Range of hyperparameter for TIMIT dataset during validation.
\r\n\tHomeostasis is brought about by a natural resistance to change when already in the optimal conditions, and equilibrium is maintained by many regulatory mechanisms. All homeostatic control mechanisms have at least three interdependent components for the variable to be regulated: a receptor, a control center, and an effector. The receptor is the sensing component that monitors and responds to changes in the environment, either external or internal. Receptors include thermoreceptors and mechanoreceptors. Control centers include the respiratory center and the renin-angiotensin system. An effector is a target acted on to bring about the change back to the normal state. At the cellular level, receptors include nuclear receptors that bring about changes in gene expression through up-regulation or down-regulation and act in negative feedback mechanisms. An example of this is in the control of bile acids in the liver.
\r\n\tSome centers, such as the renin-angiotensin system, control more than one variable. When the receptor senses a stimulus, it reacts by sending action potentials to a control center. The control center sets the maintenance range—the acceptable upper and lower limits—for the particular variable, such as temperature. The control center responds to the signal by determining an appropriate response and sending signals to an effector, which can be one or more muscles, an organ, or a gland. When the signal is received and acted on, negative feedback is provided to the receptor that stops the need for further signaling.
\r\n\tThe cannabinoid receptor type 1 (CB1), located at the presynaptic neuron, is a receptor that can stop stressful neurotransmitter release to the postsynaptic neuron; it is activated by endocannabinoids (ECs) such as anandamide (N-arachidonoylethanolamide; AEA) and 2-arachidonoylglycerol (2-AG) via a retrograde signaling process in which these compounds are synthesized by and released from postsynaptic neurons, and travel back to the presynaptic terminal to bind to the CB1 receptor for modulation of neurotransmitter release to obtain homeostasis.
\r\n\tThe polyunsaturated fatty acids (PUFAs) are lipid derivatives of omega-3 (docosahexaenoic acid, DHA, and eicosapentaenoic acid, EPA) or of omega-6 (arachidonic acid, ARA) and are synthesized from membrane phospholipids and used as a precursor for endocannabinoids (ECs) mediate significant effects in the fine-tuning adjustment of body homeostasis.
\r\n\t
\r\n\tThe aim of this book is to discuss further various aspects of homeostasis, information that we hope to be useful to scientists, clinicians, and the wider public alike.
ASR system has two important tasks—phoneme recognition and whole-word decoding. In ASR, the relationship between the speech signal and phones is established in two different steps [1]. In the first step, useful features are extracted from the speech signal on the basis of prior knowledge. This phase is known as information selection or dimensionality reduction phase. In this, the dimensionality of the speech signal is reduced by selecting the information based on task-specific knowledge. Highly specialized features like MFCC [2] are preferred choice in traditional ASR systems. In the second step, discriminative models estimate the likelihood of each phoneme. In the last, word sequence is recognized using discriminative programming technique. Deep learning system can map the acoustic features into the spoken phonemes directly. A sequence of the phoneme is easily generated from the frames using frame-level classification.
\nAnother side, end-to-end systems perform acoustic frames to phone mapping in one step only. End-to-end training means all the modules are learned simultaneously. Advanced deep learning methods facilitate to train the system in an end-to-end manner. They also have the ability to train the system directly with raw signals, i.e., without hand-crafted features. Therefore, ASR paradigm is shifting from cepstral features like MFCC [2], PLP [3] to discriminative features learned directly from raw speech. End-to-end model may take raw speech signal as input and generates phoneme class conditional probabilities as output. The three major types of end-to-end architectures for ASR are attention-based method, connectionist temporal classification (CTC), and CNN-based direct raw speech model.
\nAttention-based models directly transcribe the speech into phonemes. Attention-based encoder-decoder uses the recurrent neural network (RNN) to perform sequence-to-sequence mapping without any predefined alignment. In this model, the input sequence is first transformed into a fixed length vector representation, and then decoder maps this fixed length vector into the output sequence. Attention-based encoder-decoder is much capable of learning the mapping between variable-length input and output sequences. Chorowski and Jaitly proposed speaker-independent sequence-to-sequence model and achieved 10.6% WER without separate language models and 6.7% WER with a trigram language model for Wall Street Journal dataset [4]. In attention-based systems, the alignment between the acoustic frame and recognized symbols is performed by attention mechanism, whereas CTC model uses conditional independence assumptions to efficiently solve sequential problems by dynamic programming. Attention model has shown high performance over CTC approach because it uses the history of the target character without any conditional independence assumptions.
\nAnother side, CNN-based acoustic model is proposed by Palaz et al. [5, 6, 7] which processes the raw speech directly as input. This model consists of two stages: feature learning stage, i.e., several convolutional layers, and classifier stage, i.e., fully connected layers. Both the stages are learned jointly by minimizing a cost function based on relative entropy. In this model, the information is extracted by the filters at first convolutional layer and modeled between first and second convolutional layer. In classifier stage, learned features are classified by fully connected layers and softmax layer. This approach claims comparable or better performance than traditional cepstral feature-based system followed by ANN training for phoneme recognition on TIMIT dataset.
\nThis chapter is organized as follows: In Section 2, the work performed in the field of ASR is discussed with the name of related work. Section 3 covers the various architectures of ASR. Section 4 presents the brief introduction about CNN. Section 5 explains CNN-based direct raw speech recognition model. In Section 6, available experimental results are shown. Finally, Section 7 concludes this chapter with the brief discussion.
\nTraditional ASR system leveraged the GMM/HMM paradigm for acoustic modeling. GMM efficiently processes the vectors of input features and estimates emission probabilities for each HMM state. HMM efficiently normalizes the temporal variability present in speech signal. The combination of HMM and language model is used to estimate the most likely sequence of phones. The discriminative objective function is used to improve the recognition rate of the system by the discriminatively fine-tuned methods [8]. However, GMM has a shortcoming as it shows inability to model the data that is present on the boundary line. Artificial neural networks (ANNs) can learn much better models of data lying on the boundary condition. Deep neural networks (DNNs) as acoustic models tremendously improved the performance of ASR systems [9, 10, 11]. Generally, discriminative power of DNN is used for phoneme recognition and, for decoding task, HMM is preferred choice. DNNs have many hidden layers with a large number of nonlinear units and produce a very large number of outputs. The benefit of this large output layer is that it accommodates the large number of HMM states. DNN architectures have densely connected layers. Therefore, such architectures are more prone to overfitting. Secondly, features having the local correlations become difficult to learn for such architectures. In [12], speech frames are classified into clustered context-dependent states using DNNs. In [13, 14], GMM-free DNN training process is proposed by the researchers. However, GMM-free process demands iterative procedures like decision trees, generating forced alignments. DNN-based acoustic models are gaining much popularity in large vocabulary speech recognition task [10], but components like HMM and n-gram language model are same as in their predecessors.
\nGMM or DNN-based ASR systems perform the task in three steps: feature extraction, classification, and decoding. It is shown in Figure 1. Firstly, the short-term signal \n
General framework of automatic speech recognition system.
The prior class probability \n
DNN is a feed-forward NN containing multiple hidden layers with a large number of hidden units. DNNs are trained using the back-propagation methods then discriminatively fine-tuned for reducing the gap between the desired output and actual output. DNN-/HMM-based hybrid systems are the effective models which use a tri-phone HMM model and an n-gram language model [10, 15]. Traditional DNN/HMM hybrid systems have several independent components that are trained separately like an acoustic model, pronunciation model, and language model. In the hybrid model, the speech recognition task is factorized into several independent subtasks. Each subtask is independently handled by a separate module which simplifies the objective. The classification task is much simpler in HMM-based models as compared to classifying the set of variable-length sequences directly. Figure 2 shows the hybrid DNN/HMM phoneme recognition model.
\nHybrid DNN/HMM phoneme recognition.
On the other side, researchers proposed end-to-end ASR systems that directly map the speech into labels without any intermediate components. As the advancements in deep learning, it has become possible to train the system in an end-to-end fashion. The high success rate of deep learning methods in vision task motivates the researchers to focus on classifier step for speech recognition. Such architectures are called deep because they are composed of many layers as compared to classical “shallow” systems. The main goal of end-to-end ASR system is to simplify the conventional module-based ASR system into a single deep learning framework. In earlier systems, divide and conquer approaches are used to optimize each step independently, whereas deep learning approaches have a single architecture that leads to more optimal system. End-to-end speech recognition systems directly map the speech to text without requiring predefined alignment between acoustic frame and characters [16, 17, 18, 19, 20, 21, 22, 23, 24]. These systems are generally divided into three broad categories: attention-based model [19, 20, 21, 22], connectionist temporal classification [16, 17, 18, 25], and CNN-based direct raw speech method [5, 6, 7, 26]. All these models have a capability to address the problem of variable-length input and output sequences.
\nAttention-based models are gaining much popularity in a variety of tasks like handwriting synthesis [27], machine translation [28], and visual object classification [29]. Attention-based models directly map the acoustic frame into character sequences. However, this model differs from other machine translation tasks by requesting much longer input sequences. This model generates a character based on the inputs and history of the target character. The attention-based models use encoder-decoder architecture to perform the sequence mapping from speech feature sequences to text as shown in Figure 3. Its extension, i.e., attention-based recurrent networks, has also been successfully applied to speech recognition. In the noisy environment, these models’ results are poor because the estimated alignment is easily corrupted by noise. Another issue with this model is that it is hard to train from scratch due to misalignment on longer input sequences. Sequence-to-sequence networks have also achieved many breakthroughs in speech recognition [20, 21, 22]. They can be divided into three modules: an encoding module that transforms sequences, attention module that estimates the alignment between the hidden vector and targets, and decoding module that generates the output sequence. To develop successful sequence-to-sequence model, the understanding and preventing limitations are required. The discriminative training is a different way of training that raises the performance of the system. It allows the model to focus on most informative features with the risk of overfitting.
\nAttention-based ASR model.
End-to-end trainable speech recognition systems are an important application of attention-based models. The decoder network computes a matching score between hidden states generated by the acoustic encoder network at each input time. It processes its hidden states to form a temporal alignment distribution. This matching score is used to estimate the corresponding encoder states. The difficulty of attention-based mechanism in speech recognition is that the feature inputs and corresponding letter outputs generally proceed in the same order with only small deviations within word. However, the different length of input and output sequences makes it more difficult to track the alignment. The advantage of attention-based mechanism is that any conditional independence assumptions (Markov assumption) are not required in this mechanism. Attention-based approach replaces the HMM with RNN to perform the sequence prediction. Attention mechanism automatically learns alignment between the input features and desired character sequence.
\nCTC techniques infer the speech-label alignment automatically. CTC [25] was developed for decoding the language. Firstly, Hannun et al. [17] used it for decoding purpose in Baidu’s deep speech network. CTC uses dynamic programming [16] for efficient computation of a strictly monotonic alignment. However, graph-based decoding and language model are required for it. CTC approaches use RNN for feature extraction [28]. Graves et al. [30] used its objective function in deep bidirectional long short-term memory (LSTM) system. This model successfully arranges all possible alignments between input and output sequences during model training, not on the prior.
\nTwo different versions of beam search are adopted by [16, 31] for decoding CTC models. Figure 4 shows the working architecture of the CTC model. In this, noisy and not informative frames are discarded by the introduction of the blank label which results in the optimal output sequence. CTC uses intermediate label representation to identify the blank labels, i.e., no output labels. CTC-based NN model shows high recognition rate for both phoneme recognition [32] and LVCSR [16, 31]. CTC-trained neural network with language model offers excellent results [17].
\nCTC model for speech recognition.
End-to-end ASR systems perform well and achieve good results, yet they face two major challenges. First is how to incorporate lexicons and language models into decoding. However, [16, 31, 33] have incorporated lexicons for searching paths. Second, there is no shared experimental platform for the purpose of benchmark. End-to-end systems differ from the traditional system in both aspects: model architecture and decoding methods. Some efforts were also made to model the raw speech signal with little or no preprocessing [34]. Palaz et al. [6] showed in his study that CNN [35] can calculate the class conditional probabilities from raw speech signal as direct input. Therefore, CNNs are the preferred choice to learn features from the raw speech. Two stages of learned feature process are as follows: initially, features are learned by the filters at first convolutional layer, and then learned features are modeled by second and higher-level convolutional layers. An end-to-end phoneme sequence recognizer directly processes the raw speech signal as inputs and produces a phoneme sequence. The end-to-end system is composed of two parts: convolutional neural networks and conditional random field (CRF). CNN is used to perform the feature learning and classification, and CRFs are used for the decoding stage. CRF, ANN, multilayer perceptron, etc. have been successfully used as decoder. The results on TIMIT phone recognition task also confirm that the system effectively learns the features from raw speech and performs better than traditional systems that take cepstral features as input [36]. This model also produces good results for LVCSR [7].
\nIn this section, a brief review on conventional GMM/DNN ASR, attention-based end-to-end ASR, and CTC is given.
\nASR system performs sequence mapping of T-length speech sequence features, \n
The ASR problem is formulated within the Bayesian framework. In this method, an utterance is represented by some sequence of acoustic feature vector \n
In Eq. (2), the argument of \n
In Eq. (3), the denominator \n
where \n
where \n
\n\n
In Eq. (9), framewise likelihood function \n
\n\n
An HMM state transition represents this probability. A pronunciation dictionary performs the conversion from \n
Similarly, \n
The issue of Markov assumption is addressed using recurrent neural network language model (RNNLM) [39], but it increases the complexity of decoding process. The combination of RNNLMs and m-gram language model is generally used and it works on a rescoring technique.
\nThe approach based on attention mechanism does not make any Markov assumptions. It directly finds the posterior \n
where \n
Eq. (15) represents the encoder and Eq. (18) represents the decoder networks. \n
The input feature vector \n
It is to be noted that the computational complexity of the encoder network is reduced by subsampling the outputs [20, 21].
\n\n\n
\n\n
It is an extended version of content-based attention mechanism to deal with the location-aware attention. If \n
Here, * denotes 1-D convolution along the input feature axis, \n
The decoder network is an RNN that is conditioned on previous output \n
\n\n
\n\n
The objective function of the attention model is computed from the sequence posterior
\nwhere \n
The CTC formulation is also based on Bayes’ decision theory. It is to be noted that L-length letter sequence,
\nIn \n
is also introduced. The posterior distribution, \n
Same as Eq. (3), CTC also uses Markov assumption, i.e., \n
Same as DNN/HMM acoustic model, \n
The framewise posterior distribution, \n
where \n
By applying Bayes’ decision theory probabilistic chain rule and Markov assumption, \n
where \n
when \n
Monotonic alignment property is an important constraint for speech recognition, so ASR sequence-to-sequence mapping should follow the monotonic alignment. This property is also satisfied by HMM/DNN.
\nThe posterior, \n
Viterbi method and forward-backward algorithm are dynamic programming algorithm which is used to efficiently compute the summation over all possible \n
The CTC formulation is also same as HMM/DNN. The minute difference is that Bayes’ rule is applied to \n
CNNs are the popular variants of deep learning that are widely adopted in ASR systems. CNNs have many attractive advancements, i.e., weight sharing, convolutional filters, and pooling. Therefore, CNNs have achieved an impressive performance in ASR. CNNs are composed of multiple convolutional layers. Figure 5 shows the block diagram of CNN. LeCun and Bengio [41] describe the three states of convolutional layer, i.e., convolution, pooling, and nonlinearity.
\nBlock diagram of convolutional neural network.
Deep CNNs set a new milestone by achieving approximate human level performance through advanced architectures and optimized training [42]. CNNs use nonlinear function to directly process the low-level data. CNNs are capable of learning high-level features with high complexity and abstraction. Pooling is the heart of CNNs that reduces the dimensionality of a feature map. Maxout is widely used nonlinearity and has shown its effectiveness in ASR tasks [43, 44].
\nPooling is an important concept that transforms the joint feature representation into the valuable information by keeping the useful information and eliminating insignificant information. Small frequency shifts that are common in speech signal are efficiently handled using pooling. Pooling also helps in reducing the spectral variance present in the input speech. It maps the input from p adjacent units into the output by applying a special function. After the element-wise nonlinearities, the features are passed through pooling layer. This layer executes the downsampling on the feature maps coming from previous layer and produces the new feature maps with a condensed resolution. This layer drastically reduces the spatial dimension of input. It serves the two main purposes. The first is that the amount of parameters or weight is reduced by 65%, thus lessening the computational cost. The second is that it controls the overfitting. This term refers to when a model is so tuned to the training examples.
\nA novel acoustic model based on CNN is proposed by Palaz et al. [5] which is shown in Figure 6. In this, raw speech signal is segmented into input speech signal \n
CNN-based raw speech phoneme recognition system.
The end-to-end approach employs the following understanding:
Speech signals are non-stationary in nature. Therefore, they are processed in a short-term manner. Traditional feature extraction methods generally use 20–40 ms sliding window size. Although in the end-to-end approach, short-term processing of signal is required. Therefore, the size of the short-term window is taken as hyperparameter which is automatically determined during training.
Feature extraction is a filter operation because its components like Fourier transform, discrete cosine transform, etc. are filtering operations. In traditional systems, filtering is applied on both frequency and time. So, this factor is also considered in building convolutional layer in end-to-end system. Therefore, the number of filter banks and their parameters are taken as hyperparameters that are automatically determined during training.
The short-term processing of speech signal spread the information across time. In traditional systems, this spread information is modeled by calculating temporal derivatives and contextual information. Therefore, intermediate representation is supplied to classifier and calculated by taking long time span of input speech signal. Therefore, \n
The end-to-end model estimates \n
In this model, a number of hyperparameters are used to specify the structure of the network. The number of hidden units in each hidden layer is very important; hence, it is taken as hyperparameter. \n
Hyperparameter | \nUnits | \nRange | \n
---|---|---|
Input window size (\n | \nms | \n100–700 | \n
Kernel width of the first ConvNet layer (\n | \nSamples | \n10–90 | \n
Kernel width of the nth ConvNet layer (\n | \nSamples | \n1–11 | \n
Number of filters per kernel (\n | \nFilters | \n20–100 | \n
Max-pooling kernel width (\n | \nFrames | \n2–6 | \n
Number of hidden units in the classifier | \nUnits | \n200–1500 | \n
Range of hyperparameter for TIMIT dataset during validation.
The experiments are conducted for three convolutional layers. The speech window size (\n
End-to-end speech recognition model | \nPER (%) | \n
---|---|
CNN-based speech recognition system using raw speech as input [7] | \n33.2 | \n
Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks [36] | \n32.4 | \n
Convolutional neural network-based continuous speech recognition using raw speech signal [6] | \n32.3 | \n
End-to-end phoneme sequence recognition using convolutional neural networks [5] | \n27.2 | \n
End-to-end continuous speech recognition using attention-based recurrent NN: First results [19] | \n18.57 | \n
Toward end-to-end speech recognition with deep convolutional neural networks [44] | \n18.2 | \n
Attention-based models for speech recognition [20] | \n17.6 | \n
Segmental recurrent neural networks for end-to-end speech recognition [45] | \n17.3 | \n
Comparison of existing end-to-end speech model in the context of PER (%).
Bold value and text represent the performance of the CNN-based direct raw speech model.
Methods | \nPER (%) | \n
---|---|
GMM-/HMM-based ASR system [46] | \n34 | \n
Attention-based models for speech recognition [20] | \n17.6 | \n
Segmental recurrent neural networks for end-to-end speech recognition [45] | \n17.3 | \n
Combining time and frequency domain convolution in convolutional neural network-Based phone recognition [47] | \n16.7 | \n
Phone recognition with hierarchical convolutional deep maxout networks [48] | \n16.5 | \n
Comparison of existing techniques with CNN-based direct raw speech model in the context of PER (%).
Bold value and text represent the performance of the CNN-based direct raw speech model.
This chapter discusses the CNN-based direct raw speech recognition model. This model directly learns the relevant representation from the speech signal in a data-driven manner and calculates the conditional probability for each phoneme class. In this, CNN as an acoustic model consists of a feature stage and classifier stage. Both the stages are trained jointly. Raw speech is supplied as input to first convolutional layer, and it is further processed by several convolutional layers. Classifiers like ANN, CRF, MLP, or fully connected layers calculate the conditional probabilities for each phoneme class. After that decoding is performed using HMM. This model shows the similar performance as shown by MFCC-based conventional mode.
\nThe clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) protein modules are found to be a part of adaptive antivirus defense systems in archaea and bacteria and mediate immunity by a three-stage process called adaption, processing of the primary transcript, and interference. These systems incorporate fragments of foreign DNA (known as spacers) into CRISPR cassettes, then transcribe the CRISPR arrays including the spacers, and process them to make a guide crRNA or the clustered regularly interspaced short palindromic repeats ribonucleic acid (CRISP RNA) which is employed to specifically target and cleave the genome of the cognate virus or plasmid. Earlier classic methods such as zinc finger motif, meganucleases, and transcription activator-like effector nucleases were deployed for genome editing but due to its prerequisite for different fusion proteins, the technology raised hurdles in its applicability. The characteristic feature of single guide RNA of CRISPR to regulate Cas protein to target specific gene sequence is highly advantageous to overcome the barriers posing from classic methods. Proteins cas1 and cas2 genes are found to be the core and active part of the information processing subsystems of the three distinct types of CRISPR/Cas systems [1]. Due to the current problems with the vast diversity and complexity of the architecture of CRISPR/Cas systems, the classification is still challenging. Based on the presence of three signature genes, the classification is as follows:
Typical type I loci contain the signature cas3 gene, which codes for helicase and DNase activities within a single large protein. The detailed sequence and structural comparisons have led to the recognition of many of these proteins in the RAMP superfamily including Cas5 and Cas6 families. Type I systems are currently divided into six subtypes, I-A to I-F, each of which has its own signature gene and distinct features of operon organization [2, 3].
These contain cas9 as a signature gene encoding for a multidomain protein that combines all the functions of effector complexes and the target DNA cleavage and is essential for the maturation of the crRNA. These systems use cellular (not encoded within the CRISPR/Cas loci) RNase III and tracrRNA for the processing of pre-crRNA. Type II CRISPR-Cas systems are currently classified into three subtypes such as II-A, II-B, and II-C. Type II-A encompasses an additional signature gene csn2. Protein csn2 is found to be engaged in spacer integration. Type II-B systems belong to the Cas family of proteins with 5′-single-stranded DNA exonuclease activity. The recently proposed type II-C CRISPR-Cas systems possess only three protein-coding genes (cas1, cas2, and cas9) and are common in sequenced bacterial genomes (Figure 1) [2, 3].
Simplified model of the immunity mechanisms of class 1 and class 2 CRISPR-Cas systems. The CRISPR-Cas systems are composed of a
All type III systems possess the signature gene cas10 which encodes a multidomain protein containing a palm domain similar to that in cyclases and polymerases of the PolB family (Table 1) [2, 3].
Classification | Type I | Type II | Type III | Type V |
---|---|---|---|---|
Signature protein | Cas 3 | Cas 9 | Csm (III-A) or Cmr (III-B) | Cas 12a |
Effector | Cascade | CrRNA | sgRNA | CrRNA |
Cleavage product | SSBs | DSBs | SSBs | DSBs |
Different types of CRISPR/Cas based on signature protein, effector, and cleavage product [3].
Initially, CRISPR-Cas 9 was found to nick the DNA along with the guide RNA Cas 12a belonging to class II Type VA system, derived from
Type II is further divided into six types based on their structure and function. The Cas12a protein contains a RuvC endonuclease domain, which sequentially cleaves the non-targeting strand and the targeting strand to form DSBs (double-stranded base pairs). Compared to the CRISPR/Cas9 system, this system has several remarkable differences, including the signature protein, PAM sequence, and cleavage product [6]. CRISPR/Cas12a based sensing methods focus on fluorescence readout with reduced transduction efficiency as studies report a direct correlation between the catalysis systems with recognition elements (i.e., aptamers), thus greatly improving the working efficiency of the detection platform. Cas 13 consists of four subtypes and is involved in RNA interference activities. Off-target editing is critical to Cas 13 and requires significant attention in retrieving obstacles for protein analysis [7].
CRISPR/Cas systems generally play a role as RNA-guided endonucleases (crRNA). The crRNA guides cas proteins to specific DNA sequences whereby the hybridization leads to cas protein activation which later results in cleavage of DNA sequence [8]. Figure 2 shows the components of Cas9, Cas12a, Cas12f, and Cas13a.
Basic components of CRISPR/Cas9, Cas12a, Cas12f, and Cas13a pink triangle indicates cis-cleavage site [
Cas9 is an endonuclease and the single-guide RNA (sgRNA) of CRISPR-Cas9 systems contains a hairpin-rich region that binds to Cas9 and a 20-nucleotide “spacer” region that binds with the complementary “protospacer” region in the target strand of a dsDNA duplex. Binding between the sgRNA and the DNA target brings Cas9 into close proximity to the target (Figure 2). The His-Asn-His (HNH) domain of Cas9 cleaves the strand that is complementary to sgRNA (target strand) and the RuvC domain of Cas9 cleaves the other strand of the dsDNA (non-target strand). Single-guide RNA (sgRNA) (Figure 3) [13].
Combining functional nucleic acids and molecular translators with CRISPR/Cas technology for detection of non-nucleic acids such as proteins. Adapted from Ref. [
Recent findings indicate that the cas12a proteins have both trans and cis cleavage activities on ssDNA regardless of the sequence. Notably, Cas12a is the first Cas protein to be identified whose ternary complex has been shown to have trans-ss DNA cleavage ability. Research shows that Cas12a may have acquired single-stranded DNA ability through evolution due to the abundance of viruses in the environment. Thus, gaining a significant role as a powerful and dominating weapon to eliminate invasion by foreign ss DNA. The well-characterized variants of cas12, cas12a, and cas12f, formerly known as cas14 lack the HNH domain but nevertheless, achieved the PAM dependant cleavage with RuvC domain alone. Recent findings have reported Cas13a also called C2Ca, an RNA-guided and RNA-targeting CRISPR effector from the class 2 type VI CRISPR system, was found to have the trans-cleavage activity on RNA. Additionally, the RuvC catalytic pocket of both C2c1 and Cas12a was responsible for the cleavage of both strands of targeted dsDNA [9].
Electrochemical biosensors register physical-chemical and biological change and possess high throughput of the biological recognition process. Depending on the type of biological recognition, sensors are classified into biocatalytic devices and affinity sensors. Biocatalytic sensors integrate enzymes and whole cells as recognition elements leading to exquisite specificity and a significant rise in the rate of reaction whereas affinity sensors make use of extreme selectivity and specificity for acquiring higher sensitivity. The electrochemical transducer responds to the binding event and converts the electrical response to an output that can be amplified, stored, and displayed [14]. Due to its signal-off architecture, these electrochemical sensors provide limited sensitivity and productivity. To overcome these limitations the CRISPR/Cas12a based electron sensing biosensors have been developed for non-nucleic acid targets. CRISPR/Cas12a-based immobilization-free electrochemical biosensors can detect small molecules and proteins by adjusting regions for target recognition in RCA components [15]. Transcription factors (TFs) assay seems to be path-breaking as it is found to be involved directly in many diseases including cancers. CRISPR/Cas 12a based biosensors for the detection of transcription factors have been developed. The biosensing mechanism is based on the interaction of TF’s with double-stranded DNA activator eliminating Cas12a/crRNA from contacting and interacting with the 14 activators, thus inhibiting Cas12a activation. As a consequence of this strategy, the DNase activity of Cas12a was controlled and several TFs with well-defined binding sites could be quantified at the picomolar level with high precision [16, 17].
Recent findings report that the implementation of various nucleic acid amplification strategies led to improvements in analytical specificity and sensitivity and the development of point of care (POC) diagnostics. For example, the best-studied reaction is the amplification employing the Cas9 nickase (Cas9nAR) which when combined with polymerase and primers may substantially duplicate double-stranded DNA (dsDNA) without requiring heat cycling as does the polymerase chain reaction (PCR) [18].
In contrast to PCR, isothermal proximity assay seems to be an effective protein quantification assay for disease biomarkers and point of care diagnostics. Recent advances in the CRISPR/Cas technique specifically combining recombinase polymerase activity (RPA) and ssDNAse activity have led to the discovery of a series of isothermal assays for protein quantification. iPCCA relies on proximity binding for target recognition due to which it holds the potential for detecting non-nucleic acid targets such as proteins. However, isothermal amplification does not necessitate the use of advanced and sophisticated thermal cyclers and hence is more commonly used in biosensing [9].
The most widely used bioassay, ELISA (enzyme-linked immunosorbent assay) has revolutionized the ability to detect a wide variety of antigens. Complex chemical structure and restricted catalytic efficiency of HRP has a direct correlation with poor sensitivity in picomolar and nanomolar concentration. However, conventional ELISA is still not sensitive enough to detect ultralow concentrations of biomarkers for the early diagnosis of cancer, cardiovascular risk, neurological disorders, and infectious disease. CRISPR/Cas 13a based signal amplification strategy also called CLISA has been used to develop a 10 fold high-sensitive method for detecting low abundance. Recently, CRISPR/Cas13a has been recently demonstrated to have RNA-directed RNA cleavage ability. This RNA-guided trans-endonuclease activity is highly specific, being activated only when the target RNA has the perfect complementary sequence to the crRNA and is highly efficient. This potent signal amplification ability of CRISPR/Cas13a enables the development of direct RNA assays with a sensitivity down to the femtomolar level [19, 20].
Aptamer, a highly selective recognition element has been combined with various analytical techniques to increase the sensitivity of protein assay. Amongst these, an electrochemical technique using specific aptamers as recognition elements exhibits great promise in detecting protein duo to its attractive merits, such as high selectivity and sensitivity, the potential for miniaturization, and ease of integration with additional components [21]. Recent findings have demonstrated that the electrochemical aptasensor has been effectively used for the detection of thrombin in femtomolar concentration. It has been reported that once CRISPR RNA (crRNA)-directed Cas12a binds to a specific target DNA, the conserved RuvC nuclease domain in Cas12a will non-specifically cut single-stranded DNA (ssDNA). A homogeneous electrochemical aptasensor has been reported for sensitive and specific detection of thrombin by utilizing binding-induced DNA strand displacement strategy as the transduction element of thrombin and rolling circle amplification-regulated CRISPR/Cas12a for signal amplification. Importantly, this homogeneous electrochemical aptasensor can detect the femtomolar range of thrombin, and exhibited good specificity relative to other interfering blood-relevant proteins. The BIDSD-RCA-CRISPR/Cas12a is implemented in three steps, but this electrochemical aptasensor dispenses with the need for probe surface-immobilization procedure, simplifying the preparation process, and reducing the operating cost of the analysis. The strategy further could be applied to detect another disease-related protein biomarker in early diagnosis in the future [22].
The struggle for life between bacteria and their infecting viruses (phages) has led to the development of numerous bacterial defense mechanisms and their phage-encoded opponents. Recently, anti-CRISPR proteins have been identified, which inhibits the CRISPR/Cas system. The mechanism by which anti-CRISPR proteins inhibit CRISPR/Cas provides an extensive set of valuable tools to both understand and manipulate CRISPR [21]. Several findings report that the growing number of anti-CRISPR families has a significant impact on CRISPR/Cas function and has been a driving force in the evolution of CRISPR-Cas. These Anti-CRISPR systems rely heavily on Aca proteins due to their extensive interaction with anti-CRISPRs and the presence of Aca genes has the potential to act as anti-anti CRISPR playing a vital role in CRISPR-based antibacterial technologies [22, 23]. Anti-CRISPR ranges from 50 to 150 amino acids with no sequence similarity. Recent finding demonstrated that phage carries atleast one anti-CRISPR gene to avoid elimination by competent hosts. The unique mechanism of anti-CRISPR results in sequence-specific transcriptional repression system. Type II anti-CRISPRs have more evident biotechnological uses, given the widespread usage of CRISPR-Cas9 genome editing tools. Their application could be critical for gene drive and gene therapy technologies [24].
CRISPR/Cas based technology has a lot of potential as a tool for treating a range of medical conditions that have a genetic component, including cancer, hepatitis B, or even high cholesterol [25, 26, 27]. It is likely to be many years before CRISPR/Cas technology is used routinely in humans. CRISPR/Cas technology emerged as a versatile technology with wide application in the genome sequence editing, molecular studies of various gene functions, protein detection, gene therapy, and in the biomedical science as a diagnostic technology for detection of covid 19 like viral, bacterial, and various genetic disease [28]. Cancer is one of the fatal diseases that has severely threatened human life and caused a tremendous burden for society [29]. Early diagnosis of cancer is of great benefit to treat patients in early stages which leads to improve the survival rate of cancer patients. In body fluids detection of cancer related biomarkers is a critical kind of noninvasive technique for cancer diagnosis. Nevertheless, existing techniques of cancer biomarker detection always depends on a large-scale instruments and required sophisticated operation [30]. Clustered regularly interspaced short palindromic repeats and CRISPR-associated protein (CRISPR/Cas) based in vitro diagnosis can simplify the detection procedures and improve sensitivity and specificity, with great promise as the next-generation molecular diagnosis [31]. In the future, genome-wide screening for various genetic disorders, and cancer subtypes should be conducted to identify specific genetic and epigenetic targets for CRISPR technology to be most effective. The functionality of the identified mutations and their related signaling pathways need to be thoroughly analyzed before they are manipulated for therapy with CRISPR technology [32]. More in vivo research on Cas9 epigenetic regulation is needed to better understand its impact on cancer epigenetics. The use of synthetic biology for Cas9 modulation can be further extended to create real-time predictive algorithms for specific metastatic pathways that update as epigenetic regulation progress and the cancer advances so that treatment can always be precisely one step ahead of cancer. Ongoing research has the potential to optimize and advance CRISPR technology, culminating in the clinical realization of its full potential for breast cancer diagnosis, modeling, and treatment [29, 33, 34]. In the future, CRISPR/Cas technology will be used as a unique promising technology to study the various genes for their function, for identification of mutations and their correction, this technology will be used in tumor angiogenesis research for cancer treatment [35], CRISPR technology also used for modification of genetic sequence to develop various organisms for the benefit of human and environmental protection. Much research is still focusing on its use in animal models or isolated human cells, with the aim to eventually use the technology to routinely treat diseases in humans (Figure 4).
Applications of CRISPR/Cas system in detection of various molecules [
From many years scientists have learned about genetics and gene function by studying the effects of alteration in DNA sequence. Artificially by making a change in a gene, either in a cell line or a whole organism, it is possible to study the effect of that change to understand what the function of that gene is. For a long period geneticists used chemicals or radiation to create mutations but this approach is not precise and specific and due to its randomness for several years scientists have been using ‘gene targeting’ to introduce changes in specific places in the genome, by deletion or insertion either whole genes or single bases. Conventional gene targeting has been very valuable for studying genes and genetics, however it takes a long time to create a mutation and is fairly expensive But the CRISPR/Cas9 system based technology currently stands out as the fastest, cheapest and most reliable system for ‘editing’ genes. In the last decade CRISPR/Cas is a genome editing technology that is creating a an atmosphere of excitement in the science world because of its faster, cheaper, promising, precise, sensitive and efficient and more accurate nature than previous conventional techniques of genome engineering and it has a wide range of potential applications. CRISPR/Cas technology have made it possible to edit the genomes of most cell types precisely and efficiently hence (CRISPR)/Cas9 system is a novel, versatile and easy-to-use tool to edit genomes irrespective of their complexity, with multiple and applications in almost all branches of life science, biomedicine and facilitating the elucidation of target gene function in biology and diseases. CRISPR/Cas technology able to detect various targets starting from nucleic acids to proteins. Incorporating CRISPR/Cas systems with numerous nucleic acid amplification strategies allows the generation of amplified detection signals, enrichment of low-abundance molecular targets, enhancements in analytical specificity and sensitivity, and development of point-of-care diagnostic techniques. It is concluded that the CRISPR/Cas systems in association with functional nucleic acids (FNAs) and molecular translators permits the detection of non-nucleic acid targets, like proteins, metal ions, and tiny molecules. Productive integrations of CRISPR technology with nucleic acid amplification techniques lead to sensitive and fast detection of Protein.
The authors would like to thank Dr. B.A. Mehere, Principal and Head of the Department of Biochemistry and Biotechnology, Dr. Ambedkar College, Deekshabhoomi, Nagpur, India, for providing research space and facility.
The authors declare no conflict of interest.
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However, some of these heavy metals in high doses can be harmful to the body while others such as cadmium, mercury, lead, chromium, silver, and arsenic in minute quantities have delirious effects in the body causing acute and chronic toxicities in humans. The focus of this chapter is to describe the various mechanism of intoxication of some selected heavy metals in humans along with their health effects. Therefore it aims to highlight on biochemical mechanisms of heavy metal intoxication which involves binding to proteins and enzymes, altering their activity and causing damage. More so, the mechanism by which heavy metals cause neurotoxicity, generate free radical which promotes oxidative stress damaging lipids, proteins and DNA molecules and how these free radicals propagate carcinogenesis are discussed. Alongside these mechanisms, the noxious health effects of these heavy metals are discussed.",book:{id:"7111",slug:"poisoning-in-the-modern-world-new-tricks-for-an-old-dog-",title:"Poisoning in the Modern World",fullTitle:"Poisoning in the Modern World - New Tricks for an Old Dog?"},signatures:"Godwill Azeh Engwa, Paschaline Udoka Ferdinand, Friday Nweke Nwalo and Marian N. Unachukwu",authors:[{id:"241837",title:"Mr.",name:"Godwill Azeh",middleName:null,surname:"Engwa",slug:"godwill-azeh-engwa",fullName:"Godwill Azeh Engwa"},{id:"274194",title:"BSc.",name:"Paschaline Ferdinand",middleName:null,surname:"Okeke",slug:"paschaline-ferdinand-okeke",fullName:"Paschaline Ferdinand Okeke"},{id:"286975",title:"Dr.",name:"Friday",middleName:null,surname:"Nweke Nwalo",slug:"friday-nweke-nwalo",fullName:"Friday Nweke Nwalo"},{id:"286976",title:"Dr.",name:"Marian",middleName:null,surname:"Unachukwu",slug:"marian-unachukwu",fullName:"Marian Unachukwu"}]},{id:"57717",doi:"10.5772/intechopen.71923",title:"In Vitro Cytotoxicity and Cell Viability Assays: Principles, Advantages, and Disadvantages",slug:"in-vitro-cytotoxicity-and-cell-viability-assays-principles-advantages-and-disadvantages",totalDownloads:14818,totalCrossrefCites:78,totalDimensionsCites:157,abstract:"Cytotoxicity is one of the most important indicators for biological evaluation in vitro studies. In vitro, chemicals such as drugs and pesticides have different cytotoxicity mechanisms such as destruction of cell membranes, prevention of protein synthesis, irreversible binding to receptors etc. In order to determine the cell death caused by these damages, there is a need for cheap, reliable and reproducible short-term cytotoxicity and cell viability assays. Cytotoxicity and cell viability assays are based on various cell functions. A broad spectrum of cytotoxicity assays is currently used in the fields of toxicology and pharmacology. There are different classifications for these assays: (i) dye exclusion assays; (ii) colorimetric assays; (iii) fluorometric assays; and (iv) luminometric assays. Choosing the appropriate method among these assays is important for obtaining accurate and reliable results. When selecting the cytotoxicity and cell viability assays to be used in the study, different parameters have to be considered such as the availability in the laboratory where the study is to be performed, test compounds, detection mechanism, specificity, and sensitivity. In this chapter, information will be given about in vitro cytotoxicity and viability assays, these assays will be classified and their advantages and disadvantages will be emphasized. The aim of this chapter is to guide the researcher interested in this subject to select the appropriate assay for their study.",book:{id:"6310",slug:"genotoxicity-a-predictable-risk-to-our-actual-world",title:"Genotoxicity",fullTitle:"Genotoxicity - A Predictable Risk to Our Actual World"},signatures:"Özlem Sultan Aslantürk",authors:[{id:"211212",title:"Dr.",name:"Özlem Sultan",middleName:null,surname:"Aslantürk",slug:"ozlem-sultan-aslanturk",fullName:"Özlem Sultan Aslantürk"}]},{id:"66259",doi:"10.5772/intechopen.85270",title:"Antioxidant Compounds and Their Antioxidant Mechanism",slug:"antioxidant-compounds-and-their-antioxidant-mechanism",totalDownloads:7594,totalCrossrefCites:58,totalDimensionsCites:152,abstract:"An antioxidant is a substance that at low concentrations delays or prevents oxidation of a substrate. Antioxidant compounds act through several chemical mechanisms: hydrogen atom transfer (HAT), single electron transfer (SET), and the ability to chelate transition metals. The importance of antioxidant mechanisms is to understand the biological meaning of antioxidants, their possible uses, their production by organic synthesis or biotechnological methods, or for the standardization of the determination of antioxidant activity. In general, antioxidant molecules can react either by multiple mechanisms or by a predominant mechanism. The chemical structure of the antioxidant substance allows understanding of the antioxidant reaction mechanism. This chapter reviews the in vitro antioxidant reaction mechanisms of organic compounds polyphenols, carotenoids, and vitamins C against free radicals (FR) and prooxidant compounds under diverse conditions, as well as the most commonly used methods to evaluate the antioxidant activity of these compounds according to the mechanism involved in the reaction with free radicals and the methods of in vitro antioxidant evaluation that are used frequently depending on the reaction mechanism of the antioxidant.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Norma Francenia Santos-Sánchez, Raúl Salas-Coronado, Claudia Villanueva-Cañongo and Beatriz Hernández-Carlos",authors:[{id:"143354",title:"Dr.",name:"Raúl",middleName:null,surname:"Salas-Coronado",slug:"raul-salas-coronado",fullName:"Raúl Salas-Coronado"},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez"},{id:"193718",title:"Dr.",name:"Beatriz",middleName:null,surname:"Hernández-Carlos",slug:"beatriz-hernandez-carlos",fullName:"Beatriz Hernández-Carlos"},{id:"278133",title:"Dr.",name:"Claudia",middleName:null,surname:"Villanueva-Cañongo",slug:"claudia-villanueva-canongo",fullName:"Claudia Villanueva-Cañongo"}]},{id:"40253",doi:"10.5772/50486",title:"Lipid Nanoparticulate Drug Delivery Systems: A Revolution in Dosage Form Design and Development",slug:"lipid-nanoparticulate-drug-delivery-systems-a-revolution-in-dosage-form-design-and-development",totalDownloads:11293,totalCrossrefCites:22,totalDimensionsCites:105,abstract:null,book:{id:"2509",slug:"recent-advances-in-novel-drug-carrier-systems",title:"Recent Advances in Novel Drug Carrier Systems",fullTitle:"Recent Advances in Novel Drug Carrier Systems"},signatures:"Anthony A. 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Among these heavy metals, a few have direct or indirect impact on the human body. Some of these heavy metals such as copper, cobalt, iron, nickel, magnesium, molybdenum, chromium, selenium, manganese and zinc have functional roles which are essential for various diverse physiological and biochemical activities in the body. However, some of these heavy metals in high doses can be harmful to the body while others such as cadmium, mercury, lead, chromium, silver, and arsenic in minute quantities have delirious effects in the body causing acute and chronic toxicities in humans. The focus of this chapter is to describe the various mechanism of intoxication of some selected heavy metals in humans along with their health effects. Therefore it aims to highlight on biochemical mechanisms of heavy metal intoxication which involves binding to proteins and enzymes, altering their activity and causing damage. More so, the mechanism by which heavy metals cause neurotoxicity, generate free radical which promotes oxidative stress damaging lipids, proteins and DNA molecules and how these free radicals propagate carcinogenesis are discussed. Alongside these mechanisms, the noxious health effects of these heavy metals are discussed.",book:{id:"7111",slug:"poisoning-in-the-modern-world-new-tricks-for-an-old-dog-",title:"Poisoning in the Modern World",fullTitle:"Poisoning in the Modern World - New Tricks for an Old Dog?"},signatures:"Godwill Azeh Engwa, Paschaline Udoka Ferdinand, Friday Nweke Nwalo and Marian N. Unachukwu",authors:[{id:"241837",title:"Mr.",name:"Godwill Azeh",middleName:null,surname:"Engwa",slug:"godwill-azeh-engwa",fullName:"Godwill Azeh Engwa"},{id:"274194",title:"BSc.",name:"Paschaline Ferdinand",middleName:null,surname:"Okeke",slug:"paschaline-ferdinand-okeke",fullName:"Paschaline Ferdinand Okeke"},{id:"286975",title:"Dr.",name:"Friday",middleName:null,surname:"Nweke Nwalo",slug:"friday-nweke-nwalo",fullName:"Friday Nweke Nwalo"},{id:"286976",title:"Dr.",name:"Marian",middleName:null,surname:"Unachukwu",slug:"marian-unachukwu",fullName:"Marian Unachukwu"}]},{id:"49459",title:"Pharmacokinetics of Drugs Following IV Bolus, IV Infusion, and Oral Administration",slug:"pharmacokinetics-of-drugs-following-iv-bolus-iv-infusion-and-oral-administration",totalDownloads:15480,totalCrossrefCites:16,totalDimensionsCites:24,abstract:null,book:{id:"4491",slug:"basic-pharmacokinetic-concepts-and-some-clinical-applications",title:"Basic Pharmacokinetic Concepts and Some Clinical Applications",fullTitle:"Basic Pharmacokinetic Concepts and Some Clinical Applications"},signatures:"Tarek A. Ahmed",authors:[{id:"175649",title:"Dr.",name:"Tarek A",middleName:null,surname:"Ahmed",slug:"tarek-a-ahmed",fullName:"Tarek A Ahmed"}]},{id:"29240",title:"Oral Absorption, Intestinal Metabolism and Human Oral Bioavailability",slug:"oral-absorption-intestinal-metabolism-and-human-oral-bioavailability-",totalDownloads:27175,totalCrossrefCites:28,totalDimensionsCites:58,abstract:null,book:{id:"672",slug:"topics-on-drug-metabolism",title:"Topics on Drug Metabolism",fullTitle:"Topics on Drug Metabolism"},signatures:"Ayman El-Kattan and Manthena Varma",authors:[{id:"85539",title:"Dr.",name:"Ayman",middleName:null,surname:"El-Kattan",slug:"ayman-el-kattan",fullName:"Ayman El-Kattan"},{id:"88221",title:"Dr.",name:"Manthena",middleName:null,surname:"Varma",slug:"manthena-varma",fullName:"Manthena Varma"}]},{id:"66259",title:"Antioxidant Compounds and Their Antioxidant Mechanism",slug:"antioxidant-compounds-and-their-antioxidant-mechanism",totalDownloads:7587,totalCrossrefCites:58,totalDimensionsCites:152,abstract:"An antioxidant is a substance that at low concentrations delays or prevents oxidation of a substrate. Antioxidant compounds act through several chemical mechanisms: hydrogen atom transfer (HAT), single electron transfer (SET), and the ability to chelate transition metals. The importance of antioxidant mechanisms is to understand the biological meaning of antioxidants, their possible uses, their production by organic synthesis or biotechnological methods, or for the standardization of the determination of antioxidant activity. In general, antioxidant molecules can react either by multiple mechanisms or by a predominant mechanism. The chemical structure of the antioxidant substance allows understanding of the antioxidant reaction mechanism. This chapter reviews the in vitro antioxidant reaction mechanisms of organic compounds polyphenols, carotenoids, and vitamins C against free radicals (FR) and prooxidant compounds under diverse conditions, as well as the most commonly used methods to evaluate the antioxidant activity of these compounds according to the mechanism involved in the reaction with free radicals and the methods of in vitro antioxidant evaluation that are used frequently depending on the reaction mechanism of the antioxidant.",book:{id:"8008",slug:"antioxidants",title:"Antioxidants",fullTitle:"Antioxidants"},signatures:"Norma Francenia Santos-Sánchez, Raúl Salas-Coronado, Claudia Villanueva-Cañongo and Beatriz Hernández-Carlos",authors:[{id:"143354",title:"Dr.",name:"Raúl",middleName:null,surname:"Salas-Coronado",slug:"raul-salas-coronado",fullName:"Raúl Salas-Coronado"},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez"},{id:"193718",title:"Dr.",name:"Beatriz",middleName:null,surname:"Hernández-Carlos",slug:"beatriz-hernandez-carlos",fullName:"Beatriz Hernández-Carlos"},{id:"278133",title:"Dr.",name:"Claudia",middleName:null,surname:"Villanueva-Cañongo",slug:"claudia-villanueva-canongo",fullName:"Claudia Villanueva-Cañongo"}]},{id:"66742",title:"Introductory Chapter: Alkaloids - Their Importance in Nature and for Human Life",slug:"introductory-chapter-alkaloids-their-importance-in-nature-and-for-human-life",totalDownloads:4130,totalCrossrefCites:16,totalDimensionsCites:32,abstract:null,book:{id:"6828",slug:"alkaloids-their-importance-in-nature-and-human-life",title:"Alkaloids",fullTitle:"Alkaloids - Their Importance in Nature and Human Life"},signatures:"Joanna Kurek",authors:[{id:"214632",title:"Dr.",name:"Joanna",middleName:null,surname:"Kurek",slug:"joanna-kurek",fullName:"Joanna Kurek"}]}],onlineFirstChaptersFilter:{topicId:"19",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"83076",title:"Treatments for the Infection by SARS-CoV-2",slug:"treatments-for-the-infection-by-sars-cov-2",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.106232",abstract:"In late 2019, pneumonia cases from unknown origin were detected in Wuhan, China. The cause was a new coronavirus. The World Health Organization (WHO) named the virus SARS-CoV-2 and COVID-19 the associated disease. In the first months of 2020, this disease became a pandemic with a high lethality reported. Since then, the search for treatments began. We started by searching among treatments previously approved for human use that were not designed for COVID-19 and were considered to treat this condition. We continued searching on the therapeutics guidelines published by the WHO for the management of infection by SARS-CoV-2. Based on these results, we searched for the literature in PubMed to obtain further evidence on the drugs against SARS-CoV-2. The treatments presented in this chapter are Ivermectin, Hydroxychloroquine, Nitazoxanide, Azithromycin, Molnupiravir, Casirivimab-Imdevimab, Ritonavir-Nirmatrelvir, Ritonavir-Lopinavir, Remdesivir, and Favipiravir. Two years ahead of the start of the COVID-19 pandemic, a plenty of options for treatment have been investigated. Only a few of them have been shown to be efficient and safe. According to the WHO, Ritonavir-Nirmatrelvir outperforms other proposed therapeutics.",book:{id:"11690",title:"COVID-19 Drug Development - Recent Advances, New Perspectives, and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11690.jpg"},signatures:"Nicolás Padilla-Raygoza, Gilberto Flores-Vargas, María de Jesús Gallardo-Luna, Efraín Navarro-Olivos, Francisco Javier Magos-Vázquez and Daniel Alberto Díaz-Martínez"},{id:"83054",title:"Pulsatory Liposome: A Possible Biotechnological Device",slug:"pulsatory-liposome-a-possible-biotechnological-device",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.106347",abstract:"A unilamellar liposome filled with an osmotic solution is introduced into a hypotonic aqueous environment. Because of the mechanical tension induced by the osmotic flow, the vesicle swells up to a critical size, when suddenly a transbilayer pore appears and the vesicle relaxing stage starts. A part of the intracellular material leaks out through this pore, and the liposome membrane relaxes and finally recovers. The swelling begins again and the liposome experiences a periodical process. For this reason, we have named it a pulsatory liposome. The swelling of the liposome is described by a differential equation. All the processes which contribute to the vesicle relaxing and its coming back to the initial size are described by three differential equations. The pulsatory liposome can be programmed to work a number of cycles, established before. The activity of a pulsatory liposome can be characterized by the following parameters: (a) number of cycles, the length time of each cycle, and liposome activity life; (b) the length time of the swelling stage and the relaxation stage for each cycle; (c) the amount of solute leaked out through the pore in each cycle. The pulsatory liposome may be regarded as a two-stroke engine.",book:{id:"11814",title:"Liposomes - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11814.jpg"},signatures:"Dumitru Popescu and Alin Gabriel Popescu"},{id:"82962",title:"Pluralism Medical Treatment, Prevention, and Control of COVID-19 Infection and Its Long-Sufferings among the Older Adults in the Northeast of Thailand from 2019 to 2022",slug:"pluralism-medical-treatment-prevention-and-control-of-covid-19-infection-and-its-long-sufferings-amo",totalDownloads:48,totalDimensionsCites:0,doi:"10.5772/intechopen.106339",abstract:"COVID-19 in 2019 has brought both changes and challenges to the world. This global pandemic has an impact on people of all age levels, especially older adults. In Thailand, older persons are at high risk of COVID-19 infection. They are included in the so-called 608 groups. The objective of this review article was to synthesize and present medical pluralism, the development of drugs from herbs, and projects conducted to treat, prevent, and control the infection and long sufferings of COVID-19. The review covers 10 studies, three projects produced at Mahasarakham University, Chaiyaphum Rajabhat University, and Khon Kaen University that were reviewed, synthesized, and analyzed. The results of the synthesis indicate that modern and Thai traditional medicine can help reduce the severity of the infection and long sufferings of COVID-19. The medical pluralism between modern and Thai traditional medicine is needed to remedy COVID-19 cases among the older adults in the Northeast of Thailand.",book:{id:"11690",title:"COVID-19 Drug Development - Recent Advances, New Perspectives, and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11690.jpg"},signatures:"Pissamai Homchampa, Khemika Napattaradechanon, Parichat Yatniyom, Thawalrat Ratanasiri, Piyaporn Sansila, Thanawan Sirisuk, Thawalwong Ratanasiri and Amornrat Ratanasiri"},{id:"82353",title:"Pharmacovigilance of Biological Drugs",slug:"pharmacovigilance-of-biological-drugs",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.105520",abstract:"The use of biological drugs has significantly increased over the past decades and has allowed for the treatment of many life-threatening and chronic diseases. The patent expiration of biological innovative medicines enables copies of these drugs called biosimilars. The availability of biosimilars enhances competition, with the potential to improve patient access to biological medications and contribute to the financial sustainability of the healthcare systems. Unlike equivalent drugs, biosimilars are not identical but similar to their innovator products because of the differences in the manufacturing process, which is a biological process. However, they are considered comparable to their originators in safety, quality characteristics, biological activity, and efficacy. The regulatory procedures used for generic drugs cannot be applied for biosimilars, so they are subjected to rigorous characterization as well as comparative clinical studies. Since they are highly complex molecules produced from living cells, even small change in the production process can have major implications on their safety and effectiveness profile, causing a potential risk of immune-based adverse reactions. For all these reasons, for biological drugs, a robust long-term pharmacovigilance system is necessary. It is desirable that in the future, there are further guidance and resolution of the ongoing discussions on biosimilar labeling, naming, pharmacovigilance and interchangeability/substitution, to ensure the appropriate use of these drugs in clinical practice.",book:{id:"11679",title:"Pharmacovigilance and Regulations",coverURL:"https://cdn.intechopen.com/books/images_new/11679.jpg"},signatures:"Simona Guerzoni, Flavia Lo Castro, Carlo Baraldi, Giuliana Colella and Luca Pani"},{id:"82868",title:"Recent Strategies for Ocular Drug Delivery: Promises and Challenges",slug:"recent-strategies-for-ocular-drug-delivery-promises-and-challenges",totalDownloads:9,totalDimensionsCites:0,doi:"10.5772/intechopen.106335",abstract:"Ocular diseases include various anterior and posterior segment diseases. Due to the unique anatomy and physiology of the eye, efficient ocular drug delivery is a great challenge to researchers. The emerging nanoscience is playing an important role in the development of novel strategies for ocular disease management. Various active molecules have been designed to associate with nanocarriers to overcome ocular barriers and interact with certain ocular tissues. In this chapter, highlights will be made on barrier to intraocular delivery, general pathways for ocular absorption, and factors affecting intraocular bioavailability. The recent attempts of nanotechnology for treating anterior and posterior ocular diseases will be explored. This will include nanomicelles, nanoparticles, nanosuspensions, vesicular systems, in situ gel, dendrimers, contact lenses, implants, microneedles, and cell-based delivery systems. In addition, gene-based ocular delivery systems will be discussed. In this chapter, we will also provide a comprehensive overview of drug-device combinations used for ocular diseases such as glaucoma, dry eye disease, infections, and inflammations. Furthermore, drug delivery devices for ocular surgeries are discussed. Finally, challenges and future prospective of ocular delivery systems will be explored.",book:{id:"11688",title:"Advances in Drug Delivery Methods",coverURL:"https://cdn.intechopen.com/books/images_new/11688.jpg"},signatures:"Amal H. El-Kamel and Asmaa A. Ashour"},{id:"82727",title:"Mesoporous Silica Based Cancer Theranostic: A Modern Approach in Upcoming Medicine",slug:"mesoporous-silica-based-cancer-theranostic-a-modern-approach-in-upcoming-medicine",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.105447",abstract:"In case cancers are located deep inside the body and are very tough to diagnose, diagnostic tools like MRI/CT scans can be employed to detect these cancers. The major challenge in such cases is the delivery of MRI active agents or visualizing agents to the target site. In this context we will discuss different mesoporous nanoparticles that can be employed to target the tissue at a specific location, its functionalization to reach the target site (Folic acid), different simple dyes as well as specific dyes which offer theranostic functionality. The nanoparticles like mesoporous silica nanoparticles offer the possibility to load therapeutic and diagnostic agents. Its surface allow multiple functionalization and conjugations which offer target specific delivery of these agents. Moreover we will also overview different modern drug delivery inventions for offering theranostic application.",book:{id:"11688",title:"Advances in Drug Delivery Methods",coverURL:"https://cdn.intechopen.com/books/images_new/11688.jpg"},signatures:"Ajinkya Pote, Vikas Ahirrao and Vishal Pande"}],onlineFirstChaptersTotal:57},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:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{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"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:20,paginationItems:[{id:"82526",title:"Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge",doi:"10.5772/intechopen.105627",signatures:"Theocharis Kravaris and George A. 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Kleczyk, Karin Hayes and Rajesh Mehta",slug:"evaluating-similarities-and-differences-between-machine-learning-and-traditional-statistical-modelin",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Machine Learning and Data Mining - Annual Volume 2022",coverURL:"https://cdn.intechopen.com/books/images_new/11422.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:59,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}}]},overviewPagePublishedBooks:{paginationCount:9,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",institutionURL:null,country:{name:"Spain"}}}]},{type:"book",id:"7656",title:"Fuzzy Logic",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7656.jpg",slug:"fuzzy-logic",publishedDate:"February 5th 2020",editedByType:"Edited by",bookSignature:"Constantin Volosencu",hash:"54f092d4ffe0abf5e4172a80025019bc",volumeInSeries:3,fullTitle:"Fuzzy Logic",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. 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She is now a lecturer at the University of Witwatersrand, South Africa, and a principal researcher at the Health Economics and Epidemiology Research Office (HE2RO), South Africa. Dr. Moolla holds a Ph.D. in Psychology with her research being focused on mental health and resilience. In her professional work capacity, her research has further expanded into the fields of early childhood development, mental health, the HIV and TB care cascades, as well as COVID. She is also a UNESCO-trained International Bioethics Facilitator.",institutionString:"University of the Witwatersrand",institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419588",title:"Ph.D.",name:"Sergio",middleName:"Alexandre",surname:"Gehrke",slug:"sergio-gehrke",fullName:"Sergio Gehrke",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038WgMKQA0/Profile_Picture_2022-06-02T11:44:20.jpg",biography:"Dr. Sergio Alexandre Gehrke is a doctorate holder in two fields. The first is a Ph.D. in Cellular and Molecular Biology from the Pontificia Catholic University, Porto Alegre, Brazil, in 2010 and the other is an International Ph.D. in Bioengineering from the Universidad Miguel Hernandez, Elche/Alicante, Spain, obtained in 2020. In 2018, he completed a postdoctoral fellowship in Materials Engineering in the NUCLEMAT of the Pontificia Catholic University, Porto Alegre, Brazil. He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",country:{name:"Spain"}}},{id:"342152",title:"Dr.",name:"Santo",middleName:null,surname:"Grace Umesh",slug:"santo-grace-umesh",fullName:"Santo Grace Umesh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/342152/images/16311_n.jpg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"333647",title:"Dr.",name:"Shreya",middleName:null,surname:"Kishore",slug:"shreya-kishore",fullName:"Shreya Kishore",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333647/images/14701_n.jpg",biography:"Dr. Shreya Kishore completed her Bachelor in Dental Surgery in Chettinad Dental College and Research Institute, Chennai, and her Master of Dental Surgery (Orthodontics) in Saveetha Dental College, Chennai. She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\r\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\r\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Orthodontist, Assoc Prof in the Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"344229",title:"Dr.",name:"Sankeshan",middleName:null,surname:"Padayachee",slug:"sankeshan-padayachee",fullName:"Sankeshan Padayachee",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"315727",title:"Ms.",name:"Kelebogile A.",middleName:null,surname:"Mothupi",slug:"kelebogile-a.-mothupi",fullName:"Kelebogile A. Mothupi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"337613",title:"Mrs.",name:"Tshakane",middleName:null,surname:"R.M.D. Ralephenya",slug:"tshakane-r.m.d.-ralephenya",fullName:"Tshakane R.M.D. Ralephenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}}]}},subseries:{item:{id:"92",type:"subseries",title:"Health and Wellbeing",keywords:"Ecology, Ecological, Nature, Health, Wellbeing, Health Production",scope:"\r\n\tSustainable approaches to health and wellbeing in our COVID 19 recovery needs to focus on ecological approaches that prioritize our relationships with each other, and include engagement with nature, the arts and our heritage. This will ensure that we discover ways to live in our world that allows us and other beings to flourish. We can no longer rely on medicalized approaches to health that wait for people to become ill before attempting to treat them. We need to live in harmony with nature and rediscover the beauty and balance in our everyday lives and surroundings, which contribute to our well-being and that of all other creatures on the planet. This topic will provide insights and knowledge into how to achieve this change in health care that is based on ecologically sustainable practices.
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