Summary of other identified studies.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"},{slug:"intechopen-identified-as-one-of-the-most-significant-contributor-to-oa-book-growth-in-doab-20210809",title:"IntechOpen Identified as One of the Most Significant Contributors to OA Book Growth in DOAB"}]},book:{item:{type:"book",id:"8465",leadTitle:null,fullTitle:"Recent Trends in Computational Intelligence",title:"Recent Trends in Computational Intelligence",subtitle:null,reviewType:"peer-reviewed",abstract:"Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications.",isbn:"978-1-83880-706-1",printIsbn:"978-1-83880-705-4",pdfIsbn:"978-1-83880-707-8",doi:"10.5772/intechopen.78839",price:119,priceEur:129,priceUsd:155,slug:"recent-trends-in-computational-intelligence",numberOfPages:198,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"ed1a280abdc24c8367170d2aff2d1a68",bookSignature:"Ali Sadollah and Tilendra Shishir Sinha",publishedDate:"May 6th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8465.jpg",numberOfDownloads:8004,numberOfWosCitations:12,numberOfCrossrefCitations:21,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:32,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:65,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 13th 2019",dateEndSecondStepPublish:"September 17th 2019",dateEndThirdStepPublish:"November 16th 2019",dateEndFourthStepPublish:"February 4th 2020",dateEndFifthStepPublish:"April 4th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"147215",title:"Dr.",name:"Ali",middleName:null,surname:"Sadollah",slug:"ali-sadollah",fullName:"Ali Sadollah",profilePictureURL:"https://mts.intechopen.com/storage/users/147215/images/system/147215.jpeg",biography:"Ali Sadollah received his BSc degree in mechanical engineering, solid states, from Azad University, Semnan Branch, Iran in 2007 and MSc degree in mechanical engineering, applied mechanics, from the University of Semnan, Semnan, Iran in 2010. He obtained his Ph.D. at the Faculty of Engineering, the University of Malaya, Kuala Lumpur, Malaysia in 2013. He has also served as a postdoctoral research fellow for more than 2 years at Korea University, Seoul, South Korea. In 2016, for a year, he served as a research staff member at Nanyang Technological University, Singapore. Afterward, he was a guest assistant professor at Sharif University of Technology, Tehran, Iran for 2 years. Currently, he is an assistant professor in the Department of Mechanical Engineering at University of Science and Culture, Tehran, Iran. His research interests include algorithm development, optimization and metaheuristics, applications of soft computing methods in engineering, artificial neural networks, and computational solid mechanics.",institutionString:"University of Science and Culture",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of Science and Culture",institutionURL:null,country:{name:"Iran"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"39255",title:"Dr.",name:"Tilendra",middleName:"Shishir",surname:"Sinha",slug:"tilendra-sinha",fullName:"Tilendra Sinha",profilePictureURL:"https://mts.intechopen.com/storage/users/39255/images/system/39255.jpeg",biography:"Dr. Tilendra Shishir Sinha, born in July 1968 in Dhanbad, Jharkhand, India, received his BE in Computer Technology from Nagpur University, Maharashtra, India, in 1992. He completed his MSc(Engineering) in Electrical Engineering (Power Electronics Specialization) from the National Institute of Technology, Jamshedpur, Jharkhand, India, in 2002. He moved on to complete his PhD(Engineering) in Computer Science & Engineering from the National Institute of Technology, Durgapur, West Bengal, India, in 2009. He has more than 25 years of experience in teaching, industry, and research. He is an IEEE member, ISTE member, and fellow member of IEI and Chartered Engineer in CSE. He has co-authored numerous publications in journals and conferences, and also published four book chapters in the area of natural language processing, speech processing, signal and image processing, information security, and cyber security. \nHis research interests include natural language processing, promoting global cyber security, information security through biometrical patterns, underwater objects detection and recognition, digital signal and morphological image processing, and applied engineering mathematics in various applications of core engineering.",institutionString:"Koneru Lakshmaiah Education Foundation",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"520",title:"Machine Learning",slug:"computer-and-information-science-artificial-intelligence-machine-learning"}],chapters:[{id:"71338",title:"Boundary Element Modeling and Optimization of Three Temperature Nonlinear Fractional Generalized Photo-Thermoelastic Interaction in Anisotropic Semiconductor Structures",doi:"10.5772/intechopen.91230",slug:"boundary-element-modeling-and-optimization-of-three-temperature-nonlinear-fractional-generalized-pho",totalDownloads:748,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The main objective of this paper is to introduce a new fractional-order theory called nonlinear fractional generalized photo-thermoelasticity involving three temperatures. Due to strong nonlinearity, it is very difficult to solve the wave problems related to this theory analytically. Therefore, we propose a new boundary element algorithm and technique for simulation and optimization of the considered problems related to this theory. The genetic algorithm (GA) as an optimization method has been applied based on free form deformation (FFD) technique to improve the performance of our proposed technique. In the formulation of the considered problem, the profiles of the considered objects are determined by FFD technique, where the FFD control point positions are treated as genes, and then the chromosome profiles are defined with the gene sequence. The population is established by a number of individuals (chromosomes), where the objective functions of individuals are achieved by the boundary element method (BEM). A nonuniform rational B-spline curve (NURBS) was used to model optimized boundary where it reduces the number of control points and provides the flexibility to design several different shapes for solving the considered photo-thermoelastic wave problems. The numerical results verify the validity and accuracy of our proposed boundary element technique.",signatures:"Mohamed Abdelsabour Fahmy",downloadPdfUrl:"/chapter/pdf-download/71338",previewPdfUrl:"/chapter/pdf-preview/71338",authors:[{id:"233766",title:"Prof.",name:"Mohamed Abdelsabour",surname:"Fahmy",slug:"mohamed-abdelsabour-fahmy",fullName:"Mohamed Abdelsabour Fahmy"}],corrections:null},{id:"70944",title:"Using Discrete Geometric Models in an Automated Layout",doi:"10.5772/intechopen.90941",slug:"using-discrete-geometric-models-in-an-automated-layout",totalDownloads:662,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The application of discrete (voxel) geometric models in computer-aided design problems is shown. In this case, the most difficult formalized task of computer-aided design is considered—computer-aided layout. The solution to this problem is most relevant when designing products with a high density of layout (primarily transport equipment). From a mathematical point of view, these are placement problems; therefore, their solution is based on the use of a geometric modeling apparatus. The basic provisions and features of discrete modeling of geometric objects, their place in the system of geometric modeling, the advantages and disadvantages of discrete geometric models, and their primary use are described. Their practical use in solving some of the practical problems of automated layout is shown. This is the definition of the embeddability of the placed objects and the task of tracing and evaluating the shading. Algorithms and features of their practical implementation are described. A numerical assessment of the accuracy and performance of the developed geometric modeling algorithms shows the possibility of their implementation even on modern computers of medium power. This allows us to hope for the integration of the developed layout algorithms into modern systems of solid-state geometric modeling in the form of plug-ins.",signatures:"Leonid V. Markin",downloadPdfUrl:"/chapter/pdf-download/70944",previewPdfUrl:"/chapter/pdf-preview/70944",authors:[{id:"312671",title:"Prof.",name:"Leonid",surname:"Markin",slug:"leonid-markin",fullName:"Leonid Markin"}],corrections:null},{id:"69613",title:"Effective Pitch Value Detection in Noisy Intelligent Environments for Efficient Natural Language Processing",doi:"10.5772/intechopen.89697",slug:"effective-pitch-value-detection-in-noisy-intelligent-environments-for-efficient-natural-language-pro",totalDownloads:582,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The performance of applications based on natural language processing depends primarily on the environment in which these applications are applied. Intelligent environments will be one of the major applications used to process natural language. The methods for speaker’s gender classification can adapt and improve the performance of natural language processing applications. That is why, this chapter will present an effective speaker’s pitch value detection in noisy environments, which then allows more robust speaker’s gender classification. The chapter presents the algorithm for the speaker’s pitch value detection and performs the comparison in various noisy environments. The experiments are carried out on the part of the publically available Aurora 2 speech database. The results showed that the automatically determined pitch values deviate, on average, only by 8.39 Hz from the reference pitch value. A well-defined pitch value allows a functional speaker’s gender classification. In this chapter, presented speaker’s gender classification works well, even at low signal to noise ratios. The experiments show that the speaker’s gender classification performance at SNR 0 dB is higher than 91% when the automatically determined pitch value is used. Speaker’s gender classification can then be used further in the processes of natural language processing.",signatures:"Damjan Vlaj, Andrej Žgank and Marko Kos",downloadPdfUrl:"/chapter/pdf-download/69613",previewPdfUrl:"/chapter/pdf-preview/69613",authors:[{id:"306398",title:"Dr.",name:"Marko",surname:"Kos",slug:"marko-kos",fullName:"Marko Kos"},{id:"306773",title:"Dr.",name:"Damjan",surname:"Vlaj",slug:"damjan-vlaj",fullName:"Damjan Vlaj"},{id:"306776",title:"Dr.",name:"Andrej",surname:"Žgank",slug:"andrej-zgank",fullName:"Andrej Žgank"}],corrections:null},{id:"70393",title:"Classification Problem in Imbalanced Datasets",doi:"10.5772/intechopen.89603",slug:"classification-problem-in-imbalanced-datasets",totalDownloads:949,totalCrossrefCites:4,totalDimensionsCites:7,hasAltmetrics:0,abstract:"Classification is a data mining task. It aims to extract knowledge from large datasets. There are two kinds of classification. The first one is known as complete classification, and it is applied to balanced datasets. However, when it is applied to imbalanced ones, it is called partial classification or a problem of classification in imbalanced datasets, which is a fundamental problem in machine learning, and it has received much attention. Considering the importance of this issue, a large amount of techniques have been proposed trying to address this problem. These proposals can be divided into three levels: the algorithm level, the data level, and the hybrid level. In this chapter, we will present the classification problem in imbalanced datasets, its domains of application, its appropriate measures of performances, and its approaches and techniques.",signatures:"Aouatef Mahani and Ahmed Riad Baba Ali",downloadPdfUrl:"/chapter/pdf-download/70393",previewPdfUrl:"/chapter/pdf-preview/70393",authors:[{id:"300915",title:"Dr.",name:"Aouatef",surname:"Mahani",slug:"aouatef-mahani",fullName:"Aouatef Mahani"},{id:"310335",title:"Dr.",name:"Ahmed Riadh",surname:"BABA ALI",slug:"ahmed-riadh-baba-ali",fullName:"Ahmed Riadh BABA ALI"}],corrections:null},{id:"71118",title:"Applications of Mining Arabic Text: A Review",doi:"10.5772/intechopen.91275",slug:"applications-of-mining-arabic-text-a-review",totalDownloads:717,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches.",signatures:"Qasem Al-Radaideh",downloadPdfUrl:"/chapter/pdf-download/71118",previewPdfUrl:"/chapter/pdf-preview/71118",authors:[{id:"303628",title:"Prof.",name:"Qasem",surname:"AlRadaideh",slug:"qasem-alradaideh",fullName:"Qasem AlRadaideh"}],corrections:null},{id:"71286",title:"Categorizing Patient Disease into ICD-10 with Deep Learning for Semantic Text Classification",doi:"10.5772/intechopen.91292",slug:"categorizing-patient-disease-into-icd-10-with-deep-learning-for-semantic-text-classification",totalDownloads:742,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"How to leverage insights into big electronic health records (EHRs) becomes increasingly important for accomplishing precision medicine to improve the quality of human healthcare. When analyzing big Chinese EHRs, there are a lot of applications that we need to categorize patients’ diseases according to the medical coding standard. In this paper, we develop natural language processing (NLP), deep learning, and machine learning algorithms to automatically categorize each patient’s individual diseases into the ICD-10 standard. Experimental results show that the convolutional neural network (CNN) algorithm outperforms the recurrent neural network (RNN)-based long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms, and it generates much better results than the support vector machine (SVM), one of the most popular conventional machine learning algorithms, demonstrating the great impact of deep learning on medical big data analysis.",signatures:"Junmei Zhong and Xiu Yi",downloadPdfUrl:"/chapter/pdf-download/71286",previewPdfUrl:"/chapter/pdf-preview/71286",authors:[{id:"306304",title:"Ph.D.",name:"Junmei",surname:"Zhong",slug:"junmei-zhong",fullName:"Junmei Zhong"}],corrections:null},{id:"71081",title:"Deep Learning Approach to Key Frame Detection in Human Action Videos",doi:"10.5772/intechopen.91188",slug:"deep-learning-approach-to-key-frame-detection-in-human-action-videos",totalDownloads:814,totalCrossrefCites:5,totalDimensionsCites:6,hasAltmetrics:0,abstract:"A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of constituting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.",signatures:"Ujwalla Gawande, Kamal Hajari and Yogesh Golhar",downloadPdfUrl:"/chapter/pdf-download/71081",previewPdfUrl:"/chapter/pdf-preview/71081",authors:[{id:"312168",title:"Dr.",name:"Ujwalla",surname:"Gawande",slug:"ujwalla-gawande",fullName:"Ujwalla Gawande"},{id:"312350",title:"Mr.",name:"Kamal",surname:"Hajari",slug:"kamal-hajari",fullName:"Kamal Hajari"},{id:"312351",title:"Prof.",name:"Yogesh",surname:"Golhar",slug:"yogesh-golhar",fullName:"Yogesh Golhar"}],corrections:null},{id:"68953",title:"Machine Translation and the Evaluation of Its Quality",doi:"10.5772/intechopen.89063",slug:"machine-translation-and-the-evaluation-of-its-quality",totalDownloads:1482,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:1,abstract:"Machine translation has already become part of our everyday life. This chapter gives an overview of machine translation approaches. Statistical machine translation was a dominant approach over the past 20 years. It brought many cases of practical use. It is described in more detail in this chapter. Statistical machine translation is not equally successful for all language pairs. Highly inflectional languages are hard to process, especially as target languages. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This chapter also describes the evaluation of machine translation quality. It covers manual and automatic evaluations. Traditional and recently proposed metrics for automatic machine translation evaluation are described. Human translation still provides the best translation quality, but it is, in general, time-consuming and expensive. Integration of human and machine translation is a promising workflow for the future. Machine translation will not replace human translation, but it can serve as a tool to increase productivity in the translation process.",signatures:"Mirjam Sepesy Maučec and Gregor Donaj",downloadPdfUrl:"/chapter/pdf-download/68953",previewPdfUrl:"/chapter/pdf-preview/68953",authors:[{id:"305414",title:"Associate Prof.",name:"Mirjam",surname:"Sepesy Maučec",slug:"mirjam-sepesy-maucec",fullName:"Mirjam Sepesy Maučec"},{id:"309288",title:"Dr.",name:"Gregor",surname:"Donaj",slug:"gregor-donaj",fullName:"Gregor Donaj"}],corrections:null},{id:"70785",title:"Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges",doi:"10.5772/intechopen.90810",slug:"pedestrian-detection-and-tracking-in-video-surveillance-system-issues-comprehensive-review-and-chall",totalDownloads:1309,totalCrossrefCites:7,totalDimensionsCites:10,hasAltmetrics:0,abstract:"Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed.",signatures:"Ujwalla Gawande, Kamal Hajari and Yogesh Golhar",downloadPdfUrl:"/chapter/pdf-download/70785",previewPdfUrl:"/chapter/pdf-preview/70785",authors:[{id:"312168",title:"Dr.",name:"Ujwalla",surname:"Gawande",slug:"ujwalla-gawande",fullName:"Ujwalla Gawande"},{id:"312350",title:"Mr.",name:"Kamal",surname:"Hajari",slug:"kamal-hajari",fullName:"Kamal Hajari"},{id:"312351",title:"Prof.",name:"Yogesh",surname:"Golhar",slug:"yogesh-golhar",fullName:"Yogesh Golhar"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6806",title:"Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications",subtitle:null,isOpenForSubmission:!1,hash:"fedf4479b910cbcee3025e391f073417",slug:"fuzzy-logic-based-in-optimization-methods-and-control-systems-and-its-applications",bookSignature:"Ali Sadollah",coverURL:"https://cdn.intechopen.com/books/images_new/6806.jpg",editedByType:"Edited by",editors:[{id:"147215",title:"Dr.",name:"Ali",surname:"Sadollah",slug:"ali-sadollah",fullName:"Ali Sadollah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7607",title:"Recent Trends in Artificial Neural Networks",subtitle:"from Training to Prediction",isOpenForSubmission:!1,hash:"b7f92b054dddc49df32d4f9c9e71507c",slug:"recent-trends-in-artificial-neural-networks-from-training-to-prediction",bookSignature:"Ali Sadollah and Carlos M. 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\r\n\tThe development of RNA therapeutics has been an intense journey, with numerous stories of success and failure. The potential, and suitability, of recently discovered RNAs (for therapeutics), stemmed from several Nobel Prize-winning discoveries. Currently, novel RNA drugs are entering clinical trials almost daily. RNA therapeutics are chemically synthesized biomolecules with broad clinical applications, ranging from correcting inherited mutations, to treating cancer, and chronic conditions, improving organ transplant outcomes, and infectious disease prophylaxes.
\r\n\tRNA therapies evolved as profitable and widely applicable individualized treatment solutions. Moreover, RNA-based therapeutic vaccines (e.g., against SARS-CoV-2 infection) have been proven to be safe and effective, and several of them are approved by the United States Food and Drug Administration (FDA).
\r\n\tThis book aims to present distinct classes of RNA therapeutics, ranging from single-stranded antisense oligonucleotides (ASOs), and subclasses of RNA interferences (miRNAs and other RNAi), to in vitro transcribed mRNAs and RNA vaccines. Also, it will present some of the challenges in RNA drug engineering, delivery, and specificity. Additionally, the improvement of pharmacological effectiveness will be discussed. Monumental breakthroughs in molecular biology, computational chemistry, bioinformatics, and individualized genomics, which undoubtedly propelled RNA therapeutics through the commercialization stage, will also be examined in this book.
\r\n\tRNA therapeutics have had a significant impact on medicine, the economy, and overall public health; they are becoming prescription drugs, and this holds great promise for modernizing healthcare.
In today’s world, the performance of business and corporates of a country plays a very important role in its position as a world leader. The per capita income, employment rate and other economic variables depend a lot upon the performance of business houses in that country. The stock price of a company fluctuates according to the performance of the business and the economy as a whole. The timing and the decision about buying and selling of stock depend upon the stock price level. When an investor decides to invest in a stock he always looks for strong and growing companies, the value of the firm is reflected in the stock prices of that firm, and that is how an investor without any finance knowledge selects the stock-by-stock price movements.
One of the key sources of financing for the listed firms is the stock issue, and for successful stock issue, firms need to have a strong track record in the stock market. There are various stakeholders to the business, like shareholders, creditors, customers, employees, and government. The rising stock price is an indicator of good management and satisfaction for all the stakeholders. There are company-specific and market-related determinants of stock prices; in literature, many theories are available that explain the movement in the stock prices.
One of the most significant theories is the Efficient Market Hypothesis (EMH), which is based on the assumption that rational investors in the market react to the available information like company fundamentals and other important declaration about the company to decide on the stock buying or selling. If they feel that the information is positive, then they retain the shares if already bought or buy the one which was not purchased earlier and vice versa. The action of buying and selling stocks by the investors is responsible for changes in stock price. There are three forms of EMH—weak, semi-strong and strong form—and they vary regarding available information for public and investors. Another theory ‘Random walk’ states that stock prices are random and cannot be predicted by any means. This theory has been empirically tested many times and proved by the researchers. A random walk is consistent with EMH, as the flow of information is random which helps investors in reassessing the stock price.
The third theory ‘Behavioral Finance Theory’ is very different from the random walk and the EMH theories. This theory states that investors do not behave rationally rather they invest by psychological and behavioral factors; for example, they will invest in the stock if the stock price is increasing even if there are no significant changes in the company fundamentals.
Gordon [1] revealed that dividend payment and growth rate of the company have an impact on the intrinsic value of shares. The model was based on the assumption of constant growth in dividends which was one of the weaknesses of the model, but still, it is the highly used model to calculate the intrinsic value of the stock. This model claims that expected dividend and growth rate of the company are positive determinants of stock prices.
A considerable amount of research has been done to find out internal determinants of share price changes of companies, some of the common factors found are dividend yield, total assets, earning per share, capital structure and book value per share. Apart from internal variables, macroeconomic variables also have an impact on share prices that have been discussed by Roll and Ross [2] in his arbitrage pricing theory (APT), a framework for pricing securities for investors. According to Ross, common macroeconomic factors affecting share prices were unexpected changes in inflation, GDP and changes in the yield curve. APT model is flexible as investors can select other factors also depending on the market like for oil exporting and importing countries oil price can be an important factor affecting security prices. Mukherjee and Naka [3] supported the APT theory by confirming the impact of economic variables on the stock returns; they argued that changes in economic variables affect dividend payments and discount rates and thus have an impact on share prices as well.
In the present study, the attempt has been made to study the impact of select internal determinants and macroeconomic determinants of the share price of listed 26 nonfinancial companies in the Muscat Securities Market. A lot of work has been done on this topic, but most of the studies are based on establishing a relationship between dividend policy and stock prices. To the best of researcher’s knowledge, this study is the pioneer study on the Oman capital market, based on stock price determinants of the companies from Muscat securities market. In the previous studies from GCC countries [4, 5, 6] and studies from other countries, authors have not studied any specific sector for share price determinants. Another contribution of this study is that it is exclusively based on nonfinancial companies. The nature of balance sheet in financial companies varies from nonfinancial companies in terms of leverage, current assets and fixed assets composition. Therefore, to study the impact of company-specific determinants on share prices a separate sample of financial and nonfinancial companies would yield better results rather than studying the mix of all types of companies.
The whole chapter is organized into five sections including introduction. Section 2 describes the literature review. Section 3 discusses the methodology and data. Section 4 presents the empirical results and its discussion thereof. Section 5 presents conclusion with policy implications.
Collins [7] was the pioneering work on determinants of share prices based on the US market, the findings of the chapter recognized book value of equity, dividend, net profit and operating cash flows as the significant factors affecting share prices.
Nirmala et al. [8] used fully modified least square regression model on panel data of 37 Indian companies from 2000 to 2009. The study identified price earnings ratio, leverage and dividend per share as the major determinants of share prices. In the Indian context, this study was also conducted by Tandon and Malhotra [9]; they tried to identify determinants of stock prices for 100 companies listed in National Stock Exchange (NSE) using linear regression model from 2007 to 2012. The results indicated that firms’ book value, earning per share and price-earnings ratio have a significant positive association with firm’s stock price, while dividend yield has a significant inverse association with the market price of the firm’s stock.
Malhotra and Prakash [10] studied the determinants of stock prices of Indian companies during 1990–1999 with the help of correlation and regression analysis. Book value per share, dividend per share, market to book ratio and PE ratio emerged as the significant determinants of the share prices.
Oseni [11] studied the impact of earnings per share (EPS), oil price, dividend per share (DPS), GDP, foreign exchange rate and interest rates on share prices of 130 companies from the Nigerian stock exchange. The study revealed a strong positive correlation between stock prices and EPS, oil price, dividend per share and GDP.
Gjerde and Saettem [12] studied the relationship between stock returns and macroeconomic variables like inflation, real economic activity and oil prices in Norway. The empirical study revealed that inflation is not a significant variable for changes in stock prices. However, there was a positive relationship between oil price and stock price.
Irfan et al. [13] attempted to explain the impact of six company variables dividend yield, dividend payout ratio, leverage, size of the firm, earnings volatility and asset growth rate on stock prices of Pakistani companies during the period 1981–2000. A regression model was used to establish the relationship; the study concluded that none of the fundamental factors considered were significant for the changes in the share prices.
Al-Deehani [4] studied the impact of EPS, DPS, previous year dividend, return on equity (ROE), price to book value and cash flow per share on the share prices of companies listed in the Kuwait stock exchange. The study concluded that variables DPS, previous year dividends per share, ROE, price to book value and cash flow per share are all highly correlated with the share price.
Al-Tamimi et al. [5] investigated the key determinants of stock prices of 17 companies listed in UAE stock market during 1990–2005. The regression result indicated EPS as a strong determinant having positive impact on share prices; consumer price index was found to be statistically significant with a negative coefficient. Money supply and GDP were found to have a positive coefficient, but they were statistically insignificant.
Allen and Rachim [14] tested the effect of dividend policy on the stock price volatility with the control variables like leverage, growth, earnings volatility and firm size. The data on 173 companies listed in the Australian stock market from 1972 to 1985 were analyzed with the help of cross-sectional regression analysis. The results showed the significant positive relation between stock price volatility and leverage, size and earnings volatility. It was also concluded that dividend policy is not influencing stock price volatility. Apart from the studies mentioned above, few more important studies from different markets are identified and mentioned in Table 1.
Study | Methodology | Results of the study (+/− significant or insignificant) | Place |
---|---|---|---|
Bhattarai [15] | Multiple Regression Model | dividend payout ratio (insignificant), dividend yield (−), earnings per share (+), price earnings ratio (+), logarithm of total assets (insignificant) | Banking sector of Nepal |
Şebnem and Vuran [16] | Dynamic Panel Data Analysis | closing price of the stock at time t-1 (+), book-to-market equity (+), leverage ratio (−), firm size (+), dividend paid (−), oil price (−) | Istanbul Stock Exchange |
Gregoriou et al. [17] | Panel least squares regression model | earnings per share (insignificant), net cash from operating activities per share (+), book value per share (+), long-term debt to total assets (+), dividends per share (+) | Mobile companies from Europe, Asia, the Middle East and America |
Ibrahim Obeidat [6] | Multiple Regression Model | Earnings per share (+), dividends per share (+), book value per share (+) | Abu Dhabi Securities Market |
Mohammad Khan Ghauri [18] | Fixed effect regression model | Equity (−), dividend yield (insignificant), return on assets (insignificant), asset growth (insignificant) | Banking sector of Pakistan |
Raithatha and Bapat [19] | Fixed effect regression model | Beta (+), market capitalization (+), current ratio (insignificant), earnings per share (+), D/E ratio (insignificant), return on capital employed (insignificant) | Indian Stock Market |
Srinivasan [20] | Random effects model | Dividend per share (−), price earnings ratio (+), EPS (+), size-sales (insignificant), book value per share (insignificant) | Indian Manufacturing Companies |
Adrangi et al. [21] | Johansen and Juselius co-integration model | Inflation rate (−), economic activity (GDP) (+) | Brazil stock market |
Papapetrou [22] | Multivariate vector-auto regression (VAR) approach | Oil prices (−), real economic activity and employment | Greece stock market |
Fama and Schwert [23] | Regression Model | Expected inflation (−) and unexpected inflation (−) | New York Stock Exchange |
Summary of other identified studies.
In the existing literature, there is a mixed opinion on the determinants of stock prices and their positive or negative impact. Very few studies are based on GCC countries and none of them from Muscat securities market, Oman. This study thus fills the gap by researching the impact of select firm-specific and economic variables on the stock prices of the nonfinancial sample companies listed in Muscat securities market, Oman.
By available literature and data, the author has identified dividend payout ratio, debt ratio, earnings per share (EPS), logarithm of total assets (a proxy for company size) and price earnings ratio as the regressors of the stock price in this study. This study is based on 26 nonfinancial companies listed in Muscat securities market during 2011–2016. The sample companies selected for the study are based on the availability and fullness of the data. The selected companies are Al Saffa Foods, Salalah Mills, Oman Cement, Raysut Cement, Galfar Engineering and contracting, Anwar Ceramic Tiles, Jazeera Steel Products, National Aluminum Products, Gulf International Chemicals, Oman Chlorine, Oman Cables Industry, Voltamp Energy, Omantel telecommunications, Port Services Corporation, Almaha petroleum products, National Gas, Oman oil marketing, Shell Oman Marketing, ACWA Power Barka, SMN Power Holding, Sohar Power, United Power, Al Jazeira Services, Oman Investment and Finance, Renaissance Services and Ooredoo.
Roll and Ross [2] in his arbitrage pricing theory (APT) has proved the relevance of macroeconomic variables in stock pricing. Based on the literature, economic variables like growth rate in GDP, consumer price index and crude oil prices have also been considered as the external variables affecting stock prices. Fama and Schwert [23] the well-known study was also based on the relationship between stock prices and inflation. Oman being the net exporter and mainly depending on oil and gas export is facing the heat of low oil prices. Economy of Oman like other GCC countries is driven by oil and gas, so consideration of oil price as an independent variable makes sense.
Dividend payout ratio is the ratio of the amount of dividend paid per unit of total earnings, also represents the percentage of earnings distributed in the form of dividends to shareholders. The payout ratio is considered to be one of the important variables affecting stock price as current stock value is the discounted value of future cash flows from that stock. The second variable ‘debt ratio’ is defined as the ratio of total debt to total assets, expressed as a decimal or percentage. It can be interpreted as the proportion of a company’s assets that are financed by debt. It is a measure of financial risk on the assets of a company, and higher financial risk will affect the returns and consequently price of a stock. The third variable considered in the study is EPS, which measures the income generated on one share. It is a ratio of net income to the number of shares outstanding. In most of the studies, EPS has emerged as a significant variable having a positive impact on share prices. In literature, many studies have tried to measure the impact of the size of the company on the stock prices. Some of them have used the logarithm of sales as the proxy for company size and in some cases logarithm of total assets. Both sales and total assets are an indicator of business size. Many investors take their investment decision by company size as bigger companies are more stable regarding profit and are also less prone to the business cycle. Price-earnings ratio commonly known as PE ratio is one of the prime indicators used in the stock selection by the investors. PE ratio is the ratio of the market price of a stock to its EPS. It is a measure of investor’s confidence on stock and is a reflection of investor’s anticipation of higher growth in the future. Gordon growth model confirms the role of the growth rate of the company on the intrinsic value of the stock.
The following hypothesis statements were formulated on the basis of available literature and theory which provides the scope and depth to the study.
Hypotheses H01 to H06 are framed to test the reflection of publicly available information on the stock prices based on semi-strong form of EMH.
H01: There is no significant effect of size of the company on its share price.
H02: There is no significant effect of dividend payout ratio on share price.
H03: There is no significant effect of EPS on share price.
H04: There is no significant effect of leverage on share price.
H05: There is no significant effect of price-earnings ratio on share price.
H06: There is no significant effect of first lag of share price on current share price.
The following hypothesis are framed to confirm the impact of economic variables on the stock returns based on arbitrage pricing theory (APT),
H07: There is no significant effect of crude oil price on share price.
H08: There is no significant effect of inflation on share price.
H09: There is no significant effect of growth in GDP on share price.
Panel data analysis has been used to analyze the impact of firm-specific and macroeconomic determinants on the share price of the nonfinancial listed companies in Oman. Panel data always has advantages over time-series and cross-sectional data. Panel data analysis weakens the interaction between the variables that result in more reliable parameters, Hsiao [24]. Employment of this technique is considered more efficient as it reduces the co-linearity of the predictor variables and also it offers gain regarding the degree of freedom. The research study uses both the panel data methods, that is, fixed effect method and the random effect method. The better method is then selected applying the Hausman test. Both the models fixed effects and the random effects have been represented by the following Eqs. (1) and (2), respectively:
where, CPjt = annual closing price of firm’s stock in year t; β0 = common y-intercept; β1–β9 are the coefficients of concerned explanatory variables; ɛjt = stochastic error term for firm j at time t; β0j = firm j’s y-intercept; μjt = error term for firm j at time t.
Based on the literature on share price determinants, the following company-specific variables dividend payout ratio, leverage, earning per share, size of the company, price earnings ratio and three economy based variables growth rate in GDP, inflation rate and crude oil prices were selected as the predictor variables in the regression analysis. Apart from these variables, first lag of yearly closing price of shares was also considered as a predictor variable.
This section presents the results of panel data analysis which are reported in Table 2. Both the fixed effect and random effect model was used to measure the impact of the selected independent variables on the stock prices of sample companies. Then the Hausman specification test was used to select a better model between fixed effects and random effects model. The null hypothesis in Hausman test is that the preferred model is random effects and the alternate hypothesis is that the preferred model is fixed effects.
Variables | Fixed effects model | Random effects model | ||
---|---|---|---|---|
Coefficient | Probability value | Coefficient | Probability value | |
Constant | −7.520228 | 0.0146 | −2.388403 | 0.0112 |
Lag dependent | 0.046939 | 0.0306 | 0.049934 | 0.0116 |
Dividend | −0.085040 | 0.8511 | −0.158990 | 0.4880 |
Leverage | −0.348200 | 0.6613 | 0.694747 | 0.0286 |
EPS | 12.28314 | 0.0000 | 12.16380 | 0.0000 |
GDP | 0.004827 | 0.7650 | 0.004485 | 0.7791 |
Inflation | −0.053186 | 0.2667 | −0.080517 | 0.0778 |
Size | 0.558998 | 0.0409 | 0.082158 | 0.1875 |
Oil price | 0.349209 | 0.0432 | 0.316537 | 0.0620 |
PE ratio | −0.000300 | 0.8755 | −0.000619 | 0.7284 |
Determinants of share price according to fixed and random effect model.
According to the results of fixed effects model earnings per share, a log of total assets (a proxy for company size) and crude oil prices are found to be significant determinants of the changes in stock prices. All the three variables have a positive relationship with share prices. The macroeconomic variables growth rate in GDP and consumer price index are found to be insignificant in explaining the changes in share prices.
Results of the Hausman test are reported in Table 3, and according to that, null hypothesis is accepted. Therefore, random effects model is supposed to be a better model for analyzing this panel data. Value of R square is also quite high with 93.23% of variations in stock price explained by the regression model. In Random effects model, among the company-specific variables used in this study, lag of share prices, earnings per share and leverage are the statistically significant variables. The two variables earnings per share and first lag of share prices are even significant at 1% level of significance. The lag of the share prices has positive coefficient which means the previous hike in share prices are responsible for the increase in share price of the next year. Investors invest by stock price movement; this result supports the behavioral theory of finance. Earnings per share (EPS) is one of the most dominant determinants of share prices with the highest positive regression coefficient of 12.16 and significant at 1%. Debt to the total asset (leverage) is also significant and is positively related to sharing prices of the sample companies. The dividend has proved to be an insignificant determinant of the share prices, and this supports the irrelevance of dividend policy on the firm value. The logarithm of total assets (size of the company) and PE ratio are also not significant determinants at 5%.
Test Summary | Chi-Square Statistic | Probability |
---|---|---|
Cross-section random | 0.000000 | 1.0000 |
Result of Hausman Test.
From the three external variables, inflation rate and crude oil price are significant at 10% level of significance. The result of inflation rate is consistent with the previous studies and has a negative impact on share prices [21, 23]. Oman being an exporter of crude oil, the oil prices are significant determinants and have a positive impact on them. The growth rate in GDP is not seen as important and significant variables for share prices in Oman.
Results of random effect model indicate the rejection of the null hypothesis H03, H04 and H06 at 5% level of significance. Other null hypothesis like H01, H02 and H05 are not rejected at 5%. Hypothesis H01 to H06 were framed to test the existence of semi-strong form of EMH in capital market of Oman, which is partially met. Similarly, two null hypotheses (H07 and H08) are rejected at 10% level of significance and supports APT theory for stock prices.
The study aimed at investigating the effect of dividend payout, EPS, a log of total assets, debt ratio, PE ratio and previous year stock price on the current stock price of 26 listed nonfinancial companies in Oman. Three economic variables—growth rate in GDP, crude oil prices and consumer price index—are also considered as an independent variable in this study.
The empirical analysis is based on random effect model regression analysis with the stock price as the dependent variable. Based on the data analysis, the study finds that EPS has a significant positive effect on the price of common stock. Relatively, the value of the coefficient (12.16) for EPS is the highest among all the independent variables. In the majority of the existing studies, EPS had shown the same relationship with stock price [5, 6, 15, 19, 20]. EPS is a direct measure of shareholders earning on one share, and stocks with high EPS are commonly selected by equity analysts. Debt ratio (leverage) is also a significant variable having a positive relationship with stock price. Conceptually higher debt capital is an indication of financial risk, and hence an investor avoids these stocks. The reason for a positive relation between leverage and stock price could be a low percentage of debt capital in sample companies, as up to a certain level debt capital is favorable for stockholders which has been explained by the concept of ‘trading on equity.’
First lag of stock price is also significant and has a positive effect on current stock price consistent with Şebnem and Vuran [16]. This finding supports ‘Behavioral Finance Theory’ which explains the inconsistent behavior of investors toward theories and concepts. Dividend payout is insignificant determinant for stock prices, and results are consistent with the previous studies [9, 15, 18]. However, intrinsic value of a stock depends on future dividends; this may be because of anomalies or investors giving weightage to capital gains. The firm size is not significant; this result shows that investors are not giving any preference to bigger and established firms.
The macroeconomic changes also influence stock prices; inflation is negatively related to stock prices which support the well-known study of Fama and Schwert [23]. He justified the negative relationship by arguing that ‘an increase in inflation causes uncertainty and reduces future economic activity and thus future earnings of the firm which results in a reduction of stock price.’
The current study confirms that stock prices are affected by certain firm-specific variables and also by select economic variables. The results of the study might help investors and equity analysts in better decision-making. The study has achieved its objectives and recommends future research in the context of Oman with financial companies or with another set of variables that might have a significant effect on stock prices.
The use of both light and heat in medicine has roots that reside long back in history. In ancient times, sunlight was used to treat different kinds of skin and mental diseases. These treatments mimic, amplify, and in some cases focus on natural occurring phenomena to achieve a therapeutic goal.
\nDuring the nineteenth century, it was observed that prolonged heating, as fever or locally externally induced hyperthermia, could cause cancerous formations to disappear [1, 2, 3, 4]. Since then, many methods to treat cancer with heat were introduced, from whole body to local methods such as microwave ablation, radiofrequency ablation, and laser ablation. The main goals with innovative treatments that utilize heat are to give an alternative to patients that are not suitable for surgery and minimize the impact of the intervention on the patient. In addition, many of these methods have a lower economical impact on the treating institution budget, which enables clinics to offer treatment to a larger number of patients.
\nOther methods that do not make use of heat as treating source were also developed, such as cryogenic ablation that uses subfreezing temperatures to kill the tumor cells or photodynamic therapy (PDT) that uses a selective combination of light and photoactivatable drugs to induce radicals in the tumor.
\nInterest in focal ablation of tumors increased significantly in the last decades because of indications that local treatment may cause shrinkage of untreated, in some cases distant, tumors suggesting the involvement of the immune system in the process [5, 6, 7]. The so-called abscopal effect evoked by local treatments could be used to treat patients that lack effective treatments to date. Immune stimulating interstitial laser thermotherapy is an innovative hyperthermia treatment that uses a specifically tailored treatment protocol based on lower temperature heating for a prolonged period of time and designed to maximize the probability of triggering the immune system response to the treated tumor type. The medical device system uses laser as heat source; the same system is also used for interstitial laser ablation to burn tumorous and non-tumorous formation when imaging is challenging given its natural MR compatibility.
\nLaser-based hyperthermia, known as laser thermotherapy or laser ablation, is a focal hyperthermia technique that uses laser light as heat source. Its minimally invasive version for treatment of tumors located deeper in the body is called interstitial laser thermotherapy (LITT or ILT). The main goal in oncological treatments is to achieve tumor destruction without damaging tissue and structures surrounding the neoplastic lesion to be treated. Different factors concur to the tissue destruction, among these direct cell death and coagulation.
\nDuring laser-induced thermotherapy, light causes damage in tissue due to absorption of light and through heat conduction into the tissue of the absorbed energy. Laser thermotherapy therefore produces a lesion that is larger than the volume where light is absorbed due to this heat conduction.
\nThese two phenomena, direct light absorption and heat conduction, determine the modality and the parameters to be used to control the tumor heating and are dependent on the characteristics of the tissue to be treated.
\nThe penetration depth, which is defined as the distance at which the light is attenuated to 1/
Penetration depth depends on the tissue type since the optical properties are dependent on tissue composition and structure. For a generic tissue composition, the effective attenuation coefficient and the penetration depth can be calculated as follows:
\nValues for
The absorption,
Absorption spectra of tissue components in the window 500–1100 nm. Dotted line at 1064 nm.
The scattering,
Scattering coefficient for a generic soft tissue in the window 500–1100 nm, data from literature. Dotted line at 1064 nm.
The equation takes into consideration different scattering contributions mainly due to the different sizes of the scattering centers.
\nAll the parameters are tissue dependent. The values for a generic soft tissue in Table 1 were used in Figure 2.
\ng | \n0.95 | \n
a′ [cm−1] | \n19.1 | \n
fRay | \n0.153 | \n
bMie | \n1.091 | \n
Scattering parameters for a generic tissue [9].
The energy deposited in tissue causes an increase in temperature in the portion of tissue where laser light is absorbed. Naturally, the difference in heat evens out over time. The heat is removed from the volume where absorption of light occurs by active or passive cooling. Active cooling is achieved through blood perfusion, which varies during time according to response of the tissue to heat and is dependent on the perfusion rate and therefore on the tissue type. Passive cooling is due to heat conduction and is described by the second law of thermodynamics which asserts that heat flows spontaneously from hot to cold bodies, in this case from the heated portion of tissue to the portion of tissue at body temperature.
\nIf the delivered energy is high enough, the heat conduction concurs to the progression of the damage since heat conduction can cause tissue temperatures to rise well above the threshold for permanent damage. The threshold for permanent tissue damage is discussed in the following paragraphs.
\nPennes’ equation models heat distribution in the tissue:
\nThe equation describes the heat flow in the tissue as the combination of (passive) heat conduction, (active) heat transport due to blood perfusion and dependent on the temperature difference, metabolic heat source which is the heat produced by the tissue itself, and the external heat source, in this case the laser energy [10, 11, 12].
\nEffects on biological tissues induced by lasers can vary in nature and can be classified in several groups among which are photochemical damage, when light triggers a chemical reaction in the tissue, and thermal effects, when heat is the cause of the outcome. Photochemical damage includes radical formation and tissue inflammation, while examples of thermal damage are protein denaturation and burning. The type of damage triggered depends mainly on the characteristics of the light beam (wavelength, power, pulse properties, exposure time, spot size) and if the beam is collimated, i.e., laser source.
\nThermal effects are caused when the temperature in the tissue is locally increased over the physiological temperature; the threshold is generally set to 40°C. Conditional to the specific tissue properties, beam characteristics and exposure times, the tissue can undergo hyperthermia (<60°C), coagulation, vaporization, carbonization, or pyrolysis. Hyperthermia can be reversible or irreversible depending on the combination of temperature reached and exposure time. Local ablation techniques, such as microwave, radiofrequency, or laser ablation, aim at achieving a temperature of at least 60°C in the whole treated volume, therefore inducing cell death by coagulation; vaporization and carbonization may occur.
\nClassic laser ablation is used to treat solid tumor masses in a variety of organs and aims at heating the whole tumor volume at a temperature of at least 60°C in order to coagulate the tissue in the area to be treated. In this way, near to instant cell death is achieved. An optical fiber is placed in the center of the region of interest, and light is delivered for a period of time of 1–10 minutes depending on the volume to ablate and the device used. The treatment can be repeated directly after to achieve larger coagulation volume either inserting the fiber in a new position or utilizing the so-called pull-back technique, meaning performing a new ablation along the insertion track by pulling the fiber back.
\nImmune stimulating interstitial laser thermotherapy (imILT) is a local ablation method that works at non-coagulating temperatures at the tumor border. The technique consists in creating a temperature gradient in the tumor that results in a heating to 46°C at the tumor border or some millimeters outside it. The temperature is then kept for a prolonged period of approximately 30 minutes to achieve an immunogenic cell death (ICD) at the tumor border, visible only 48–72 hours after treatment, which activates an immune response [13, 14]. An example of ablation achieved performing an imILT treatment is shown in Figure 3. The biological process is not fully understood to date, but the hypothesis is that imILT creates inflammation in the tumor. Damage-associated molecular pattern (DAMP) signal is created, and antigens, which are not coagulated due to the low temperatures, are released [7, 15, 16, 17]. The antigens are picked up by antigen-presenting cells (APCs) that in turn trigger an immune response [18, 19, 20, 21].
\nEffect of imILT treatment on porcine healthy skeletal muscle tissue. Coagulation is achieved within the yellow circle, and immunogenic cell death (ICD) is achieved along the ablation border, between the yellow and the blue line.
The method can in principle be used to treat all types of solid tumors, but some types will be more responsive than others depending on the tumor biology, which is true for immunotherapies in general. Some results from proof-of-concept preclinical and clinical studies are presented in this chapter.
\nThe CE-marked and FDA-approved TRANBERG® Thermal Therapy System for imILT consists of three main parts: a laser generator, a laser applicator, and a thermometry system. The laser generator is a diode-based system that emits light at a wavelength of 1064 nm and with a maximum accessible power of 25 W continuous wave. The unit has a built-in temperature feedback system that is able to measure the temperature in the tissue by means of a minimally invasive temperature probe and to drive the laser emission in order to maintain a stable temperature, set by the user between 43 and 50°C, for a treatment time of up to 30 minutes. The laser applicator consists of a non-cooled optical fiber and an introducer to enable insertion of the fiber in the tissue. The non-cooled optical fiber is available in different tip designs tailored to the ablation volume and shape to be achieved and the tissue to be treated.
\nAll the procedures are performed under image guidance, using MRI, ultrasound, computed tomography (CT), or a combination of the previous depending on the availability of these techniques at the clinic. While it is only possible to perform imILT treatments using ultrasound or CT guidance due to limitations in the temperature probe design, the design of the laser applicator allows laser ablation procedure to be performed with MRI guidance, for example, when performing a focused laser ablation (FLA) for the treatment of early prostate cancer or benign prostatic hyperplasia (BPH).
\nExtensive preclinical studies were performed to prove the immune stimulating effects of imILT. One specific study aimed at comparing the immunologic memory evoked by imILT if compared to resection [22].
\nResearch was conducted on 280 rats divided in four groups: (1) rats with tumor implanted in the liver that were treated with imILT, (2) rats with tumors implanted in the liver that were treated with surgical resection, (3) rats without tumor that were treated with imILT ablating normal liver tissue (sham imILT), and (4) rats without tumors that were treated with resection of a part of a healthy liver (sham resection).
\nRats in groups 1 and 2 were implanted with adenocarcinoma and treated after 6–8 days. A second challenging tumor of the same kind was implanted in another lobe 2, 5, or 10 weeks later, and the animals were followed for up to 48 days after rechallenge unless they showed signs of inactivity or distress earlier. Vital tumor at sacrifice was evaluated together with other immune system markers. Group 1, tumor treated with imILT, showed a distinct behavior if compared with the other three groups. In groups 2, 3, and 4, the challenging tumor, second implanted, displayed a growth so substantial that none of the rats survived for 48 days. On the contrary, rats in group 1 showed eradication of the challenging tumor at day 48. The extent of the tumor burden for the four groups is represented in Figure 4. These findings, combined with results from immunology markers from blood tests, indicate that imILT invokes a strong immune response and an immunologic memory against the treated cancer.
\nTumor burden after implantation of challenging tumor. Only rats having been treated with imILT of primary tumor survived for 48 days after implantation of challenging tumor. All other rats in the 48-day study group had to be euthanized within 10–30 days after the tumor challenge due to extensive tumor. Image: Mats Ekelund.
A number of pre-marketing clinical studies on imILT were performed at Lund University Hospital, Lund, Sweden, where the method was developed for the first time. These studies demonstrated the recruitment of immunocompetent cells in breast cancer patients which indicate a favorable antitumor activity [23, 24, 25, 26, 27].
\nMore recently, initial findings from the clinical study program designed to evaluate the safety and the usability of the method performed using the TRANBERG®|Thermal Therapy System (Clinical Laserthermia Systems, AB, Sweden) were published [28]. A variety of solid tumors are included in the study program; the data was reported after 12 patients were treated, out of which 4 were female and 8 were male. Indications treated were breast cancer (n = 1), breast cancer metastasis (n = 1), colon cancer metastasis (n = 2), malignant melanoma metastasis (n = 2), pancreatic carcinoma (n = 1), and primary pancreatic carcinoma (n = 5); the latter two were treated in open surgery, while the other percutaneously. All the treatments were performed using CT or ultrasound guidance. All patients included in the study underwent numerous previous treatments due to comorbidity. Immunotherapy was delivered on two malignant melanoma patients before imILT treatment but not during the study period.
\nOne serious adverse event was reported out of nine patients within the sponsor initiated clinical study; the frequency of serious adverse events is in line with previous data on other local ablative techniques, including laser ablation [29, 30], indicating that the procedure can be safely performed.
\nUsability results vary among the different study clinics. Preliminary indications suggest that insertion and placement of the instrumentation within the volume to be treated are the main challenge, while sterile access, removal from the tissue, and handling of disposable are perceived as less complicated. Handling of the laser unit needs further investigation as the data is spread [28].
\nThe safety studies were not designed to collect statistically significant efficacy results. Each study included different indications to gather safety data and input to future efficacy studies as extensive as possible leading to a low number of patients per indication, and therefore no indication-based data was published. Future ongoing publications will include indicative efficacy and quality-of-life results from these studies.
\nThis case is a 53-year-old patient with pancreatic cancer diagnosed about 2 years before and treated with first-line chemotherapy, FOLFIRINOX 16 cycles, for tumor reduction. Disease progression was registered after 12 cycles. Due to intolerable toxicity, the treatment regimen was changed to second- and third-line chemotherapies, gemcitabine and protein-bound paclitaxel 16 cycles, after which partial response was achieved. At the time of the first imILT treatment 2 years after the diagnosis, the patient presented with pancreatic carcinoma and three liver metastases (stage IV). PET-CT showed a hypermetabolic focus around the biliary stent, but no clearly visible tumor in the pancreas, and three metastases in the liver (segments VI, V/VI, and V/peri-gallbladder area).
\nThe first treatment was performed on a 19 mm liver metastasis in segment VI that was metabolically active; see Figure 5. The intervention was performed percutaneously under CT guidance, and a first treatment was performed by placing the tip of the radial laser applicator in the metastasis—see Figure 6—and a temperature needle at a distance of approximately 10 mm. The temperature needle was used to regulate the laser emission based on the measured temperature and achieve ICD in a region of the lesion that presented as metabolically active from the PET scan. A temperature of 44–45°C was kept during a period of 30 minutes according to the imILT protocol. A second overlapping ablation was performed after repositioning the laser applicator to necrotize the whole volume of the metastasis. Track ablation was performed to minimize risk for track seeding of tumor cells along the insertion track. A post-procedure CT scan was performed to ensure the ablation of the entire tumor, which was achieved as shown in Figure 7 (black arrow). The patient suffered slight pain and rise in temperature (38°C) posttreatment, but no other discomfort was registered; the patient was discharged after 3 days. No complications were reported during the first 3 months following therapy [31].
\nPET-CT (left) and CT (right) scans showing the position of the treated metastasis during the first treatment session [
Laser applicator positioning visualized using CT scan while placing the instrumentation for the first treatment [
Posttreatment CT that shows the ablation cavity (black arrow) and the biliary stent (white arrow). First treatment session [
Partial response in liver metastasis and total response in pancreas primary tumor were registered 21 months later. However, 3 months later disease progression was noticed, and the patient was treated with imILT for a second time 24 months after the initial treatment. The targeted metastasis was a 35 × 50 mm liver metastasis evaluated at ultrasound at the time of the treatment. The metastasis was treated performing one imILT treatment combined with an overlapping LITT treatment of about 5 minutes to necrotize the whole metastatic mass; the imILT treatment was achieved positioning the radial laser applicator off center within the tumor and the temperature probe at a distance of approximately 11 mm from the applicator. The temperature measured by the probe was kept at 43–45°C for 20 minutes.
\nLastly, a third imILT treatment was performed after 40 months from the first treatment because of new disease progression. A new 20 mm liver metastasis was treated using a diffuser laser applicator combined with an introducer with built-in temperature sensors, which resulted in only one puncture. The laser applicator was inserted in the center of the metastasis, and the sensors were positioned 25 mm from the applicator tip to achieve a lesion of 25–30 mm in diameter. To date, 4 months after the last treatment, no complications connected to the laser treatment have been reported [32].
\nLocal ablation of tumors is receiving increasing attention for the treatment of metastatic disease because of observed effects on distant tumorous masses suggesting the involvement of the immune system following local therapy.
\nOne technique for local tumor eradication is laser ablation which kills the tumor mass by heating the tissue through direct light absorption and heat transfer resulting in tissue coagulation. imILT is an interstitial laser ablation method tailored to evoke an immune response against the treated tumor. The technique utilizes a laser applicator to deliver energy in the form of laser light to the tissue; the energy delivered to the tissue is precisely controlled based on the temperature measured by a sensor inserted in the tissue at the periphery of the tumor to obtain a lower temperature ablation that aims at maximizing the immune cell death (ICD) volume of the ablation.
\nPreclinical results indicate that imILT invokes an immune response against the treated tumor, if compared with resection in a rat tumor model. Clinical studies suggest that the procedure can be safely performed since the frequency of the adverse events is in line with previous data on other local ablation techniques. The case of a pancreatic cancer patient treated with imILT was presented.
\nThis publication was founded and made possible by Clinical Laserthermia Systems AB, Lund, Sweden.
\nCristina Pantaleone is the Technical Manager of Product Development at Clinical Laserthermia Systems, AB.
\nI would like to thank Belarmino Gonçalves for the pictures relative to the case report and Karin Peterson, Gunilla Savring, Emily Emilsson Rossander, Maria Luisa Verteramo, and Dennis Laks for review and support.
\n\n absorption coefficient scattering coefficient anisotropy factor scaling factor that equals the reduced scattering coefficient at 500 nm fraction of Rayleigh scattering scattering power (Mie scattering) tissue density blood density tissue thermal conductivity tissue heat capacity blood heat capacity blood perfusion rate difference between the heated tissue and the blood or the surrounding tissue metabolic heat external heat sources benign prostate hyperplasia damage associated molecular pattern computed tomography immunogenic cell death interstitial laser thermotherapy immune stimulating interstitial laser thermotherapy laser-induced thermotherapy photodynamic therapy
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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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