functionally connected to temporo-parietal areas where the multimodal integration of sensory information—proprioceptive, vestibular, and visual—takes place [64, 65].
\nAlthough motor movements in humans are smooth, but the motor performance shows a large variability from trial to trial. This large variability in the movements is a reflection of inherent noise in the motor circuits, also called signal generated noise, in addition to the noise present in sensory circuits, and the external source of sensory inputs. Optimum feedback control is used by the central nervous system to modify feedback signal to control some index of motor function, such as minimization of endpoint errors or achieving a maximum jump. The control of motor outcome is optimum when it meets the spatiotemporal constraints of a task, such as hand meeting a ball during a catch. As a result, there is a decrease in the variations along the trajectory of the task; however, this is accompanied by the increase in variations in task-irrelevant trajectories. State estimator functions provide outputs to the feedback controller which helps to produce the optimum outcome [67].
\nA modification of optimum feedback control would consider the feedforward motor output via synergy recruitment. State estimator would recruit a limited number of muscle synergies that would account for 80–90% of variability of the movements according to VAF or CD criteria. The mechanism to explain the control of the recruitment of synergies must take into the constraints that limit movements. Muscle contraction velocity profiles, due to the constraints of viscoelastic properties as well as the biochemical events underlying sliding filament mechanism, are bell-shaped. The allowed muscle contractions must minimize the energy cost in the use of skeletal muscles, which is imposed as inheritable features on neural circuits due to evolutionary pressures. Furthermore, only certain movements are allowed by the joints, which depend on various factors, such as the shape and the structure of joints. The function of state estimator depends upon sensory inputs and efferent copy of cortical motor commands, which provides estimation of online changes occurring in the state of the musculoskeletal system [67].
\nPlease note that sensory inputs and efferent copies of motor commands must be integrated with time-dimension in order to serve as state estimator for the optimum control of motor movements via synergy recruitment. The early online control of movements likely involves the integration of unimodal sensory state estimates instead of a single multimodal state estimate [68]. Studies have shown that during reaching movement task, joint angle variability peaked mid-way during the task [69], but there is a high accuracy at the endpoint [70], which suggests optimum feedback control of movements.
\nOptimum feedback control depends on a set of distributed circuits, among which the primary motor cortex appears to play a key role. It has been shown that the primary motor cortex receives inputs from several brain areas, which include the premotor cortex, primary somatosensory cortex, posterior parietal cortex, and pathways via the thalamus from the cerebellum, forming some of the structures involved in optimum feedback control. A recent study also shows that if feedback controller is represented in the primary motor cortex, the optimum feedback control describes multiple representation of preferred directions of torque or movements in which muscles are most active [71]. This is consistent with the role of the primary motor cortex as the site where individual synergies, involved in constructing voluntary movements, are recruited by the activation of individual spatial synergies by time-varying signals.
\nNewton’s third law of motion states that for every action, there is an equal and opposite reaction. Newton’s third law can be applied to analyze the interaction of the brain with the four-dimensional physical surroundings. The action, which results from movements, produces the reactional forces on the human body, resulting in changes in the activity of mechanoreceptors, which then modifies the activity of the musculoskeletal system responsible for movements in a feedback process. Thus, pairs of forces, action, and reaction forces during interaction between primates and their environment lead to changes in the activities of sensory and motor functions of the brain via feedback processes. In addition, successful interaction between the brain and the external environment depends on several other factors, which includes the representation of time and spatial dimensions in neural circuits of the brain. During this interaction, there is a complex interplay between feedforward motor process and sensory inputs. An important example of such a complex interaction is a driver controlling a motor vehicle (Figure 3). The control of the vehicle involved is due to two main motor actions, pressing the gas pedal to change the speed of the vehicle and the steering wheel to change the direction. The motor control of the vehicle, which is the feedforward motor prediction based on internally generated top-down feedforward output, is directed by a negative feedback process involving sensory inputs of different modalities.
\nIt depicts the interaction between feedforward motor output and feedback sensory input resulting from the stimulation of proprioceptors by reactional forces acting on the foot and hand. Visual information from the scene is actively gathered to provide additional but crucial sensory input.
Visual input, after processing in the posterior parietal cortex, is relayed to the primary motor cortex, which will be important for the control of movements with the help of visual cues. The premotor cortex is involved with the planning of movements and the internally generated signals, while the primary somatosensory cortex plays a role in processing inputs related to the perturbations of musculoskeletal system during movements.
\nScene processing involves recognizing the environment, searching the information in the environment, and navigating through the environment [72]. Visual scene-selective regions are occipital place area, parahippocampal place area, and retrosplenial complex, which are on the lateral occipital, ventral temporal, and medial parietal cortical surfaces, respectively [72, 73]. Parahippocampal place area is believed to reflect a wide range of properties, such as spatial frequency, orientation, texture, object identity, as well as size of a space [72]. A recent study has shown that the networking of the visual cortex with retrosplenial cortex is important for mental time travel, which would play a role in constructing prospective scenes [74], such as seeing an increasing number of brake lights will suggest a slowing traffic. The self-projection in time, called mental time travel, relies on various neural structures, encoding memory, mental imagery, and self, which is critical for the judgments during interactions with the physical environment [6]. Mental time travel recruits a network, which includes the anteromedial temporal, posterior parietal, inferior frontal, temporo-parietal, and insular cortices [6].
\nA recent study of review-based evidence, which examined a large number of studies, suggests a key role of the dopaminergic system in various temporal functions of the brain [75]. These conclusions are based on various combinations of genetic, pharmacological, physiological, and psychophysical evidence [75]. This review suggests an important role of certain key molecules, which influence the concentration of synaptic dopamine, in various timing functions of the brain. These molecules includes catechol-O-methyltransferase (COMT) that degrades synaptic dopamine and dopamine transporter (DAT), which removes dopamine from the synaptic cleft. Studies of common gene polymorphisms, COMT gene (COMT Val158Met) and DAT gene (SLC6A3 3’-VNTR variant), suggest the involvement of dopaminergic system in time perception.
\nStudy of the effects of dopamine agonists, such as cocaine and metamphetamine and dopamine antagonists, haloperidol, on peak interval task suggests that both attentional and clock mechanisms are dependent on dopaminergic neurotransmission to some extent [76]. As argued earlier, cognitive functions are functionally dependent on timing as time-dimension is a part of the physical environment with which the brain interacts. In addition, neural oscillations, which represent time-dimension in neural circuits [16], help to form networks that form the basis of perception and action coupling [27]. Thus, we propose that dopaminergic system is one of the main chemical bases of timing circuits. This is consistent with the anatomical evidence, showing the extensive presence of dopamine terminals in layer I, with more specific presence in deeper cortical layers V and VI, which has neurons, projecting to the thalamus and striatum [77]. Dopamine can also play roles in the maintenance of homeostasis of neural circuits via cortico-striatal-thalamic-cortical loops [16]. This is consistent with the importance of dopaminergic system in cognitive and timing functions [78]. This view is further supported by the well-known role played by abnormal dopaminergic system in schizophrenia, which is a disorder of cognitive functions and time perception. Furthermore, the genetic variations in expression of molecules related to dopaminergic system in the brain are likely to contribute significantly to the variations in timing and cognitive functions between individuals.
\nTime-dimension plays a key role in all aspects of the brain functions. However, the focus study of time-dimension has been on a limited number of functions, which include timing behavior, subjective time perception, and temporal order. In this book chapter, we have extended the importance of time-dimension in the study of other aspects, such as movements, perception, and highlighted the importance of temporal coupling of neural and physical events during the interaction with external environment.
\n3D printing is a process whereby a real object is created starting with a virtual 3D digital model. It was first developed in 1986 by Hull and Lewis which is an improved stereolithography system using photochemical processes in which light causes chemical monomers to link together to form polymers and generate a solid object [1]. This technology is capable to fabricate a super complex geometry or features by accurately follow the computer-aided design (CAD) model. The fabrication requires appropriate materials that gradually released and overlapped in layer-by-layer fashion by 3D printer. The type of material chosen is crucial to ensure the printed object that can be used for further settings and applications. Various types of metals, polymers, ceramics, and composites such as polycaprolactone (PCL), polyethylene glycol (PEG), polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) plastic, stainless steel, titanium, calcium phosphate, and silica can be used as starting materials in 3D printing [2, 3, 4].
\nGenerally, there are four main applications of 3D printing in the medical field, which are as follows: (a) drug delivery, (b) surgical devices/implants, (c) operative planning, and (d) tissue engineering [5, 6, 7, 8, 9, 10, 11, 12]. The 3D printing application for drug delivery is extensively used in the pharmaceutical industry to develop sustained release medication [5]. Modulation of the shell thickness as well as the shape of the 3D printed capsule allows precise control of the drug release rate [13]. 3D printing enables a fast and cost-effective way of fabricating personalized medical implants. The capability of producing custom implants gets rid of the need for adjustments during surgery that saves time as well as reduces the cost of operation and the risk of medical complications. This is particularly beneficial where metal implant interfaces with living bone and tissue. The electron-beam melting (EBM) and direct metal laser sintering (DMLS) technologies are both now used in the production of standard and customized implants. Surgical tools are generally designed to work with many patients. However, by fabricating patient-specific tools, it would decrease the risk of complications during surgery [13, 14]. Patient anatomy will be imaged using imager and transferred into the 3D design in CAD to create suitable tools that can be easily controlled during operation. In operative planning, the 3D printing also would provide surgeons with a visualization of the complex injuries. They can plan and strategize their work and choose specific tools required. Some of the common applications that require a 3D model are complex pelvic trauma [15], pediatric deformities [16], and osteotomies [17]. Furthermore, advances in 3D printing technology enable the possibilities of constructing living human tissues in the lab hoping to demonstrate structural and functional similarities as native tissue in the human body [12]. The biggest challenge is to construct thick tissue and to ensure the diffusion of oxygen and nutrients for cellular viability [14].
\nThe conventional method to develop an engineered-tissue product involved the initial fabrication of specific native tissue design followed by the provision of cells and biomolecules. However, this approach could contribute to two major drawbacks including limitation in cell distribution and reduction in cell growth due to low nutrient concentration at the core area [10]. Very commonly used techniques for fabricating 3D scaffolds include freeze casting, solvent casting, gas foaming, and salt leaching [18]. The technology advancement in tissue engineering has been contributed to the current approach through computer-aided layered manufacturing technique, which is also known as 3D bioprinting. Briefly, the 3D bioprinting technology involves the combination of the primary ingredients known as “bio-ink” that functions as a biological framework and various types of cells with the presence of chemical factors, and biomolecules to form a solid and functional
There are four different techniques under 3D bioprinting including inkjet printing, extrusion-based methods, light-induced (photopolymerization) methods and particle fusion-based methods [7, 20, 21, 22, 23, 24, 25, 26, 27]. The first three abovementioned techniques have been widely used to fabricate biomaterial designs [7].
\nThe inkjet-based 3D bioprinting (\nFigure 1\n), first developed by Thomas Boland from Clemson University in 2003, is a low-cost manufacturing process that performs high-speed printing for 3D structure [21]. Besides, it provides high-resolution printing output up to 50 μm and widely proven to support cell viability and growth [22]. However, the main drawbacks are dealing with a low concentration of printing ink could hamper the reliability of cell encapsulation and significantly affect print fidelity [23]. Besides, this approach potentially could damage the printed cells on the plantar under shear stress created through the inkjet-based printing but no concrete evidence is reported so far [21, 24]. Three main stages could affect the printable ink such as the production of the droplet, droplet/substrate close-interaction, and polymerization of the droplet. The two mechanisms, which have been involved under droplet generation through inkjet-based 3D bioprinting, are drop-on-demand and continuous inkjet [25]. The size of ink droplets produced via drop-on-demand and continuous injection is in the range of 25–50 and 100 μm, respectively [19]. Drop-on-demand inkjet has been conveniently used for tissue engineering applications.
\nThe inkjet-based 3D bioprinting provides high resolution of printing output around 50 μm.
The inkjet-based technology can be categorized into three as follows: thermal-based, piezoelectric-based, and magnetic-based inkjet printing [26]. The thermal induction can reach until 100–300°C that is required to nucleate a bubble and directly increase the appropriate pressure in the printhead lead to droplet expulsion [28]. There is no dead effect on the cells due to the presence of high temperature only for a microsecond and the previous study demonstrated consistency in cell viability post-inkjet-based 3D bioprinting [29]. Besides, the ink drop production can be induced by a piezoelectric method that focuses on the pulse pressure or acoustic waves generated from a piezoelectric actuator to expel printing ink drop. Another method to generate the drop expulsion is by using the electromagnetic approach depending on the Lorentz force and permanent magnet-based configurations. However, it produces a larger size of ink droplets as compared to thermal-based and piezoelectric-based approaches [28].
\nThe second approach of 3D bioprinting is the extrusion-based method (\nFigure 2\n) that can be divided into two types consisting of fused deposition modeling and direct ink writing [19]. It is easy to handle, customized-based design bioprinter, and versatile with the developed current system. The principle of this 3D bioprinting method is that the printed ink extruded from the nozzle in liquid or molten state forms a particular line on the platform before polymerizing [30]. The bioprinting ink is commonly in the form of solid coil or filament that goes through the hot nozzle (temperature of around 200°C) before extrusion onto the platform. The extrusion from the printing nozzle is controlled by a specific system using various interventions including pressure-based control, pneumatic or mechanical control, or solenoid control before forming layered printed ink as required by the computerized set up to build up the 3D biomaterial designs [7]. The biopolymer should have an excellent solid-to-melt transition property to produce high-resolution 3D cell-laden on the printer platform [31]. However, the extrusion-based 3D bioprinting potentially could generate high mechanical force and shear stress together with high viscous of substrate lead to cellular apoptosis [5]. Further adjustment and optimization of this extrusion-based bioprinting can mitigate the drawbacks but it reduces the bioprinter resolution and speed [32]. Besides, the low concentration of ink viscosity supported cell proliferation and sustained the cell viability by introduced a composite-modified printing ink [33].
\nThe extrusion-based 3D bioprinting is easy to handle, customized-based design bioprinter, and versatile with the developed current system. It can be categorized into the fused deposition modeling and direct ink writing.
Light or laser-assisted 3D bioprinting, also known as stereolithography (SLA) (\nFigure 3\n), focuses on polymer resins manufacturing [19]. There are many variations of light or laser printing approaches for 3D fabrication. The advantages of these approaches are that they provide excellent accuracy, and good resolution between 10 and 50 μm [21]. This technique involves the patterning of a laser beam toward photo-based polymer to generate physical hardened polymer. This procedure is repeatedly applied to fabricate multi-layered polymer in the build-up stage. The other two types of laser-assisted 3D bioprinting techniques that are primarily applied in tissue engineering are digital light processing-based bioprinting (DLP) and the two-photon polymerization-based bioprinting (TPP) [34, 35]. The DLP technology uses a digitalized micro-mirror device chip (DMD) that contains around 2 million micro-mirrors. It functions to ensure light projection patterning precisely and is easy to modulate either on or off while the printing process is running on the platform. This technology consists of two 3D printing platform systems, namely, dynamic optical projection stereolithography (DOPS) and microscale continuous optical printing (μCOP) that support dynamic printing and continuous printing, respectively [7]. The TPP printing mechanism depends on the absorption of two photons by a molecule associated with light intensity square [36]. This phenomenon contributed to the printing of voxel dimension below 1 μm3. Thus, this printing approach is an ideal method to generate nanoscale and microscale printing 3D output. However, high-resolution printing limits the construct size and printing speed. Nonetheless, the TPP printing speed is still faster than that in extrusion-based bioprinting and at a similar rate with inkjet-based bioprinting [37].
\nLight- or laser-assisted 3D bioprinting approaches supported the high cell viability, accuracy, and good resolution between 10 and 50 μm. Two types consist of digital light processing-based bioprinting (DLP) and the two-photon polymerization-based bioprinting (TPP).
Due to the limitation in technology to support the formation of the adequate and functional vascular network
Bone tissue is one of the earliest tissues that were 3D printed and clinically used due to the ability of this technique to fabricate scaffolds according to the required shape, strength, and porosity. 3D printing enables fabrication of scaffold in any shape, which is not possible with many conventional fabrication techniques [41]. Furthermore, the materials commonly used for bone substitute production, such as hydroxylapatite (HA), synthetic calcium phosphate ceramics, polymethylmethacrylate, polylactides/polyglycolide and copolymer ceramics, tricalcium phosphate (TCP), bioglass, titanium, and other composite materials, are very compatible with the 3D printing technology [42]. The bone 3D printing had started as early as the 1990s, which utilized a powder-based freeform fabrication method [43]. Today, the bone substitute can be fabricated using the 3D plotting/direct ink writing, laser-assisted bioprinting (LAB), selective laser sintering (SLS), stereolithography (SLA), and fused deposition modeling (FDM) [42]. For example, Goriainov et al. prototyped hip joint implants using computer-aided design-computer-assisted manufacturing (CAD-CAM) and fabricated the scaffold using direct metal laser sintering from titanium alloy [44]. The custom-designed implants were seeded with autologous bone marrow aspirate before the implantation to 11 patients who were unsuitable for standard revision hip surgery. The postoperative results showed extensive new bone formation in the patients and a certain level of load-bearing function at the hip joint. The
The other tissue that has a high potential to utilize 3D printing technology to repair and regenerate is skin. Although skin substitutes made by conventional tissue engineering techniques such as Matriderm®, Integra®, Dermagraft®, and OrCel® have been commercialized and been used in clinics for wound treatment, there are still challenges that are yet to be resolved by these skin substitutes. These skin substitutes are expensive, require long production time with prolonged healing time, have limited tissue functionality, and resulted in scarring in some cases [45]. Besides, these skin substitutes lack hairs, sweat glands, sebaceous glands, and other skin appendages as well as pigmentation. The 3D bioprinting technology has led to the paradigm shift in the skin substitute production where this transformative technology enables simultaneous and accurate deposition of multiple types of skin cells, the formation of scaffolds with complex macro- and micro-architecture, creation of vascular networks, and construction of stratified layer [46].
\nThe commonly used skin 3D bioprinting techniques are microextrusion, inkjet, stereolithography, and laser-assisted bioprinting [47]. The materials commonly used in skin 3D bioprinting are mainly natural polymers such as alginate, gelatine, collagen, fibrin, and hyaluronic acid. However, biocompatible synthetic materials such as polycaprolactone (PCL), polyglycolide (PGA), polyethylene glycol (PEG), poly(lactic-co-glycolic acid) (PLGA), and polylactide (PLA) are commonly combined with natural polymers to increase the mechanical strength of the skin substitute [46, 47]. The bio-inks serve either as the cell carrier or sacrificial support that is removed after the printing, or both as a carrier and mechanical support material that provides greater strength and microarchitecture that supports the function of the skin even after the implantation on to the patients [46]. The on-site bioprinting of either autologous or allogeneic dermal fibroblasts and epidermal keratinocytes directly into a wound area is the latest development in skin 3D bioprinting. The direct deposition of the cells in fibrinogen/collagen solution in a layer-by-layer method onto porcine full-thickness wound has shown to promote the wound closure, reduce contraction, and enhance the re-epithelialization, and the regenerated skin tissue had the composition similar to healthy skin [48].
\nThe other important and potential use of 3D bioprinting technology is the fabrication of vascularized tissues for passage of blood, air, lymph, and other vital fluids in the human body. The cells in dense tissue need to be within 200 mm from a vessel supplying oxygen and nutrients to survive [49]. The conventional technologies faced a major hindrance in fabricating vascular network structure in the dense engineered tissues, which is very crucial for the functioning of the implanted tissue or organ substitute, due to the technological limitation [50]. However, 3D bioprinting technology had enabled the fabrication of complex tissues with an integrated vasculature system, which in turn enabled the integration of the implant vasculature system with that of the host and long-term exchange of air, nutrient, and waste between the native and the implanted tissues [51].
\nThe construction of the vasculature network throughout the tissue is achieved through the design and fabrication of the hollow tube structure in the micrometer scale. This hollow structure is also seeded with vascular cell types or angiogenic factors to promote the formation of functional microvascular networks structure, especially the branching that can size-up to the nanoscale range and also permeation capability [51]. The two main additive manufacturing concepts used for vascularized tissue formation are indirect and direct printing. In the indirect printing, a sacrificial material or negative mold is printed using thermo-reversible hydrogels such as Pluronic F-127 in combination with another material as the permanent scaffold. Upon completion of the 3D printing, the sacrificial mold is removed to form the vascular network that was cellularized with vascular cells [52]. In the direct printing method, the vascular structure is actively printed either with cell-loaded biomaterial or cell-compatible bio-ink. The bio-ink utilized in this process normally has quick gelation/cross-linking ability, or extrinsically induced to crosslink/cured, to maintain a stable hollow structure [52].
\n3D bioprinting has been utilized to prepare vascular networks in several studies. Miller et al. printed a 3D carbohydrate-glass lattice that was late embedded within an engineered tissue with living cells. Then, the 3D carbohydrate-glass lattice is removed, leaving interconnected hollow structures that can be seeded with endothelial cells to form the vasculature [53]. Later on, the same group of researchers proved that the vascular patch prepared using this technique can guide angiogenesis
Besides the tissues discussed above, various other tissues have been and being fabricated with the still-evolving 3D bioprinting techniques. Many of these 3D printed tissues had also been implanted on patients as part of a clinical study [55, 56, 57] and systematic clinical trials are also being conducted for many of these products, which have been reviewed by Mehrotra et al. [58]. The 3D printed implants are in the clinical trial phase mostly as implants for an ankle injury, bone fracture, disease and deformation, and breast reconstruction. Among the other tissues that are in lab-scale fabrication and optimization but have a high potential for therapeutic use are liver tissue [59, 60], cardiac tissue [61, 62], kidney tissue [63, 64], pancreas tissue [65, 66], cartilage [67, 68], and neural tissues [69, 70].
\nAlthough the 3D bioprinting is a new technology, a few types of tissues produced by this technology are already utilized for therapeutic use. However, for the other tissues that have complex microarchitecture, and regulated by multiple signaling factors and cues from surrounding host tissues, it might need a longer time for the 3D printed tissue substitutes to be used in the clinical setting. The 3D printing of complex tissues needs more synergistic research from researches in various fields and various angles before it could fully mimic the native tissue’s function. Another aspect to be considered will be the scaling up of the production using the clinical-grade materials and commercial-scale 3D printers as most of the current studies are being done with experimental materials and lab-scale 3D printer technologies.
\nPersonalized medicine, also known as precision medicine, is a concept in medicine that emphasizes that each patient should be managed differently based on an individual’s condition. This tailored therapy shall be able to provide the best treatment plan for the patients to improve their prognosis. In personalized medicine, all the patient’s specific characteristics such as age, gender environment, height, weight, diet, environment, and genetics are being considered during the prevention, diagnosis, and treatment phase. Personalized medicine can improve the quality of patient care and reduce the cost by avoiding unnecessary diagnostic testing and treatments [71, 72, 73]. Personalized medicine is not only limited to drugs but also for tissue engineering and regenerative medicine. Tissue engineering is highly personalized as a specific tissue-engineered substitute is needed for each patient. For example, different burn patients are presented with different degrees of injury and varied wound location, size, and dimension. Thus, a unique engineered skin needs to be prepared in the current good manufacturing practice (cGMP) facility for each patient.
\n3D bioprinting is one of the techniques that allow the preparation of personalized tissue-engineered substitutes. One of the major advantages of 3D bioprinting in the field of tissue engineering is the possibility of producing personalized living tissue comprising of stem cells, cell-friendly matrix, and bioactive compound in the dimension uniquely suited for different patients. 3D bioprinting can be used to print simple living tissues like skin to a more complex hollow structure like a trachea and very complex organ like heart and kidney. This is something other living tissue fabrication techniques cannot achieve as these techniques do not allow precise deposition of cells at the space wanted. With the advances in the 3D bioprinting technology, nowadays, it is possible to print multiple types of cells, biomaterials, and bioactive compounds at different spaces to create a complex tissue that mimics the native tissue cellular arrangement and mechanical properties. Maturation of the 3D printed tissues can be achieved using a bioreactor.
\nTo prepare the personalized 3D bioprinted living tissue, the image of the targeted tissue in specific patients needs to be taken and reconstructed into 3D, which will be used to guide the 3D printer to print the tissue in the dimension wanted layer-by-layer to form the 3D tissue [74]. Initially, 3D bioprinting is used to prepare engineered tissue
The personalized 3D bioprinted living tissue has been printed layer-by-layer to form the 3D tissue.
Apart from personalized engineered tissue substitutes, 3D bioprinting also can be utilized for the preparation of personalized drug delivery systems and functional tissue models for personalized drug screening and disease modeling. Various models have been developed, including the liver [78], heart [79], blood vessel [80], skin [81], skeletal muscle [82], and cancer [83]. The development of these models can greatly improve the medical care the patients will receive as distinctive prevention and treatment strategies can be designed individually.
\nThe invention of 3D bioprinting has revolutionized biomedical research and significant development in translational research closing the gap from bench to bedside. In the pharmaceutical industry, the value of 3D bioprinting is expected in lowering the attrition rate of a new drug since 3D bioprinting has the potential to precisely position multiple cell types as needed according to the tissue of interest (\nFigure 5\n). Thus, 3D bioprinting enables a more robust design of drug screening, drug delivery, high-throughput drug testing, and ADME assays. The application of 3D bioprinting in the development of in vitro tissue or organoid models for drug discovery is discussed in this section.
\nThe potential development of organoid models for drug discovery such as for cancer model, skin, cornea, intestines, muscle, cardiac tissue, and liver.
The ability of 3D bioprinting in replicating tumor microenvironment (TME) provides a better model to assess drug response, tumor proliferation, and metastasis. By 3D bioprinting, a tumor model with hypoxic core and necrosis could be recreated similar to the
The human skin’s inherent multi-layered, multicellular composition is in demand commercially for pharmaceutical and dermatological testing. Dermal skin equivalent has been successfully created using 3D bioprinting through several approaches. One of them is via direct cell printing of fibroblasts and keratinocytes in the collagen-based hydrogel to recreate the skin stratification [88]. The incorporation of melanocytes and fibroblasts in collagen/fibroblast bio-ink was also reported [89]. Maturation and stratification of 3D bioprinted skin construct could be achieved via exposure to the air-liquid interface as shown by Lee et al. with skin construct expressing skin-specific markers [90]. These skin-like constructs are of value in drug toxicity screening as shown by Tseng et al. where five different drugs, i.e. all-trans retinoic acid, dexamethasone, doxorubicin, 5′-fluorouracil, and forskolin, use their 3D bioprinted fibroblasts [91].
\nCorneal in vitro/ex vivo model is desperately needed as cornea function as major barrier in penetration of drugs into eye; thus, drug absorption thru cornea need to be optimized for topical ocular drug application. Hence, many studies were done in an animal model which is not cost-effective. The complex arrangement of collagen lamellae could be recapitulated using a 3D bioprinting system. Such a corneal model has been successfully produced utilizing extrusion-based bioprinting (EBB) of collagen/alginate/keratinocyte bio-ink [92]. Similar studies utilizing 3D bioprinting with a promising outcome have also been reported such as the generation of 3D multi-lamellar silk film incorporated with human corneal stromal stem cells (hCSSCs). The silk film architecture supports the growth and differentiation of hCSSCs in producing matured corneal stroma with the desired optical and mechanical properties close to the native cornea [93].
\nDrugs are commonly absorbed in the intestine; hence, an in vitro intestinal tissue model is of value in the early phase of drug screening. Such a model was fabricated successfully using the Organovo 3D NovoGen bioprinter system with epithelial cells and myofibroblast that has a polarized columnar epithelium with tight junctions and specialized cells that express cytochromes P450 (CYP450). The above said model is a good model for Crohn’s disease and internal bowel disease (IBD) that could be used in early-phase drug screening or toxicology study [94].
\nDevelopment of drugs that are delivered through intramuscular injection or for muscle injuries and muscular dystrophy require an in vitro muscle model for screening and testing. Alginate and Pluronic mixed with murine C2C12 cells have been successfully printed using the EBB method to create a 3D muscle construct that is used to screen several drugs and observe the cell viability, myogenic differentiation, and tissue contractile force against the drug [95, 96].
\nCardiovascular disease (CVD) is the leading cause of death in the world. Cardiotoxicity is the primary cause of CVD drug retraction from the market and is often done in 2D cell cultures. Therefore, the development of cardiovascular disease modeling and drug screening platform is a necessity. Most work focuses on recreating the left ventricular myocardium where cardiac pathologies occur. A spontaneously and synchronously contracting tissue was successfully developed with aligning endothelial cells that are used for cardiotoxicity screening [97]. In another study, Lind et al. fabricated self-assembled rat-derived cardiac cells by direct printing of six functional bio-inks that are highly conductance, piezoresistive, and biocompatible material. This model exhibits inotropic responses similar to isolated post-natal whole rat heart to several CVD drugs, i.e., L-type calcium channel blocker, verapamil, and β adrenergic agonist isoproterenol [98].
\n3D bioprinting approaches have been utilized in creating a liver disease model and liver tissue. Hepatotoxicity study of any drug introduced in the market is essential in any preclinical drug development. The establishment of in vitro liver models includes the incorporation of primary hepatocytes, hepatic cell lines, and stem cell-derived hepatic cells [99, 100, 101]. Kang et al. created a five-layer 3D hepatic structure using alginate and mouse induced hepatocyte-like cells that express albumin, ASGR1, and HNF4a [102]. Biomimetic liver tissue builds by Ma et al. showed better liver-specific function and drug metabolism potential compared to 2D monolayer culture [103].
\nThe application of 3D bioprinting technology in the development of in vitro tissue or organoid models for drug discovery has fruitfully shown a better model in mitigating the risk associated with drug development. A 3D environment provides a better representation of an in vivo model in addition to reducing or eliminating the use of animal model early in the drug development process. All in all, the reliable prediction of safety and efficacy means a significant reduction of time and financial investment of a particular drug in question.
\nAlthough tissue engineering emerged with this glory for a few decades, the initial attempts took way long [104], whereas 3D printing of complex biomaterials is a promising means of scaffold designing.
\nThere are different types of 3D printers: laser-, inkjet-, and extrusion-based. However, inkjet-based is more popular in tissue engineering, where cells or biomaterials are incorporated into the substrate, as per digitally set instruction, to recreate a functional organ or tissue. Multiple printheads can be used in the case of organs/tissue containing different types of cells. However, there are several challenges to address while designing a 3D printed engineered tissue [105].
\nThe form of material input is important for this specialized process of 3D printing. Hence, it is important to think through before choosing a material, whether it is compatible to form a filament or powder or pellet or solution, that is required for that process. Another important feature to be considered while choosing the material is the expected mechanical strength of the scaffold and their biocompatibility and biodegradability.
\nThe sole intent of engineered tissue is to replace and regenerate damaged tissue or organ. To comply with this requirement, the scaffold material of the transplanted tissue should be subject to remodeling and absorption. They should be able to degrade in equal or similar pace with the regeneration of extracellular matrix and differentiation of cells. This phenomenon depends on several factors, including hydrophilicity of the scaffold, surface area, porosity, degree of crystallinity, presence or absence of certain enzymes, etc. The most critical part here is harmonization in these factors, so that the degradation of biomaterial and stress release to the surrounding tissue is well synchronized, to ensure healing of the damaged tissue.
\nBiodegradation rate affects the cell viability and mobility, despite the general concept of this biodegradation being non-cytotoxic. The study finds that the fast degradation of the polymer may affect the cells negatively due to the formation of acidic byproduct. However, more research is required to support these data and to develop the degradation profile of the materials.
\nCells are described to be sensitive toward the mechanical strength of the polymer scaffold. Rigid and non-flexible material may hinder the cytoskeleton assembly, cell organization, and receptor recruitment into “focal adhesion plaques,” which is crucial for cell signaling and anchoring. On the other hand, highly pliable material may not be able to provide the mechanical strength for anchoring or cytoskeleton assembly and thus affecting the cellular function as well.
\nDifferent tissues require different porosities for the optimum effect. However, little knowledge is available. A general range of pore size is suggested to be considered for any type of cell, based on observations, rather than the established theory of optimum pore size for each cell type.
\nA study by Yin et al. describes that the microgrooves on the scaffold surface directly affect the cardiac function and susceptibility to arrhythmias [106]. This indicates the importance of the scaffold surface microenvironment, which positively or negatively affects the success of the tissue transplant.
\nIt is stated that surface roughness may enhance adhesion between cell and extracellular matrix. At the same time, too rough surface of the scaffold may exhaust the cell adhesion capability. On the other side, if the scaffold material is too sharp, the cells may get damaged. However, choosing a smooth surfaced scaffold material may require consideration of further modification or coating, as this feature does not facilitate cell adhesion.
\nSmall and simpler organ printing has been successful, without much difficulty. However, it is not simple when comes to bigger and complex organ, due to difficulty in vascularization. Small tissues are avascular, and most of the time, aneural, alymphatic, and thin or hollow. They can receive nutrition from host vasculature. But when the transplanted tissue is thicker than 150–200 μm, oxygen cannot be diffused from host tissue to it. As such, to create a functional bigger and complex tissue or organ, an integrated vascular system is to be created, which is still not in place [105].
\nThe homogenous distribution of the cells throughout the scaffold is important for the effectivity of the tissue. The conventional usage of Petri dish may not be adequate to ensure the uniform seeding of these cells. The bioreactor technology can influence a successful cell seeding, throughout the depth of the scaffold, evenly.
\nDespite all the challenges, 3D bioprinting offers great potential and diverse applications for the medical and healthcare sector.
\nAlthough few technical aspects are still to be figured out, rapid prototyping creates possibilities to generate complex organs like kidney, liver or even heart, despite having a heterogeneous cellular composition. With the fast pace of advancement in technology and the number of researches going on in this field, the current challenges are expected to be resolved eventually. It has been foreseen that within 20 years, 3D printed organs will be commercially available for transplantation [105].
\nThe animal study is a mandatory part of drug designing, which applies to tissue engineering and cell therapy. It has been estimated that about 115 million animals are being used in the biomedical industry per year [107]. The printed organs can replace these animal tests of safety, efficacy, and toxicology, saving a number of animals, and resolving the ethical conflict in this issue. At the same time, these printed organs can be more “close to the human subject” model than the animals.
\nIn an optimistic vision, it may be possible to have a printed piece of patient’s tissue to test which drug is suitable and effective for that particular patient, before applying on them, using this technology.
\nConventional tissue engineering involves customized scaffold preparation and manual cell seeding. Hence, the success rate is not consistent and the production cost is high, thereby resulting in very costly tissue that many people cannot afford. With the automation and advancement of bioreactor technique in conjunction with rapid prototyping, mass production of the complete organ is a very likely prospect [104]. This will increase the efficiency of the procedure of organ formation, and mass production capability will be economic and more affordable.
\nThe organ donation rate has always been far less than the requirement in a given period. On top of that, immunogenicity, rejection, and graft-versus-host disease make the transplantation process further difficult. With rapid prototyping, the scarcity of human organs can be resolved, with less immune rejection and higher effectivity.
\nIn situ generation of skin has already been achieved. With the progress of this technology, it is deemed that in future, a small piece of any tissue can be bioprinted in situ, during surgery, in no time, with precision [105].
\nRegenerative medicine is the new big thing in the medical and healthcare areas. Due to the promising outcome and compatibility for the human body, this alternative treatment method might compete and/or take over conventional medicine soon. Rapid prototyping has a very wide prospect in regenerative medicine, medical device, and pharmaceuticals. Incorporating the knowledge of cellular biology, biomaterial design, tissue engineering, bioreactors, and so on, organ regeneration will be much more precise and effective. With this speed of progression of science, the remaining challenges will be resolved soon, thereby opening a new era of healthcare and a better life for human beings.
\nThe authors declare no conflict of interest.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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