Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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In 2010, she defended her doctoral thesis, and in 2019 she obtained habilitation and she was granted a Marie Sklodowska-Curie Actions (MSCA) to participate in the Postgraduate School of Industrial Ecology (PSIE) - IndEcol at the Norwegian University of Science and Technology in Trondheim. She has published over 77 papers, including 69 papers in world-known peer-reviewed scientific journals from the JCR list. Her works have been cited more than 300 times and she has an h-index: 15. She is the author of 5 patents. She cooperates with many universities in the field of multidisciplinary projects. 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The report titled ‘Reimagining India’s Media & Entertainment Sector’ published by FICCI and E&Y, 2018 states that OTT subscription is expected to touch 20 billion INR by 2020 in India. The content broadcast over these OTT platforms depict a bold, rough and rugged youth culture crossing the boundaries of sophistication that has remained a part of Indian television screen for a long time. Emergence of transnational OTT platforms like Netflix, Amazon Prime Video, Hulu etc. offer multiple options to the audience for content viewing. The chapter is commentary in nature and aims to address how content streaming on these transnational platforms are framing the new emerging identities of the Indian youth. The protagonists of the stories over these platforms are out rightly rejecting the stereotypical images of young people in the country thus challenging the preoccupied identities of the socio-cultural class distinctions. The chapter attempts to understand contemporary mediated culture and how and in what ways it impacts the young people who are exposed to them regularly. 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1. Introduction
Artificial intelligence (AI) technique is the most effective technology used in the modern healthcare area. The rapidly growing accessibility of healthcare medical data and also the advances of big data diagnostic techniques has completed the potential of the current successful uses of artificial intelligence (AI) in healthcare system. With the help of important medical questions, potential artificial intelligence (AI) techniques can disengage healthcare-appropriate information secreted in the huge quantity of data, which can maintain healthcare decision-making. Modern healthcare technology in various medical areas has spread to the several pioneering startups in the world, which helps people in healthier and longer lives. The advances have initially been determined by the beginning of mobility and software, permitting the health sector to digitize several of the pen- and paper-based processes and operations that are presently held up service release. Nowadays, computer software has become far more intelligent and autonomous. These new abilities are discussed under the same cover of machine learning (ML) and artificial intelligence (AI), which are accelerating the tempo of improvement in healthcare. The applications of machine learning (ML) and artificial intelligence (AI) in healthcare region have allowed the area to employ some of its major challenges in particular domains like drug discovery, personal genetics, and disease identification and management. Every time an innovative technical tool comes into the healthcare system, it also faces several challenges. Most of the common issues of artificial intelligence (AI) technique in healthcare system are regulatory compliance requirements, patient and provider adoption, and also lack of data exchange. The Artificial intelligence (AI) has moved from all of these concerns, reducing the areas in which it can accomplish something. The purpose of artificial intelligence (AI) and machine learning (ML) in healthcare system is redesigning the industry and creating what was once impracticable into a real truth. For artificial intelligence (AI)/machine learning (ML) to take its place in the healthcare system, sustained access to appropriate data is necessary to succeed. Artificial intelligence (AI) can be used to analyze and identify patterns in large and complex datasets faster and more precisely than has previously been possible. It can also be used to search the scientific literature for relevant studies and to combine different kinds of data, for example, to aid drug discovery. Artificial intelligence (AI) health apps have the potential to empower people to evaluate their own symptoms and care for themselves when possible. Artificial intelligence (AI) systems that aim to support people with chronic health conditions or disabilities could increase people’s sense of dignity, independence, and quality of life, and enable people who may otherwise have been admitted to care institutions to stay at home for longer. Artificial intelligence (AI) depends on digital data, so inconsistencies in the availability and quality of data restrict the potential of artificial intelligence (AI). Also, significant computing power is required for the analysis of large and complex datasets. Clinical practice often involves complex judgments and abilities that artificial intelligence (AI) currently is unable to replicate, such as appropriate knowledge and the ability to read social cues. With the help of machine learning process, structured data like genetic data, electro physical data (EP), and imaging data are properly investigated. Machine learning makes the information analytical algorithms to extract characteristics from the input data. Input data generally in machine learning algorithms involve with patient’s natures as well as the intermittently apprehension healing effects. A patient’s nature generally includes bottom line data, such as gender, disease history, age, gene expressions, electrophysiological data (EP) test, analytical imaging, idea test results, and medicinal symptoms. Support vector machine was also applied in cancer diagnosis. Even supposing complicated data, machine learning represents the support for artificial intelligence (AI). At this moment in time, an innovative advancement is happening in the subfield of neural networks. This has created notable interest in various domains of healthcare science, in addition to drug analysis and also the area of public health. Deep neural networks can implement in addition to the most exceptional human clinicians in specific diagnostic tasks. Also, artificial intelligence techniques are already promising in healthcare-based apps, which can be performed by any network machine like modern smart mobile phone. Artificial intelligence has the ability to address imperative health challenges, but it is limited due to the unavailability of good health data. Employing artificial intelligence (AI) involves some ethical issues including the probable for artificial intelligence (AI) to make mistaken assessments and then the question of responsibility occurs.
2. Artificial intelligence (AI) devices
Basically, artificial intelligence (AI) devices are categorized by two main types: the first one is machine learning (ML) category [1], which generally analyses the structured data, for example, electrophysiological data (EP), genetic data, and imaging data. For healthcare applications, the machine learning (ML) processes try to gather patients’ individuality or understand the possibility of the disease effects [2]. The second type of artificial intelligence (AI) device is the natural language processing (NLP) technique [3], which can take out the information from free or unstructured data such as medical observations or health journals to enhance structured health check data. The natural language processing (NLP) processes objects at revolving contents toward the machine-understandable structured records and can then be considered by machine learning (ML) procedures [4]. Figure 1 explains the road plan from medical data making, during natural language processing (NLP) data improvement and machine learning (ML) data investigation, to medical judgment creating. In this figure, the road plan starts and ends with medical activities. As dominant as artificial intelligence (AI) procedures, they can be inspired by medical/healthcare troubles and also be practical to help out the medical performance at the end.
Figure 1.
The road plan from generation of medical data, during natural language processing (NLP) data improvement and machine learning (ML) data investigation.
2.1 Machine learning (ML) processes
Machine learning (ML) builds the data investigative algorithms to extort characteristics from the data. Inputs to machine learning (ML) algorithms consist of patient ‘characters’ and occasionally therapeutic effects of concern. A patient’s characters generally contain bottom line data, for example, gender, age, disease history, and also disease explicit data, for instance, gene expressions, analytical imaging, electrophysiological data (EP) test, objective test results, medication, and medical symptoms. In addition to the attributes of the patients medical results are frequently composed for medical investigation. These contain syndrome pointers, patients’ endurance periods, and quantitative syndrome stages such as the size of tumor. Here jth characteristic of the ith numbers of patient is denoted by Pij and Qi is representing the effect of concern. Regarding whether to integrate the results, machine learning (ML) algorithms can also be separated into two main types: supervised learning and unsupervised learning. One more type is also available named as semisupervised learning. Figure 2 represents all these three types of learning procedures. Unsupervised learning is also identified for feature removal, whereas supervised learning is appropriate for analytical representation by constructing several interactions involving patient individuality (input) and result of concern (output). In recent times, semisupervised learning has been projected as a hybrid involving supervised learning and unsupervised learning, which is appropriate for circumstances wherever the effect is omitted for definite issues.
Figure 2.
Representation of (A) unsupervised learning, (B) supervised learning, and (C) semisupervised learning.
There are two major unsupervised learning techniques available such as (i) principal component analysis (PCA) technique and (ii) clustering technique. Principal component analysis is basically for element reduction, mainly while the characteristic is documented in a huge number of elements, such as the number of genes in a genome-mixt connection revise. Principal component analyses (PCA) project the data on a small number of principal component (PC) guidelines, without trailing in excess of information regarding the issues. Occasionally, PCA is used to decrease the element of the data, after which clustering technique is used to fraction the issues. All these fraction issues with related characteristics are gathered together, without applying any result information. This algorithm’s result output helps the cluster tags for the patients throughout maximizing as well as minimizing the parallel of the patients and also involving the clusters. These accepted clustering algorithms contain (i) Gaussian mixture clustering, (ii) K-means clustering, and (iii) hierarchical clustering. Alternatively, supervised learning reflects on the topics’ outcomes in cooperation with their characteristics and goes via a definite training procedure to find out the finest outputs connected through the inputs, which are nearby the standard outcomes. Generally, the formulations of output contrast through the concern outcomes. Such that, the outcome can be the possibility of receiving an exact clinical result, the projected value of a disease stage or the projected endurance time. Evaluated by unsupervised learning and supervised learning, which offers extra clinically applicable results; therefore Artificial Intelligence (AI) relevance in healthcare system most regularly apply supervised learning. Unsupervised learning may be applied as a component of the preprocessing stage to or find out subgroups or decrease dimensionality, which consecutively makes summarizing supervised learning stage more capable. Appropriate methods contain logistic regression, linear regression, decision tree, naïve Bayes, random forest, discriminate analysis, nearest neighbor, neural network, and support vector machine (SVM). Neural network and SVM are the most accepted supervised learning methods in healthcare applications [5]. The mechanisms of neural networks and support vector machine (SVM) techniques process together with relevant examples in the cardiovascular disease, neurological disease, and cancer.
2.2 Neural network
Neural network is basically known as the expansion of linear regression for confining the difficult nonlinear relationships dividing the input parameters and outcome data. In this neural network, the relations involving the input parameters and the outcome are represented throughout the multiple unknown layer grouping of preindividual functional. The aim is to calculate approximately the weights via input data and also the outcome data so that the average error involving the outcome and their calculation is reduced. Here, this technique is described via following some examples. Neural network was used in stroke diagnosis [6], where the input parameters were given as Xi1, …, Xip and p = 16 stroke-related symptoms, together with acute confusion, problem of vision and mobility, paresthesia of the leg or arm, etc. Yi represents the binary outcome, where Yi = 1/0 represents that the ith patient has or does not have stroke. The output factor of importance is the possibilities of stroke (ai), which represents the equation given below:
ai=hΣK=1Dw2lfkΣl=1pw1lXil+w10+w20E1
In this equation, w10 and w20 are not equal to zero, where Xij, fk = 0; fks and h are prespecified functions, which indicate that the weighted grouping influences the disease threat as a whole. Figure 3 represents the neural network system.
Figure 3.
Neural network diagram.
The instruction’s aim is to find out the weight of wij, which can minimize the calculation in accuracy given by Σi=1nYi−ai2. The minimization can be done via standard optimization algorithms, for instance, local quadratic estimate or gradient decline optimization, which are integrated in both R and MATLAB software. The latest data were issued from the similar population and the results of wij are also applied to calculate the outcomes rooted in their particular characters [7]. This is the same as methods have been applied to identify cancer treatment [8], where the input efforts and outcomes are the principal components (PC) predictable from 6567 genes and the tumor groups. A neural network was applied to identify breast cancer, where the inputs represent the surface information from mammographic images and where the outcomes are tumor indicators [9]. Another problematical neural network model was analyzed to identify Parkinson’s disease derived where the input parameters are motor and nonmotor indications and neuroimages [10].
2.3 The support vector machine (SVM)
The supporting vector machine is mostly applied for categorizing the topics into two different clusters, where the result Yi, Yi = −1 or 1 indicates whether the ith patient is in set 1 or 2 correspondingly. This procedure can be completed for circumstances with more than 2 sets. The fundamental hypothesis is that the subject matters can be divided into two different groups via a decision boundary distinct on the characteristics Xij, which can be represented as:
ai=Σj=1pwjXij+bE2
where wj represents the weight put on the jth characteristic to mark edits’ comparative implication on moving the outcome between the others. If ai > 0, the ith patient is categorized to group 1, that is, Yi = −1; and if ai < 0, the patient is categorized to group 2, that is, Yi = 1. Furthermore, assuming that the new patients come from the same population, the resulting Wj can be applied to classify these new patients based on their traits. An important property of SVM is that the determination of the model parameters is a convex optimization problem so the solution is always global optimum. Additionally, many obtainable rounded optimization technique applications are readily available for the SVM performance. SVM has been widely applied in healthcare research. For example, SVM was used to recognize imaging biomarkers of psychiatric and neurological disease [11]. SVM was also applied in cancer diagnosis [12]. SVM and other statistical methods can also be used to reach early detection of Alzheimer’s syndrome [13]. SVM was applied to analyze the power of an offline human and device interface, which can control the upper-limb prostheses [14].
2.4 Deep learning method
Deep learning method is a contemporary expansion of the traditional neural network method. Figure 4 represents deep learning like a neural network with multicovers.
Figure 4.
Multilayer neural network.
Rapid growth of current computing allowed deep learning for constructing the neural networks along with huge amount of covers, which is impossible for traditional neural networks. Basically, this technique helps to investigate many critical nonlinear models in the information. One more cause for the recent acceptance of deep learning techniques is owing to the enhancement of the critical and volume of data [15]. Dissimilar to the traditional neural network, this process generally applies more hidden levels in order that the algorithms can handle critical data with different structures [5]. In the healthcare applications, the generally applied deep learning algorithms consist of recurrent neural network, convolution neural network technique, deep neural network, and deep belief network. Convolution neural network is the most accepted one in 2016. The convolution neural network is extended to analyzing the ineptitude of the traditional machine learning algorithms when conducting high dimensional data, that is, data with a huge number of characteristics. Conventionally, the machine learning algorithms are considered to examine data when the number of characteristics is little. The image data are physically high dimensional because each image generally includes thousands of pixels as characteristics. One explanation is to present dimension decrease: primarily preselect an object of pixels as elements and then complete the machine learning algorithms on the ensuing lower dimensional traits. However, heuristic feature selection events may drop the information in the images. Unsupervised learning methods such as clustering or PCA can be applied for data-determined dimension decrease. The convolution neural network was first projected the high-dimensional image investigation [16], where the inputs for convolution neural network are the accurately regulated pixel values on the images. The convolution neural network then transmitted the pixel values in the image throughout weighting in the difficulty layers and variety in the subsampling layers instead. The ultimate output is a recursive purpose of the weighted input values. The weights are skilled to reduce the average error involving the predictions and the outcomes. The performance of convolution neural network has been incorporated in trendy software packages such as Caffe from Berkeley AI Research [17] and Tensor Flow from Google [18]. Recently, the convolution neural network has been effectively executed in the healthcare area to help disease identification. It is used to identify the congenital cataract disease throughout learning the ocular images [19], though it has over 90% accuracy on identification and treatment implication. Convolution neural network was performed to identify skin cancer from clinical images [20]. Convolution neural network is applied to identify referable diabetic retinopathy via the retinal fundus photographs [21]. The specificity and sensitivity of the algorithm are both over 90%, which expressed the usefulness of using the method in the analysis of diabetes. It is importance to declare that in all this type of applications, the presentation of the convolution neural network is competitive beside an experienced physician in the truthfulness for categorizes both usual and disease stages.
2.5 Natural language processing
Genetic data and EP plus image are all machine-comprehensible, that is why the machine learning (ML) algorithms can be straightly presented after quality control processes or appropriate preprocessing. Though huge extents of medical data are like descriptive content, like a substantial examination, operative notes, and an experimental laboratory reports and release abstracts, these are formless and inconceivable for computer programming. Below this background, natural language processing (NLP) targets removing helpful data from the descriptive text to support the medical conclusion making [3]. A natural language processing (NLP) pipeline includes two main components: (i) classification and (ii) text processing. During text processing, the natural language processing (NLP) recognizes a sequence of disease-appropriate keywords at clinical remarks related to the past records [22]. After that, keyword subsets are preferred during analyzing their achievements in the arrangement in the normal abnormal cases. The authorized keywords then enter and enhance the controlled information to support medical choice making. The natural language processing pipelines have been developed to help the medical choice making on attentive treatment preparations and monitoring critical effects. For instance, it was showed that establishment of natural language processing, for analyzing the chest X-ray reports would help the antibiotic assistant system to aware physicians for the probable necessitate for anti-infective therapy [23]. Natural language processing was used to mechanically monitor laboratory-based difficult effects. Moreover, the natural language processing pipelines can also assist with disease analysis [24]. A recognized of 14 cerebral aneurysm disease-associated changeable during executing natural language processing (NLP), based on the clinical remarks [25]. Resulting variables are effectively applied for categorizing the common patients and the patients with cerebral problems, with 86% to 95% accuracy rates on the validation and training trials correspondingly. A natural language processing was implemented to extort the peripheral arterial disease-allied keywords from description clinical remarks. The keywords are then applied to categorize the common patients and the patients who have peripheral arterial disease, which reaches over 90% accurate [22].
3. Artificial intelligence (AI) applications in healthcare system
In spite of few limitations, artificial intelligence (AI) are applied in healthcare system. Researchers mainly focus on the region of major three diseases: cardiovascular disease, nervous system disease, and life-threatening cancer also. In cardiology, [26] explained the prospective uses of the AI system for making a diagnosis of the cardiac diseases with the help of cardiac images. Cardiac stroke is a natural and commonly stirring disease that has an effect on more than 500 million people all around the world. It is the most leading cause of death in world. It has also high medical expenses across the world nearly about US$ 689 billion, which causes serious trouble to patient families [27, 28]. For that reason, research on anticipation and medical treatment for stroke has a great impact. Recently, artificial intelligence (AI) processes have been used in additional and supplementary stroke-connected studies. In stroke-concerned cases, AI procedures help in the three main areas: before time for disease calculation and analysis, healing, and in addition to conclusion forecast and diagnosis assessment. About 85% of the time, stroke is caused by cerebral infarction, that is, thrombus in the vessel. For require of finding pre stroke indication, only some patients could obtain appropriate treatment. A movement-detecting device was developed for predicting early stroke [29]. For model structure resolution, two machine learning algorithms like PCA and genetic fuzzy finite state machine are mainly used. The revealing method is attached with a patient human action detection phase and the starting of the stroke detection phase. Ideally, the typical model is remarkably different from the patient movement, and an attentive model that can detect stroke can stimulate and assess medical action and make it immediately feasible. Correspondingly, a device that is wearable was proposed for gathering data for regular and pathological steps for calculation of stroke [30]. The data can be removed and copied by SVM and unseen Markov models, and this algorithm could suitably organize 91% of information to the exact group. For some identification of the stroke, neuro-imaging processes like CT scan and MRI are also essential for disease estimation. Several studies have attempted to concern machine learning techniques to neuro-imaging data to support with stroke analysis. SVM was used in resting-state functional MRI data, where endophenotypes of motor disability behind stroke were classified and recognized [31]. This algorithm can precisely distinguish patients with a precision of 87.6%. T1-weighted MRI, [32] helps to rearrange the stroke injury. This effect is similar for human-proficient physical injury explanation. Kamnitsas et al. [33] attempted 3D CNN aimed at injury fragmentation in multisculpt brain MRI. It likewise used fully associated provisional casual field representation for ultimate postprocessing of the CNN’s soft segmentation plots. With the help of Gaussian process regression method, stroke anatomical MRI images were analyzed,and also establish the vortex pattern performed well than injury load/area like the expecting elements [34]. Machine learning (ML) techniques are also useful to examine stroke patients with CT scans. A free-floating intraluminal thrombus can be created like injury post stroke, and this is complicated to discriminate by carotid sign in CT imaging. Three machine learning (ML) algorithms were used to categorize two quantitative types: shape analysis with linear classification analysis, SVM, and artificial neural network [35]. Machine learning is also used in expecting and evaluating the presentation for stroke cure. In a critical emergency phase determination, the result of intravenous thrombolysis (tPA) has a sturdy link for the diagnosis per durance rate. With CT scan, SVM can be used for expecting whether the patients by thrombolysis (tPA) cure can build up suggestive intracranial hemorrhage [36]. In SVM, complete brain images were used as input, which acted healthier than traditional radiology-based procedures. For improving the medical result making procedure of thrombolysis (tPA) healing, a stroke treatment model was proposed for investigating perform guiding principle, clinical trials and meta-analysis with Bayesian principle network [37]. The model consisted of 56 different types of variables and 3 decisions aimed at investigating the process for analysis, cure, and effective calculation. An interaction tree was used, where the subgroup investigated suitable thrombolysis (tPA) dosage as per patient individuality, taking into consideration the healing efficacy and the possibility of bleeding [38]. Several issues can influence stroke diagnosis and syndrome mortality. Evaluating with traditional methods, machine learning techniques have returns in progressing calculation activity. To enhance and maintain the medical assessment making procedure, a model was proposed for expecting a three-month healing outcome by examining the physiological considerations for the duration of 48 hours following stroke with logistic degeneration [39]. A database was observed with 107 patient’s medical information through acute anterior stroke and also posterior stroke via intra-arterial therapy [18]. Here, the data were examined through SVM and artificial neural network and achieved calculation accurateness of more than 70%. Machine learning procedures was used to recognize the control effect in brain arterio-venous abnormality satisfied with endo-vascular embolization. Though typical degeneration analysis representation could only reach a 43% precision rate, this technique’s exertion is much enhanced with 97.5% exactness. An optimal algorithm was analyzed to calculate 30 days mortality test and gained additional exact calculation than surviving techniques [40]. Likewise, SVM was used to calculate the stroke mortality via discharge. Additionally, the application of the synthetic alternative oversampling procedure was proposed to decrease the stroke effect calculation prejudice reasoned among class inequality between several datasets. Brain images were examined for calculating the effect of stroke cure. CT scan data were examined through machine learning procedure for estimating the cerebral edema through hemispheric infraction [41]. A random forest was constructed to involuntarily recognize the cerebrospinal fluid (CSF) and examined the changes in the CT scan, and this is more precise and capable compared to the traditional procedures. Functional connectivity was extracted from magnetic resonance imaging (MRI) and practical magnetic resonance imaging (MRI) data, and ridge degeneration and multitasking intellect were also applied for cognitive deficit calculation following stroke [42]. A relationship was examined, which involved injuries extorted from magnetic resonance imaging (MRI) and the cure effect through Gaussian method regression technique [43]. The model was used to calculate the difficulty of cognitive damages during stroke and the way of retrieval in due course. In Arterys Cardio DL process, where artificial intelligence (AI) is help to make available programmed and also changeable ventricle segmentations related on traditional MRI of cardiac images [44]. In nervous system disease, an artificial intelligence (AI) method was developed [45] for repairing the regulation of body movement in quadriplegia patients. Farina et al. experienced the control of the offline man–machine edge, which applies the release timings for the spinal motor neurons for controlling the prosthesis of the upper limb. IBM Watson for the oncology diagnosis can be a consistent AI for cancer diagnosis from start to the end, which was explained by Somashekhar et al. [46] by a double-blinded validation study. A clinical image was examined for recognizing skin cancer subtypes [20]. The applications of these three types’ diseases are not absolutely unpredicted. These three diseases are principal death causes; for that reason, analyzing the stages of the disease before time is vital to avoid worsening of the patients’ health condition. Moreover, quick diagnoses can prospectively reach throughout recovering the analysis measures on electrophysiological (EP) or electronic medical record (EMR), imaging and genetic, and this is the major power of the artificial intelligence (AI) technique. Moreover, apart from the three main diseases, artificial intelligence (AI) system has been used in another disease too: to examine the ocular image data for diagnosing inherited cataract diseases [19]. A referable diabetic retinopathy was detected by the retinal fundus photographs [21].
4. Application of artificial intelligence in modern medicine
Artificial intelligence in modern medicine and medial area has been a mostly upcoming hot topic in current years. Although there is wisdom of excessive prospective in the use of artificial intelligence in modern medicine, there are also worries about the defeat of the ‘human touch’ in such an important and person-motivated work. Artificial intelligence in modern medicine denotes to the practice of artificial intelligence tools and programmed procedures in the identification and cure of patients who need care. At the same time as analysis and cure may appear like modest phases, there are numerous other circumstantial procedures that come to pass in demand for a patient designate properly taken to attention, such as:
Collecting information data from patient discussions and checks
Treating and examining outcomes of result
Applying several causes of information data to derive an exact identification
Defining an applicable cure technique
Arranging and controlling the selected cure technique
Patient observing
Rehabilitation, continuation arrangements
Disagreement for enlarged use of artificial intelligence in modern medicine is that reasonably a various of the beyond could be programmed—computerization often means jobs are finished more swiftly, and it also help to frees up the time of a medical expert’s when they could be acting other responsibilities, which cannot be computerized, and hence are appreciated as a more cherished practice of human wealth. For instance, technology application has improved in all regions of daily life. Now, there are unbelievable volumes of tools and robotics in association with modern medicine; all medical information is digitized, online appointments can be arranged, and with the help of different healthcare apps in smartphone, it can be easy to find out nearest medical clinics or any health centers. Artificial intelligence is already being used in healthcare modern medicine nowadays. As a medical assessment support system, DXplain [47] is an artificial intelligence system that can help to perform on a set of medical outcomes like symptoms, marks, laboratory files, etc. to make a hierarchical list of identification that can describe the medical indices. Germwatcher [48] is another artificial intelligent system that is considered to notice, and examine taints in needy patients. In medical robotic surgical technology, the “da Vinci robotic surgical system” [49] with defined movement, robotic arms, and magnetized visualization permits surgeons to perform surgery that is not possible through an exclusively manual method. The probable for enlarged artificial intelligence practice in modern medicine is not objective in a decrease of physical jobs and reducing doctor’s time, growing proficiency and output-it also offers the prospect for healthcare system to change to further accuracy of modern medicine.
5. Alginate and AI in biomedical fields
Smart biomedical and medical packaging with the application of polymers is a generally and rapidly growing area of interest for academia and industries. Among a variety of polymers such as alginate, many uses have been created such as in biomedical field, medicine, packaging, and food sector [50]. For example, in modern drug delivery systems, a mesh completed of nanofibers created by the electro-spinning process is highly desired. Electro-spinning for biomedicine is based on the application of natural substances and biopolymers, along with the mixture of drugs such as sulfisoxazole, naproxen, and essential oils with antibacterial properties such as eugenol and tocopherol. In recent times, there has been an enormous thrust in the usage of biopolymers for a number of applications, especially in the biomedical and also in pharmaceutical areas [51, 52]. The functional effectiveness of the biopolymer molecules depends on the physicochemical properties, structural features, and composition [53]. It is feasible to rationally design the structure and composition of the biopolymer to gain suitable useful features [54]. The internal structure of the polymer molecule determines many functional characteristics, for example permeability, integrity, and chargeability [55]. The strength of the biopolymer particles and their summative capability is influenced by the electrical characteristics. Molecules of biopolymers and their electrical properties influence the contact with other molecules present in the neighboring environment. Alginate is one of the most popular natural biopolymers and intensely studied [56, 57]. It is an anionic biopolymer consisting of units of guluronic acid and mannuronic acid in uneven blocks [58]. Guluronic acid and mannuronic acid are linked by glycosidic linkages [59, 60], whereas the guluronic acid forms α bonds (1 → 4) and β (1 → 4) bonds with mannuronic acid [61]. The stiffness of molecular chains is ensured by the rigid and bent conformations of guluronic acid [62]. Hecth et al. have recently discussed their study on the characterization of calcium alginate and sodium alginate with particular importance on their structure [63]. Different applications and properties of alginate have also been examined. Alginate characteristics used biomedical especially in biomedicine can be formed by adjusting the accessibility of their hydroxyl and carboxyl groups [64]. It influences the characteristics of alginates, such as hydrophobicity, solubility, and their biological activity [65]. Alginate hydrogels were formed by cross-linking polymer chains [66]. The chemical properties of alginate hydrogels were found to depend on the cross-linking density of the chain [67]. The cellular viability of MG-63 osteosarcoma cells was improved by blending alginate bioink solution with N-acetyl cysteine (NAC) [68]. One of the techniques used in the design of alginate hydrogels is intermolecular cross-linking, wherein only the alginate guluronan groups react with the divalent cation, most frequently the calcium used to gel the alginate [69].
6. Conclusions
Artificial intelligence (AI) in healthcare offered a variety of healthcare information results that artificial intelligence (AI) has examined and reviewed the most important types of diseases that artificial intelligence (AI) has arranged. Machine learning (ML) and natural language processing are two major groups of artificial intelligence (AI) devices. For machine learning (ML) process, two most accepted traditional methods are available, that is, neural network and SVM. A typical artificial intelligence (AI) system must have the machine learning (ML) component that can help for conducting the structured data such as EP data, images, and genetic data and another natural language processing (NLP) module for the deduction of unstructured works. The complicated algorithm requires to be taught during the healthcare results previous to the system which can support the physicians for the disease analysis and plans which should be required for treatment. This technique focuses on how computer-oriented assessment methods, within the same roof as artificial intelligence (AI), can help in improving health and clinical area. Even though sophisticated information and machine learning present the base for artificial intelligence (AI), at present, there are revolutionary progresses happening in the subfield of neural networks. This has produced remarkable enthusiasm in several fields of healthcare science, as well as drug analysis and public health. Deep neural networks can execute as well as the most excellent human clinicians in definite diagnostic responsibilities. Additionally, artificial intelligence (AI) tools are already emerging in health-based apps, which can be engaged in handheld, network machines such as smart mobile phones. The major obstructions to be defeated in building health and healthcare data information are the space between digital data and human cognition. Data information regarding an entity patient is mostly gained in forms designed to be available to healthcare personnel. Typical data may consist of MRI or X-ray or ultrasound pictures of the patient, visual records of lung or heart function differing with time, or verbal similes of the patient as seen by the medical personnel. Alternatively, when data are accumulated in data information process and applied, in health research or to expand treatment procedures, it is regularly concentrated to statistical information that is mainly digital. The transfer of analog input into digital output is an oppressive task and may result in a defeat of important information, which would have been cooperative to the consumer.
\n',keywords:"artificial intelligence (AI), machine learning (ML), natural language processing (NLP), medical imaging, SVM",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/70446.pdf",chapterXML:"https://mts.intechopen.com/source/xml/70446.xml",downloadPdfUrl:"/chapter/pdf-download/70446",previewPdfUrl:"/chapter/pdf-preview/70446",totalDownloads:1680,totalViews:0,totalCrossrefCites:10,dateSubmitted:"September 20th 2019",dateReviewed:"November 11th 2019",datePrePublished:"December 12th 2019",datePublished:"February 5th 2020",dateFinished:"December 12th 2019",readingETA:"0",abstract:"Artificial intelligence (AI) has the potential of detecting significant interactions in a dataset and also it is widely used in several clinical conditions to expect the results, treat, and diagnose. Artificial intelligence (AI) is being used or trialed for a variety of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery. In this chapter, we will discuss the application of artificial intelligence (AI) in modern healthcare system and the challenges of this system in detail. Different types of artificial intelligence devices are described in this chapter with the help of working mechanism discussion. Alginate, a naturally available polymer found in the cell wall of the brown algae, is used in tissue engineering because of its biocompatibility, low cost, and easy gelation. It is composed of α-L-guluronic and β-D-manuronic acid. To improve the cell-material interaction and erratic degradation, alginate is blended with other polymers. Here, we discuss the relationship of artificial intelligence with alginate in tissue engineering fields.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/70446",risUrl:"/chapter/ris/70446",signatures:"Sudipto Datta, Ranjit Barua and Jonali Das",book:{id:"8019",type:"book",title:"Alginates",subtitle:"Recent Uses of This Natural Polymer",fullTitle:"Alginates - Recent Uses of This Natural Polymer",slug:"alginates-recent-uses-of-this-natural-polymer",publishedDate:"February 5th 2020",bookSignature:"Leonel Pereira",coverURL:"https://cdn.intechopen.com/books/images_new/8019.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78985-642-2",printIsbn:"978-1-78985-641-5",pdfIsbn:"978-1-83968-558-3",isAvailableForWebshopOrdering:!0,editors:[{id:"279788",title:"Dr.",name:"Leonel",middleName:null,surname:"Pereira",slug:"leonel-pereira",fullName:"Leonel Pereira"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"304764",title:"Dr.",name:"Sudipto",middleName:null,surname:"Datta",fullName:"Sudipto Datta",slug:"sudipto-datta",email:"dattadip440v@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Artificial intelligence (AI) devices",level:"1"},{id:"sec_2_2",title:"2.1 Machine learning (ML) processes",level:"2"},{id:"sec_3_2",title:"2.2 Neural network",level:"2"},{id:"sec_4_2",title:"2.3 The support vector machine (SVM)",level:"2"},{id:"sec_5_2",title:"2.4 Deep learning method",level:"2"},{id:"sec_6_2",title:"2.5 Natural language processing",level:"2"},{id:"sec_8",title:"3. Artificial intelligence (AI) applications in healthcare system",level:"1"},{id:"sec_9",title:"4. Application of artificial intelligence in modern medicine",level:"1"},{id:"sec_10",title:"5. Alginate and AI in biomedical fields",level:"1"},{id:"sec_11",title:"6. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'James G, Witten D, Hastie T, et al. An Introduction to Statistical Learning with Applications in R. 1st ed. Springer; 2013'},{id:"B2",body:'Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. 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Centre for Healthcare Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, India
Department of Chemistry, Calcutta University, Uttarpara, Hooghly, India
'}],corrections:null},book:{id:"8019",type:"book",title:"Alginates",subtitle:"Recent Uses of This Natural Polymer",fullTitle:"Alginates - Recent Uses of This Natural Polymer",slug:"alginates-recent-uses-of-this-natural-polymer",publishedDate:"February 5th 2020",bookSignature:"Leonel Pereira",coverURL:"https://cdn.intechopen.com/books/images_new/8019.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78985-642-2",printIsbn:"978-1-78985-641-5",pdfIsbn:"978-1-83968-558-3",isAvailableForWebshopOrdering:!0,editors:[{id:"279788",title:"Dr.",name:"Leonel",middleName:null,surname:"Pereira",slug:"leonel-pereira",fullName:"Leonel Pereira"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"174758",title:"Prof.",name:"Rudolf",middleName:null,surname:"Riehl",email:"rrriehl@feb.unesp.br",fullName:"Rudolf Riehl",slug:"rudolf-riehl",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"2",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"Sao Paulo State University",institutionURL:null,country:{name:"Brazil"}}},booksEdited:[],chaptersAuthored:[{id:"48509",title:"A New FPN Cancellation Circuit for Time-Domain CMOS Image Sensors",slug:"a-new-fpn-cancellation-circuit-for-time-domain-cmos-image-sensors",abstract:"A fixed-pattern noise correction technique for time-domain CMOS imagers with high dynamic range is presented in this chapter. 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Ulson, José Eduardo C.\nCastanho, Bruno Albuquerque de Castro and Rudolf Riehl",authors:[{id:"39440",title:"Dr.",name:"Fernando",surname:"de Souza Campos",fullName:"Fernando de Souza Campos",slug:"fernando-de-souza-campos",email:"fcampos@feb.unesp.br"},{id:"152326",title:"Dr.",name:"José Alfredo",surname:"Covolan Ulson",fullName:"José Alfredo Covolan Ulson",slug:"jose-alfredo-covolan-ulson",email:"ulson@feb.unesp.br"},{id:"152327",title:"Dr.",name:"José Eduardo",surname:"Cogo Castanho",fullName:"José Eduardo Cogo Castanho",slug:"jose-eduardo-cogo-castanho",email:"castanho@feb.unesp.br"},{id:"174758",title:"Prof.",name:"Rudolf",surname:"Riehl",fullName:"Rudolf Riehl",slug:"rudolf-riehl",email:"rrriehl@feb.unesp.br"},{id:"175721",title:"MSc.",name:"Bruno",surname:"Albuquerque De Castro",fullName:"Bruno Albuquerque De Castro",slug:"bruno-albuquerque-de-castro",email:"brunocastro@hotmail.com"}],book:{id:"4607",title:"Optoelectronics",slug:"optoelectronics-materials-and-devices",productType:{id:"1",title:"Edited Volume"}}},{id:"49535",title:"Analysis and Methodology for Determining the Parasitic Capacitances in VSI-fed IM Drives Based on PWM Technique",slug:"analysis-and-methodology-for-determining-the-parasitic-capacitances-in-vsi-fed-im-drives-based-on-pw",abstract:"Three-phase induction motors present stray capacitances. The aim of this chapter is to present a methodology to experimentally determine these capacitances and also evaluate the effects of electromagnetic interference on motors in common mode. The proposed procedures for this methodology consist of: a) identifying the motor equivalent electrical circuit parameters through characteristic tests performed in the laboratory; b) setting up configurations between the PWM inverter and the motor for voltage and current measurements: common mode and shaft voltages, leakage and shaft (bearing) currents by using a dedicated measuring circuit; c) calculating the parasitic capacitance values between stator and frame, stator and rotor, rotor and frame and bearings of the motor using the capacitance characteristic equation; d) using the dedicated software Pspice to simulate the system composed by the three-phase induction motor fed by PWM inverter with the equivalent electrical circuit parameters; e) determining the characteristic waveforms involved in the common mode phenomenon.",signatures:"Rudolf Ribeiro Riehl, Fernando de Souza Campos, Alceu Ferreira\nAlves and Ernesto Ruppert Filho",authors:[{id:"39440",title:"Dr.",name:"Fernando",surname:"de Souza Campos",fullName:"Fernando de Souza Campos",slug:"fernando-de-souza-campos",email:"fcampos@feb.unesp.br"},{id:"174758",title:"Prof.",name:"Rudolf",surname:"Riehl",fullName:"Rudolf Riehl",slug:"rudolf-riehl",email:"rrriehl@feb.unesp.br"},{id:"37044",title:"Prof.",name:"Ernesto",surname:"Ruppert",fullName:"Ernesto Ruppert",slug:"ernesto-ruppert",email:"ruppert@fee.unicamp.br"},{id:"177415",title:"Prof.",name:"Alceu",surname:"Ferreira Alves",fullName:"Alceu Ferreira Alves",slug:"alceu-ferreira-alves",email:"alceu@feb.unesp.br"}],book:{id:"4688",title:"Induction Motors",slug:"induction-motors-applications-control-and-fault-diagnostics",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"16260",title:"Prof.",name:"Yong-Gang",surname:"Zhang",slug:"yong-gang-zhang",fullName:"Yong-Gang Zhang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"16262",title:"Prof.",name:"Yi",surname:"Gu",slug:"yi-gu",fullName:"Yi Gu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"101486",title:"Prof.",name:"Mikhail E.",surname:"Belkin",slug:"mikhail-e.-belkin",fullName:"Mikhail E. Belkin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"102312",title:"Prof.",name:"Shiyong",surname:"Yang",slug:"shiyong-yang",fullName:"Shiyong Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Institute of Chemistry",institutionURL:null,country:{name:"Slovakia"}}},{id:"153340",title:"Dr.",name:"Jin-Gang",surname:"Liu",slug:"jin-gang-liu",fullName:"Jin-Gang Liu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"173809",title:"Dr.",name:"Fuxing",surname:"Gu",slug:"fuxing-gu",fullName:"Fuxing Gu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173809/images/4332_n.jpg",biography:null,institutionString:null,institution:{name:"University of Shanghai for Science and Technology",institutionURL:null,country:{name:"China"}}},{id:"175835",title:"Prof.",name:"Alexander S.",surname:"Sigov",slug:"alexander-s.-sigov",fullName:"Alexander S. Sigov",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"175836",title:"Dr.",name:"Leonid",surname:"Belkin",slug:"leonid-belkin",fullName:"Leonid Belkin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"175837",title:"Dr.",name:"Alexey",surname:"Loparev",slug:"alexey-loparev",fullName:"Alexey Loparev",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"176235",title:"Dr.",name:"Vladimir",surname:"Iakovlev",slug:"vladimir-iakovlev",fullName:"Vladimir Iakovlev",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"open-access-funding-funders-list",title:"List of Funders by Country",intro:"
If your research is financed through any of the below-mentioned funders, please consult their Open Access policies or grant ‘terms and conditions’ to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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IMPORTANT: You must be a member or grantee of the listed funders in order to apply for their Open Access publication funds. Do not attempt to contact the funders if this is not the case.
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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The model parameters deciphered here are the amplitude coefficient (k), horizontal location (x0), depth of the body (z), and shape (q). Inversion of the model parameter suggests that constraining the horizontal location and the shape factor offers the most reliable results. Investigation of convergence rate, histogram, and cross-plot examination suggest that the interpretation method developed for the self-potential anomalies is stable and the model parameters are within the estimated ambiguity. Inversion of synthetic noise-free and noise-corrupted data for single structures and multiple structures in addition to real field information exhibits the viability of the method. The model parameters estimated by the present technique were in good agreement with the real parameters. The method has been used to invert two field examples (Sulleymonkoy anomaly, Ergani, Turkey, Senneterre area of Quebec, Canada) with application of subsurface mineralized bodies. 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In fact, under some specific conditions the NCMs could be used either as effective adsorbent material or alternative source of minerals. This chapter presents an outline of a general review of factors that affect the application ability of NCMs and a descriptive analysis of NH4+ and REE adsorption behavior and extraction of rare earth elements (REE) by an ion-exchange with NH4+ ions onto NCMs. Clays and NCMs both effectively remove various contaminants from aqueous solution and serve as alternative sources of minerals, as extensively discussed in this chapter. This review compiles thorough literature of current research and highlights the key findings of adsorption (NH4+ and REE) that use different NCMs as adsorbents or alternative sources of minerals (i.e., REE). The review confirmed that NCMs excellently remove different cations pollutants and have significant potential as alternative source of REE. However, modification and further development of NCMs applications for getting the best adsorption and the best extraction of REE onto NCMs, which would enhance pollution control and leaching system is still needed.",book:{id:"7315",slug:"minerals",title:"Minerals",fullTitle:"Minerals"},signatures:"Aref Alshameri, Xinghu Wei, Hailong Wang, Yang Fuguo, Xin Chen, Hongping He, Chunjie Yan and Feng Xu",authors:[{id:"172947",title:"Prof.",name:"Xin",middleName:null,surname:"Chen",slug:"xin-chen",fullName:"Xin Chen"},{id:"250327",title:"Dr.",name:"Aref",middleName:null,surname:"Alshameri",slug:"aref-alshameri",fullName:"Aref Alshameri"},{id:"306625",title:"Dr.",name:"Aref",middleName:null,surname:"Alshameri",slug:"aref-alshameri",fullName:"Aref Alshameri"},{id:"306656",title:"Prof.",name:"Fuguo",middleName:null,surname:"Yang",slug:"fuguo-yang",fullName:"Fuguo Yang"},{id:"306658",title:"Dr.",name:"Wei",middleName:null,surname:"Xinghu",slug:"wei-xinghu",fullName:"Wei Xinghu"},{id:"306660",title:"Prof.",name:"Wang",middleName:null,surname:"Hailong",slug:"wang-hailong",fullName:"Wang Hailong"},{id:"306664",title:"Prof.",name:"Yan",middleName:null,surname:"Chunjie",slug:"yan-chunjie",fullName:"Yan Chunjie"},{id:"306665",title:"Dr.",name:"Xu",middleName:null,surname:"Feng",slug:"xu-feng",fullName:"Xu Feng"},{id:"306671",title:"Prof.",name:"He",middleName:null,surname:"Hongping",slug:"he-hongping",fullName:"He Hongping"}]}],mostDownloadedChaptersLast30Days:[{id:"71052",title:"Enhanced Humidity Sensing Response in Eu3+-Doped Iron-Rich CuFe2O4: A Detailed Study of Structural, Microstructural, Sensing, and Dielectric Properties",slug:"enhanced-humidity-sensing-response-in-eu-sup-3-sup-doped-iron-rich-cufe-sub-2-sub-o-sub-4-sub-a-deta",totalDownloads:596,totalCrossrefCites:7,totalDimensionsCites:7,abstract:"The CuFe(2−x)EuxO4 (where x = 0.00, 0.01, 0.02, 0.03) nanoparticles are synthesized by solution combustion method. The influence of Eu3+ on the structural, morphological, dielectrical, and humidity sensing study is recorded. The XRD pattern peaks of the as-prepared CuFe(2−x)EuxO4 (where x = 0.00, 0.01, 0.02, 0.03) nanoparticle confirm the polycrystalline spinel cubic structure with a small amount of CuO impurity phase at 38.87° and 48.96°. Surface morphology of the samples was studied by scanning electron microscope (SEM) images of the nanoparticles, and their respective average grain size was estimated using Image software. Chemical composition of all prepared samples was analyzed by EDS spectra. The dielectric parameters of AC conductivity, electric modulus, and impedance of the samples were measured over a range of frequencies from 0.1 KHz to 1 MHz at room temperature. Europium-doped copper ferrite samples showed good humidity sensing response, response and recover times, and stability over a %RH range of 11–91%. These types of samples are very useful for sensor application, battery applications, electronic applications, and automotive applications.",book:{id:"9247",slug:"mineralogy-significance-and-applications",title:"Mineralogy",fullTitle:"Mineralogy - Significance and Applications"},signatures:"I.C. Sathisha, K. Manjunatha, V. Jagadeesha Angadi, B. Chethan, Y.T. Ravikiran, Vinayaka K. Pattar, S.O. Manjunatha and Shidaling Matteppanavar",authors:[{id:"266255",title:"Dr.",name:"Veerabhadrappa",middleName:null,surname:"Jagadeesha Angadi",slug:"veerabhadrappa-jagadeesha-angadi",fullName:"Veerabhadrappa Jagadeesha Angadi"},{id:"321561",title:"Dr.",name:"I.C.",middleName:null,surname:"Sathisha",slug:"i.c.-sathisha",fullName:"I.C. Sathisha"},{id:"321562",title:"Dr.",name:"K.",middleName:null,surname:"Manjunatha",slug:"k.-manjunatha",fullName:"K. Manjunatha"},{id:"321564",title:"Dr.",name:"B.",middleName:null,surname:"Chethan",slug:"b.-chethan",fullName:"B. Chethan"},{id:"321565",title:"Dr.",name:"Y.T.",middleName:null,surname:"Ravikiran",slug:"y.t.-ravikiran",fullName:"Y.T. Ravikiran"},{id:"321566",title:"Dr.",name:"Vinayaka K.",middleName:null,surname:"Pattar",slug:"vinayaka-k.-pattar",fullName:"Vinayaka K. Pattar"},{id:"321567",title:"Dr.",name:"S.O.",middleName:null,surname:"Manjunatha",slug:"s.o.-manjunatha",fullName:"S.O. Manjunatha"},{id:"321568",title:"Dr.",name:"Shidaling",middleName:null,surname:"Matteppanavar",slug:"shidaling-matteppanavar",fullName:"Shidaling Matteppanavar"}]},{id:"65826",title:"Introductory Chapter: Mineral Exploration from the Point of View of Geophysicists",slug:"introductory-chapter-mineral-exploration-from-the-point-of-view-of-geophysicists",totalDownloads:1635,totalCrossrefCites:3,totalDimensionsCites:3,abstract:null,book:{id:"7315",slug:"minerals",title:"Minerals",fullTitle:"Minerals"},signatures:"Khalid S. Essa and Marc Munschy",authors:[{id:"102766",title:"Prof.",name:"Khalid S.",middleName:null,surname:"Essa",slug:"khalid-s.-essa",fullName:"Khalid S. Essa"},{id:"292929",title:"Prof.",name:"Marc",middleName:null,surname:"Munschy",slug:"marc-munschy",fullName:"Marc Munschy"}]},{id:"69811",title:"Chemical Synthesis and Characterization of Luminescent Iron Oxide Nanoparticles and Their Biomedical Applications",slug:"chemical-synthesis-and-characterization-of-luminescent-iron-oxide-nanoparticles-and-their-biomedical",totalDownloads:564,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The syntheses and characterizations of biocompatible luminescent magnetic iron oxide nanoparticles has drawn particular attention as diagnostic and drug delivery tools for treatment of cancer and many other diseases. This chapter focuses on the chemical synthetic methods, magnetic and luminescent properties, including the biomedical applications of iron oxide nanomaterials and luminescent magnetic iron oxide-based nanocomposite materials. The influences of functionalizing with short ligands such as dopamine and L-cysteine on the magnetic properties of synthesized nanoparticles are described. The chapter contains some data on necessary reagents and protocols for bioconjugation aimed at cell culture and step by step the MTT assays used to evaluate cytotoxicity are also presented. In the final section of the chapter, we focus on the biomedical applications specifically for diagnosis and treatment of breast cancer treatment. This chapter also investigates the application of various characterization techniques for analysis of the structural, optical and magnetic properties of the iron oxide nanoparticles and as their nanocomposites.",book:{id:"9247",slug:"mineralogy-significance-and-applications",title:"Mineralogy",fullTitle:"Mineralogy - Significance and Applications"},signatures:"Martin Onani, Leandre Brandt and Zuraan Paulsen",authors:[{id:"258023",title:"Dr.",name:"Martin",middleName:null,surname:"Onani",slug:"martin-onani",fullName:"Martin Onani"},{id:"302723",title:"Dr.",name:"Leandré Bianca",middleName:null,surname:"Brandt",slug:"leandre-bianca-brandt",fullName:"Leandré Bianca Brandt"},{id:"302725",title:"MSc.",name:"Zuraan",middleName:null,surname:"Paulsen",slug:"zuraan-paulsen",fullName:"Zuraan Paulsen"}]},{id:"27429",title:"An Introduction to Mineralogy",slug:"an-introduction-to-mineralogy",totalDownloads:6621,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"1600",slug:"an-introduction-to-the-study-of-mineralogy",title:"An Introduction to the Study of Mineralogy",fullTitle:"An Introduction to the Study of Mineralogy"},signatures:"Cumhur Aydinalp",authors:[{id:"98959",title:"Prof.",name:"Cumhur",middleName:"---",surname:"Aydinalp",slug:"cumhur-aydinalp",fullName:"Cumhur Aydinalp"}]},{id:"27435",title:"A Review of Pathological Biomineral Analysis Techniques and Classification Schemes",slug:"a-review-of-pathological-biomineral-analysis-techniques-and-classification-schemes",totalDownloads:4303,totalCrossrefCites:1,totalDimensionsCites:6,abstract:null,book:{id:"1600",slug:"an-introduction-to-the-study-of-mineralogy",title:"An Introduction to the Study of Mineralogy",fullTitle:"An Introduction to the Study of Mineralogy"},signatures:"Maria Luigia Giannossi and Vito Summa",authors:[{id:"101919",title:"PhD.",name:"Maria Luigia",middleName:null,surname:"Giannossi",slug:"maria-luigia-giannossi",fullName:"Maria Luigia Giannossi"},{id:"108348",title:"Dr.",name:"Vito",middleName:null,surname:"Summa",slug:"vito-summa",fullName:"Vito Summa"}]}],onlineFirstChaptersFilter:{topicId:"651",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81626",title:"Use of Natural Safiot Clay for the Removal of Chemical Substances from Aqueous Solutions by Adsorption: A Combined Experimental and Theoretical Study",slug:"use-of-natural-safiot-clay-for-the-removal-of-chemical-substances-from-aqueous-solutions-by-adsorpti",totalDownloads:24,totalDimensionsCites:0,doi:"10.5772/intechopen.101605",abstract:"The main objective of this work was to investigate the potential of Natural Safiot Clay (NSC), as an adsorbent for the removal of two cationic dyes such as Basic Blue 9 (BB9) and Basic Yellow 28 (BY28) from single and binary systems in aqueous solutions. For this, the effects of three factors controlling the adsorption process, such as initial dye concentration, adsorbent dose, and initial pH on the adsorption extent, were investigated and examined. The natural safiot clay was characterized using the following technique: energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), DRX, and Fourier transform infrared (FT-IR) and pH of the point of zero charge (pHZPC). Energy-dispersive X-ray spectroscopy results indicate high percentages of Silica and Alumina. FT-IR spectrum identified kaolinite as the major mineral phase in the presence of quartz, calcite, and dolomite. The quantum theoretical study confirms the experimental results, through the study of the global and local reactivity and the electrophilicity power of the dyes. The electrophilicity power of dyes affects the removal efficiency. The theoretical study proves that BB9 (ω = 6.178) is more electrophilic than BY28 (ω = 2.480) and more interactions with surface sites. The results of the molecular dynamics simulation indicate that the dyes are adsorbed parallel to the surface of natural Safi clay (kaolinite), implying the strong interaction with the kaolinite atoms. All the results of quantum chemistry calculations and simulations of molecular dynamics are in perfect agreement with the results of the experimental study.",book:{id:"11137",title:"Mineralogy",coverURL:"https://cdn.intechopen.com/books/images_new/11137.jpg"},signatures:"Aziz El Kassimi, Mohammadine El Haddad, Rachid Laamari, Mamoune El Himri, Youness Achour and Hicham Yazid"},{id:"80866",title:"Normative Mineralogy Especially for Shales, Slates, and Phyllites",slug:"normative-mineralogy-especially-for-shales-slates-and-phyllites",totalDownloads:44,totalDimensionsCites:0,doi:"10.5772/intechopen.102346",abstract:"First, an insight into normative mineralogy and the most important methods for calculating the standard or norm minerals, such as the CIPW norm, is given. This is followed by a more detailed explanation of “slatenorm” and “slatecalculation” for low and very low metamorphic rocks, such as phyllites, slates, and shales. They are particularly suitable for fine-grained rocks where the mineral content is difficult to determine. They enable the determination of a virtual mineral inventory from full chemical analysis, including the values of carbon dioxide (CO2), carbon (C), and sulfur (S). The determined norm or standard minerals include the minerals—feldspars, carbonates, micas, hydro-micas, chlorites, ore minerals, and quartz. The advantages of slatenorm and slatecalculation compared to other methods for calculating normal minerals of sedimentary rocks are discussed.",book:{id:"11137",title:"Mineralogy",coverURL:"https://cdn.intechopen.com/books/images_new/11137.jpg"},signatures:"Hans Wolfgang Wagner"},{id:"80770",title:"Mg-Ilmenite from Kimberlites, Its Origin",slug:"mg-ilmenite-from-kimberlites-its-origin",totalDownloads:57,totalDimensionsCites:0,doi:"10.5772/intechopen.102676",abstract:"The main regularities of the saturation of kimberlite rocks with the accessory mineral Mg-ilmenite (Ilm), the peculiarities of the distribution of Ilm compositions in individual pipes, in different clusters of pipes, in diamondiferous kimberlite fields, are considered as the example of studies carried out within the Yakutian kimberlite province (Siberian Craton). Interpretation of different crystallization trends in MgO-Cr2O3 coordinates (conventionally named “Haggerty’s parabola”, “Steplike”, “Hockey stick”, as well as the peculiarities of heterogeneity of individual zonal and polygranular Ilm macrocrysts made it possible to propose a three-stage model of crystallization Ilm: (1) Mg-Cr poor ilmenite crystallizing from a primitive asthenospheric melt; (2) Continuing crystallization in the lithospheric contaminated melt by MgO and Cr2O3; (3) Ilmenite subsequently underwent sub-solidus recrystallization in the presence of an evolved kimberlite melt under increasing oxygen fugacity (ƒO2) conditions.",book:{id:"11137",title:"Mineralogy",coverURL:"https://cdn.intechopen.com/books/images_new/11137.jpg"},signatures:"Sergey I. Kostrovitsky"},{id:"80553",title:"Investigation of Accessory Minerals from the Blatná Granodiorite Suite, Bohemian Massif, Czech Republic",slug:"investigation-of-accessory-minerals-from-the-blatn-granodiorite-suite-bohemian-massif-czech-republic",totalDownloads:48,totalDimensionsCites:0,doi:"10.5772/intechopen.102628",abstract:"The Central Bohemian magmatic complex belongs to the Central European Variscan belt. The granitic rocks of this plutonic complex are formed by several suites of granites, granodiorites, and tonalites, together with small bodies of gabbros, gabbro diorites, and diorites. The granodiorites of the Blatná suite are high-K, calc-alkaline to shoshonitic, and metaluminous to slightly peraluminous granitic rocks. Compared to the common I-type granites, granodiorites of the Blatná suite are enriched in Mg (1.0–3.4 wt.% MgO), Ba (838–2560 ppm), Sr. (257–506 ppm), and Zr (81–236 ppm). For granodiorites of the Blatná suite is assemblage of apatite, zircon, titanite, and allanite significant. Zircon contains low Hf concentrations (1.1–1.7 wt.% HfO2). The composition of titanite ranges from 83 to 92 mol.% titanite end-member. Allanite is relatively Al-poor and displays Feox. ratio 0.2–0.5.",book:{id:"11137",title:"Mineralogy",coverURL:"https://cdn.intechopen.com/books/images_new/11137.jpg"},signatures:"Miloš René"},{id:"80423",title:"Minerals as Prebiotic Catalysts for Chemical Evolution towards the Origin of Life",slug:"minerals-as-prebiotic-catalysts-for-chemical-evolution-towards-the-origin-of-life",totalDownloads:106,totalDimensionsCites:0,doi:"10.5772/intechopen.102389",abstract:"A transition from geochemistry to biochemistry has been considered as a necessary step towards the emergence of primordial life. Nevertheless, how did this transition occur is still elusive. The chemistry underlying this transition is likely not a single event, but involves many levels of creation and reconstruction, finally reaching the molecular, structural, and functional buildup of complexity. Among them, one apparent question is: how the biochemical catalytic system emerged from the mineral-based geochemical system? Inspired by the metal–ligand structures in metalloenzymes, many researchers have proposed that transition metal sulfide minerals could have served as structural analogs of metalloenzymes for catalyzing prebiotic redox conversions. This assumption has been tested and verified to some extent by several studies, which focused on using Earth-abundant transition metal sulfides as catalysts for multi-electron C and N conversions. The progress in this field will be introduced, with a focus on the CO2 fixation and ammonia synthesis from nitrate/nitrite reduction and N2 reduction. Recently developed methods for screening effective mineral catalysts were also reviewed.",book:{id:"11137",title:"Mineralogy",coverURL:"https://cdn.intechopen.com/books/images_new/11137.jpg"},signatures:"Yamei Li"},{id:"80338",title:"Ionic Conductivity of Strontium Fluoroapatites Co-doped with Lanthanides",slug:"ionic-conductivity-of-strontium-fluoroapatites-co-doped-with-lanthanides",totalDownloads:54,totalDimensionsCites:0,doi:"10.5772/intechopen.102410",abstract:"Britholites derivatives of apatite’s that contain lanthanium and neodymium in the serial compounds Sr8La2−xNdx(PO4)4(SiO4)2F2 with 0 ≤ x ≤ 2 were subject of the present investigation. The solid state reaction was the route of preparing these materials. Several techniques were employed for the analysis and characterization of the synthesized powders. The chemical analysis results indicated that molar ratio Sr+La+NdP+Si was of about 1.67 value of a stoichiometric powder. The X-ray diffraction data showed single-phase apatites crystallizing in hexagonal structure with P63/m space group were successively obtained. Moreover, the substitution of lanthanium by neodymium in strontium phosphosilicated fluorapatite was total. This was confirmed by the a and c lattice parameters contraction when (x) varies coherently to the sizes of the two cations. The infrared spectroscopy and the 31P NMR (MAS) exhibited the characteristic bands of phosphosilicated fluorapatite. The pressureless sintering of the material achieved a maximum of 89% relative density. The sintered specimens indicated that the Nd content as well as the heating temperature affected the ionic conduction of the materials and the maximum was 1.73 × 10−6 S cm−1 obtained at 1052 K for x = 2.",book:{id:"11137",title:"Mineralogy",coverURL:"https://cdn.intechopen.com/books/images_new/11137.jpg"},signatures:"Khouloud Kthiri, Mohammed Mehnaoui, Samira Jebahi, Khaled Boughzala and Mustapha Hidouri"}],onlineFirstChaptersTotal:10},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. 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He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. 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Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. 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His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. 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He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. 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