Planned versus actual coverage of the survey.
\r\n\tIt is an exceedingly complex condition often with contradictory findings. Diagnosis has been controversial since Leo Kanner's narrow autism to the broader autism spectrum of today. Screening and diagnostic instruments are also problematic and have limitations and broader instruments are better for example the DISCO. The National Institute for Clinical Excellence recommends no specific instruments and states that diagnosis is a clinical task and the instruments are only to be used as adjunctive. While genetic factors are highly significant, there is huge complexity in the genotype and involves a vast array of genes of small effect. Environmental factors in autism are equally complex and controversial, ranging from prenatal and perinatal factors to drugs, testosterone, environmental toxins and fever inducing factors, etc. The natural history and the effect of intervention and treatment on the outcome is very important. Huge resources are devoted to research on autism with increasing numbers of publications.
",isbn:"978-1-83881-012-2",printIsbn:"978-1-83881-005-4",pdfIsbn:"978-1-83881-013-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b9c36a2454fac16e70ba00562cb6f009",bookSignature:"Dr. Michael Fitzgerald",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9634.jpg",keywords:"History, Cognitive Phenotype, Aq, CARS, Epidemiology, Risk, Neurochemistry, Brain Function, Drug Vaccinations, Testosterone, Long Term, Effect of Interventions",numberOfDownloads:94,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 18th 2020",dateEndSecondStepPublish:"October 16th 2020",dateEndThirdStepPublish:"December 15th 2020",dateEndFourthStepPublish:"March 5th 2021",dateEndFifthStepPublish:"May 4th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Prof Michael Fitzgerald was the winner of the “Excellence in Psychiatry” award 2017 and was nominated as one of the top 4 Psychiatrists by Hospital Professional News Ireland – Top 100 Professionals in Ireland 2017. The first Professor of Child and Adolescent Psychiatry in Ireland, specializing in Autism, Aspergers Syndrome, and ADHD. He was the first Psychoanalyst recognized by the International Psychoanalytic Association founded by Sigmund Freud to work in the Republic of Ireland.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"205005",title:"Dr.",name:"Michael",middleName:null,surname:"Fitzgerald",slug:"michael-fitzgerald",fullName:"Michael Fitzgerald",profilePictureURL:"https://mts.intechopen.com/storage/users/205005/images/system/205005.jpg",biography:"Professor Michael Fitzgerald was the first Professor of Child and\nAdolescent Psychiatry in Ireland (Retired). Specialising in autism spectrum\ndisorders, he has diagnosed over 5000 patients. He has a\nlarge number of peer-reviewed publications and has written,\nco-written, and co-edited 34 books with Japanese, Dutch, and\nPolish translations. Professor Simon Baron-Cohen of the University\nof Cambridge described one of his books on autism as 'the\nbest book on autism” and described him as an 'exceptional scholar.” He has lectured\nextensively throughout the world, including the Royal Society/British Academy\nand the British Library in London. He was the overall winner of the 'Excellence in\nPsychiatry” Award 2017 and was nominated as one of the top four psychiatrists by\nHospital Professional News Ireland—Top 100 Professionals in Ireland 2017.",institutionString:"Trinity College",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Trinity College Dublin",institutionURL:null,country:{name:"Ireland"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"74828",title:"An Observationally and Psychoanalytically Informed Parent-Toddler Intervention for Young Children at Risk of ASD: An Audited Case Series and Convergences with Organicist Approaches",slug:"an-observationally-and-psychoanalytically-informed-parent-toddler-intervention-for-young-children-at",totalDownloads:13,totalCrossrefCites:0,authors:[null]},{id:"74695",title:"Associations between Monocyte Cytokine Profiles and Co-Morbid Conditions in Autism Spectrum Disorders",slug:"associations-between-monocyte-cytokine-profiles-and-co-morbid-conditions-in-autism-spectrum-disorder",totalDownloads:30,totalCrossrefCites:0,authors:[null]},{id:"74353",title:"L1-79 and the Role of Catecholamines in Autism",slug:"l1-79-and-the-role-of-catecholamines-in-autism",totalDownloads:36,totalCrossrefCites:0,authors:[null]},{id:"74867",title:"Exposure to Xenobiotics and Gene-Environment Interactions in Autism Spectrum Disorder: A Systematic Review",slug:"exposure-to-xenobiotics-and-gene-environment-interactions-in-autism-spectrum-disorder-a-systematic-r",totalDownloads:17,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5498",title:"Autism",subtitle:"Paradigms, Recent Research and Clinical Applications",isOpenForSubmission:!1,hash:"7a4a04bc1ec60da290315a53de5043b8",slug:"autism-paradigms-recent-research-and-clinical-applications",bookSignature:"Michael Fitzgerald and Jane Yip",coverURL:"https://cdn.intechopen.com/books/images_new/5498.jpg",editedByType:"Edited by",editors:[{id:"205005",title:"Dr.",name:"Michael",surname:"Fitzgerald",slug:"michael-fitzgerald",fullName:"Michael Fitzgerald"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8430",title:"Neurodevelopment and Neurodevelopmental Disorder",subtitle:null,isOpenForSubmission:!1,hash:"696c96d038de473216e48b199613c111",slug:"neurodevelopment-and-neurodevelopmental-disorder",bookSignature:"Michael Fitzgerald",coverURL:"https://cdn.intechopen.com/books/images_new/8430.jpg",editedByType:"Edited by",editors:[{id:"205005",title:"Dr.",name:"Michael",surname:"Fitzgerald",slug:"michael-fitzgerald",fullName:"Michael Fitzgerald"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7835",title:"Autism Spectrum Disorders",subtitle:"Advances at the End of the Second Decade of the 21st Century",isOpenForSubmission:!1,hash:"2cfcf44e79e12e620251aaa9d08a4a3e",slug:"autism-spectrum-disorders-advances-at-the-end-of-the-second-decade-of-the-21st-century",bookSignature:"Michael Fitzgerald",coverURL:"https://cdn.intechopen.com/books/images_new/7835.jpg",editedByType:"Edited by",editors:[{id:"205005",title:"Dr.",name:"Michael",surname:"Fitzgerald",slug:"michael-fitzgerald",fullName:"Michael Fitzgerald"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/6550.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. Mauricio Barría"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"18028",title:"Multiscale Modeling of Myocardial Electrical Activity: From Cell to Organ",doi:"10.5772/22907",slug:"multiscale-modeling-of-myocardial-electrical-activity-from-cell-to-organ",body:'Cardiac arrhythmias represent a leading cause of morbidity and mortality in developed countries. In spite of intense research, the mechanisms of generation, maintenance and termination of theses arrhythmias are still not clearly understood. The ability to predict, prevent and treat these arrhythmias remains a major scientific challenge. In the last years, new technologies have produced a huge amount of information at different levels: subcellular, cellular, tissue, organ and system. However, the knowledge of each component is not sufficient by itself to understand its behavior and an integrative approach, which should take into account the complex interactions between all components is needed. This understanding is necessary to improve prevention and treatment of cardiac arrhythmias.
Traditionally, cardiac research has been based on experimental and clinical studies. However, in the last decades, a quantitative understanding of the biophysical and biochemical properties of individual cardiac myocytes and of the anatomical structure of the heart has permitted the development of computational models of single myocytes, portions of tissue and even the whole heart. These models have opened a new approach in studying the complex mechanisms underlying cardiac arrhythmias by means of computer simulations.
Integrated and multiscale models of the heart are improving very fast, as a large body of research is being made in this field. These models integrate, with a great degree of detail, the different levels of the organ, from the genetic characteristics of membrane ionic channels, to the electrophysiological characteristics of cardiac cells and to the anatomical structure of the different cardiac tissues. In this chapter, the basis of cardiac multiscale modeling will be reviewed and recent and specific case studies for the different levels will be described focusing on the understanding of cardiac arrhythmias during myocardial ischemia and drug treatment.
Comprehensive models of the electrical activity of cardiac ion channels were formulated for the first time in 1952 (Hodgkin & Huxley, 1952). These models were based on the Hodgkin-Huxley mathematical formalism, which allows the reproduction of macroscopic ionic currents. This approach computes the conductance of a certain ion channel as a function of the open probability of each gate of the channel and the maximum conductance of the membrane for that particular ion. In additon, gate transitions from closed to open and vice versa follow a first order voltage dependent behavior at a rate that is independent of the remaining gate states.
The improvements in the experimental techniques employed to measure and characterize the ionic currents responsible for the cardiac action potential have provoked a paralell evolution in the ion channel models, which have acquired an increasing complexity and realism. Two examples are the separation of certain currents into its components, like the slow and rapid delayed rectifier potassimum currents, and the discovery of ion current dependencies on electrolyte concentrations. Importantly, experimental data have evidenced the existence of state dependent transitions of ion channels that Hodgkin-Huxley formalism fails to reproduce. This fact has favored the development of Markov models for ion channels, which are based on the assumption that the transitions between channel states depend on the present conformation of the channel and not on its past history. Furthermore, recent developments in molecular biology and in the genetics of ion channels have made possible the characterization of the electrophysiological properties of a certain mutated ion channel (Tomaselli et al., 1995), allowing the development of its mathematical model using Markov models. Although Markov models can provide a more realistic formulation of ion channels, Hodgkin-Huxley models are also currently used because their computational requirements are significantly less demanding. Both formalisms are used to model ion channel-drug interactions and pathological situations.
The ionic current and action potential models developed in the last decades make it possible to simulate the electrical activity of cardiac cells both in normal and abnormal conditions. Among the latter, acute myocardial ischemia is one of the most threatening abnormalities that can occur in cardiac tissue because it can easily trigger malignant and potentially mortal reentrant tachyarrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF) (Janse & Wit, 1989). Indeed, VF subsequent to ischemia is considered as the first cause of mortality in Europe, the USA and a significant part of Asia (Ross, 1999).
Myocardial ischemia occurs when a coronary artery is occluded. When this happens, a certain zone of the myocardium (formed by the cells that depended on the occluded artery for their blood and nutrient supply) begins to suffer metabolic and electrophysiological changes. Specifically, K+ ions begin to rapidly accumulate in the extracellular medium (a phenomemon termed hyperkalemia), the media (both intra and extracellular) become acidotic (with a drop in pH from 7.2-7.4 to 6.2-6.4 in 10-15 minutes), and oxygen levels decline (hypoxia). Hypoxia provokes in turn a depletion in intracellular ATP levels and a concomitant increase in ADP levels, which results in the partial activation of a specific type of K+ current called ATP-sensitive potassium current (IK(ATP)) (Noma, 1983).
To properly and predictively simulate the cardiac arrhythmias arising from myocardial ischemia, the model must be of a multi-scale nature and ischemia has to be modeled in the ion channel level, the cell level and the tissue level. While tissue modeling of ischemia will be adressed further in this chapter, ion channel ischemia modeling should be done as follows. First, hypoxia must be included in the model through its effects in IK(ATP) current activation (see below). Second, hyperkalemia must be simulated by increasing [K+]o levels (Rodriguez et al., 2002). Elevated [K+]o in turn alters the conductance of a number of sarcolemmal channels, such as IK1 or IKr, and these effects are included in most ion channel models published to date. Third, acidosis must be modeled through its effects in the maximum conductances and kinetic properties of the Na+ and Ca2+ inward currents (Ferrero, Jr. et al., 2003; Shaw & Rudy, 1997a).
The IK(ATP) current, which is normally dormant in normoxic situations but is partially (though only slightly) activated in acute myocardial ischemia, is carried by K+ ions (Noma, 1983), shows voltage-dependent inward rectification (but is otherwise non-voltage dependent) caused by Mg2+ and Na+ ions (Horie et al., 1987), and is gated by intracellular ATP with intracellular ADP playing a modulating role in the ATP-mediated deactivation (Weiss et al., 1992). All these facts have to be present in an ion channel model of the current. In 1995, Ferrero et al. published the first comprehensive model for the myocardial IK(ATP) current (Ferrero, Jr. et al., 1996), which has been further improved more recently (Michailova et al., 2005).
The mathematical model for the IK(ATP) current includes all the aforementioned effects, which are represented in the following equation:
where σ is the channel density, g0 the unitary conductance (which is [K+]o dependent), γir is a term that includes the inward rectification properties of the channels, fATP is a term that expresses the ATP and ADP dependence of the current, Vm is membrane potential and EK the K+ equilibrium (Nernst) potential. The inward rectification term is expressed as follows:
where Kh,Mg and Kh,Mg are voltage and [K+]o dependent. Finally, the ATP-ADP dependence follows another Hill-type equation as follows:
where both the semi-inhibition constant KATP and the Hill exponent H depend on the intracellular ADP concentration.
Also at the ionic channel level, the effect of antiarrhythmic drugs can be mathematically formulated. Models that simulate effects of drugs and reproduce the associated clinical electrocardiography signal are highly relevant to assess and predict antiarrhythmic actions, and help to improve the efficacy and safety of pharmacologic therapy. Additionally, it starts to be considered as an important technological tool for helping the pharmaceutical industry to develop new antiarrhythmic drugs spending less time and money. In this field, Dr. Starmer is one of the pioneers in modeling drug effects on sodium channels and an increasing number of research groups are developing 3D models of the heart to test drug actions. In the last years, our group has carried out different studies of antiarrhythmic and proarrhytmic effects of different drugs, such as pinacidil, dofetilide and lidocaine on cardiac arrhythmias.
To simulate the effects of drugs on ion channels, different kinds of models can be used (Brennan et al., 2009). Sometimes it is sufficient to change the conductance of ion channels by introducing the effect of the drug, but to be able to reproduce use- and frequency-dependent block it is necessary to use a state-dependent block model. In the state-dependent block model, drug interaction with ionic channels depends on the state of the channel. Drug-associated channels (blocked channels) do not conduct. The fraction of blocked non-conducting channels depends on the equilibrium between blocked and unblocked states. Two hypotheses have been proposed to reproduce the state-dependent block: the modulated receptor hypothesis and the guarded receptor hypothesis. The Modulated Receptor (MR) hypothesis (Hille, 1977; Hondeghem & Katzung, 1977) states that the affinity of a drug changes with the state of the channel. Additionally, the rate constants of state transitions are different for bound and unbound satates. This is the most general model to reproduce the effects of drugs; however a great number of parameters have to be estimated. The Guarded Receptor (GR) hypothesis (Starmer et al., 1984) assumes that the drug has a fixed and state-independent affinity for their receptor sites, but its access to the binding sites is controlled by the voltage-dependent channel gates. In this hypothesis, the rate constants for the state transitions do not change when the drug binds to the channel. The GR model is less complex than the MR model for the same drug-ion channel interaction as fewer parameters have to be estimated.
In the present section three different models of drugs: pinacidil, lidocaine, and dofetilide are described.
Pinacidil is a vasodilator drug and is known to increase IK(ATP) in cardiomyocytes, as well as in vascular smooth muscle and pancreatic Beta cells (Fan et al., 1990; Nakayama et al., 1990). The activation of ATP-sensitive potassium (KATP) channels in heart tissue, especially during myocardial ischemia, markedly changes action potential (AP) configuration by reducing AP duration (APD) (Nichols et al., 1991) and plays an important role in cardiac arrhythmogenesis (Janse & Kleber, 1981). To analyze the controversial effects of this drug through computer simulations, the first step is to build a model of the effects of pinacidil at the ion channel level. Pinacidil enhances the activity of IK(ATP) increasing the fraction of open KATP channels (fATP) (Fan et al., 1990). The equation of IK(ATP) formulated by Ferrero et al. (Ferrero, Jr. et al., 1996) was modified, as indicated in the following equations:
The equation of fATP was based on the pharmacokinetic scheme depicted in Fig. 1. This scheme describes the mechanism of action by which ATP molecules close KATP channels, while pinacidil molecules open it. ATP, ADP and pinacidil molecules bind to different sites in the protein receptor (Nichols et al., 1991; Weiss et al., 1992). According to the open states when molecules of pinacidil and/or ADP are bound to the protein channel, the fraction of activated KATP channels was expressed in equation (5). Experimental data (Fan et al., 1990; Nakayama et al., 1990; Weiss et al., 1992) were fitted to equation (5) to determine the values of equilibrium dissociation constants and the Hill coefficient (H) (see Trenor et al. 2005 for details). A factor (fPIN) addressing the voltage-dependent block of KATP channels by pinacidil was also included in IK(ATP) equation, as in Fan et al. (Fan et al., 1990).
Cyclic scheme representing different possibilities of binding to the channel receptor protein. R, P, A and D stand for the unoccupied receptor, pinacidil, ATP and ADP respectively. ki are the overall equilibrium dissociation constants for the different steps of the cyclic reaction, and H is the Hill coefficient.
Lidocaine is a class I antiarrhythmic drug that exerts its effect by blocking inward sodium current (INa) in a use-dependent manner, and is more effective for high stimulation frequencies (Clarkson et al., 1988). The block of lidocaine exists both as neutral and charged forms at a physiological pH. The charged form predominates at low pH (6.4) due to the protonation of the neutral form with hydrogen molecules. Experimental data have confirmed that lidocaine is more effective and recovery from block is slowed (Broughton et al., 1984; Moorman et al., 1986; Wendt et al., 1993) under acidosis.
The formulated model of lidocaine-INa interaction takes into account the modulatory effect of pH on the Na+ channel block and is based on the GR theory proposed by Starmer (Starmer & Courtney, 1986). Three main processes have been considered: the hydrophobic pathway, the hydrophilic pathway, and coupling between blocked channels by charged and neutral forms using a proton exchange process. Several experimental studies have suggested that the block of neutral drug takes place during the activated (O), inactivated (I) and resting (R) states (Bean et al., 1983; Clarkson et al., 1988), whereas the interaction between the charged form with the channel only takes place in the activated state (Liu et al., 2003; Moorman et al., 1986; Yeh & Tanguy, 1985). Fig. 2 represents the kinetic diagram of the complete model, and equations (6) and (7) mathematically describe the interaction of the neutral and charged form with Na+ channels, respectively.
bN and bC stands for the fraction of channels blocked by the neutral and charged form. The association and dissociation rate constants for the different states (activated, inactivated, and resting) are kO, kI, kR, lO, lI, and lR, respectively. The activation channel gate is m and the inactivaction channel gates are h and j. Additionally, we have taken into account the rate constants kp and lp to determine the proton exchange process. In equation (7), the association and dissociation rate constants are kC and lC, respectively.
Lidocaine interaction with Na+ channels in both forms of the drug: neutral (bN) and charged (bC).
As stated in the GR hypothesis, we introduced the factor (1-b), where b is the sum of the bN and bC in the INa equation.
Dofetilide is a specific and potent blocker of the rapid component of the delayed rectifier K+ current (IKr) with an IC50 in the nanomolar range (3.9-31 nmol/L) for ventricular myocytes (Carmeliet, 1992; Jurkiewicz & Sanguinetti, 1993; Weerapura et al., 2002b). Dofetilide is classified as a pure class III antiarrhythmic agent and provokes a prolongation of action potential duration (APD) without any effect on the resting membrane potential, action potential amplitude or maximum rate of depolarization (Jurkiewicz & Sanguinetti, 1993; Tande et al., 1990).\n\t\t\t\t
To model the effects of dofetilide on IKr, the formulation of IKr proposed by Zeng and Rudy (Zeng et al., 1995) for guinea pig ventricular cells was used. This formulation is based on experimental data obtained by Sanguinetti and Jurkiewicz (Sanguinetti & Jurkiewicz, 1990). In their model, IKr channels present three possible states: Closed (C), Open (O) and Inactivated (I). The formulation of IKr in the ventricular cell model (Zeng et al., 1995) is expressed in equation (8).
where Vm is the membrane potential, EKr is the reversal potential, GKrmax is the maximum conductance of IKr, Xr is the activation gate and R is the time-independent inactivation gate. In our dofetilide-IKr model, the effect of dofetilide is represented by introducing the factor (1b) in the IKr formulation (where b is the fraction of channels blocked by the drug). Thus, the new formulation of the rapid component of the delayed rectifier K+ current taking into account the effect of dofetilide is
The blocking activity (b) on IKr equation is modeled using the GR hypothesis (Starmer et al., 1984). Dofetilide experimental studies have shown drug-receptor interaction in open and inactivated states but not in closed states (Weerapura et al., 2002a; Yang et al., 1997). It has been also observed that the drug is trapped when the channel closes, thereby the transition between open and closed state is possible but dissociation of the drug does not occur at closed states (Carmeliet, 1992; Weerapura et al., 2002a). Taking into account these experimental evidences we suggest the model of IKr block by dofetilide shown in Fig. 3.
Using this model, the blocking factor b for a concentration of dofetilide D can be calculated as:
\n\t\t\t\tSchematic model of dofetilide interaction with IKr channels in the different states: Closed (C), Open (O) and Inactivated (I1 and I2) potassium channels. k and r are the association and dissociation constants for the different states.
The electrical activity of the cell is determined by the interaction of the multiple ionic channels and ion exchangers present in the cellular membrane. Their activity conditions the cardiac AP, which is intimately synchronized with the mechanical activity of the heart. Several computational models of cardiac cellular electrical activity have been developed, and characterize the activity of ventricular myocytes, atrial myocytes, sinus node and Purkinje fibers for different animal species. The degree of complexity of these models increases going from heuristic models describing the physical process of cell excitation and propagation, such as Fitz-Hugh-Nagumo model (Fitzhugh, 1961), to phenomenological models (Bueno (Bueno-Orovio et al., 2008) model), and to extremely detailed ionic models. The most detailed ionic models for AP include transmembrane currents, ion transfer through transporters and dynamic changes of ion concentrations during the action potential. The ventricular AP models most extensively used in simulations because of their detailed and realistic formulation are Luo and Rudy model (Faber & Rudy, 2000; Luo & Rudy, 1994; Zeng et al., 1995) for guinea pig, Shannon (Shannon et al., 2005) and Mahajan (Mahajan et al., 2008) models for rabbit, and ten Tusscher (ten Tusscher & Panfilov, 2006) and Grandi (Grandi et al., 2010) models for human. As regards atrial myocytes, Nygren (Nygren et al., 1998) model and Courtemanche (Courtemanche et al., 1998) model have also had an important repercussion. Luo Rudy AP model was one of the first detailed models formulated and has been a fundamental base on which other models have been built. This model has been improved and extensively used to simulate the ventricular electrical activity. In these detailed AP models, the formulation of the ionic currents follow Hodgkin-Huxley, Goldmann-Hodgkin-Katz (GHK) or Markov formalisms, and the membrane potencial Vm is related to the ionic currents through equation (11).
where the total ionic current through the cell membrane (Im) is expressed as the sum of all ionic currents (Is through ionic channels and Ip through pumps and exchangers) plus the current through the membrane equivalent capacitor.
Fig. 4 shows a scheme of the main ionic currents of a ventricular myocyte (panel A) and the time course of membrane potential and some ionic currents (panel B) obtained by solving the non-linear differential equations system defined by equation (11) and the different ionic current equations in Luo and Rudy model (Faber & Rudy, 2000; Zeng et al., 1995).
Panel A: Schematic representation of a ventricular myocyte and the most significant ionic currents through the sarcolemma. Panel B: Temporal evolution of the membrane potential Vm, Ca2+, Na+ and K+ currents, and currents through the Na+/K+ pump (INaK) and the Na+/Ca2+ exchanger.
Acutely ischemic action potentials can be simulated using these AP models appropriately modified to represent ischemia-activated currents (such as IK(ATP)) and ischemic-modified currents (e.g. the inward Na+ and Ca2+ currents), as explained previously.One of the main components of acute ischemia is hypoxia (i.e. lack of oxygen in the tissue), and the main change exerted by hypoxia to myocardial action potentials is a significant shortening in APD (Morena et al., 1980). The model proposed by Ferrero et al. for the ATP-sensitive K+ current (Ferrero, Jr. et al., 1996) yielded the first theoretical proof of the determinant role of the IK(ATP) current in hypoxic action potential shortening (Ferrero, Jr. et al., 1996), which is an arrhythmic factor due to the APD dispersion it generates across the myocardium (Janse & Wit, 1989). Indeed, as shown in Fig. 5, although very few KATP channels do activate during acute myocardial ischemia (<1% in the first 15minutes), this degree of activation is enough to account for the >50% shortening in APD which is observed in experimental conditions (Morena et al., 1980).
When the simulated myocyte is subject to hyperkalemia and acidosis, as well as hypoxia, the main features of the ischemic AP waveforms are correctly reproduced (Ferrero, Jr. et al., 2003; Shaw & Rudy, 1997a). A shown in Fig. 6, ischemia produces APD shortening, resting potential elevation, upstroke phase division into two phases (the first one mediated by inward Na+ current, the second one by inward Ca2+ current through the L-type channels). Moreover, acute ischemia changes important intrinsic AP properties which are pivotal in arrhythmogenesis (e.g., postrepolarization refractoriness). Overall, this ischemic AP simulations are the first level of the multi-scale ischemia simulations that will be described further in this chapter.
Hypoxic action potentials simulated using a modified version of the dynamic Luo & Rudy model (Luo & Rudy, 1994) in which the IK(ATP) model formulated by Ferrero was included (Ferrero, Jr. et al., 1996). Numbers indicate percentage of KATP channel activation.
As mentioned before, the electrical activity of the cell is altered under the effects of drugs. Once the effects of the drug have been formulated at the ionic channel level, the alterations of the electrical activity of the cell can be analyzed including the modified current in the AP model. In this way, the effects of pinacidil and lidocaine on ischemic cells are described. Also the effects of dofetilide on different types of ventricular cells will be analyzed.
Action potentials and inward ionic currents during control conditions (panel A) and 10 minutes of acute ischemia (panel B). The lower panels correspond to a zoomed view of the first 30 ms of the action potentials shown on the top panels. A significant decrease in inward Na+ current, together with an increase in Ca+2 current, is observed. The low INa in ischemia provoques the division of the upstroke phase of the action potential into two distinct phases, as seen experimentaly (Janse & Wit, 1989).
As mentioned in section 2, pinacidil shortens the APD by the activation of KATP channels. To validate pinacidil model, the formulation of the effect of pinacidil (equations (4) and (5)) on IK(ATP) was introduced into the Luo and Rudy model (Luo & Rudy, 1994; Zeng et al., 1995). Simulations were carried out combining different concentrations of pinacidil with various [ATP]i and [ADP]i values corresponding to several stages of early acute ischemia (Cole et al., 1991; Nakayama et al., 1990; Weiss et al., 1992). The resulting APs are shown in Fig. 7. Consistent with experimental findings (Cole et al., 1991; Nakayama et al., 1990), our results show that increasing concentrations of pinacidil and/or decreasing intracellular ATP levels reduce APD. It is noteworthy that changes concerning intracellular ATP and ADP concentrations, occurring during acute ischemia, significantly enhance the effects of pinacidil. These changes in APD (and hence in cell refractoriness) could be key in determining the reentry probability in acute ischemia after the administration of the drug.
Set of simulated single cell action potentials in the presence of different concentrations of pinacidil: 0, 5 and 10 mol/L from left to right. Shortening of APD can be also observed under different concentrations of ATP: 6.8 mmol/L (solid line), 5 mmol/L (dashed line) and 2 mmol/L (discontinuous line).
The block of INa by lidocaine leads to a decrease in the maximal AP upstroke (dV/dtmax). To simulate the effects of lidocaine on AP features, we incorporated the model of lidocaine into the Luo and Rudy AP model (Luo & Rudy, 1994). Specifically, we measured the reduction of the maximum AP upstroke for different stimulation frequencies and pH values when increasing concentrations of the drug. We examined the response of dV/dtmax to changes in pH from 7.4 to 6.4 for different concentrations of the drug (50 and 100 of µmol/L lidocaine) and at different stimulation frequencies. Panel A in Fig. 8, shows a decrease of 66% and 83% in dV/dtmax from the control value for 50 and 100 µmol/L lidocaine, respectively, at a basic cycle length (BCL) of 500 ms and pH 6.4. For a pH of 7.4 (see Fig. 8 panel B), dV/dtmax was reduced in 45% and 61% for the same concentrations and BCL values. These data support the fact that lidocaine has a greater effect under lower pH. Furthermore, the use-dependent effect is more pronounced at lower pH, as the differences in the decrease of dV/dtmax for different stimulation frequencies are higher for low pH.
The effects of lidocaine on dV/dtmax at pH values of 6.4 and 7.4 at different BCLs. The steady-state dV/dtmax was normalized (Nor) with respect to its value in the absence of the drug.
The steady-state effects of dofetilide concentration on AP characteristics for the different kinds of guinea pig ventricular cells at different BCL values are shown in Fig. 9. In all simulations, once dofetilide was applied the cellular models were stimulated until steady- state was reached. Only magnitudes associated to the last stimulation pulse are shown. Fig. 9A shows that the prolongation of APD90, for all tested dofetilide concentrations, significantly depends on the type of cell. APD90 of M-cells was markedly prolonged compared to endocardial and epicardial cells. For a concentration of 10 nmol/L, APD90 of endocardial cells was prolonged in 8.8%, 11.5% and 12.4% of control value (APD90 before the application of drug) for BCL of 300, 1000 and 2000 ms, respectively. For the same conditions, APD90 of epicardial cells was increased in 5.8%, 6.9% and 7.1% of control, respectively. However, under these conditions, APD90 of M-cells was substantially increased in 14.8%, 36.1% and 292.7% of control, respectively. The three types of cells present reverse use-dependency. Due to this effect, the prolongation of APD90 increased whith the BCL. Fig. 9B shows the relative increase of APD90 (ΔAPD90 in%) induced by different concentrations of dofetilide at different BCLs.
Fig. 9B shows that dofetilide presents reverse use-dependence in the range of tested concentrations (10 nmol/L to 1 µmol/L) with a much more pronounced effect in M-cells than endocardial or epicardial cells. Even more, when the concentration of dofetilide increased, the reverse use-dependency was enhanced.
Effects of different concentrations of dofetilide on steady-state AP waveform (panel A) and APD90 prolongation (panel B) from guinea pig endocardial, epicardial and M-cells at different BCL values.
Abnormalities in impulse propagation are involved in the genesis and maintenance of cardiac arrhythmias and many experimental and theorical studies have analyzed action potential propagation both in normal and pathological situations. The electrical coupling between cells and the propagation process is described in the bidomain model (Tung, L., 1978). This model consists of two continuous domains, the intracellular and the extracellular domain, where electrical currents are governed by Ohm´s law. The following equations describe the bidomain model:
where Di and De are the intracellular and extracellular conductivity tensors, respectively, Ve and Vm stand for the extracellular and the membrane potentials, respectively, Cm is membrane capacitance, Iion represents the total ionic current density computed using AP models, and Ist is the stimulus current density.
Considering that anisotropy similarly affects the intracellular and the extracellular conductivity tensors (Di=De, being a scalar variable), the bidomain model can be simplified into the monodomain model and the previous equations can be reduced to the following one:
where
\n\t\t\tSubsequently, the monodomain model consists of an elliptic partial differential equation and a parabolic partial differential equation coupled to a system nonlinear ordinary differential equations describing the ionic current through the cellular membrane. As this model is mathematically simpler and less computational demanding than the bidomain model, it is widely used for cardiac electrophysiology simulations. Heterogeneous structures of cardiac tissue can also be considered in tissue models allowing a wide use of cardiac models in the study of ionic mechanims of arrhythmias.
In the case of pathological tissues, not only the altered AP models have to be considered but also the spatial inhomogeneities in the electrophysiological changes distributed within the tissue. If we consider the ischemic pathology, after coronary occlusion, the lack of oxygen and blood flow provokes important and heterogeneous electrophysiological changes in the affected ventricular cells defining an ischemic zone and a border zone, setting the stage for reentrant arrhythmias and VF. A large body of clinical and experimental studies try to unravel the mechanisms by which acute myocardial ischemia provokes life-threatening arrhythmias. However, the rapid changes arising during acute myocardial ischemia and the limitations in experimental techniques hamper the complete understanding of this pathology. In this way, computer simulations overcome these limitations providing a helpful tool to understand the electrophysiological changes arising during myocardial ischemia and the underlying mechanisms of reentrant arrhythmias observed during its acute phase. At the tissue level, our group has developed a regional model of ischemia, and the consideration of the temporal evolution of ischemia has allowed undertaking computational studies to assess the changes in the vulnerability of the ischemic tissue to reentry. Not only ionic currents have been analyzed to understand the process of reentry initiation but also the traditional indicator for reentry, i.e. dispersion of repolarization, and the safety factor (SF) for conduction (a parameter that quantifies the source-sink relationship of propagation) as an indicator for conduction block, have been evaluated.
A regional phase Ia ischemic 2D anisotropic monodomain tissue (Ferrero, Jr. et al., 2003; Romero et al., 2009; Trenor et al., 2007) was modeled with a high degree of electrophysiological detail. Changes in electrophysiological parameters were modeled according to experimental results (Coronel, 1994; Ferrero, Jr. et al., 1996; Irisawa & Sato, 1986; Weiss et al., 1992; Yatani et al., 1984). These changes are described in Fig. 10A, which shows the time-course of the three main components of acute ischemia: hypoxia, hyperkalemia and acidosis. Fig. 10B represents the distribution of a simulated cardiac 2D tissue comprising a normal zone (NZ), a circular central ischemic zone (CZ), and a ring-shaped ischemic border zone (BZ) surrounding the CZ. In the BZ all parameters affected by ischemia follow linear spatial gradients, as experimentally recorded (Coronel et al., 1988). Membrane kinetics was simulated by using a modified version of the Luo-Rudy dynamic AP model (LRd00) (Faber & Rudy, 2000) (see Romero et al. 2009 for details), which includes the mathematical formulation of IK(ATP) proposed by Ferrero et al. 1996 (Ferrero, Jr. et al., 1996).
A basic stimulus (S1) followed by a prematures stimulus (S2) at different coupling intervals (CIs) was applied on the edge of the tissue to quantify the vulnerable window (VW), an indicator of the vulnerability to reentry. The VW was defined as the interval of CIs that led to reentry. Fig. 10C shows gray-coded voltage snapshots of a figure-of-eight reentry developed after premature stimulation at minute 8.25 of ischemia. Our results demonstrated that during myocardial ischemia phase Ia, the VW of a bidimensional tissue had an asymmetric unimodal distribution, peaking at minute eight after coronary occlusion (see Fig. 10D), which is in close agreement with experimental observations (Cascio, 2001). We also analyzed the SF for conduction in the regional ischemic tissue using an improved version (Romero et al., 2009) of the formulation proposed by Shaw and Rudy (Shaw & Rudy, 1997c). Our results also indicate that, as ischemia progresses, the SF decreased and the heterogeneity of refractoriness grows. Therefore, a lack of correlation between dispersion of refractoriness and vulnerability to reentry was observed, although our results corroborate the fact that dispersion of refractoriness is essential to reentry generation. Moreover, we found that, in approximately half of the simulated reentries, the area where unidirectional block (UDB) took place was completely recovered from refractoriness when the AP propagation failed. However, the line where the SF dropped below unity matched very much the area where propagation failed in every simulation. Thus, it seemed that the reduction of the source-sink ratio is the ultimate cause of the UDB leading to reentry.Reentrant patterns of activation during regional acute ischemia have been mechanistically analyzed also under the effects of pharmacologic agents, such as pinacidil or lidocaine.
Panel A: Evolution of electrophysiological parameters after the onset of myocardial ischemia in the CZ: [K+]o, fATP, and relative activation of INa (fNa) and ICaL (fCa). Panel B: Regional ischemic tissue layout comprising a NZ, a BZ, and a CZ. Spatial gradients of electrophysiological parameters are described above the tissue.
To analyze the arrhythmogenic effects of pinacidil, different sets of simulations were carried out using different concentrations of the drug. In each set the regionally ischemic 2D tissue described above was stimulated using the aforementioned S1-S2 protocol and the VW was evaluated. Table 1 shows that increasing the concentration of pinacidil had an interesting non-monotonic effect on the duration of the VW. Indeed, low concentrations of the drug increased the VW, until a maximum of 54 ms was reached for [P]=3 m/L. If the concentration was further increased, the VW decreased, until the VW vanished for [P]=10 m/L.
The simulations allowed to unravel the mechanisms underlying pinacidil-induced changes in vulnerability, through the analysis of the patterns of excitation in the tissue and the time evolution of ionic currents and membrane potential. Our results showed the existence of two opposite effects of pinacidil in terms of AP propagation, which modulate the biphasic effects on the VW. On the one hand, pinacidil enhances IK(ATP) (Ferrero, Jr. et al., 1996), which counteracts inward currents responsible for depolarization, mainly INa (which is depressed as a consequence of ischemia) and ICa(L), so that AP reaches lower values of Vm during the depolarization phase. In this way, the potential gradient in the direction of propagation is also reduced, and less electrotonic current flows to downstream cells (Shaw & Rudy, 1997a), reducing axial current. In these conditions, the SF is reduced (Shaw & Rudy, 1997c) and propagation failure may develop. On the other hand, the IK(ATP) enhancement provoked by pinacidil reduces APD, which results in an earlier recovery of excitability (Shaw & Rudy, 1997b), favoring AP propagation. Both effects of pinacidil are related as follows: the earlier the cell recovers its excitability, the less axial current is needed to elicit an AP.
Width of the vulnerable window (VW) for different concentrations of pinacidil.
Width of the VW in ms for different degrees of ischemia (5, 6, and 7 minutes after occlusion) and concentrations of lidocaine (20, 50, and 100 µmol/L).
We also simulated the effects of lidocaine on a regionally ischemic bidimensional tissue prone to reentrant circuits. Simulations were also carried out using an S1-S2 protocol applied on one edge of the tissue and the VW for reentry was calculated for various degrees of severity of ischemia and various concentrations of the drug (20, 50, and 100 µmol/L). The VWs for reentry are shown in Fig. 11, and follow an unimodal behavior with ischemia time course, peaking (35 ms) for 6 minutes after the onset of ischemia. When the concentration of lidocaine was increased, the VW became wider, indicating a higher vulnerability to reentry in the presence of the drug. This effect can be due in part to the heterogeneous action of the drug in the diverse zones of the tissue, contributing to the dispersion of CV and setting the stage for reentry.
In the case of dofetilide, a class III antiarrhythmic drug recently approved by FDA (Food and Drug Administration) for the treatment of persistent atrial fibrillation and flutter, different studies have questioned the antiarrhythmic action of dofetilide in preventing and terminating ventricular tachycardias. Indeed, dofetilide promotes the prolongation of QT interval, which has been related to the trigger of a polymorphic ventricular tachycardia called torsade de pointes. The study of the proarrhythmic effects of dofetilide has also been undertaken with computer simulations at the tissue level, where pseudo ECGs have been evaluated, giving understanding about the mechanisms by which QT interval is altered by this drug.
APD90 distribution in a heterogeneous linear strand, APs of three cells belonging to the different regions of the tissue and pseudoECG, in two different conditions: before the application of dofetilide (control) and when the steady-state in drug-binding was reached after the application of dofetilide 100 nmol/L (BCL= 1000 ms), for normal coupled fiber (panel A) and under poor coupling conditions (panel B).
To study the effect of dofetilide on transmural dispersion of repolarization (TDR), a linear strand including the three kinds of cells was used. Fig. 12A illustrates the steady-state APD90 distribution along a well-coupled heterogeneous fiber and the steady-state APs time-course of the three types of cells belonging to the different regions of the tissue (right side in Fig. 12) under two different conditions: before (control) and after the application of dofetilide 100 nmol/L. The pseudo ECGs are also represented before and after the application of the drug.Our results show that in a well-coupled strand under control conditions, at a BCL of 1000 ms, M-cells present a maximum APD90 of 191 ms, which is 15 ms and 35 ms longer than the shortest APD of endocardial (176 ms) and epicardial (156 ms) cells, respectively. The application of dofetilide 100 nmol/L increased APD90 dispersion (difference between the maximum and the minimum APD90 in the fiber) from 35 ms to 58 ms, being the maximum APD90 recorded in the M-zone and the minimum in the epicardial zone. The electrograms show an increment in the QT interval induced by the application of dofetilide from 220.8 ms (control) to 274.6 ms for 100 nmol/L dofetilide.
The effect of decreasing the intercellular coupling (intercellular coupling resistance Ri multiplied by three) at a BCL of 1000 ms is shown in Fig. 12B. Poor coupling increased the APD90 dispersion from 35 ms (normal coupling) to 58 ms. Even more, under poor coupling conditions dofetilide induced a steeper distribution of APD along the fiber and the APD90 dispersion increased to 102 ms, for dofetilide concentration of 100 nmol/L. The QT intervals were higher than the ones observed in a well coupled fiber. The application of 100 nmol/L dofetilide induced a sharper increase of QT from a value of 251.8 ms in control, to 318 ms.
Whole heart models are needed to study arrhythmias that critically depend on the spatial organization of the heart. Some spatial physiological features may be localization dependent, such as regional ischemia or localized infarct scars. Furthermore, certain arrhythmias are uniquely encountered in the whole heart, such as large reentrant circuits in acute ischemia or initiation of atrial fibrillation by pulmonary venous foci. Other important considerations of the whole heart models are the cardiac fibers arrangement and transmural inhomogeneity in repolarization. Cardiac fibers are arranged as counter-wound helices encircling the ventricular cavities, and the local orientation of these fibers depends on transmural localization (Helm et al., 2005). On the other hand, the heterogeneous expression of IKs, Ito, and INCX, among others, leads to transmural inhomogeneity in repolarization (Antzelevitch & Fish, 2001; Antzelevitch et al., 1991). The vertiginous progress in medical images allows building realistic models of the heart including its highly complex anatomical structure of ventricles and/or atria (see Clayton et al. 2011 for review).
The mathematical problem associated to the resolution of the differential equations modeling the electrical propagation in a 3D heart model does not have an analytical solution. Indeed, electric conduction is described by a set of partial differential equations (PDEs) and ionic currents through the cell mambrane are described with a nonlinear stiff system of ordinary differential equations (ODEs). The numerical solution of the equations is very computationally demanding (Heidenreich et al., 2010b) and require the discretization in space and time of PDEs, as well as the integration of nonlinear systems of ODEs. The operator splitting technique for the time discretization (Qu & Garfinkel, 1999) is used to solve the equations system. The numerical methods used, such as finite differences method or finite elements method, are computationally demanding and require the use of high performance computing techniques.
Whole heart models also permit to relate the arrhythmic behavior with its manifestation in electrograms on the cardiac surface and in electrograms on the body surface, through a torso model (Weiss et al., 2009).
The use of these ventricular 3D models has shed light into the mechanisms by which reentrant arrhythmias are initiated during regional myocardial ischemia. As an example, a 3D model of the human ventricles has been recently used to study the appearance of figure-of-eight reentry under regional acute ischemic conditions (Heidenreich et al., 2010a; Heidenreich et al., 2010b).
Fig. 13 shows different views of the simulated human ventricles. The detailed realistic local fiber orientation obtained from DTI images (diffusion tensor imaging) was introduced in the model and is key in determining the electrical propagation. An ischemic region was introduced in the left ventricle, following the same normal-border-central zone scheme explained in a previous section of this chapter. The central and right panels in Fig. 13 depict the values and gradients of the main ischemic parameters (namely [K+]o, [ATP]i, INa maximum conductance and ICa(L) maximum conductance).
Anatomically realistic human ventricles subject to regional acutely ischemic conditions (Heidenreich et al., 2010b). Top A panel: render image of the simulated ventricles. Bottom A panel: fiber orientation as obtained from DTI. Panel B: distribution of [K+]o, [ATP]i, INa and ICa(L) in the CZ, the NZ and the different epicardial BZs.
Electrical activity in the simulated 3D human ventricles shown in Fig. 13 after the delivery of a premature extrastimuls in the BZ after a train of regular conditioning stimuli. A figure-of-eight reentry is initiated, which is very similar to experimentalty observed ischemic reentry (Janse & Wit, 1989).
As shown in Fig. 14, the model predicted the generation of figure-of-eight reentries, which cross the central ischemic zone formed in the epicardial surface due to the longer refractory period of the midmyocardial layers. Also, focal activity experimentally observed in the epicardium could be caused by re-entrant wavefronts propagating in the mid-myocardium that reemerge in the heart surface.
Multiscale modeling of the electrical cardiac activity represents nowadays a very valuable tool in cardiac disease research which complements experimental and clinical studies. Although significant progresses are made in this field, further improvements are required, such as the modeling of electromechanical coupling of the heart activity, which currently represents an important challenge for modelers. The vertiginous technical improvements in the obtention of medical images and genetic, molecular and ionic measurements, represent a great help to the modeling field, which is in the process of creating a virtual human.
This work was partially supported by the European Commission preDiCT grant (DG-INFSO-224381), by the Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica del Ministerio de Ciencia e Innovación of Spain (TEC2008-02090; TIN2004-03602; TEC 2005-04199/TCM; TIC 2001-2686), by the Programa de Apoyo a la Investigación y Desarrollo (PAID-06-09-2843) de la Universidad Politécnica de Valencia, by the Dirección General de Política Científica de la Generalitat Valenciana (GV/2010/078).
Research methodology is the path through which researchers need to conduct their research. It shows the path through which these researchers formulate their problem and objective and present their result from the data obtained during the study period. This research design and methodology chapter also shows how the research outcome at the end will be obtained in line with meeting the objective of the study. This chapter hence discusses the research methods that were used during the research process. It includes the research methodology of the study from the research strategy to the result dissemination. For emphasis, in this chapter, the author outlines the research strategy, research design, research methodology, the study area, data sources such as primary data sources and secondary data, population consideration and sample size determination such as questionnaires sample size determination and workplace site exposure measurement sample determination, data collection methods like primary data collection methods including workplace site observation data collection and data collection through desk review, data collection through questionnaires, data obtained from experts opinion, workplace site exposure measurement, data collection tools pretest, secondary data collection methods, methods of data analysis used such as quantitative data analysis and qualitative data analysis, data analysis software, the reliability and validity analysis of the quantitative data, reliability of data, reliability analysis, validity, data quality management, inclusion criteria, ethical consideration and dissemination of result and its utilization approaches. In order to satisfy the objectives of the study, a qualitative and quantitative research method is apprehended in general. The study used these mixed strategies because the data were obtained from all aspects of the data source during the study time. Therefore, the purpose of this methodology is to satisfy the research plan and target devised by the researcher.
The research design is intended to provide an appropriate framework for a study. A very significant decision in research design process is the choice to be made regarding research approach since it determines how relevant information for a study will be obtained; however, the research design process involves many interrelated decisions [1].
This study employed a mixed type of methods. The first part of the study consisted of a series of well-structured questionnaires (for management, employee’s representatives, and technician of industries) and semi-structured interviews with key stakeholders (government bodies, ministries, and industries) in participating organizations. The other design used is an interview of employees to know how they feel about safety and health of their workplace, and field observation at the selected industrial sites was undertaken.
Hence, this study employs a descriptive research design to agree on the effects of occupational safety and health management system on employee health, safety, and property damage for selected manufacturing industries. Saunders et al. [2] and Miller [3] say that descriptive research portrays an accurate profile of persons, events, or situations. This design offers to the researchers a profile of described relevant aspects of the phenomena of interest from an individual, organizational, and industry-oriented perspective. Therefore, this research design enabled the researchers to gather data from a wide range of respondents on the impact of safety and health on manufacturing industries in Ethiopia. And this helped in analyzing the response obtained on how it affects the manufacturing industries’ workplace safety and health. The research overall design and flow process are depicted in Figure 1.
Research methods and processes (author design).
To address the key research objectives, this research used both qualitative and quantitative methods and combination of primary and secondary sources. The qualitative data supports the quantitative data analysis and results. The result obtained is triangulated since the researcher utilized the qualitative and quantitative data types in the data analysis. The study area, data sources, and sampling techniques were discussed under this section.
According to Fraenkel and Warren [4] studies, population refers to the complete set of individuals (subjects or events) having common characteristics in which the researcher is interested. The population of the study was determined based on random sampling system. This data collection was conducted from March 07, 2015 to December 10, 2016, from selected manufacturing industries found in Addis Ababa city and around. The manufacturing companies were selected based on their employee number, established year, and the potential accidents prevailing and the manufacturing industry type even though all criterions were difficult to satisfy.
It was obtained from the original source of information. The primary data were more reliable and have more confidence level of decision-making with the trusted analysis having direct intact with occurrence of the events. The primary data sources are industries’ working environment (through observation, pictures, and photograph) and industry employees (management and bottom workers) (interview, questionnaires and discussions).
Desk review has been conducted to collect data from various secondary sources. This includes reports and project documents at each manufacturing sectors (more on medium and large level). Secondary data sources have been obtained from literatures regarding OSH, and the remaining data were from the companies’ manuals, reports, and some management documents which were included under the desk review. Reputable journals, books, different articles, periodicals, proceedings, magazines, newsletters, newspapers, websites, and other sources were considered on the manufacturing industrial sectors. The data also obtained from the existing working documents, manuals, procedures, reports, statistical data, policies, regulations, and standards were taken into account for the review.
In general, for this research study, the desk review has been completed to this end, and it had been polished and modified upon manuals and documents obtained from the selected companies.
The study population consisted of manufacturing industries’ employees in Addis Ababa city and around as there are more representative manufacturing industrial clusters found. To select representative manufacturing industrial sector population, the types of the industries expected were more potential to accidents based on random and purposive sampling considered. The population of data was from textile, leather, metal, chemicals, and food manufacturing industries. A total of 189 sample sizes of industries responded to the questionnaire survey from the priority areas of the government. Random sample sizes and disproportionate methods were used, and 80 from wood, metal, and iron works; 30 from food, beverage, and tobacco products; 50 from leather, textile, and garments; 20 from chemical and chemical products; and 9 from other remaining 9 clusters of manufacturing industries responded.
A simple random sampling and purposive sampling methods were used to select the representative manufacturing industries and respondents for the study. The simple random sampling ensures that each member of the population has an equal chance for the selection or the chance of getting a response which can be more than equal to the chance depending on the data analysis justification. Sample size determination procedure was used to get optimum and reasonable information. In this study, both probability (simple random sampling) and nonprobability (convenience, quota, purposive, and judgmental) sampling methods were used as the nature of the industries are varied. This is because of the characteristics of data sources which permitted the researchers to follow the multi-methods. This helps the analysis to triangulate the data obtained and increase the reliability of the research outcome and its decision. The companies’ establishment time and its engagement in operation, the number of employees and the proportion it has, the owner types (government and private), type of manufacturing industry/production, types of resource used at work, and the location it is found in the city and around were some of the criteria for the selections.
The determination of the sample size was adopted from Daniel [5] and Cochran [6] formula. The formula used was for unknown population size Eq. (1) and is given as
where n = sample size, Z = statistic for a level of confidence, P = expected prevalence or proportion (in proportion of one; if 50%, P = 0.5), and d = precision (in proportion of one; if 6%, d = 0.06). Z statistic (Z): for the level of confidence of 95%, which is conventional, Z value is 1.96. In this study, investigators present their results with 95% confidence intervals (CI).
The expected sample number was 267 at the marginal error of 6% for 95% confidence interval of manufacturing industries. However, the collected data indicated that only 189 populations were used for the analysis after rejecting some data having more missing values in the responses from the industries. Hence, the actual data collection resulted in 71% response rate. The 267 population were assumed to be satisfactory and representative for the data analysis.
The sample size for the experimental exposure measurements of physical work environment has been considered based on the physical data prepared for questionnaires and respondents. The response of positive were considered for exposure measurement factors to be considered for the physical environment health and disease causing such as noise intensity, light intensity, pressure/stress, vibration, temperature/coldness, or hotness and dust particles on 20 workplace sites. The selection method was using random sampling in line with purposive method. The measurement of the exposure factors was done in collaboration with Addis Ababa city Administration and Oromia Bureau of Labour and Social Affair (AACBOLSA). Some measuring instruments were obtained from the Addis Ababa city and Oromia Bureau of Labour and Social Affair.
Data collection methods were focused on the followings basic techniques. These included secondary and primary data collections focusing on both qualitative and quantitative data as defined in the previous section. The data collection mechanisms are devised and prepared with their proper procedures.
Primary data sources are qualitative and quantitative. The qualitative sources are field observation, interview, and informal discussions, while that of quantitative data sources are survey questionnaires and interview questions. The next sections elaborate how the data were obtained from the primary sources.
Observation is an important aspect of science. Observation is tightly connected to data collection, and there are different sources for this: documentation, archival records, interviews, direct observations, and participant observations. Observational research findings are considered strong in validity because the researcher is able to collect a depth of information about a particular behavior. In this dissertation, the researchers used observation method as one tool for collecting information and data before questionnaire design and after the start of research too. The researcher made more than 20 specific observations of manufacturing industries in the study areas. During the observations, it found a deeper understanding of the working environment and the different sections in the production system and OSH practices.
Interview is a loosely structured qualitative in-depth interview with people who are considered to be particularly knowledgeable about the topic of interest. The semi-structured interview is usually conducted in a face-to-face setting which permits the researcher to seek new insights, ask questions, and assess phenomena in different perspectives. It let the researcher to know the in-depth of the present working environment influential factors and consequences. It has provided opportunities for refining data collection efforts and examining specialized systems or processes. It was used when the researcher faces written records or published document limitation or wanted to triangulate the data obtained from other primary and secondary data sources.
This dissertation is also conducted with a qualitative approach and conducting interviews. The advantage of using interviews as a method is that it allows respondents to raise issues that the interviewer may not have expected. All interviews with employees, management, and technicians were conducted by the corresponding researcher, on a face-to-face basis at workplace. All interviews were recorded and transcribed.
The main tool for gaining primary information in practical research is questionnaires, due to the fact that the researcher can decide on the sample and the types of questions to be asked [2].
In this dissertation, each respondent is requested to reply to an identical list of questions mixed so that biasness was prevented. Initially the questionnaire design was coded and mixed up from specific topic based on uniform structures. Consequently, the questionnaire produced valuable data which was required to achieve the dissertation objectives.
The questionnaires developed were based on a five-item Likert scale. Responses were given to each statement using a five-point Likert-type scale, for which 1 = “strongly disagree” to 5 = “strongly agree.” The responses were summed up to produce a score for the measures.
The data was also obtained from the expert’s opinion related to the comparison of the knowledge, management, collaboration, and technology utilization including their sub-factors. The data obtained in this way was used for prioritization and decision-making of OSH, improving factor priority. The prioritization of the factors was using Saaty scales (1–9) and then converting to Fuzzy set values obtained from previous researches using triangular fuzzy set [7].
The researcher has measured the workplace environment for dust, vibration, heat, pressure, light, and noise to know how much is the level of each variable. The primary data sources planned and an actual coverage has been compared as shown in Table 1.
Planned versus actual coverage of the survey.
The response rate for the proposed data source was good, and the pilot test also proved the reliability of questionnaires. Interview/discussion resulted in 87% of responses among the respondents; the survey questionnaire response rate obtained was 71%, and the field observation response rate was 90% for the whole data analysis process. Hence, the data organization quality level has not been compromised.
This response rate is considered to be representative of studies of organizations. As the study agrees on the response rate to be 30%, it is considered acceptable [8]. Saunders et al. [2] argued that the questionnaire with a scale response of 20% response rate is acceptable. Low response rate should not discourage the researchers, because a great deal of published research work also achieves low response rate. Hence, the response rate of this study is acceptable and very good for the purpose of meeting the study objectives.
The pretest for questionnaires, interviews, and tools were conducted to validate that the tool content is valid or not in the sense of the respondents’ understanding. Hence, content validity (in which the questions are answered to the target without excluding important points), internal validity (in which the questions raised answer the outcomes of researchers’ target), and external validity (in which the result can generalize to all the population from the survey sample population) were reflected. It has been proved with this pilot test prior to the start of the basic data collections. Following feedback process, a few minor changes were made to the originally designed data collect tools. The pilot test made for the questionnaire test was on 10 sample sizes selected randomly from the target sectors and experts.
The secondary data refers to data that was collected by someone other than the user. This data source gives insights of the research area of the current state-of-the-art method. It also makes some sort of research gap that needs to be filled by the researcher. This secondary data sources could be internal and external data sources of information that may cover a wide range of areas.
Literature/desk review and industry documents and reports: To achieve the dissertation’s objectives, the researcher has conducted excessive document review and reports of the companies in both online and offline modes. From a methodological point of view, literature reviews can be comprehended as content analysis, where quantitative and qualitative aspects are mixed to assess structural (descriptive) as well as content criteria.
A literature search was conducted using the database sources like MEDLINE; Emerald; Taylor and Francis publications; EMBASE (medical literature); PsycINFO (psychological literature); Sociological Abstracts (sociological literature); accident prevention journals; US Statistics of Labor, European Safety and Health database; ABI Inform; Business Source Premier (business/management literature); EconLit (economic literature); Social Service Abstracts (social work and social service literature); and other related materials. The search strategy was focused on articles or reports that measure one or more of the dimensions within the research OSH model framework. This search strategy was based on a framework and measurement filter strategy developed by the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) group. Based on screening, unrelated articles to the research model and objectives were excluded. Prior to screening, researcher (principal investigator) reviewed a sample of more than 2000 articles, websites, reports, and guidelines to determine whether they should be included for further review or reject. Discrepancies were thoroughly identified and resolved before the review of the main group of more than 300 articles commenced. After excluding the articles based on the title, keywords, and abstract, the remaining articles were reviewed in detail, and the information was extracted on the instrument that was used to assess the dimension of research interest. A complete list of items was then collated within each research targets or objectives and reviewed to identify any missing elements.
Data analysis method follows the procedures listed under the following sections. The data analysis part answered the basic questions raised in the problem statement. The detailed analysis of the developed and developing countries’ experiences on OSH regarding manufacturing industries was analyzed, discussed, compared and contrasted, and synthesized.
Quantitative data were obtained from primary and secondary data discussed above in this chapter. This data analysis was based on their data type using Excel, SPSS 20.0, Office Word format, and other tools. This data analysis focuses on numerical/quantitative data analysis.
Before analysis, data coding of responses and analysis were made. In order to analyze the data obtained easily, the data were coded to SPSS 20.0 software as the data obtained from questionnaires. This task involved identifying, classifying, and assigning a numeric or character symbol to data, which was done in only one way pre-coded [9, 10]. In this study, all of the responses were pre-coded. They were taken from the list of responses, a number of corresponding to a particular selection was given. This process was applied to every earlier question that needed this treatment. Upon completion, the data were then entered to a statistical analysis software package, SPSS version 20.0 on Windows 10 for the next steps.
Under the data analysis, exploration of data has been made with descriptive statistics and graphical analysis. The analysis included exploring the relationship between variables and comparing groups how they affect each other. This has been done using cross tabulation/chi square, correlation, and factor analysis and using nonparametric statistic.
Qualitative data analysis used for triangulation of the quantitative data analysis. The interview, observation, and report records were used to support the findings. The analysis has been incorporated with the quantitative discussion results in the data analysis parts.
The data were entered using SPSS 20.0 on Windows 10 and analyzed. The analysis supported with SPSS software much contributed to the finding. It had contributed to the data validation and correctness of the SPSS results. The software analyzed and compared the results of different variables used in the research questionnaires. Excel is also used to draw the pictures and calculate some analytical solutions.
The reliability of measurements specifies the amount to which it is without bias (error free) and hence ensures consistent measurement across time and across the various items in the instrument [8]. In reliability analysis, it has been checked for the stability and consistency of the data. In the case of reliability analysis, the researcher checked the accuracy and precision of the procedure of measurement. Reliability has numerous definitions and approaches, but in several environments, the concept comes to be consistent [8]. The measurement fulfills the requirements of reliability when it produces consistent results during data analysis procedure. The reliability is determined through Cranach’s alpha as shown in Table 2.
Internal consistency and reliability test of questionnaires items.
K stands for knowledge; M, management; T, technology; C, collaboration; P, policy, standards, and regulation; H, hazards and accident conditions; PPE, personal protective equipment.
Cronbach’s alpha is a measure of internal consistency, i.e., how closely related a set of items are as a group [11]. It is considered to be a measure of scale reliability. The reliability of internal consistency most of the time is measured based on the Cronbach’s alpha value. Reliability coefficient of 0.70 and above is considered “acceptable” in most research situations [12]. In this study, reliability analysis for internal consistency of Likert-scale measurement after deleting 13 items was found similar; the reliability coefficients were found for 76 items were 0.964 and for the individual groupings made shown in Table 2. It was also found internally consistent using the Cronbach’s alpha test. Table 2 shows the internal consistency of the seven major instruments in which their reliability falls in the acceptable range for this research.
Face validity used as defined by Babbie [13] is an indicator that makes it seem a reasonable measure of some variables, and it is the subjective judgment that the instrument measures what it intends to measure in terms of relevance [14]. Thus, the researcher ensured, in this study, when developing the instruments that uncertainties were eliminated by using appropriate words and concepts in order to enhance clarity and general suitability [14]. Furthermore, the researcher submitted the instruments to the research supervisor and the joint supervisor who are both occupational health experts, to ensure validity of the measuring instruments and determine whether the instruments could be considered valid on face value.
In this study, the researcher was guided by reviewed literature related to compliance with the occupational health and safety conditions and data collection methods before he could develop the measuring instruments. In addition, the pretest study that was conducted prior to the main study assisted the researcher to avoid uncertainties of the contents in the data collection measuring instruments. A thorough inspection of the measuring instruments by the statistician and the researcher’s supervisor and joint experts, to ensure that all concepts pertaining to the study were included, ensured that the instruments were enriched.
Insight has been given to the data collectors on how to approach companies, and many of the questionnaires were distributed through MSc students at Addis Ababa Institute of Technology (AAiT) and manufacturing industries’ experience experts. This made the data quality reliable as it has been continually discussed with them. Pretesting for questionnaire was done on 10 workers to assure the quality of the data and for improvement of data collection tools. Supervision during data collection was done to understand how the data collectors are handling the questionnaire, and each filled questionnaires was checked for its completeness, accuracy, clarity, and consistency on a daily basis either face-to-face or by phone/email. The data expected in poor quality were rejected out of the acting during the screening time. Among planned 267 questionnaires, 189 were responded back. Finally, it was analyzed by the principal investigator.
The data were collected from the company representative with the knowledge of OSH. Articles written in English and Amharic were included in this study. Database information obtained in relation to articles and those who have OSH area such as interventions method, method of accident identification, impact of occupational accidents, types of occupational injuries/disease, and impact of occupational accidents, and disease on productivity and costs of company and have used at least one form of feedback mechanism. No specific time period was chosen in order to access all available published papers. The questionnaire statements which are similar in the questionnaire have been rejected from the data analysis.
Ethical clearance was obtained from the School of Mechanical and Industrial Engineering, Institute of Technology, Addis Ababa University. Official letters were written from the School of Mechanical and Industrial Engineering to the respective manufacturing industries. The purpose of the study was explained to the study subjects. The study subjects were told that the information they provided was kept confidential and that their identities would not be revealed in association with the information they provided. Informed consent was secured from each participant. For bad working environment assessment findings, feedback will be given to all manufacturing industries involved in the study. There is a plan to give a copy of the result to the respective study manufacturing industries’ and ministries’ offices. The respondents’ privacy and their responses were not individually analyzed and included in the report.
The result of this study will be presented to the Addis Ababa University, AAiT, School of Mechanical and Industrial Engineering. It will also be communicated to the Ethiopian manufacturing industries, Ministry of Labor and Social Affair, Ministry of Industry, and Ministry of Health from where the data was collected. The result will also be availed by publication and online presentation in Google Scholars. To this end, about five articles were published and disseminated to the whole world.
The research methodology and design indicated overall process of the flow of the research for the given study. The data sources and data collection methods were used. The overall research strategies and framework are indicated in this research process from problem formulation to problem validation including all the parameters. It has laid some foundation and how research methodology is devised and framed for researchers. This means, it helps researchers to consider it as one of the samples and models for the research data collection and process from the beginning of the problem statement to the research finding. Especially, this research flow helps new researchers to the research environment and methodology in particular.
There is no “conflict of interest.”
IntechOpen celebrates Open Access academic research of women scientists: Call Opens on February 11, 2018 and closes on March 8th, 2018.
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\n\nThe submissions are now closed. All applicants will be notified on the results in due time. Thank you for participating!
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