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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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Pesticides approval and registration are performed "taking into account the economic, social and environmental costs and benefits of the use of any pesticide". The present book documents the various adverse impacts of pesticides usage: pollution, dietary intake and health effects such as birth defects, neurological disorders, cancer and hormone disruption. Risk assessment methods and the involvement of molecular modeling to the knowledge of pesticides are highlighted, too. The volume summarizes the expertise of leading specialists from all over the world.',isbn:null,printIsbn:"978-953-307-531-0",pdfIsbn:"978-953-51-4525-7",doi:"10.5772/1003",price:139,priceEur:155,priceUsd:179,slug:"pesticides-the-impacts-of-pesticides-exposure",numberOfPages:460,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"1879242be09e94a3e79eb5fbd809e623",bookSignature:"Margarita Stoytcheva",publishedDate:"January 21st 2011",coverURL:"https://cdn.intechopen.com/books/images_new/492.jpg",numberOfDownloads:93971,numberOfWosCitations:92,numberOfCrossrefCitations:46,numberOfCrossrefCitationsByBook:4,numberOfDimensionsCitations:108,numberOfDimensionsCitationsByBook:6,hasAltmetrics:1,numberOfTotalCitations:246,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 20th 2010",dateEndSecondStepPublish:"May 18th 2010",dateEndThirdStepPublish:"September 22nd 2010",dateEndFourthStepPublish:"October 22nd 2010",dateEndFifthStepPublish:"December 21st 2010",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,8,9",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"6375",title:"Prof.",name:"Margarita",middleName:null,surname:"Stoytcheva",slug:"margarita-stoytcheva",fullName:"Margarita Stoytcheva",profilePictureURL:"https://mts.intechopen.com/storage/users/6375/images/1631_n.jpg",biography:"Professor Margarita Stoytcheva graduated from the University of Chemical Technology and Metallurgy of Sofia, Bulgaria, with titles of Chemical Engineer and Master of Electrochemical Technologies. She has a Ph.D. and DSc. degrees in chemistry and technical sciences. She has acted in research and teaching in several Universities in Bulgaria, Algeria and France. From 2006. to the present she has participated in activities of scientific research, technological development and teaching in Mexico at the University of Baja California, Institute of Engineering, Mexicali, as a full time researcher. Since 2008. she has been a member of the National System of Researchers of Mexico. Her interests and areas of research are analytical chemistry and biotechnology.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"10",institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"368",title:"Pestology",slug:"agricultural-and-biological-sciences-plant-biology-pestology"}],chapters:[{id:"13220",title:"Pesticide Pollution, Resistance and Health Hazards",doi:"10.5772/13758",slug:"pesticide-pollution-resistance-and-health-hazards",totalDownloads:7272,totalCrossrefCites:1,totalDimensionsCites:12,hasAltmetrics:0,abstract:null,signatures:"M. Ahmed Azmi and S.N.H. 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Saleh",coverURL:"https://cdn.intechopen.com/books/images_new/10668.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"144691",title:"Prof.",name:"Hosam M.",middleName:null,surname:"Saleh",slug:"hosam-m.-saleh",fullName:"Hosam M. Saleh"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"11651",leadTitle:null,title:"Bone Tumors - Recent Updates",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tThis book intends to present the latest information and modern management of bone-related tumours. Not only from Benign to malignant but also tumours conditions are covered in a detailed and succinct way. It will aim to cover an array of areas in particular tumour including pathology, pathogenesis, genetic basis, oncology modern methods of diagnosing, screening for tumours and aetiological causes, and advice on how to prevent and other early diagnosing strategies. The current concept of bone tumours and tumour management has changed rapidly over the past decades. Therefore, a fresh look at this topic is needed and is timely.
\r\n\r\n\tThe book will aim to include the latest information used in current practice and current research areas on which the future practice will be based on. Not only on modern investigation and diagnosing tools biopsy techniques and radiological imaging but also modern concepts for managing these tumours. The three main areas in managing involve radiotherapy chemotherapy and surgical oncology and the latest advances in these fields are intended to be discussed. This book will aim to benefit not only trainees of surgery, oncology medicine, orthopaedics but also medical students, general practitioners, and anybody interested in the field of bone tumour management.
",isbn:"978-1-80356-930-7",printIsbn:"978-1-80356-929-1",pdfIsbn:"978-1-80356-931-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"cf7dd688b160a1ba07e3179613684f16",bookSignature:"Dr. Hiran Wimal Amarasekera",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11651.jpg",keywords:"Osteosarcoma, Choindro - Sarcoma, Ewing's Sarcoma, Osteoma, Chondroma, Enchondromas, Bone Cysts, Myelo Proliferative Disease, Plasma Cells, Lymphomas, Soft Tissue Sarcomas, Bone",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 6th 2022",dateEndSecondStepPublish:"June 15th 2022",dateEndThirdStepPublish:"August 14th 2022",dateEndFourthStepPublish:"November 2nd 2022",dateEndFifthStepPublish:"January 1st 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Works as a clinician, orthopaedic surgeon, Researcher, academic teacher, examiner, and educator in the field of medicine and orthopaedics. Pioneering work on anatomy and blood supply to joints mainly hip joints and causative factors leading to avascular necrosis was done at the University of Warwick, and the University of California Los Angeles.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"67634",title:"Dr.",name:"Hiran",middleName:"Wimal",surname:"Amarasekera",slug:"hiran-amarasekera",fullName:"Hiran Amarasekera",profilePictureURL:"https://mts.intechopen.com/storage/users/67634/images/system/67634.png",biography:"Hiran Amarasekera is a Consultant Orthopaedic Surgeon Currently practicing in Sri Lanka. After obtaining the MBBS from Kasturba medical college, Manipal, Inda, he completed the MS in Surgical sciences from the University of Colombo. He obtained the fellowship of the Royal College of Surgeons of Edinburgh (FRCS Ed) and board certification in 2003. \n\nHis special interests are in the areas of young adult hip and knee problems, sports injuries, lower limb arthroplasty, and keyhole joint surgery, and revision arthroplasty. His present research is focused on non-surgical and minimally invasive alternative treatment for osteoarthritis. He worked and trained in many countries for over twenty including India, Sri Lanka, Australia, United States, and the UK.\n\nAs a keen researcher, he has completed an MPhil from the University of Warwick and completed a research fellowship at the University of California Los Angeles, (UCLA). \n\nPresently, he works as a medical educator, as an honorary senior lecturer at the University of Kelaniya and Kothalawela Defense University in Sri Lanka. He is an examiner of medical students both in Sri Lanka and the UK and a course provider for Trauma courses run by the college of surgeons and was elected a fellow of Sri Lanka College of surgeons in 2013.\n\nDr. Amarasekera is the editor of the Journal of Sri Lanka Orthopaedic association and council member. He is a reviewer for the Journal of Bone and Joint Surgery (Br) e and Bone and Joint Journal (BJJ) and a member of the editorial board of the Sri Lanka Journal of Surgery (SLJS). \n\nHe has over 50 international publications, presentations and several book chapters to his credit and has reviewed over 100 papers for journals of BJJ and SLJS.\n\nAfter joining IntechOpen in 2012 he authored three book chapters and edited several open access books with them.",institutionString:"University of Warwick Science Park",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Warwick Science Park",institutionURL:null,country:{name:"United Kingdom"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"429341",firstName:"Paula",lastName:"Gavran",middleName:null,title:"Ms.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"paula@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"9500",title:"Recent Advances in Bone Tumours and Osteoarthritis",subtitle:null,isOpenForSubmission:!1,hash:"ea4ec0d6ee01b88e264178886e3210ed",slug:"recent-advances-in-bone-tumours-and-osteoarthritis",bookSignature:"Hiran Amarasekera",coverURL:"https://cdn.intechopen.com/books/images_new/9500.jpg",editedByType:"Edited by",editors:[{id:"67634",title:"Dr.",name:"Hiran",surname:"Amarasekera",slug:"hiran-amarasekera",fullName:"Hiran Amarasekera"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6755",title:"Recent Advances in Arthroscopic Surgery",subtitle:null,isOpenForSubmission:!1,hash:"5c122c5b88bdc03c130d34ad2ac2d722",slug:"recent-advances-in-arthroscopic-surgery",bookSignature:"Hiran Wimal Amarasekera",coverURL:"https://cdn.intechopen.com/books/images_new/6755.jpg",editedByType:"Edited by",editors:[{id:"67634",title:"Dr.",name:"Hiran",surname:"Amarasekera",slug:"hiran-amarasekera",fullName:"Hiran Amarasekera"}],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. 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In principle, both zero-inflation and autocorrelation may be present in such series. Failing to adequately accommodate temporal dynamics and a high frequency of zeros can lead to incorrect inferential conclusions. Developing a general modeling framework that accounts for these characteristics poses a daunting challenge.
\nIn characterizing data comprised of counts with excess zeroes, two types of models are commonplace: a model that assumes a Poisson mixture distribution, and a model that assumes a binomial mixture distribution. A considerable literature exists for regression models based on the zero-inflated Poisson (ZIP) distribution to deal with count data that are independently distributed [1]. Many researchers have extended the classical ZIP model to analyze repeated measures data by incorporating independent random effects, as these can account for within-subject correlation and between-subject heterogeneity [2, 3]. To deal with count time series with excess zeros, some researchers have proposed parameter-driven ZIP models that accommodate the temporal dynamics by incorporating correlated random effects, which can be represented by a latent autoregressive process [4, 5]. However, for data arising from a binomial mixture distribution, a survey of the literature for analogous frameworks reflects an absence of work dealing with binomial time series with excess zeros. To handle such data, we propose two general classes of models: a class of observation-driven ZIB (ODZIB) models, and a class of parameter-driven ZIB (PDZIB) models. The inspiration for the two proposed modeling frameworks arises from the work of Hall [6], Yau et al. [4], and Yang et al. [5, 7].
\nDepending on how the temporal correlation is conceptualized, count time series models can be classified as either observation-driven or parameter-driven [8]. For the former, serial correlation is characterized by specifying that the conditional mean of the current response depends explicitly on its past values [9–14]. For the latter, such correlation is characterized through an unobservable underlying process [15–19]. In this chapter, we employ the partial likelihood framework to formulate the ODZIB model, as this largely simplifies parameter estimation with negligible loss of information. The ODZIB model can be viewed as an extension of the observation-driven binomial model [20]. Such a model is often fit using standard statistical software available for classical ZIB regression models. For the PDZIB model, we employ a state-space approach, as this framework allows for the investigation of the underlying latent processes that govern the temporal correlation and zero inflation. Due to the non-Gaussian distribution of the count response, and the non-linear nature of modeling its conditional mean, traditional state-space methods using the Kalman filter and the Kalman smoother are not available for parameter estimation. We thereby adopt a Monte Carlo Expectation Maximization (MCEM) algorithm based on the particle filter [21] and the particle smoother [22].
\nThe remainder of the chapter is organized as follows. In Section 2, we briefly introduce a class of observation-driven models for a zero-inflated count time series that arises from a binomial mixture. Section 3 proposes a class of parameter-driven models in the state-space framework, and presents the MCEM algorithm devised to fit such models. A comprehensive simulation study is provided in Section 4. In Section 5, we illustrate the proposed methodology through a practical application. Section 6 concludes with a brief discussion.
\nA popular approach for modeling independent zero-inflated binomial data is the ZIB model proposed by Hall [6]. This model assumes that data are generated from a mixture distribution, comprised of a binomial distribution and a degenerate distribution at zero. For response variable
Here,
In the preceding,
In this section, we introduce an autoregressive model for binomial time series with excess zeros based on an observation-driven approach. We retain the same model structure as that introduced in Section 2.1 to account for the binomial mixture, yet we employ lagged responses as covariates to resolve the temporal correlation. The proposed model can be viewed as an extension of the binomial time series model presented by Kedem and Fokianos [20].
\nLet
so as to represent all that is known to the observer at time
Similarly,
where
The partial data likelihood of the observed series is
\nwhere
The log-likelihood for the observation-driven ZIB model is
\nThe vector \n
Similar to Section 2.1, we can apply the EM algorithm or the Newton–Raphson method to obtain the MPLE. This estimation process can be conveniently conducted in practice using standard software tools available for fitting classical ZIB models. In SAS, we can use the finite mixture models (FMM) procedure to fit the observation-driven ZIB model, while we can use function gamlss in the package generalized additive models for location scale and shape (GAMLSS) for model fitting in R. Hypothesis testing for
An alternative approach to describe binomial time series with excess zeros is based on parameter-driven ZIB models. This class of models can be viewed as an analogue of the parameter-driven ZIP models presented by Yang et al. [5].
\nTo account for temporal dynamics in the series, we introduce a latent stationary autoregressive process {
Here,
Let
Similar to the previous model parameterizations,
where
For the parameter-driven ZIB model, the conditional mean and variance of the response variable
Obviously, the presence of zero-inflation (
We can write the parameter-driven ZIB model in the following hierarchical form:
\nwhere
In Eq. (15),
The transition matrix
To fit the parameter-driven ZIB model, in principle, one would first obtain the marginal likelihood of the observed data
Let
To develop an EM algorithm for parameter estimation of the mixture model, Eqs. (15)–(17), we begin by formulating the complete-data likelihood; i.e., the joint density of
Here, the initial state vector
Up to an additive constant, the complete-data log-likelihood is given by
\nThe complete-data log-likelihood can be described as the sum of three functionally independent parameter forms, such that
With the implementation of the EM algorithm, we need to compute the conditional expectation of
To simplify the notation, we let \n
where particle filtering and smoothing techniques are used to approximate the conditional expectations. The details of the particle methods for the parameter-driven ZIB model are presented in Section 3.3.
\nThe following partial derivatives are applied to maximize
At the
In addition, we can easily compute
To offset the slow convergence and to reduce the computational cost of the EM algorithm, starting with good initial parameters is essential. For the proposed parameter-driven ZIB model, we suggest using the estimates of the parameters from a classical ZIB model or from the observation-driven ZIB model discussed in Section 2.2.
\nStandard errors of the parameter estimators can be obtained either by using the inverse of the observed information to approximate the variance/covariance matrix, or by employing a collection of replicated bootstrapped parameter estimates. Given the computational cost of the MCEM algorithm, we pursue the first approach by applying Louis\'s formula [27] to compute the observed information matrix
where
The first-order derivatives of
The second-order derivatives of
Again, particle filtering and smoothing techniques are used to approximate the conditional expectations in
In principle, the variance/covariance matrix can be approximated by taking the inverse of the observed information matrix. However, the computation of the inverse is often problematic. As indicated by Kim and Stoffer [25], the observed information matrix is not guaranteed to be numerically positive definite. To address this problem, we slightly modify Louis’s formula by introducing a slack variable
where
Particle filtering [21] and particle smoothing [22] belong to the class of sequential Monte Carlo (SMC) methods [28]. These particle methods can be viewed as the non-linear and non-Gaussian extensions of the popular Kalman filtering and smoothing algorithms for traditional state-space models. Rather than yielding a single estimate for the filter or the smoother, as computed through conventional Kalman filtering and smoothing, particle methods provide a set of particles with associated weights to approximate the conditional densities governing the filters and smoothers. Implemented via sequential importance sampling (SIS), in the E-step of the EM algorithm, particle methods provide approximate solutions to the intractable integrals corresponding to the conditional expectations of functions of the latent components given the observed data. However, sample degeneracy is a typical problem for SIS methods. In particular, degeneracy occurs when particles have small weights or even negative weights, rendering their contributions to the conditional density negligible. Resampling (e.g., bootstrapping) offers a recourse for eliminating particles with negligible effects. Kim [29] provides an elegant treatment of particle filtering and smoothing for state-space models.
\nFor the parameter-driven ZIB model, we implement particle filtering by first generating \n
(F.1) Generate \n
(F.2) Compute the filtering weights
\nwhere \n
(F.3) Generate \n
As a byproduct of the particle filtering, the observed-data log-likelihood can be approximated by
\nwhere
Next, we employ the particle smoothing algorithm proposed by Godsill et al. [22] to obtain the conditional expectations of the functions of the latent variables given the complete set of observed data. In this step, we first choose \n
(S.1) Calculate the smoothing weights
\n(S.2) Choose \n
We obtain independent realizations by repeating the preceding process for
In this section, we investigate through simulation two salient issues pertaining to the proposed modeling frameworks. In the first part, we explore the convergence of the MCEM algorithm through simulated examples, and investigate the finite sample distributional properties of the parameter estimators through a comprehensive simulation study. In the second part, we present a simulation study to compare the performance of the proposed ZIB models to their counterpart ZIP models in characterizing zero-inflated count time series.
\nWe consider time series data simulated from four different parameter-driven models: ZIB + AR(2), binomial + AR(2), ZIB + AR(1), and binomial + AR(1). The sample size is set to 300 and the number of cases
where
For the rest of the models considered, the corresponding parameters are set to 0 if no such a form is included. Autoregressive (AR) coefficients are chosen to assure stationarity of the series. In fitting the models, the number of particle filters (
\n | ||||||
---|---|---|---|---|---|---|
True | \n0.300 | \n2.000 | \n−3.000 | \n0.800 | \n−0.600 | \n0.500 | \n
Binomial + AR(1) | \n\n | 1.984 | \n−2.968 | \n0.800 | \n\n | 0.540 | \n
ZIB + AR(1) | \n0.283 | \n2.124 | \n−2.930 | \n0.781 | \n\n | 0.563 | \n
Binomial + AR(2) | \n\n | 1.989 | \n−3.012 | \n0.852 | \n−0.620 | \n0.499 | \n
ZIB + AR(2) | \n0.293 | \n1.992 | \n−2.872 | \n0.831 | \n−0.576 | \n0.506 | \n
True and estimated parameters for the simulated examples.
Figure 1 shows the trace plots of the log-likelihood for the four fitted parameter-driven models. Note that the log-likelihood of the MCEM algorithm is not strictly increasing at each iteration due to the introduction of Monte Carlo errors. However, the log-likelihood stabilizes after a few dozen iterations with slight fluctuations around the maximal value. Figure 2 shows the trace plots for the parameter estimates from the most complex fitted model, ZIB + AR(2). The plots indicate that the parameter estimates converge to the MLEs quickly with negligible fluctuations. The trace plots of the parameter estimates for the other three models exhibit similar patterns (results not shown). In practice, we recommend always checking the trace plots of the estimates to assess convergence of the MCEM algorithm.
\nTrace plots of the log-likelihood for fitted parameter-driven models based on simulated data.
Trace plots of the estimated parameters for the fitted ZIB + AR(2) model.
We next investigate the finite sample distributional properties of the parameter estimators from the MCEM algorithm. We consider the same parameter-driven models presented in the preceding simulated example. For each model structure, 500 replications are generated based on sample sizes of 200 and 500. We employ the proposed MCEM algorithm to fit models based on these replications, and record the subsequent parameter estimates and their standard errors. As the MECM algorithm is computationally expensive, we set the number of particles for both filters and smoothers to 200, and the stopping iteration for the MCEM algorithm at 100. In Tables 2–3, we provide the simulation results based on the most complex model, ZIB + AR(2).
\n\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
0.300 | \n0.299 | \n0.295 | \n0.032 | \n0.032 | \n|
2.000 | \n1.999 | \n1.992 | \n0.139 | \n0.166 | \n|
−3.000 | \n−2.992 | \n−2.980 | \n0.235 | \n0.224 | \n|
0.800 | \n0.743 | \n0.757 | \n0.120 | \n0.165 | \n|
−0.600 | \n−0.563 | \n−0.572 | \n0.104 | \n0.145 | \n|
0.500 | \n0.504 | \n0.508 | \n0.063 | \n0.098 | \n
Summary statistics for replicated parameter estimates from fitted ZIB + AR(2) models with sample size 200.
\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
0.300 | \n0.300 | \n0.299 | \n0.020 | \n0.020 | \n|
2.000 | \n2.002 | \n2.002 | \n0.081 | \n0.104 | \n|
−3.000 | \n−3.006 | \n−3.007 | \n0.138 | \n0.139 | \n|
0.800 | \n0.754 | \n0.754 | \n0.076 | \n0.095 | \n|
−0.600 | \n−0.566 | \n−0.573 | \n0.067 | \n0.086 | \n|
0.500 | \n0.509 | \n0.510 | \n0.039 | \n0.057 | \n
Summary statistics for replicated parameter estimates from fitted ZIB + AR(2) models with sample size 500.
In general, the mean and median of the estimates converge to the true parameters, with a minor degree of negative bias associated with the estimation of the AR coefficients. The empirical standard deviations (ESDs) are reasonably close to the average asymptotic standard errors (ASEs). Therefore, the standard errors calculated by Louis’s method prove to be sufficient. As the sample size increases from 200 to 500, the bias for the estimation of the AR coefficients attenuates, and the standard errors tend to diminish. The two behaviors indicate that weak convergence holds. The results for the other three parameter-driven models are analogous to those presented in Tables 2–3. Tables 4–9 show the simulation results for the binomial + AR(2) model, ZIB + AR(1) model, and binomial + AR(1) model, respectively.
\n\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
2.000 | \n1.998 | \n2.003 | \n0.108 | \n0.167 | \n|
−3.000 | \n−3.002 | \n−3.010 | \n0.179 | \n0.174 | \n|
0.800 | \n0.783 | \n0.783 | \n0.087 | \n0.101 | \n|
−0.600 | \n−0.593 | \n−0.596 | \n0.080 | \n0.094 | \n|
0.500 | \n0.496 | \n0.494 | \n0.052 | \n0.062 | \n
Summary statistics for replicated parameter estimates from fitted binomial + AR(2) models with sample size 200.
\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
2.000 | \n1.995 | \n1.989 | \n0.070 | \n0.101 | \n|
−3.000 | \n−2.994 | \n−2.995 | \n0.113 | \n0.108 | \n|
0.800 | \n0.791 | \n0.791 | \n0.057 | \n0.063 | \n|
−0.600 | \n−0.593 | \n−0.595 | \n0.053 | \n0.059 | \n|
0.500 | \n0.498 | \n0.496 | \n0.032 | \n0.038 | \n
Summary statistics for replicated parameter estimates from fitted binomial + AR(2) models with sample size 500.
\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
0.300 | \n0.299 | \n0.299 | \n0.031 | \n0.032 | \n|
2.000 | \n1.971 | \n1.971 | \n0.208 | \n0.251 | \n|
−3.000 | \n−2.982 | \n−2.969 | \n0.199 | \n0.210 | \n|
0.800 | \n0.763 | \n0.770 | \n0.073 | \n0.067 | \n|
0.500 | \n0.500 | \n0.502 | \n0.056 | \n0.063 | \n
Summary statistics for replicated parameter estimates from fitted ZIB + AR(1) models with sample size 200.
\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
0.300 | \n0.299 | \n0.299 | \n0.020 | \n0.021 | \n|
2.000 | \n1.984 | \n1.989 | \n0.135 | \n0.168 | \n|
−3.000 | \n−2.992 | \n−2.991 | \n0.133 | \n0.132 | \n|
0.800 | \n0.781 | \n0.785 | \n0.041 | \n0.040 | \n|
0.500 | \n0.500 | \n0.499 | \n0.035 | \n0.040 | \n
Summary statistics for replicated parameter estimates from fitted ZIB + AR(1) models with sample size 500.
\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
2.000 | \n2.006 | \n2.024 | \n0.192 | \n0.233 | \n|
−3.000 | \n−2.987 | \n−2.988 | \n0.165 | \n0.167 | \n|
0.800 | \n0.782 | \n0.788 | \n0.054 | \n0.056 | \n|
0.500 | \n0.497 | \n0.496 | \n0.051 | \n0.052 | \n
Summary statistics for replicated parameter estimates from fitted binomial + AR(1) models with sample size 200.
\n | True | \nMean | \nMedian | \nESD | \nASE | \n
---|---|---|---|---|---|
2.000 | \n1.997 | \n1.996 | \n0.125 | \n0.168 | \n|
−3.000 | \n−2.997 | \n−2.994 | \n0.106 | \n0.106 | \n|
0.800 | \n0.787 | \n0.789 | \n0.035 | \n0.035 | \n|
0.500 | \n0.499 | \n0.499 | \n0.030 | \n0.033 | \n
Summary statistics for replicated parameter estimates from fitted binomial + AR(1) models with sample size 500.
Difference in AIC | \nLevel of empirical support for model with larger AIC | \n
---|---|
0−2 | \nSubstantial | \n
4−7 | \nConsiderably less | \n
>10 | \nEssentially none | \n
Guidelines for assessing AIC differences.
Model | \nAIC | \n||||||
---|---|---|---|---|---|---|---|
PDZIB(1) | \n922.98 | \n0.248 | \n−3.349 | \n−0.249 | \n−0.223 | \n\n | 0.430 | \n
\n | \n | (0.034) | \n(0.051) | \n(0.120) | \n(0.160) | \n\n | (0.044) | \n
PDZIP(1) | \n923.31 | \n0.248 | \n−3.389 | \n−0.242 | \n−0.241 | \n\n | 0.410 | \n
\n | \n | (0.034) | \n(0.051) | \n(0.116) | \n(0.166) | \n\n | (0.043) | \n
ODZIB(1) | \n1039.80 | \n0.341 | \n−3.184 | \n−0.319 | \n−0.007 | \n\n | \n |
\n | \n | (0.061) | \n(0.024) | \n(0.086) | \n(0.002) | \n\n | \n |
ODZIP(1) | \n1030.04 | \n0.341 | \n−3.224 | \n−0.309 | \n−0.007 | \n\n | \n |
\n | \n | (0.061) | \n(0.046) | \n(0.084) | \n(0.004) | \n\n | \n |
PDZIB(2) | \n922.98 | \n0.248 | \n−3.359 | \n−0.237 | \n−0.120 | \n0.264 | \n0.426 | \n
\n | \n | (0.034) | \n(0.054) | \n(0.126) | \n(0.166) | \n(0.153) | \n(0.046) | \n
PDZIP(2) | \n924.09 | \n0.248 | \n−3.395 | \n−0.230 | \n−0.119 | \n0.263 | \n0.402 | \n
\n | \n | (0.034) | \n(0.052) | \n(0.118) | \n(0.178) | \n(0.158) | \n(0.045) | \n
ODZIB(2) | \n1038.11 | \n0.341 | \n−3.250 | \n−0.275 | \n−0.008 | \n0.007 | \n\n |
\n | \n | (0.061) | \n(0.033) | \n(0.088) | \n(0.002) | \n(0.002) | \n\n |
ODZIP(2) | \n1028.49 | \n0.341 | \n−3.288 | \n−0.266 | \n−0.007 | \n0.007 | \n\n |
\n | \n | (0.061) | \n(0.058) | \n(0.087) | \n(0.004) | \n(0.004) | \n\n |
Model fitting results for eight different zero-inflated models.
The normality of the parameter estimators is assessed by Q-Q plots based on the sets of replicated estimates (figures not shown). For the most complex ZIB + AR(2) model, approximate normality holds for the finite sample distribution of the parameter estimators, with slightly non-normal tail behavior (thick or thin) evident for the estimated AR coefficients. As the sample size is increased from 200 to 500, this non-normal behavior is attenuated. Similar patterns are observed for the other three parameter-driven models.
\nAs previously mentioned, based on a Poisson mixture distribution, extensive methodology has been published to deal with count time series with excess zeros. In addition, the Poisson distribution provides an accurate approximation to the binomial distribution when the sample size is large and the success probability is small. Therefore, one may question whether Poisson-type models are sufficient for approximating binomial-type models when data are generated from a binomial mixture distribution. In this section, we try to address this question through a simulation study.
\nTwo different types of ZIB models are proposed in this work: the parameter-driven ZIB model, and the observation-driven ZIB model. To evaluate the propriety of the binomial-type models, we consider two corresponding Poisson-type counterparts: the parameter-driven ZIP model, and the observation-driven ZIP model. We assess the performance of the four models under two scenarios: first, where data are generated from the parameter-driven ZIB model, and second, where data are generated from the observation-driven ZIB model.
\nTo denote the parameter-driven ZIB/ZIP model with an AR(
In the first scenario, data are generated from a PDZIB(2) model having the same form as that provided in Section 4.1. To reduce the computational burden associated with fitting the models, 100 replicated series of length 200 are generated. We fit four different zero-inflated models to each of the series. For the two parameter-driven models, we specify a latent autoregressive process of order two, and employ the MECM algorithm to fit the models. For the two observation-driven models, we incorporate the lagged responses
In the second scenario, data are generated from an ODZIB(2) model featuring the following structures:
\nHere,
Again, we generate 100 replications of length 200 based on the preceding model. The same four zero-inflated models are fit to each of the replications. The Akaike information criterion (AIC) [30] is used to guide the selection of an optimal model in both scenarios. To evaluate the magnitude of the absolute difference in AIC values, Burnham and Anderson [31] provide the following guidelines (Table 10).
\nThus, a difference in AIC values of two or more is considered meaningful, and a difference of 10 or more is considered pronounced.
\nFigure 3 illustrates the performance of the four zero-inflated models, in terms of AIC differences, when data are generated from a PDZIB(2) model. The PDZIB(2) model serves as the reference for model comparison. Each point represents the difference in the AIC value between the target model and the reference model. As evident from the figure, the PDZIB(2) model markedly outperforms the other three models for all 100 replications, with AIC differences over 50. Although vastly inferior to the PDZIB(2) model, the PDZIP(2) model performs better than the two observation-driven models. The ODZIB(2) performs the worst among the four models considered. Parameter-driven models clearly exhibit a substantial advantage over observation-driven models when the underlying data arise via a parameter-driven approach.
\nAIC differences of zero-inflated fitted models relative to parameter-driven ZIB fitted models.
Figure 4 shows the performance of the four zero-inflated models, in terms of AIC differences, when data are generated from an ODZIB(2) model. Similarly, the ODZIB(2) model serves as the reference. The ODZIB(2) model easily performs the best among all four models for all 100 replications, reflecting a substantial improvement in model fit over the other three models based on AIC differences (>20). Compared to the two parameter-driven models, the ODZIP(2) model accommodates the data much more appropriately. Between the two parameter-driven models, the PDZIB(2) model is substantially favored over the PDZIP(2) model. Thus, observation-driven models markedly outperform parameter-driven models when the underlying data arise via an observation-driven approach.
\nAIC differences of zero-inflated fitted models relative to observation-driven ZIB fitted models.
We close this section with a brief discussion of issues germane to model selection. These issues are relevant not only in evaluating the results of the preceding simulations, but also in facilitating the choice of a model in practice.
\nFirst, one may question which class of models should be considered when coping with binomial time series data with excess zeros. In the simulation sets, the fitted parameter-driven models markedly outperform the fitted observation-driven models when data are generated via a parameter-driven approach. Although parameter-driven models are computationally expensive to fit, observation-driven models do not appear to provide an adequate characterization of the data in such settings. Additionally, unlike observation-driven models, parameter-driven models provide a description of the underlying latent processes that govern the temporal correlation and zero inflation. Observation-driven models, in contrast, outperform parameter-driven models when the underlying data are generated via an observation-driven approach. In general, the selection of the class of models depends on the conceptualization of the model structure and the perceived value of recovering and investigating the underlying latent processes. However, in the context of zero-inflated count time series, since an understanding of the phenomenon that gives rise to the data will rarely inform the practitioner as to whether the parameter-driven or observation-driven conceptualization is more appropriate, we recommend the use of AIC or an alternate likelihood-based selection criterion in choosing between these two model classes.
\nSecond, one may question which distribution should be used when dealing with count time series with excess zeros. The Poisson-type model with an offset is often considered an appropriate approximating model for a binomial-type model when the sample size is large and the success probability is low. However, in the presence of zero inflation, our simulation results indicate the necessity of using binomial-type models over their Poisson counterparts when the underlying distribution is actually a binomial mixture. In practice, if the dynamics of the phenomenon that gives rise to the data do not inform the underlying data generating distribution, we again recommend the use of AIC or another likelihood-based criterion in choosing an appropriate distribution.
\nIn this section, to illustrate our proposed methodology, we consider an application pertaining to the diagnosis coding of a severe disease, Kaposi’s sarcoma (KS). The application concerns the assessment of a particular level change for a primary KS diagnosis. The data used are extracted from the Healthcare Cost and Utilization Project (HCUP) database. We identify all hospitalizations during the period from January 1998 through December 2011 during which a primary or secondary diagnosis of KS is received. For case ascertainment, we use the International Classification of Diseases, 9
Monthly time series plots of primary KS hospitalizations (top panel) and overall KS hospitalizations (bottom panel) from January 1998 to December 2011.
A coding change was implemented in early 2008, during which many hospitals may have modified the coding convention by switching the primary code to secondary, as this modification may lead to an increase in hospital reimbursements. During the study period, a large number of zero counts is observed and data among adjacent points seem to be highly correlated. Since the primary KS count series exhibits a relatively large degree of zero-inflation (appropriately 25% of the values are zero), we apply our proposed ZIB models to characterize the data.
\nOur analysis focuses on two objectives. First, we aim to model the dynamic pattern of the primary KS series; in particular, we are interested in determining the appropriate order of the autoregressive process embedded in the series, and evaluate whether there is a significant level change at January 2008. Second, we aim to compare the performance of our proposed ODZIB(
For potential autocorrelation structures, we let
Specifically, for the two PDZIB(
where
For the two ODZIB(
where
In addition, we consider four comparable Poisson-type models based on the work by Yang et al. [5, 7]. For the two PDZIP(
For the two ODZIP(
Here,
Table 11 features results for the eight fitted candidate models. The parameter estimates along with their standard errors are presented. All eight models indicate a significant level change for the primary KS series after the introduction of the potential coding change practice (
Count time series featuring a preponderance of zeros are commonly encountered in a variety of scientific applications. In characterizing such series, modeling frameworks that assume a Poisson mixture distribution have been extensively studied. However, minimal work has been focused on modeling frameworks that assume a binomial mixture distribution. When data are more naturally assumed to arise from the latter, a Poisson-type model with an offset is often employed; however, the propriety of such an approximation is unclear.
\nWe propose two general classes of models to effectively characterize a count time series that arises from a zero-inflated binomial mixture distribution. The observation-driven ZIB model, formulated in the partial likelihood framework, is fit using the Newton–Raphson algorithm. The parameter-driven ZIB model, formulated in the state-space framework, is fit using the MCEM algorithm. When data are generated from a binomial mixture, our proposed ZIB models outperform their Poisson-type counterparts. We illustrate our methodology with an application that assesses a particular level change for a diagnosis code.
\nFuture work involves extending the current frameworks to the zero-inflated beta-binomial (ZIBB) model. Both observation-driven and parameter-driven ZIBB models can be formulated and fit based on methodological developments similar to those presented in this work. However, weak identifiability could arise as a potentially problematic issue in fitting the parameter-driven ZIBB model, as not only the overdispersion explicitly induced by the beta distribution but also the correlated random effects account for any excess variability in the data [5]. In addition, we could consider more complicated correlation structures by incorporating moving average components in the linear predictors for parameter-driven models. Such an extension necessitates non-trivial revisions to the state-space model formulation and the complete-data likelihood, which warrant further investigation.
\nDifferential diagnosis and treatment of tachycardias is a common dilemma encountered by physicians or cardiologists. Although such tachycardias often occur in patients with a normal heart, they may cause bothersome symptoms and rarely represent life-threatening conditions. Among these tachycardias with a heart rate greater than 100 beats per minute (bpm), the narrow QRS complex tachycardias (NCTs) are defined by the presence in a 12-lead electrocardiogram (ECG) of a QRS complex duration less than 120 ms and the wide QRS complex tachycardias (WCTs) are defined by the presence in a 12-lead ECG of a QRS complex duration more than 120 ms (Figure 1) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. The NCTs are typically of supraventricular origin above or within the His bundle, although rarely narrow complex ventricular tachycardias (VT) have been reported in the literature in which early activation of the His bundle can also occur in high septal VT, resulting in relatively narrow QRS complexes of 110–140 ms (Table 1, [1, 2, 3, 4, 5]]. The WCTs can be VT or supraventricular tachycardia (SVT) with right or left bundle branch block (BBB) or right or left accessory pathway (Table 1, [6, 7, 8, 9, 10]]. Because administration of medications based on misdiagnosis of these tachycardias can be harmful and sometimes fatal, diagnosis of these tachycardias is critical [11, 12, 13]. The accurate, rapid diagnosis in patients with these tachycardias still remains a significant clinical dilemma, because the published numerous ECG algorithms and criteria are complicated and difficult to recall in urgent clinical situations [11, 12, 13]. We have reviewed ECG findings of the NCTs and WCTs in order to reduce the possible diagnostic errors on the ECGs.
Differential diagnostic algorithm of NCTs and WCTs.
The NCTs with QRS duration less than 120 ms | The WCTs with QRS duration more than 120 ms |
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Differential diagnosis of NCTs and WCTs.
SVT; supra-ventricular tachycardia, AVNRT; atrio-ventricular nodal re-entrant tachycardia, AVRT; atrio-ventricular reciprocating tachycardia, AV; atrio-ventricular, VT; ventricular tachycardia, BBB; bundle branch block, AF; atrial fibrillation, VA; ventriculo-atrial.
The NCTs are common problems encountered in clinical situations [1, 2, 3, 4, 5, 14, 15, 16, 17, 18, 19, 20, 21]. The key to approaching the diagnosis of these arrhythmias is identifying atrial activity (P waves) on the ECG and classifying these tachycardias according to the presence of AV dissociation (Figure 2) and then re-classifying according to long RP or short RP (Table 2) [1, 2, 3, 4, 5, 14, 15, 16, 17, 18, 19, 20, 21]. On the basis of these algorithm, a differential diagnosis can be generated, logical therapy can be delivered for termination of the tachycardia, and a plan can be developed to prevent recurrence.
Differential diagnostic algorithm of NCTs with regular rhythm.
Short RP (RP < PR) | Long RP (RP > PR) |
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Differential diagnosis of NCTs according to RP interval.
AVNRT; atrio-ventricular nodal re-entrant tachycardia, AVRT; atrio-ventricular reciprocating tachycardia, PJRT; Permanent junctional reciprocating tachycardia.
Short RP tachycardias are defined as regular tachycardias in which interval from QRS complex to P wave (upper arrows, Figure 3) much less than interval from P wave to subsequent QRS complex, whereas long RP tachycardias are defined as regular tachycardias in which interval from QRS complex to P wave much more than interval from P wave to subsequent QRS complex (lower arrows, Figure 3) [1, 2, 3, 4, 5, 14, 15, 16, 17, 18, 19, 20, 21].
Schematic demonstration of short RP and long RP.
Sinus tachycardia is defined as an increase in sinus rate to more than 100 bpm with regular rhythm. The rate increases gradually and may show beat to beat variation. Although generally identifiable by a P wave of normal morphologic features that precedes each QRS complex, sinus tachycardia can be difficult to recognize when the P wave begins to fuse with the T wave of the preceding QRS complex. Sinus tachycardia is usually a physiological response such as fever, anxiety, pain, hyperthyroidism but may be precipitated by sympathomimetic drugs or endocrine disturbances [5, 14, 15, 16, 17, 18, 19, 20, 21].
The morphologic appearance of sinus nodal reentrant tachycardia is identical to that of sinus tachycardia. In contrast to sinus tachycardia, the rate is very regular and initiation and termination are abrupt without an underlying physiological stimulus. Vagal maneuver may be successful in stopping the arrhythmia [5, 14, 15, 16, 17, 18, 19, 20, 21].
Atrial tachycardia (AT) is usually a NCTs accounting for 5–15% of SVT. Other than sinus tachycardia, AT is the most common long RP tachycardia. In AT, an atrial source outside the sinoatrial node due to focal automatic activity or re-entry circuit activates the atria. Accordingly, P-wave morphologic characteristics vary depending on the site of this source. Digitalis toxicity should be suspected in patients with paroxysmal AT with AV block [5, 14, 15, 16, 17, 18, 19, 20, 21, 22].
Atrial flutter is a reentrant rhythm of the right atrium typically with an atrial rate of 250 to 350 beats/min. The flutter may circulate in a counterclockwise direction around the tricuspid annulus in the frontal plane (typical, counterclockwise flutter) or in a clockwise direction (atypical, clockwise flutter). P waves have a characteristic “sawtooth” appearance, and 2:1 AV block is common. Because one flutter wave occurs in the ST-T segment and another flutter wave occurs before each QRS complex in atrial flutter with 2:1 AV conduction, atrial flutter is neither a short RP nor a long RP tachycardia [5, 14, 15, 16, 17, 18, 19, 20, 21, 22].
Non-paroxysmal junctional tachycardia (NPJT) is a tachycardia that arises in the AV junction. Although often described as a short RP tachycardia, because NPJT causes ventricular activation almost concurrently with atrial activation, a substantial portion (25%), which is described as a long RP tachycardia, actually show P waves that slightly precede the QRS complex. and in some cases, AV dissociation may be present. Unlike AVNRT and AVRT, initiation and termination are gradual. NPJT is often associated with digitalis intoxication, inferior myocardial infarction, myocarditis, and mitral valve surgical procedures [5, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23].
AV nodal reentrant tachycardia (AVNRT) is characterized by a tachycardia with supraventricular origin, with sudden onset and termination generally at rates between 150 and 250 beats/min and is the most common cause of SVT except atrial fibrillation, atrial flutter, and sinus tachycardia. In majority of patients (noted as the “typical” or “slow-fast” AVNRT), anterograde conduction to the ventricle occurs over the “slow” pathway and retrograde conduction to the atrium occurs over the “fast” pathway and the atria are activated either simultaneously with or just after activations of the ventricles and this common type is classified as a short RP tachycardia. Rarely, in “atypical” or “fast-slow” AVNRT, the reentry occurs in the opposite direction in which anterograde conduction occurs over the “fast” pathway, while retrograde conduction occurs over “slow” pathway, and this rare type is classified as a long RP tachycardia [24, 25, 26, 27, 28, 29, 30, 31].
AV reentrant tachycardia (AVRT) involves reentry between the atria and ventricles with use of the AV node-His bundle conduction as the anterograde and slow pathway and an accessory conduction as the retrograde and fast pathway. This pattern is also known as orthodromic reciprocating tachycardia (ORT). This type is not apparent by analysis of the ECG during sinus rhythm because the ventricle is not pre-excited and the accessory pathway is said to be “concealed”. In tachycardia, retrograde conduction over the accessory pathway is fast and yields a short RP tachycardia [24, 25, 26, 27, 28, 29, 30, 31].
In contradistinction to ORT resulting in NCTs, antidromic AVRT has anterograde conduction over the accessory pathway and retrograde conduction over the AV node-His bundle resulting in WCTs [24, 25, 26, 27, 28, 29, 30, 31].
The following factors are important differences between AVNRT and AVRT [24, 25, 26, 27, 28, 29, 30, 31]:
In contradistinction to AVNRT, an 1:1 relationship is necessary for AVRT because both the atria and the ventricles are part of the reentry circuit. Therefore, if AV block occurs during tachycardia, AVRT is excluded.
If bundle branch block occurs during ORT and the length of the tachycardia cycle increases, AVNRT is excluded because the His-Purkinje system is not part of the tachycardia reentry circuit in AVNRT. The converse is not necessarily true because the absence of cycle length change with the occurrence of bundle branch block does not exclude AVRT.
As discussed with AVRT, certain types of reentrant circuits exist in which the accessory AV connection has AV nodal properties such as slow conduction. In PJRT, excitation over the postero-septal accessory pathway conducts very slowly, because of a long and tortuous route of pathway. Tachycardia is maintained by anterograde AV nodal conduction and retrograde conduction over slow accessory pathway. Because of slow conduction property of accessory pathway, retrograde atrial activation is delayed, and a long RP tachycardia results. Patients with this type of accessory pathway almost never have preexcitation (a delta wave) on ECGs during sinus rhythm [5, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23].
Atrial tachycardia with atrioventricular block is typically seen with digoxin toxicity. The ventricular rhythm is usually regular but may be irregular if atrioventricular block is variable [5, 14, 15, 16, 17, 18, 19, 20, 21].
MAT is characterized by P waves with variable morphologies and variable PR intervals. Differential diagnosis between MAT and atrial fibrillation can be possible by the presence of isoelectric baselines between the P waves in MAT. MAT is seen typically in patients with chronic obstructive pulmonary disease or digoxin toxicity [5, 14, 15, 16, 17, 18, 19, 20, 21, 32].
Atrial flutter is due to a re-entry circuit in the right atrium with secondary activation of the left atrium. This produces atrial contractions at a rate of about 300 beats/min as flutter (F) waves. F waves show broad and saw-tooth appearances and are best seen in lead V1 and the inferior leads [5, 14, 15, 16, 17, 18, 19, 20, 21].
This is the most common sustained arrhythmia with overall prevalence is 1% to 1.5%. Atrial fibrillation is caused by multiple re-entrant circuits or “wavelets” of activation sweeping around the atrial myocardium without effective atrial contraction. Atrial fibrillation is seen on the ECG as irregular baseline undulations of variable amplitude and morphology (called f waves) discharging at a frequency of 350 to 600 beats/min.
With normal conduction, ventricular rate shows frequency between 100 and 150 beats/min. Atrial fibrillation with slow ventricular responses or AV block is seen typically in patients with digoxin toxicity [5, 14, 15, 16, 17, 18, 19, 20, 21, 33, 34, 35, 36].
Monomorphic ventricular tachycardia is common in patients with a history of previous myocardial infarction. Other rare causes of monomorphic VT include right or left ventricular outflow tract ventricular tachycardia and right ventricular dysplasia.
Ventricular flutter appears as a sine wave pattern with regular, large oscillations on the ECG and can progress to ventricular fibrillation [37, 38, 39, 40, 41, 42, 43, 44].
Antidromic AVRT includes a reentrant circuit with accessory pathway as the anterograde pathway, and AV node–His bundle as the retrograde pathway. Some patients (3 to 8%) with WPW syndrome show mechanisms of antidromic AVRT [24, 25, 26, 27, 28, 29, 30, 31].
SVT with aberrant conduction/BBB
Atrial tachycardia with accessory pathway
Junctional tachycardia with accessory pathway
Polymorphic VT is most commonly caused by abnormalities of ventricular muscle repolarization. The predisposition to this problem usually manifests on the ECG as a prolongation of the QT interval. Congenital problems include long QT syndrome and catecholaminergic polymorphic ventricular tachycardia. Acquired problems are usually related to drug toxicity or electrolyte abnormalities, myocardial ischemia. Class III anti-arrhythmic drugs such as sotalol and amiodarone prolong the QT interval and may in some circumstances be pro-arrhythmic. Other relatively common drugs include some antibiotics and antihistamines [37, 38, 39, 40, 41, 42, 43, 44].
Ventricular fibrillation is a terminal arrhythmia in which ventricular contractions are uncoordinated and too weak to eject blood. The ECG shows irregular, chaotic deflections of varying amplitude and shape [37, 38, 39, 40, 41, 42, 43, 44].
Antidromic AVRT with variable VA conduction
Pre-excited AF (AF with ventricular pre-excitation)
Torsades de pointes
The ECG demonstrates a polymorphic VT characterized by the QRS complexes of changing amplitude that appear to twist around the isoelectric line and occur at the rates of 200 to 250 beats/min. Most data suggest that early afterdepolarizations are responsible for both the QT prolongation and the torsades de pointes. The most common causes are congenital severe bradycardia, potassium depletion and use of class IA and IC drugs. Clinical features depend on whether torsades de pointes is due to acquired or congenital long QT syndrome. Some episodes may persist and progress to ventricular fibrillation, leading to sudden death. In congenital long QT syndrome, long QT intervals predispose the patient to an R-on-T phenomenon, wherein the R-wave, representing ventricular depolarization, occurs during the relative refractory period at the end of repolarization [37, 38, 39, 40, 41, 42, 43, 44].
AF or atrial flutter or focal atrial tachcyardia with varying block conducted with aberration
There are several algorithms that are currently used to help distinguish Supraventricular Tachycardia (SVT) with aberrancy and Ventricular Tachycardia (VT) (Table 3). Many of these algorithms and criteria have limitations [44, 45, 46, 47, 48, 49, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
[RBBB morphology] Identical activation vector = SVT.
If the initial 20 ms of the QRS are the same in WCT as in sinus rhythm, SVT is favored with a positive predictive value (PPV) of 92%. The sinus rhythm ECG must be available for this analysis.
[RBBB morphology] An rSR’ where S crosses baseline = SVT with a PPV of 91%.
[RBBB morphology] Triphasic QRS in V1 = SVT with a PPV of 92%.
[RBBB morphology, LBBB morphology] Precordial concordance = VT. A QRS, which is predominantly positive or predominantly negative in every precordial lead, overwhelmingly favors VT with specificity of 95–100% and a PPV of 89–100% [44, 45, 46, 47, 48, 49, 50, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
AV dissociation = VT. Of all criteria, this is the most secure with specificity of 100% and PPV of 100%. It holds true regardless of bundle branch pattern or other morphology criteria.
[RBBB morphology] QRS duration >140 ms = VT with specificity of 57–75% and PPV of 89%.
[RBBB morphology] Left axis deviation = VT with PPVs of 88–94%. With extreme left axis (more negative than −90°), the PPV is 98%.
[RBBB morphology] Mono- or biphasic QRS morphologies in V1 favors VT with PPV of 82–83
If the V1 QRS is triphasic, an R:S ratio < 1 in V6 (that is, R wave smaller than S wave) favors VT with PPV of 90%.
[RBBB morphology] Rsr’ (‘Rabbit ears’) = VT. In an unusual triphasic V1, with the left R wave taller than the right, and the S wave not crossing the baseline, favors VT with PPV of 100% [44, 45, 46, 47, 48, 49, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
A history of myocardial infarction, QRS morphology in leads aVF and V1 ([1] predominant negative deflection in aVF in tachycardia with RBBB pattern and Q wave, [2] a monophasic or biphasic waveform in V1 in tachycardia with RBBB pattern, [3] QS or qR waveform in tachycardia with LBBB pattern favored a diagnosis of VT) and frontal plane axis > 40° when compared with baseline the ECG favored a diagnosis of VT. The presence of AV dissociation and/or the presence of premature ventricular beats during sinus rhythm that show morphologies same to that observed in tachycardia favored a diagnosis of VT [51, 52, 53, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
[LBBB morphology] V1 or V2 with initial R > 30 ms = VT.
[LBBB morphology] V1 or V2 QRS onset to nadir of S wave >60 ms = VT.
[LBBB morphology] V1 or V2 with notching on the S wave downstroke = VT.
[LBBB morphology] Any Q in V6 = VT [54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
An R-wave peak time, with the interval from QRS onset to first change in polarity (R or S peak) in lead II ≥ 50 ms, independent of whether the complex is positive or negative, has been reported to have a sensitivity of 93% and specificity of 99% for identifying VT (Figure 4) [55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
The R-wave peak time (RWPT) in lead II.
Vereckei et al. published four-step algorithms with the incorporation of new criteria of Vi/Vt (Figure 5) [56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
Vereckei aVR algorithm.
The four steps were used in the following sequence:
If an initial R wave was present in lead aVR, VT was diagnosed.
If an initial, non-dominant q or r in aVR > 40 ms, VT was diagnosed.
If the morphology of WCT did not correspond to BBB or fascicular block, VT was diagnosed.
In the last step when the Vi/Vt ratio, obtained by measuring the voltage of the initial 40 ms (Vi) and the terminal 40 ms of a QRS (Vt) in any ECG lead, was ≤1 the diagnosis of VT, if the Vi/Vt was >1 the diagnosis of SVT was made (Figure 5).
During WCT due to SVT, after the initial rapid septal activation over the normal His-Purkinje system, the slow intraventricular activation occurs in the mid to terminal portion of the QRS, thus the Vi/Vt > 1.
Brugada algorithm is the most widely known and commonly used algorithm (Figure 6) [57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71].
Brugada algorithm.
Brugada algorithm is as follows:
Is there concordance present in the precordial leads (leads V1-V6)?
“Are all of the QRS complexes completely upright, or downward in the precordial leads?”
If the answer is yes, then VT is the diagnosis
Is the R to S interval (between the onset of the R wave and the nadir of the S wave) > 100 ms in any one precordial lead?
If the answer is present, then VT is the diagnosis
Is AV dissociation present?
“AV dissociation occurs when P waves are seen at different rates than the QRS complexes.”
If the answer is present, then VT is the diagnosis
Examine the morphology of the QRS complex to see if it meets the specific criteria for VT, as Table 4.
QRS duration | |
---|---|
| |
| |
| |
| |
| |
| |
| |
Positive or negative concordance in all precordial leads | |
| |
| |
| |
| |
Lead V1 | Lead V6 |
|
|
| |
| |
Lead V1–2 | Lead V6 |
|
|
| (Absence of Q wave favors SVT) |
| |
ECG criteria favoring ventricular rather than supra-ventricular tachycardia in WCTs.
RBBB; right bundle branch block, LBBB; left bundle branch block, SVT; supra-ventricular tachycardia.
Right bundle branch block morphology | Lead V1: Monophasic R, biphasic qR, broad R (>40 ms), Rsr’ (the so-called ‘rabbit ears’ sign) Lead V6: R:S ratio < 1 |
---|---|
Left bundle branch block morphology | Lead V1–2: Broad R wave, slurred or notched downstroke of S wave, delayed nadir of S wave Lead V6: Q or QR or QS wave |
Morphology criteria for VT in leads V1, V2 and V6.
The ECG criteria or algorithms for the diagnosis of NCTs and WCTs has undergone evolution and development in concert with the field of cardiology itself, but the necessity of a correct diagnosis remains unchanged [57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]. The world has not yet seen the ‘one criterion to end all criteria’ or ‘simplest criterion’ with high sensitivity and specificity, and it seems unlikely to appear in our near future. Therefore, physicians or cardiologists should be cautioned against overreliance in these ECG criteria or algorithms for the interpretation of the ECGs.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. 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Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. 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Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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