Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3111",leadTitle:null,fullTitle:"Hydraulic Conductivity",title:"Hydraulic Conductivity",subtitle:null,reviewType:"peer-reviewed",abstract:"This book is a research publication that covers original research on developments within the Hydraulic Conductivity field of study. 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\r\n\tMinerals are one of the four groups of essential nutrients, beside vitamins, essential fatty acids, and essential amino acids. \r\n\tThis book intends to cover major mineral deficiency problems such as calcium, iron, magnesium, sodium, potassium and zinc. These minerals have very important task either on intracellular or extracellular level as well as regulatory functions in maintaining body homeostasis.
\r\n
\r\n\t \r\n\tBoth macrominerals and trace minerals (microminerals) are equally important, but trace minerals are needed in smaller amounts than major minerals. The measurements of these minerals quite differ. Mineral levels depend on their uptake, metabolism, consumption, absorption, lifestyle, medical drug therapies, physical activities etc.
\r\n
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1. Introduction
The extensive use of computers and information technology has led toward the creation of extensive data repositories from a very wide variety of application areas [1]. Such vast data repositories can contribute significantly towards future decision making provided appropriate knowledge discovery mechanisms are applied for extracting hidden, but potentially useful information embedded into the data [2].
Data mining (DM) is one of the phases in knowledge discovery in databases.It is the process of extracting the useful information and knowledge in which the data is abundant, incomplete, ambiguous and random [3], [4], [5]. DM is defined as an automated or semi-automated exploratory data analysis of large complex data sets that can be used to uncover patterns and relationships in data with an emphasis on large observational databases [6].Modern statistical and computational technologies are applied to the problem in order to find useful patterns hidden withina large database [7], [8], [9].To uncover hidden trends and patterns, DM uses a combination of an explicit knowledge base, sophisticated analytical skills, and domain knowledge.In effect, the predictive models formed from the trends and patterns through DM enable analysts to produce new observations from existing data. DM methods can also be viewed as statistical computation, artificial intelligence (AI) and database approach[10].However, these methods are not replacing the existing traditional statistics; in fact, it is an extension of traditional techniques.For example, its techniques have been applied to uncover hidden information and predict future trends in financial markets.Competitive advantages achieved by DM in business and finance include increased revenue, reduced cost, and improved market place responsiveness and awareness [11]. It has also been used to derive new information that could be integrated in decision support, forecasting and estimation to help business gain competitive advantage [9].In higher educational institutions, DM can be used in the process of uncovering hidden trends and patterns that help them in forecasting the students’ achievement.For instance, by using DM approach, a university could predict the accuracy percentage of students’ graduation status, whether students will or will not be graduated, the variety of outcomes, such as transferability, persistence, retention, and course success[12], [13].
The objective of this study is to investigate the impact of various data representations on predictive data mining models.In the task of prediction, one particular predictive model might give the best result for one data set but gives a poor results in another data set although these two datasets contain the same data with different representations [14],[15],[16], [17].This study focuses on two predictive data mining models, which are commonly used for prediction purposes, namely neural network (NN) and regression model.A medical data set (known as Wisconsin Breast Cancer) and a business data (German credit) that has Boolean targets are used for experimental purposes to investigate the impact of various data representation on predictive DM model. Seven data representations are employed for this study; they are As_Is, Min Max normalization, standard deviation normalization, sigmoidal normalization, thermometer representation, flag representation and simple binary representation.
This chapter is organized as follows.The second section describes data mining, and data representation is described in the third section.The methodology and the experiments for carrying out the investigations are covered in Section 4.The results are the subject of discussion which is presented in Section 5.Finally, the conclusion and future research are presented in Section 6.
2. Data mining
It is well known that DM is capable of providing highly accurate information to support decision-making and forecasting for scientific, physiology, sociology, the military and business decision making [13].DM is a powerful technology with great potential such that it helps users focus on the most important information stored in data warehouses or streamed through communication lines.DM has a potential to answer questions that were very time-consuming to resolve in the past.In addition, DM can predict future trends and behavior, allowing us to make proactive, knowledge-driven decisions [18].
NN, decision trees, and logistic regression are three classification models that are commonly used in comparative studies [19]. These models have been applied to a prostate cancer data set obtained from SEER (the Surveillance, Epidemiology), and results program of the National Cancer Institute. The results from the study show that NN performed best with the highest accuracy, sensitivity and specificity, followed by decision tree and then logistic regression.Similar models have been applied to detect credit card fraud. The results indicate that NN give better performance than logistic regression and decision tree [20].
3. Data representation
Data representation plays a crucial role on the performance of NN, “especially for the applications of NNs in a real world." In data representation study,[14] used NNs to extrapolate the presence of mercury in human blood from animal data.The effect of different data representations such as As-is, Category, Simple binary, Thermometer, and Flag on the prediction models are investigated.The study concludes that the Thermometer data representation using NN performs extremely well.
[16], [21] used five different data representations (Maximum Value, Maximum and Minimum Value, Logarithm, Thermometer (powers of 10), and Binary (powers of 2)) on a set of data to predict maize yield at three scales in east-central Indiana of the Midwest USA [17]. The data used to consist of weather data and yield data from farm, county and state levels from the year 1901 to 1996. The results indicate that data representation has a significant effect on NN performance.
In another study, [21] investigate the performance of data representation formats such as Binary and Integer on the classification accuracy of network intrusion detection system.Three data mining techniques such as rough sets, NN and inductive learning were applied on binary and integer representations. The experimental results show that different data representations did not cause significant difference to the classification accuracy.This may be due to the fact that the same phenomenon were captured and put into different representation formats [21]. In addition, the data was primarily discrete values of qualitative variables (system class), and different results could be obtained if the values were continuous variables.
Numerical encoding schemes (Decimal Normalization and Split Decimal Digit representation) and bit pattern encoding schemes (Binary representation, Binary Code Decimal representation, Gray Code representation, Temperature code representation, and Gray Coded Decimal representation) were applied on Fisher Iris data and the performance of the various encoding approaches were analyzed.The results indicate that encoding approaches affect the training errors (such as maximum error and root mean square error) and encoding methods that uses more input nodes that represent one single parameter resulted in lower training errors.Consequently, [22] work laid an important foundation for later research on the effect of data representation on the classification performance using NN.
[22] conducted an empirical study based on a theoretical provided by [15] to support the findings that input data manipulation could improve neural learning in NN.In addition, [15] evaluated the impact of the modified training sets and how the learning process depends on data distribution within the training sets.NN training was performed on input data set that has been arranged so that three different sets are produced with each set having a different number of occurrences of 1’s and 0’s. The Temperature Encoding is then employed on the three data sets and then being used to train NN again. The results show that by employing Temperature Encoding on the data sets, the training process is improved by significantly reducing the number of epochs or iteration needed for training. [15]’s findings proved that by changing input data representation, the performance in a NN model is affected.
4. Methodology
The methodology for this research is being adapted from [14] by using different data representations on the data set, and the steps involved in carrying out the studies are shown in Figure1 [14].The study starts with data collection, followed by data preparation stage, analysis and experiment stage, and finally, investigation and comparison stage.
Figure 1.
Steps in carrying out the study
4.1. Data collection
At this stage, data sets have been acquired through the UCI machine learning repository which can be accessed at http://archive.ics.uci.edu/ml/ datasets.html. The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for conducting empirical studieson machine learning algorithms. Two types of data have been obtained from UCI; they are Wisconsin Breast Cancer data set and German credit data set.
4.2. Data preparation
After the data has been collected in the previous stage, data preparation would be performed to prepare the data for the experiment in the next stage. Each attribute is examined and missing values are treated prior to training.
4.2.1. Data description
In this study, two sets of data are used, namely Wisconsin Breast Cancer and German Credit.Each data set is described in details in the following subsections.
4.2.1.1. Wisconsin breast cancer data set
Wisconsin breast cancer data set is originated from University of Wisconsin Hospitals, Madison donated by Dr. William H. Wolberg. Each instance or data object from the data represents one patient record.Each record comprises of information about Breast Cancer patient whose cancer condition is either benign or malignant.A total of 699 cases in the data set with nine attributes (excluding Sample Code Number) that represent independent variables and one attribute, i.e. Class represent the output or dependent variable.
Table 1 describes the attribute in the data set, code which represents the short form for this attribute, type, which shows the data type for particular attribute, domain, which represents the possible range in the value and the last column,showsthe missing values in all attributes in the study. From Table 1, only one attribute has been missing values (a total of 16 instances), and this attribute is Bare Nuclei.
No
Attribute description
Code
Type
Domain
Missing value
1
Sample code number
CodeNum
Continues
Id number
0
2
Clump Thickness
CTHick
Discrete
1 – 10
0
3
Uniformity of Cell Size
CellSize
Discrete
1 – 10
0
4
Uniformity of Cell Shape
CellShape
Discrete
1 – 10
0
5
Marginal Adhesion
MarAd
Discrete
1 – 10
0
6
Single Epithelial Cell Size
EpiCells
Discrete
1 – 10
0
7
Bare Nuclei
BareNuc
Discrete
1 – 10
16
8
Bland Chromatin
BLChr
Discrete
1 – 10
0
9
Normal Nucleoli
NormNuc
Discrete
1 – 10
0
10
Mitoses
Mito
Discrete
1 – 10
0
11
Class:
Cl
Discrete
2 for benign 4 for malignant
0
Table 1.
Attribute of Wisconsin Breast Cancer Dataset
Based on the condition of Breast Cancer patients, a total of 65.5% (458) of them has benign condition and the rest (34.5% or 241) is Malignant.
4.2.1.2. German credit dataset
German credit data set classifies applicants as good or bad credit risk based upon a set of attributes specified by financial institutions. The original data set is provided by Professor Hofmann contains categorical and symbolic attributes.A total of 1000 instances have been provided with 20 attributes, excluding the German Credit Class (Table 2). The applicants are classified as good credit risk (700) or bad (300) with no missing value in this data set.
No.
Attribute description
Code
Type
Domain
Missing value
1
Status of existing checking account
SECA
Discrete
1, 2, 3, 4
0
2
Duration in month
DurMo
Continuous
4- 72
0
3
Credit history
CreditH
Discrete
0, 1, 2, 3, 4
0
4
Purpose
Purpose
Discrete
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
0
5
Credit amount
CreditA
Continuous
250 - 18424
0
6
Savings account/bonds
SavingA
Discrete
1, 2, 3, 4, 5
0
7
Present employment since
EmploPe
Discrete
1, 2, 3, 4, 5
0
8
Instalment rate in percentage of disposable income
InstalRate
Continuous
2 – 4
0
9
Personal status
PersonalS
Discrete
1, 2, 3, 4, 5
0
10
Other debtors / guarantors
OtherDep
Discrete
1, 2, 3
0
11
Present residence since
PresentRe
Discrete
1 – 4
0
12
Property
Property
Discrete
1, 2, 3, 4
0
13
Age in years
Age
Continuous
19 – 75
0
14
Other instalment plans
OtherInst
Discrete
1, 2, 3
0
15
Housing
Housing
Discrete
1, 2, 3
0
16
Number of existing credits at bank
NumCBnk
Discrete
1,2,3
0
17
Job
Job
Discrete
1, 2, 3, 4
0
18
Number of people being liable to provide maintenance for
Numppl
Discrete
1, 2
0
19
Telephone
Telephone
Discrete
1, 2
0
20
Foreign worker
ForgnWor
Discrete
1, 2
0
21
German Credit Class
GCL
Discrete
1 good 2 bad
0
Table 2.
Attribute of German Credit Dataset
4.2.2. Data cleaning
Before using the data that has been collected in the previous stage, missing values should be identified. Several methods that could be performed to solve missing values on data, such as deleting the attributes or instances, replacing the missing values with the mean value of a particular attribute, or ignore the missing values. However, which action would be performed to handle the missing values depends upon the data that has been collected.
German credit application data set has no missing values (refer to Table 2); therefore, no action was taken on German credit data set. On the other hand, Wisconsin breast cancer data set has 16 missing values of an attribute Bare Nuclei (see Table 1). Therefore, these missing values have been resolved by replacing the mean value to this attribute. The mean value to this attribute is 3.54, since the data type for this attribute is categorical so the value was rounded to 4. Finally, all the missing values have been replaced by value 4.
4.3. Analysis and experiment
The data representations used for the experiments are described in the following subsections.
4.3.1. Data representation
Each data set has been transformed into data representation identified for this study, namely As_Is, Min Max Normalization, Standard Deviation Normalization, Sigmoidal Normalization, Thermometer Representation, Flag Representation and Simple Binary Representation.In As_Is representation, the data remain the same as the original data without any changes.The Min Max Normalization is used to transform all values into numbers between 0 and 1. The Min Max Normalization applies linear transformation on the raw data, keeping the relationship to the data values in the same range.This method does not deal with any possible outliers in the future value, and the min max formula [25] is written in Eqn. (1).
V\'=(v-Minvi)/(Maxvi-\n\tMinvi)E1
Where V’ is the new value,Min(v(i)) is the minimum value in a particular attribute, Max(v(i)) the maximum value in a particular attribute and v is the old value.
The Standard Deviation Normalization is a technique based on the mean value and standard deviation function for each attribute on the data set. For a variable v, the mean value Mean (v) and the standard deviation Std_dev(v) is calculated from the data set itself. The standard deviation normalization formula [25] is written as in Eqn. (2).
V\'=\n\t(v-meanv)std_dev(v)E2
where meanvv=\n\tSumvn\n\t std_dev(v)= sqr(sum(v2)-(sum(v)2/n)/(n-1))
The Sigmoidal Normalization transforms all nonlinear input data into the range between -1 and 1 using a sigmoid function. It calculates the mean value and standard deviation function value from the input data. Data points within a standard deviation of the mean are converted to the linear area of the sigmoid. In addition, outlier points to the data are compacted along the sigmoidal function tails. The sigmoidal normalization formula [25] is given by Eq. (3).
V\'=\n\t(v-meanv)std_dev(v)E3
Where a=v-meanvstddevvstd_dev(v)= sqr(sum(v2)-(sum(v)2/n)/(n-1))
In the Thermometer representation, the categorical value was converted into a binary form prior to performing analysis. For example, if the range of values for a category field is 1 to 6, thus value 4 can berepresented in thermometer format as "111100" [15].
In the Flag format, digit 1 is represented in the binary location for the value. Thus, following the same assumption that the range values in a category field is 1 to 6, if the value 4 needs to be represented in Flag format, the representation will be shown as "000100." The representation in Simple Binary is obtained by directly changing the categorical value into binary.Table 3 exhibits the different representations of Wisconsin Breast Cancer and German Credit data set.
Table 3.
Various dataset representations
4.3.2. Logistic regression
Logistic regression is one of the statistical methods used in DM for non-linear problems either to classify or for prediction.Logistic Regression is one of the parts of statistical models, which allows one to predict a discrete outcome (known as dependent variable), such as group membership, from a set of variables (also known as independent variables) that may be continuous, discrete, dichotomous, or a combination of any of these. The logistic regression aims to correctly predict the category of outcome for individual cases using the most parsimonious model. In order to achieve the goal, a model is created, which comprises of all predictor (independent) variables that are useful in predicting the desired target. The relationship between the predictor and the target is not linear instead; the logistic regression function is usedwhose equation can be written as Eqn. (4) [26].
θ=exp(β0+β1X1+…+βkxk)1+exp(β0+β1X1+…+βkxk)E4
Whereα = the constant from the equation andβ = the coefficient of the predictor variables. Alternatively, the logistic regression equation can be written as Eqn. (5).
logit[θ(x)]=log[θ(x)1−θ(x)]=α+(β0+β1X1+…+βkxk)E5
Anodd\'s ratio is formed from logistic regression that calculates the probability or success over the probability of failure. For example, logistic regression is often used for epidemiological studies where the analysis result shows the probability of developing cancer after controlling for other associated risks. In addition, logistic regression also provides knowledge about the relationships and strengths among the variables (e.g., smoking 10 packs a day increases the risk for developing cancer than working in asbestos mine)[27].
Logistic regression is a model which is simpler in terms of computation during training while still giving a good classification performance [28]. The simple logistic regression model has the form as in Eqn. (6), viz:
<&#OMath>meanv=\n\tSumvnE6
Taking the antilog of Eqn. (1) on both sides, an equation to predict the probability to the occurrence of the outcome of interest is as follows:
logitY=naturalloglogodds=lnϞ1-Ϟ=\n\tϏ+ϐXE7
WhereϞ=ProbabilityY=outcomeofinterestX=x,\n\taspecificvalueofX)=\n\teϏ+ϐx1+\n\t\t\teϏ+ϐsis theprobability for the outcome of interest or “event,” α is the intercept, ß is the regression coefficient, and e = 2.71828 is the base forthe system of natural logarithmsϞ can becategorical or continuous, but X is alwayscategorical.
For the Wisconsin Breast Cancer dataset, there are ten independent variables and one dependent variable for logistic regression as shown in Figure 2.However, the CodeNum is not included for analysis.
Figure 2.
Independent and dependent variables of Wisconsin Breast Cancer dataset
Similar approach is applied to German Credit dataset.
4.3.3. Neural network
NN or artificial neural network (ANN) are one of the DM techniques; defined as an information-processing system which is inspired from the function of the human brain whose performance characteristics are somehow in common with biologicalNN[30]. It comprises of a large number of simple processing units, called artificial neurons or nodes. All nodes are interconnected by links known as connections.These nodes are linked together to perform parallel distributed processing in order to solve a desired computational taskby simulating the learning process [3].
There are weights associated with the links that represent the connection strengths between two processing units. These weights determine the behavioron the network.The connection strengths determine the relationship between the input and the output for the network, and in a way represent the knowledge stored on the network. The knowledge is acquired by NN through a process of training during which the connection strengths between the nodes are modified. Once trained, the NN keeps this knowledge, and it can be used for the particular task it was designed to do [29].Through training, a network understands the relationship of the variables and establishes the weights between the nodes.Once the learning occurs, a new case can be loaded over the network to produce more accurate prediction or classification [31].
NN models can learn from experience, generalize and “see through” noise and distortion, and also abstract essential characteristics in the presence of irrelevant data [32].NN model is also described as a ‘black box’ approach which has great capacity in predictive modelling.NN models provide a high degree of robustness and fault tolerance since each processing node has primarily local connections[33]. NNs techniques are also advocated as a replacement for statistical forecasting methods because of its capabilities and performance [33], [34], [33]. However, NNs are very much dependent upon the problem at hand.
The techniques of NNs have been extensively used in pattern recognition, speech recognition and synthesis, medical applications (diagnosis, drug design), fault detection, problem diagnosis, robot control, and computer vision [36], [37]. One major application areas of NNs is forecasting, and the NNs techniques have been used as to solve many forecasting problems ([33], [36], [39], [38].
There are two types of perceptron in NN, namely simple or linear perceptron and MLP. Simple perceptron consists of only two layers; the input layer and output layer. MLP consists of at least three layers input layer, hidden layer and output layer. Figure 3illustrates the two types of perceptron.
The basic operation of NN involves summing its input weights and the activation function is applied to these layers to yield the output. Generally, there are three types of activation functions used in NN, which are threshold function, Piecewise-linear function and Sigmoid function (Figure4).Among these sigmoid function is the most commonly used in NN.
Figure 3.
Simple and MLP architecture
Figure 4.
Activation function for BP learning
Multilayer Perceptron (MLP) is one of the most common NN architecture that has been used for diverse applications, particularly in forecasting problems [40]. The MLP network is normally composed of a number of nodes or processing units, and it is organized into a series of two or more layers. The first layer (or the lowest layer) is named as an input layer where it receives the external information while the last layer (or the highest layer) is an output layer where the solution to the problem is obtained. The hidden layer is the intermediate layer in between the input layer and the output layer, and may compose with one or more layers. The training of MLP could be stated as a nonlinear optimization problem. The objective of MLP learning is to find out the best weights that minimize the difference between the input and the output. The most popular training algorithm used in NN is Back propagation (BP), and it has been used in solving many problems in pattern recognition and classification. This algorithm depends upon severalparameters such as a number of hidden nodes at the hidden layers ‘learning rate, momentum rate, activation function and the number of training to take place. Furthermore, these parameters could change the performance on the learning from bad to good accuracy [23].
There are three stages involved when training the NN using BP algorithm[36]. The first step is the feed forward of the input training pattern, second is calculating the associated error from the output with the input. The last step is the adjustment to the weight. The learning process basically starts with feed forward stage when each of input units receives the input information and sends the information to each of the hidden units at the hidden layer. Each hidden unit computes the activation and sends its signal to each output unit, and applies the activation to form response of the net for given input pattern. The accuracy of NN is provided by a confusion matrix. In a confusion matrix, the information about actual values and the predictive values are illustrated in Table 4.Each row of the matrix represents the actual accounts of a class of target for the actual data, while each column represents the predictive value from the actual data. To obtain the accuracy of NN, the summation of the correct instance will be divided by the summation for all instances. The accuracy of NN is calculated using Eqn. (7).
YE8
Based on Table 4, the Percentage of correct is calculated as:
Experiments are conducted to obtain a set of training parameters that gives the optimum accuracy for both data sets.Figure.5shows general architecture of NN for the Wisconsin Breast Cancer data set.Note that the ID number is not including in the architecture.
Figure 5.
Neural Network architecture for Wisconsin Breast Cancer
Similar architecture can be drawn for German Credit dataset; however, the number of hidden units and output units will be different from the Wisconsin Breast Cancer.
4.4. Investigation and comparison
The accuracy results obtained from previous experiments are compared and investigated further.Two data sets are considered for this study, the Logistic regression and Neural Network.Logistic regression is a statistical regression model for binary dependent variables [24], which is simpler in terms of computation during training while still giving a good classification performance [27]. Figure 6shows the general steps involve in performing logistic regression and NN experiments using different data representations in this study.
Figure 6.
Illustration of Data Representation for NN/ Regression analysis experiments
5. Results
Investigating the prediction performance on different data sets involves many uncertainties for a different data type.In the task of prediction, one particular predictive model might give the best result for one data set but gives the poor results in another data set although these two data sets contain the same data with different representations [14],[15],[16], [17].
Initial experimental results of correlation analysis on Wisconsin Breast Cancer indicate that all attributes (independent variables)has significant correlation with the dependent variable (target).However, German Credit data set indicates otherwise.Therefore, for German Credit data set, two different approaches (all dependent variables and selected variables) were performed in order to complete the investigation.
Based on the results exhibited in Table 5, although NN obtained the same percentage of accuracy, As_Isachieved the lowest training results (98.57%, 96.24%).On the other hand, regression exhibits the highest percentage of accuracy for ThermometreandFlag representation (100%) followed by Simple Binary representation.
Referring to the result shown in Figure 7, similar observation has been noted for German Credit data set when all variables are considered for the experiments.As_Isrepresentation obtained the highest percentage of accuracy (79%) for NN model.For regression analysis, Thermometer and Flag, representation obtained the highest percentage of accuracy (80.1%).Similar to earlier observation on the Wisconsin Breast Cancer dataset. Simple Binary representation obtained the second highest percentage of accuracy (79.5%).
Wisconsin Breast Cancer
Neural Network
Regression
Train
Test
Accuracy
As_Is representation
96.24%
98.57%
96.9%
Min Max normalization
96.42%
98.57%
96.9%
Standard Deviation normalization
96.42%
98.57%
96.9%
Sigmoidal normalization
96.60%
98.57%
96.9%
Thermometer representation
97.14%
98.57%
100.0%
Flag representation
97.67%
98.57%
100.0%
Simple Binary representation
97.14%
98.57%
97.6%
Table 5.
Percentage of accuracy for Wisconsin Breast Cancer Dataset
Figure 7.
German Credit All Variables accuracy for Neural Network and Regression
When selected variables of German Credit data set was tested with NN, the highest percentage accuracy was obtained using As_Is representation (80%), followed by Standard Deviation Normalization (79%) Min Max Normalization (78%) and Thermometer (78%) representation.The regression results show similar patterns with results illustrated in Figure.In other words, the data representation techniques, namely Thermometer (77.4%) and Flag(77.4%) representations produce the highest and second highest percentage of accuracy for selected variables of German Credit.
Figure 8.
German Credit Selected Variables accuracy for Neural Network and Regression
For brevity, Table 6 exhibits NN parameters that produce the highest percentage of accuracy for Wisconsin Breast Cancer, and German Credit data set using all variables as well as selected variables in the experiments.
Neural Network
Wisconsin Breast lCancer
German credit using all variables
German credit using selected variables
Percentage of Accuracy
98.57%
80.00%
79.00%
Input units
9
20
12
Hidden units
2
6
20
Learning rate
0.1
0.6
0.6
Momentum rate
0.8
0.1
0.1
Number of epoch
100
100
100
Table 6.
The summary of NN experimental results using As_Is representation
The logistic regression and correlation results for Wisconsin Breast Cancer data set are exhibited in Table 7.Note that based on Wald Statistics, variables such as CellSize, Cellshape, EpiCells, NormNuc and Mito are not significant in the prediction model.However, these variables have significant correlation with Type of Breast Cancer.Thus, the logistic regression independent variables include all variables listed in Table 7.
Logistic Regression
Correlation
Variables
B
Sig.
R
p
CTHick
.531
.000
CellSize
.006
.975
.818(**)
.000
CellShape
.333
.109
.819(**)
.000
MarAd
.240
.036
EpiCells
.069
.645
.683(**)
.000
BareNuc
.400
.000
BLChr
.411
.009
NormNuc
.145
.157
.712(**)
.000
Mito
.551
.069
.423(**)
.000
Constant
-9.671
.000
Table 7.
List of variables included in logistic regression of Wisconsin breast cancer
For German Credit data set, NN obtained the highest percentage of accuracy when all variables are considered for the training (see Table 6).The appropriate parameters for this data set are also listed in the same table. The summary of logistic regression results is shown in Table 8.All shaded variables displayed in Table 8 are significant independent variables for determining whether a credit application is successful or not.
Note also that variable age is not significant to German Credit target.However, its correlation with the target is significant. Therefore, these are variable included in logistic regression equation that represents German credit application.
Regression (Thermometer representation)
German Credit using all variables (80%)
Variables
Logistic Regression
Correlation
B
Sig.
R
p
SECA
-.588
000
-.348(**)
.000
DurMo
.025
.005
.206(**)
.000
CreditH
-.384
.000
-.222(**)
.000
CreditA
-.384
.018
.087(**)
.003
SavingA
-.240
.000
-.175(**)
.000
EmploPe
-.156
.029
-.120(**)
.000
InstalRate
.300
.000
.074(**)
.010
PersonalS
-.267
.022
-.091(**)
.002
OtherDep
-.363
.041
-0.003
.460
Property
.182
.046
.141(**)
.000
Age
-.010
.246
-.112(**)
.000
OtherInst
-.322
.004
-.113(**)
.000
Forgn Work
-1.216
.047
-.082(**)
.005
Constant
4.391
.000
Table 8.
List of variables included in logistic regression of German Credit dataset
6. Conclusion and future research
In this study, the effect of different data representations on the performance of NN and regression was investigated on different data sets that have a binary or boolean class target.The results indicate that different data representation produces a different percentage of accuracy.
Based on the empirical results, data representation As_Isis a better approach for NN with Boolean targets (see also Table 9). NN has shown consistent performance for both data sets.Further inspection of the results exhibited in Table 6 also indicates that for German Credit data set, NN performance improves by 1%.This leads to suggestion that by considering correlation and regression analysis, both NN results using As_Isand Standard Deviation Normalization could be improved.For regression analysis, Thermometer, Flag and Simple Binary representations produce consistent regression performance.However, the performance decreases when the independent variables have been reduced through correlation and regression analysis.
As for future research, more data sets will be utilized to investigate further on the effect of data representation on the performance of both NN and regression.One possible area is to investigate which cases fail during training, and how to correct the representation of cases such that the cases will be correctly identified by the model.Studying the effect of different data representations on different predictive models enable future researchers or data mining model\'s developer to present data correctly for binary or Boolean target in the prediction task.
German Credit All Variables
German Credit Selected Variables
Neural Network
Regn
Neural Network
Regn
Train
Test
Train
Test
As_Is representation
77.25
79.00
77.0
75.00
80.00
76.8
Min Max normalization
76.50
76.00
77.0
75.25
78.00
76.8
Standard Deviation normalization
76.75
77.00
77.0
75.13
79.00
76.8
Sigmoidal normalization
76.75
77.00
77.0
74.00
75.00
76.6
Thermometerrepresentation
78.38
78.00
80.1
77.00
78.00
77.4
Flag representation
76.75
77.00
80.1
75.13
73.00
77.4
Simple Binary representation
75.75
74.00
79.5
70.63
70.00
77.1
Table 9.
Summary of NN and regression analysis of German Credit dataset
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Rajib Hasan",authors:[{id:"17159",title:"Dr.",name:"Fadzilah",middleName:null,surname:"Siraj",fullName:"Fadzilah Siraj",slug:"fadzilah-siraj",email:"fadzilahsiraj@gmail.com",position:null,institution:null},{id:"157227",title:"Mr.",name:"Md Rajib",middleName:null,surname:"Hasan",fullName:"Md Rajib Hasan",slug:"md-rajib-hasan",email:"rajib@live.com.my",position:null,institution:{name:"Northern University of Malaysia",institutionURL:null,country:{name:"Malaysia"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Data mining",level:"1"},{id:"sec_3",title:"3. Data representation",level:"1"},{id:"sec_4",title:"4. Methodology",level:"1"},{id:"sec_4_2",title:"4.1. Data collection",level:"2"},{id:"sec_5_2",title:"4.2. Data preparation",level:"2"},{id:"sec_5_3",title:"Table 1.",level:"3"},{id:"sec_6_3",title:"4.2.2. Data cleaning",level:"3"},{id:"sec_8_2",title:"4.3. 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School of Computing, College of Arts and Sciences, University Utara Malaysia, Sintok, Kedah, Malaysia
School of Computing, College of Arts and Sciences, University Utara Malaysia, Sintok, Kedah, Malaysia
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Nurgat",authors:[{id:"141606",title:"Dr.",name:"Ahmed",middleName:null,surname:"Al-Jedai",fullName:"Ahmed Al-Jedai",slug:"ahmed-al-jedai"},{id:"144489",title:"Mr.",name:"Zubeir",middleName:null,surname:"Nurgat",fullName:"Zubeir Nurgat",slug:"zubeir-nurgat"}]},{id:"38580",title:"Incorporating Domain Knowledge into Medical Image Mining",slug:"incorporating-domain-knowledge-into-medical-image-mining",signatures:"Haiwei Pan",authors:[{id:"144521",title:"Dr.",name:"Haiwei",middleName:null,surname:"Pan",fullName:"Haiwei Pan",slug:"haiwei-pan"}]},{id:"38569",title:"Discovering Fragrance Biosynthesis Genes from Vanda Mimi Palmer Using the Expressed Sequence Tag (EST) Approach",slug:"discovering-fragrance-biosynthesis-genes-from-vanda-mimi-palmer-using-the-expressed-sequence-tag-est",signatures:"Seow-Ling Teh, Janna Ong Abdullah, Parameswari Namasivayam and Rusea Go",authors:[{id:"140525",title:"Dr.",name:"Janna",middleName:"Ong",surname:"Abdullah",fullName:"Janna Abdullah",slug:"janna-abdullah"}]},{id:"38664",title:"Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization",slug:"region-of-interest-based-image-classification-a-study-in-mri-brain-scan-categorization",signatures:"Ashraf Elsayed, Frans Coenen, Marta García-Fiñana and Vanessa Sluming",authors:[{id:"149756",title:"Dr.",name:"Frans",middleName:null,surname:"Coenen",fullName:"Frans Coenen",slug:"frans-coenen"}]},{id:"38572",title:"Visual Exploration of Functional MRI Data",slug:"visual-exploration-of-functional-mri-data",signatures:"Jerzy Korczak",authors:[{id:"145711",title:"Prof.",name:"Jerzy",middleName:null,surname:"Korczak",fullName:"Jerzy Korczak",slug:"jerzy-korczak"}]},{id:"38579",title:"Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)",slug:"data-mining-techniques-in-pharmacovigilance-analysis-of-the-publicly-accessible-fda-adverse-event-re",signatures:"Elisabetta Poluzzi, Emanuel Raschi, Carlo Piccinni and Fabrizio De Ponti",authors:[{id:"140549",title:"Prof.",name:"Fabrizio",middleName:null,surname:"De Ponti",fullName:"Fabrizio De Ponti",slug:"fabrizio-de-ponti"},{id:"142567",title:"Dr.",name:"Elisabetta",middleName:null,surname:"Poluzzi",fullName:"Elisabetta Poluzzi",slug:"elisabetta-poluzzi"},{id:"142568",title:"Dr.",name:"Emanuel",middleName:null,surname:"Raschi",fullName:"Emanuel Raschi",slug:"emanuel-raschi"},{id:"142569",title:"Dr.",name:"Carlo",middleName:null,surname:"Piccinni",fullName:"Carlo Piccinni",slug:"carlo-piccinni"}]},{id:"38584",title:"Examples of the Use of Data Mining Methods in Animal Breeding",slug:"examples-of-the-use-of-data-mining-methods-in-animal-breeding",signatures:"Wilhelm Grzesiak and Daniel Zaborski",authors:[{id:"143973",title:"Prof.",name:"Wilhelm",middleName:null,surname:"Grzesiak",fullName:"Wilhelm Grzesiak",slug:"wilhelm-grzesiak"},{id:"156311",title:"Dr.",name:"Daniel",middleName:null,surname:"Zaborski",fullName:"Daniel Zaborski",slug:"daniel-zaborski"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"67299",title:"Salivary Diagnostics in Oral Diseases",doi:"10.5772/intechopen.85831",slug:"salivary-diagnostics-in-oral-diseases",body:'\n
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1. Introduction
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Early diagnosis of the disease, quantification of the disease and prognosis of the treatment are the vital steps in controlling and preventing the diseases that would damage the person’s quality of life. Diagnosing these diseased conditions has become challenging and thus necessitates complementing clinical evaluation with laboratory testing [1]. It is essential to have a thorough knowledge in order to control or prevent a disease. When we know the particular clinical, radiological, and histological and laboratory characteristics of the disease, it is easy to prevent the disease condition.
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Chronic non-communicable diseases are the major public health problems faced by many of the developed and developing countries in the world. Unlike most of the communicable diseases, chronic non-communicable diseases are initiated by multiple risk-factors. Identifying such risk factors is vital to control the disease burden. Identifying unique compound in the diseased body, which is sensitive and specific to that particular disease can help in identifying and measuring the disease status.
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2. Why saliva is used for diagnosis?
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Saliva was used as a screening tool for cystic fibrosis in the early 1960s [2, 3]. Saliva is the exudate of serum; hence saliva also contains all the biological compounds like hormones, growth factors, antibodies, enzymes, microbes and their products [2, 4, 5]. Salivary diagnostics has become popular these days as collecting saliva is non-invasive, inexpensive, less technique sensitive and easy to perform as compared to serum.
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3. Oral diseases
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3.1 Dental caries and saliva
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Dental caries is a multifactorial oral disease resulting in demineralization of mineralised tissues and denaturation of organic tissues of the teeth initiated by acid production by cariogenic bacteria. These are the major reasons for the loss of teeth among the population. Saliva has protective action in maintaining oral health by its buffering action, antibacterial action and cleansing effect. The awareness of functional properties of saliva as well as those of its distinct components may permit a better valuation of dental caries susceptibility [6].
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4. Caries susceptible tests
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It refers to intrinsic propensity of the host tissue, the tooth, to be affected by the carious process. It uses saliva as a diagnostic tool in detection and progression of dental caries. Saliva used for caries susceptibility measurement can be explained in four parts:
5.1 Sampling oral microbial community through saliva (salivary microbiomics)
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When considering the hard tissue diseases in oral cavity (dental caries), sampling from the acquired pellicle (AP) provides more sensitive and precise protein profile compared to that of saliva [7, 8, 9, 10]. Here saliva plays like carrier of the disease biomarkers.
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Only few fractions of known proteins found in human saliva (130/2290 proteins), which are originated from acquired pellicle on dental enamel. The exact biological functions of 51% of known proteins are unknown [8]. This lack of knowledge will provide a new vista to further research.
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The particular species of microbial community grow on the AP which is specific to individual oral disease. The quality and quantification of these species can provide a clearer picture on diagnosis and find out the severity of the oral disease.
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For example, patients exhibiting dental caries demonstrates dominated acidogenic and acid-tolerant Gram-positive bacteria (i.e., Streptococcus and Lactobacilli sp) [11].
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Some of the salivary determinant tests are as follows
Lactobacillus colony count test: assessment of the number of acidogenic bacteria can be done by calculating the number of colonies appearing on Tomato peptone agar plates (pH 5.0) after inoculation with a sample of saliva.
Streptococcus mutans level in saliva: this measures the number of S. mutans colony forming units per unit volume of saliva.
Saliva/tongue blade method: estimation of the number of S. mutans in Mutans Salivarius Bacitracin (MSB) agar inoculated by paraffin-stimulated saliva/Saliva-contaminated wooden spatulas [12].
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5.2 Colorimetric methods
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Snyder’s test [7, 13]: the caries susceptibility is correlated with production of acid that is assumed to result due to the fermentation of specific amount of glucose by cariogenic Lactobacillus species by inoculating saliva into agar containing bromocresol green indicator.
Albans test: this is a simpler version of Snyder’s test in which the patient expectorates directly into tubes that contain the medium. In this test, somewhat softer medium is used that permits the transmission of saliva and acids without the requirement of melting the medium [7].
Swab test: the principle of this test is same as that of Snyder’s test i.e., capacity of salivary microorganism to form organic acids from a specific carbohydrate medium. The medium encompasses a colour changing indicator dye, bromocresol, which changes its colour when the environment changes its Ph.
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5.3 Enzymatic biomarkers
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Salivary reductase test: this test quantifies the activity of the reductase enzyme present in salivary bacteria.
Peroxidase: peroxidase is a salivary enzyme which neutralises the toxic compound (hydrogen peroxide) produced by oral microorganisms and reduces production of acid in the dental plaque. This severely reduces the plaque accumulation thereby reduces the plaque related diseases like dental caries and periodontal diseases [14].
Collagenase: it represents in extracellular fluids like serum or saliva represents tissue destruction or cell death. In saliva it indicates destruction of pulp (severe dental caries) or destruction of periodontal tissue [15].
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6. Physical properties of saliva
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Salivary buffer capacity test:
This test measures the amount of millilitres of acid involved in lowering the Ph of saliva via an arbitrary Ph interval, such as, from Ph 7.0 to 6.0 or the quantity of acid or base essential to bring colour indicators to their end point [16].
Population level researches salivary flow rate and buffer effect show a contrary correlation with caries susceptibility [17].
Flow rate:
Many researches showed that, higher the flow rate showed quicker is the salivary clearance [17, 18, 19] and greater is the buffer capacity [18] Reduced salivary flow rate and the associated reduction of oral defence systems may cause severe caries and mucosal inflammations [16, 17, 20].
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7. Salivary ions
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Calcium ion is the most extensively researched salivary ion for dental caries and periodontal disease. Demineralisation of teeth or bone leads to leach out excess of calcium ion into saliva. Increased levels of salivary calcium ion indicate the presence and severity of oral diseases. Similarly increased selenium content in saliva and food is the indicative of dental caries in subjects [21].
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8. Periodontal disease and saliva
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Diagnosing active phases of periodontal diseases, and identifying those at risk for active disease has been challenging for both clinicians and investigators. Since saliva can be easily collected which contains the locally derived and systemically derived markers of periodontal disease, it can provide specific diagnostic test for periodontitis.
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Enzymes present in saliva are contributed by the cells of the salivary glands, oral microorganisms, PMNs, epithelial cells and GCF entering the oral cavity. Studies have shown a reliable relationship between enzyme activity and periodontal status and its response to the periodontal treatment.
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Saliva can be categorised in to two types, whole saliva and saliva from specific glands [22, 23, 24]. Differences in the amount of fluid and constituents of each gland can be determined in gland specific saliva.
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Whole saliva which consists of oral fluids, secretions from the major and minor salivary glands, non-salivary constituents, GCF, bronchial secretions, serum, blood cells, food debris and microorganisms along with their products can also be used as a diagnostic tool.
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It has been found that, proposed markers for diseases such as proteins of host origin (i.e., enzymes, Ig), phenotypic markers such as epithelial keratins, host cells, hormones, microorganisms, volatile compounds and ions are found in saliva [25].
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Some of the major salivary biomarkers are discussed below:
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1. Salivary proteins:
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Salivary proteins are formed from combined Direction of DNA and RNAs. Any disparity in DNA or RNA stands can lead to altered protein formation which leads to disease condition and the altered protein becomes the marker of that disease. A patient’s salivary protein mapping can provide details on entire body’s health because saliva is an exudate of blood and contains juices from gingival crevicular fluid along with major and minor salivary glands, and is much less invasive and more acceptable to the patients compared to blood sampling [10, 26, 27, 28] Salivary proteins play a major role in adhesion of microbes on tooth surface through AP formation by stereo-specific mechanism [10, 29]. Salivary proteins can regulate adherence of microorganism by using the carboxylterminal of histatin and of acidic proline-rich proteins (PRPs) by promoting or reducing the attachment to the protein [30, 31, 32].
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Some of the proteins involved are
Enzymes:
Lysozyme
Peroxidase
Collagenase
Acidic and alkaline phosphatase
Glycoproteins and proline rich proteins
Dextran
Acquired pellicle forming proteins
Lactic acid and pyruvic acid
Fibronectin
Hormones
Histatin
Matrix metallo proteins
Other proteins
Cystatin
Amino acids
Growth factors and vascular endothelial growth factors
Salivary enzymes:
Lysozyme: lysozyme is an antimicrobial enzyme secreted in human saliva with the ability to hydrolase the 1,4-beta-linkages between N-acetylmuramic acid and N-acetyl-d-glucosamine existing in peptidoglycan, which is the major component of Gram-positive cell wall. Patients with low levels of lysozyme in saliva are more vulnerable to accumulation of dental plaque, which is considered a risk factor for oral disease and increased salivary lysozyme activity indicates recent infection in oral cavity [33].
Peroxidase: it inhibits the hydrogen peroxide formation by microbes and there by prevents accumulation of plaque formation. This directly stops the plaque related oral diseases like periodontitis and dental caries. Quantification of this enzyme directly proportional to the severity of the disease status [14].
Collagenase: collagenase is also known to as MMP-13, is collagenolytic MMP with remarkably extensive substrate specificity. Presence of this in extracellular fluid like serum and saliva indicate tissue destruction or cell death. In saliva this may indicate destruction of periodontal tissue, pulp tissue, infective necrosis or carcinogenic destructions [15].
Acid and alkaline phosphatase: the enzyme alkaline phosphatase (ALP) and acid phosphatase (ACP) are the twin counterpart enzymes which occur in many organisms ranging from bacteria to man, basically functions by catalysing or blocking the hydrolysis of monoesters of phosphoric acid and also catalyse or block a trans-phosphorylation reaction in the presence of large concentrations of phosphate acceptors [34]. Some researches conveyed amplified activity of acid phosphatase and alkaline phosphatase in the acute stage of periodontal disease, and also observed recovery of enzyme level to the normal range after periodontal therapy [35, 36].
Glycoproteins and proline rich proteins:
Dextran: dextran is a complex, branched glucan (polysaccharide made of many glucose molecules) composed of chains of varying lengths which helps the plaque to attach to the host tissue. Measuring the level of salivary dextran level can helps in calculating the plaque attachment status. There by calculating the plaque related diseases quantitatively [37].
Acquired pellicle forming proteins: salivary proteins play a chief role in bonding of microbes on tooth surface through acquired pellicle formation by stereo-specific mechanism [29]. These salivary proteins decide the type of microbes to grow. Hence identifying these proteins which are specific to grow selective bacteria will provide knowledge about the type of disease.
Fibronectin: fibronectin is a glycoprotein that mediates adhesion between cells and encourages selective adhesion and colonisation of certain favourable bacterial species. It also involved in inflammation, chemotaxis, and wound healing and tissue repair [23, 38]. Quantification of fibronectin in saliva provides a clear picture on periodontal disease status.
Hormones:
Cortisol: cortisol is a hormone highly sensitive to emotional changes and is stress related. It provides anti-inflammatory and immunosuppressive effect. This has direct effect on oral infection and dental plaque related illness [38, 39]. Even though lots of researches indicate the relation of cortisol and oral diseases, more specific and confirmatory researches are needed.
Histatin:
Histatin is a salivary protein secreted from parotid and submandibular glands with definite antimicrobial properties. It interacts with endotoxic lipopolysaccharides situated in the membrane of Gram-negative bacteria and neutralises it. It also has antihistaminic action, hence influences oral inflammation [38, 40, 41].
Matrix metallo proteins:
They are host proteinases accountable for both tissue degradation and remodelling. The presence of these metallo proteins indicates tissue destruction. MMP 8 is the most predominant MMP found in ill periodontal tissue and GCF. Recent researches reviled that the level of MMP-8 was highly raised in saliva of patients with periodontal disease [38, 42].
Other proteins:
Cystatin: this is a proteolytic enzyme produced by pathogenic bacteria, inflammatory cells, fibroblasts and osteoclasts which have collagenolytic property. It helps in spreading of oral disease to different planes also progress the disease status [23, 38]. Measuring the level in saliva can explain the severity of the oral diseases like periodontal disease and potentially malignant disease.
Amino acids: some of the amino acids like Proline shows increased levels in saliva of periodontally ill subjects compared to the healthy individuals. This may be due to degradation of salivary proteins by bacterial activities [38, 43, 44]. Presence of proline in saliva directly proportional to the level of bacteria in dental plaque. Hence it quantifies the plaque related oral diseases.
Growth factors and vascular endothelial growth factors: these are angiogenic cytokines associated with inflammation and healing tissue. Higher level of these proteins in saliva observed in the inflammatory conditions (periodontal diseases) or growth of tumour (Malignant tumours) [23, 38].
Human salivary immune system:
Immunoglobulins: immunoglobulins are specific first-line defence mechanism of saliva. The chief immunoglobulin in saliva is secretory IgA (sIgA), which is produced by the plasma cells in the salivary glands. IgA has 2 subclasses, IgA1 which is predominated in serum and IgA2, which is predominantly present in secretions like saliva, milk and sweats [38, 45]. Many researches show the positive correlation between severity of inflammation (Periodontal disease) and IgA concentration [14, 46].
Salivary neutrophil count: neutrophils play a major role in the innate immune response. Most of the oral diseases are associated with infection and inflammations, which are explained in the form of neutrophil count in saliva. Very meagre research directed to correlate the neutrophils in plaque, saliva, and gingival crevicular fluid (GCF) to periodontally healthy and diseased subjects. These researches reviled that there is positive correlation between oral diseases with PMN counts [47, 48, 49].
Salivary ions:
Calcium ion in saliva indicates demineralization of teeth or alveolar bone which leaches out into saliva indicating severity of dental and periodontal diseases [21].
Oxidative stress assessment:
Redox (reduction and oxidation) reactions are common in all cells. But imbalance between reduction and oxidation process can lead to oxidative stress within the cell which destroys the cell. Oxidative stress was involved in the progression of periodontal diseases [50, 51, 52]. In chronic periodontitis, there was lower serum total antioxidant level when equated to the control individuals [52, 53] Biomarkers of lipid peroxidation (one of the oxidative stress-mediated pathways) such as 8-isoprostane and malondialdehyde (MDA) were elevated in patients with chronic periodontitis [52, 54, 55, 56].
Salivary Microbiomics:
The particular species of microbial community grow on the AP which is specific to individual oral disease. The quality and quantification of these species can provide a clearer picture on diagnosis and find out the severity of the oral disease. Patients with periodontal disease Express increased percentage of obligately anaerobic bacteria (i.e., Gram-negative species) [10, 57].
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Some of the literatures on salivary biomarkers to detect periodontal diseases are as follows:
levels of caprylate esterase lipase, leucine, valine and cysteine aminopeptidases, trypsin, B-galactosidase, B-glucuronidase and B-glucosidase, sub gingival black pigmented bacteroides and motile organisms
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A decrease was seen in the levels of all the biomarkers after the treatment in AP
TIMP-1 was lower in patients with periodontal disease and total collagenase activity was higher in diseased patients
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9. Oral cancer and saliva
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Oral cancer is 6th most common human malignancies with approximately 50% mortality rate in 5 years. Oral cancer is a potentially lethal disease and the result of the treatment and prognosis largely determined by primary diagnosis. For prevention, treatment and for prognosis, it is essential to measure the disease objectively and accurately in quantitative manner. Quantification of biochemical or molecular specific products of cancers in serum or localised body juices can be one of the current methods of measuring oral cancer objectively. Salivary diagnostics has influenced several researchers and has been verified as an important tool in the diagnosis of many systemic conditions and prognosis of the disease. Developments in the ground of molecular biology, salivary genomics and proteomics have directed to the detection of novel molecular markers for oral cancer diagnosis, therapeutics and prognosis.
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Several research groups have found that salivary levels of specific proteins are increased in whole saliva of patients with oral squamous cell carcinoma. For example, CD44 (a cell surface glycoprotein involved in cell-to-cell interaction), 44 Cyfra 21-1 (a fragment of cytokeratin 19), tissue polypeptide antigen (TPS), and cancer antigen 125 (CA-125) have been suggested as oral cancer biomarkers [2, 66].
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Abundant biomarkers have been studied for finding of oral potentially malignant diseases and cancer, until now no further research has been done. Whole saliva was used to conduct most of the research projects. Whole saliva comprises of juices from all major and minor salivary glands, as well as liquids from mucosal and periodontal tissues, which are influenced by oral and systemic environments and by host immune responses and carry the essence of disease product with them. Measuring the levels of disease product will provide a specific diagnostic and prognostic value. Additionally, least requirement of money, material and manpower makes it more economical, less time consumption, and no special training make this methodology easily accepted.
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10. Usage of stimulated or un-stimulated saliva for collection
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Unstimulated saliva is considered as an ideal sample because it has no effect on flow rate and salivary composition of salivary glands. But some researches shows that stimulated saliva also can provide equally or more precise detection of cancer biomarkers [67, 68]. Limited researches were performed in these directions on the effect of stimulated and unstimulated saliva on salivary biomarkers of cancer. Hence, it needs to be studied to further standardise the salivary biomarkers.
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11. Storage and transportation of saliva
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Saliva is very sensitive sample, which is influenced by systemic, physiological, microbial, environmental (food products) and biochemical changes in oral cavity. It also fluctuates with time of collection, Ph of the surrounding environment, temperature type of saliva collection method, and storage methods [69, 70, 71, 72, 73] Some study reported that storage at −80°C provides less biochemical and microbial changes in saliva hence gives better results as compared to −20°C [72] . To avoid altered results, there should be least time gap between sample collection and analysis.
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12. Classification of salivary biomarkers for oral cancer
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Biomarkers have been classified based on biomolecules and disease states [74, 75].
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Based on biological molecules:
DNA biomarkers
RNA biomarkers
Protein biomarkers
Enzymes
Hormones
Glycoproteins
Salivary ions
Oxidative stress assessment
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Based on disease state
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Diagnostic biomarkers
Prognostic biomarkers
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More than 100 potential salivary biomarkers have been reported till date, they can be explained with following headings:
Peptides
Proteins
DNA
Salivary mRNA
Peptides: some of the researches explain that polypeptides like defensin which possess cytotoxic and antimicrobial properties, which exhibits their presence in azurophil granules of polymorphonuclear leukocytes is one of the potent biomarker of oral cancer. OSCC can be distinguished even in their earlier stages by the raised levels of salivary defensin-1 matched with healthy controls [67, 76].
Proteins: major portion of salivary biomarkers are protein in nature. Most of the proteins share their presence with other diseases or environmental factors (food), this exhibits higher levels of sensitivity with low specificity. Some of the protein biomarkers showed significant elevated level in saliva such as interleukins (8, 6, 1b), matrix metalloproteinase (MMP 2, 9), transforming growth factor (TGF-1), Ki67, cyclic D1, transferrin, amylase, tumour necrosis factor (TNF-a) and catalase among saliva of oral squamous cell carcinoma by various studies [77]. Protein CD44 showed elevated levels in saliva (oral rinse) of oral squamous cell carcinoma patients (n = 102) matched to controls (n = 69) [78]. some of the researches confirmed that IL-8 and IL-6 are informative biomarkers for OSCC, where IL-8 and IL6 showed elevated levels of concentration in saliva and serum respectively [79].
Enzymes:
Lysozyme: lysozyme is an antimicrobial enzyme secreted in human saliva with the capability to hydrolase peptidoglycan, which is the major constituent of Gram-positive cell wall. Lysozyme shows antitumor properties by direct activation of immune cells or it can raise tumour cell immunogenicity. And also, lysozyme can release elements from bacteria (peptidoglycans and/or polyribopyrimidinic acids) responsible for immune-potentiation and therefore antitumor activity [80].
Pyruvate kinase M2 isoenzymes: cancer cell uses metabolic alteration (Warberg’s effect) for their survival and growth. They chose anaerobic respiration for their energy needs even though abundant availability of oxygen. Pyruvate kinase muscle iso-enzyme M2 (PKM2) is a glycolytic enzyme and a vital enzyme in tumour cell metabolism and growth. Increased level in serum and saliva indicate presence of malignancy [81].
Collagenase: collagenase is the major intracellular enzyme explains the destruction of the tissue if found in extracellular fluids like saliva. In saliva this may indicate destruction of periodontal tissue, pulp tissue, infective necrosis or carcinogenic destructions [15].
Acid and alkaline phosphatase: a study clarifies that the salivary ALP enzyme displays significantly higher levels in subjects with diabetes mellitus, smokers and subjects with potentially malignant disorders without any periodontitis associated to systemically healthy persons [82]
Glycoproteins:
Lactic acid and pyruvic acid: pyruvic acid and lactic acids were produced as the end product in the physiologic process of glycolysis [83, 84]. This energy creation cascade carry on by using the end product (pyruvate) of glycolysis as a fuel to Krebs cycle in mitochondria by oxidative phosphorylation. But this process gets interrupted in malignant cells where cell undergoes fermentation of sugar molecule (anaerobic respiration) even though enough presence of oxygen. This process is known as Warberg’s effect. This leads to accumulation of the excess PA and LA. Hence quantification can give a clearer picture on the stages of cancer [83].
Salivary ions: most of the researches explain excess and deficient levels of copper or zinc were significant correlation with oral cancer risk [85]
Oxidative stress assessment: oxidative stress is the product of a discrepancy between oxidant factors and protective antioxidant systems; it may occur due to an excess of free radicals, or by the shrinking of the antioxidant systems.
Oxidative stress was also associated with oral cancer, as amplified lipid peroxidation and reduced antioxidants was reported in patients suffering from stage II, III, and IV oral cancer [52, 86].
DNA: DNA is highly specific type of biomarker used for detection of cancer. It requires high end operating machineries and also requires special training to operate it. This makes it more expensive biomarker to detect. Boyle et al. observed that OSCC exhibits p53 mutations in 71% of saliva samples by using plaque hybridization technique [87]. Aberrant methylation of p16, MGMT and DAP-K in OSCC patients was recognised by Rosas et al.
Salivary mRNAs: oral carcinogenesis can be detected by measuring the elevated six mRNA molecules such as DUSP1, H3F3A, IL 1B, IL 8, SAT and S100 [88].
DUSP1 (dual specificity phosphatase 1): DUSP mRNA involved in protein modification, oxidative stress, and signal transduction and participates in MAPK (Mitogen Activated Protein Kinase) pathway. Molecular studies showed that the carcinogenesis is associated with hypermethylation of DUSP1 gene [89].
H3F3A: these proteins are nuclear proteins, located in chromosome 1, responsible for the structural integrity of chromosomal nucleosome and acts as a one of the proliferative marker for oral cancer.
IL IB: Interleukin 1 beta is a chemical mediator of cell proliferation, differentiation, and apoptosis and also inflammation. Though it represents its presence in other pathological and physiological aspects, it shows Elevated serum levels in patients with oral squamous cell carcinoma.
IL 8: Interleukin 8 (neutrophil chemotactic factor) is a pro-inflammatory cytokine which plays a key role in tumour angiogenesis, cell adhesion, and cell cycle arrest. By observing various studies on salivary biomarkers, it concluded that IL 8 in saliva is the best biomarker for squamous cell carcinoma [79].
SAT: Spermidine/spermine N1-acetyltransferase 1 a protein that participates in the catabolism of polyamines. Researches show elevated levels of SAT in the saliva of oral cancer patients compared to the healthy controls.
S100 P: it is a calcium binding protein P, which is located in the cytoplasm or in the nucleus. Its levels in saliva get deviated among oral cancer subjects.
Salivary microRNA: MicroRNAs (miRNAs) are short parts of RNA transcripts. They are associated with most of the cellular functions like cell growth, apoptosis, differentiation, motility, and immunity. miRNAs are more specific and potent compared to mRNAs. They exhibit high sensitivity which effectively detect and differentiate poorly differentiated carcinomas. Some of these include miR-125a, miR-200a and miR-31 [88].
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13. Conclusion
\n
Saliva contains numerous substances which are potential biomarker of many oral diseases. Saliva could be a key cause of biochemical records capable of identifying some diseases. Hence would be useful for ideal methods to diagnosis, prognosis, and monitoring and management of patients with oral diseases. Even though saliva provides some evidence in early detection of oral diseases with advantages like easy, inexpensive, safe, less time and technique sensitive and non-invasive approach over serum, it showed resistance in the diagnostic and clinical usage. Salivary diagnostics faces lots of challenges for the use of saliva-based oral fluid diagnostics for future application. Salivary diagnostic procedures required more sensitive technological support and quality researches to generalise and implement the procedures.
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\n\n',keywords:"oral malignant disorders, dental caries, periodontal disease",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67299.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67299.xml",downloadPdfUrl:"/chapter/pdf-download/67299",previewPdfUrl:"/chapter/pdf-preview/67299",totalDownloads:569,totalViews:0,totalCrossrefCites:1,dateSubmitted:"October 12th 2018",dateReviewed:"March 14th 2019",datePrePublished:"May 28th 2019",datePublished:"October 23rd 2019",dateFinished:null,readingETA:"0",abstract:"Common oral diseases like dental caries, periodontal diseases and oral cancer have major impact on quality of life. For prevention, treatment and prognosis, it is essential to measure the disease objectively and accurately in a quantitative manner. Quantification of biochemical or molecular specific products of cancers in serum or localized body juices can be one of the current methods of measuring oral diseases objectively. Salivary diagnostics has influenced several researchers and has been verified as an important tool in the diagnosis of many systemic conditions and prognosis of the disease. Developments in the field of molecular biology, salivary genomics and proteomics have directed to the detection of novel molecular markers for oral disease diagnosis, therapeutics and prognosis.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67299",risUrl:"/chapter/ris/67299",signatures:"Manohar Bhat and Devikripa Bhat",book:{id:"7905",title:"Saliva and Salivary Diagnostics",subtitle:null,fullTitle:"Saliva and Salivary Diagnostics",slug:"saliva-and-salivary-diagnostics",publishedDate:"October 23rd 2019",bookSignature:"Sridharan Gokul",coverURL:"https://cdn.intechopen.com/books/images_new/7905.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"82453",title:"Dr.",name:"Gokul",middleName:null,surname:"Sridharan",slug:"gokul-sridharan",fullName:"Gokul Sridharan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"280750",title:"Dr.",name:"Manohara",middleName:null,surname:"Bhat",fullName:"Manohara Bhat",slug:"manohara-bhat",email:"manoharpangala@gmail.com",position:null,institution:null},{id:"296530",title:"Dr.",name:"Devikripa",middleName:null,surname:"Bhat",fullName:"Devikripa Bhat",slug:"devikripa-bhat",email:"kripa_95@hotmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Why saliva is used for diagnosis?",level:"1"},{id:"sec_3",title:"3. Oral diseases",level:"1"},{id:"sec_3_2",title:"3.1 Dental caries and saliva",level:"2"},{id:"sec_5",title:"4. Caries susceptible tests",level:"1"},{id:"sec_6",title:"5. Bacterial count measurement",level:"1"},{id:"sec_6_2",title:"5.1 Sampling oral microbial community through saliva (salivary microbiomics)",level:"2"},{id:"sec_7_2",title:"5.2 Colorimetric methods",level:"2"},{id:"sec_8_2",title:"5.3 Enzymatic biomarkers",level:"2"},{id:"sec_10",title:"6. Physical properties of saliva",level:"1"},{id:"sec_11",title:"7. Salivary ions",level:"1"},{id:"sec_12",title:"8. Periodontal disease and saliva",level:"1"},{id:"sec_13",title:"9. Oral cancer and saliva",level:"1"},{id:"sec_14",title:"10. Usage of stimulated or un-stimulated saliva for collection",level:"1"},{id:"sec_15",title:"11. Storage and transportation of saliva",level:"1"},{id:"sec_16",title:"12. Classification of salivary biomarkers for oral cancer",level:"1"},{id:"sec_17",title:"13. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Malathi N, Mythili S, Vasanthi HR. Salivary diagnostics: A brief review. ISRN Dentistry. 2014;2014:1-8\n'},{id:"B2",body:'Javaid MA, Ahmed AS, Durand R, Tran SD. Saliva as a diagnostic tool for oral and systemic diseases. Journal of Oral Biology and Craniofacial Research. 2016;6(1):67-76\n'},{id:"B3",body:'Mandel ID, Kutscher A, Denning CR, Thompson RH, Zegarelli EV. Salivary studies in cystic fibrosis. American Journal of Diseases of Children. 1967;113(4):431-438\n'},{id:"B4",body:'Gröschl M. The physiological role of hormones in saliva. BioEssays. 2009;31(8):843-852\n'},{id:"B5",body:'Pfaffe T, Cooper-White J, Beyerlein P, Kostner K, Punyadeera C. Diagnostic potential of saliva: Current state and future applications. Clinical Chemistry. 2011;57(5):675-687\n'},{id:"B6",body:'Dowd FJ. Saliva and dental caries. 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Archives of Oral Biology. 1984;29(9):735-738\n'},{id:"B44",body:'Syrjänen S, Piironen P, Markkanen H. Free amino-acid content of wax-stimulated human whole saliva as related to periodontal disease. Archives of Oral Biology. 1987;32(9):607-610\n'},{id:"B45",body:'Delacroix DL, Dive C, Rambaud J, Vaerman J. IgA subclasses in various secretions and in serum. Immunology. 1982;47(2):383\n'},{id:"B46",body:'Sandholm L, Grönblad E. Salivary immunoglobulins in patients with juvenile periodontitis and their healthy siblings. Journal of Periodontology. 1984;55(1):9-12\n'},{id:"B47",body:'Klinkhamer JM. Quantitative evaluation of gingivitis and periodontal disease I. The orogranulocytic migratory rate. Periodontics. 1968;6(5):207-211\n'},{id:"B48",body:'Rindom Schiött C, Löe H. The origin and variation in number of leukocytes in the human saliva. Journal of Periodontal Research. 1970;5(1):36-41\n'},{id:"B49",body:'Bhadbhade SJ, Acharya AB, Thakur S. Correlation between probing pocket depth and neutrophil counts in dental plaque, saliva, and gingival crevicular fluid. Quintessence International. 2012;43(2):111-117\n'},{id:"B50",body:'Kanzaki H, Wada S, Narimiya T, Yamaguchi Y, Katsumata Y, Itohiya K, et al. Pathways that regulate ROS scavenging enzymes, and their role in defense against tissue destruction in periodontitis. Frontiers in Physiology. 2017;8:351\n'},{id:"B51",body:'Kataoka K, Ekuni D, Tomofuji T, Irie K, Kunitomo M, Uchida Y, et al. Visualization of oxidative stress induced by experimental periodontitis in keap1-dependent oxidative stress detector-luciferase mice. International Journal of Molecular Sciences. 2016;17(11):1907\n'},{id:"B52",body:'Kumar J, Teoh SL, Das S, Mahakknaukrauh P. Oxidative stress in oral diseases: Understanding its relation with other systemic diseases. Frontiers in Physiology. 2017;8:693\n'},{id:"B53",body:'Ahmadi-Motamayel F, Goodarzi MT, Jamshidi Z, Kebriaei R. 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Oral Diseases. 2019;25(1):80-86\n'},{id:"B86",body:'Manoharan S, Kolanjiappan K, Suresh K, Panjamurthy K. Lipid peroxidation & antioxidants status in patients with oral squamous cell carcinoma. The Indian Journal of Medical Research. 2005;122(6):529\n'},{id:"B87",body:'Boyle P, Levin B. World Cancer Report 2008. IARC Press, International Agency for Research on Cancer; 2008\n'},{id:"B88",body:'Liu J, Duan Y. Saliva: A potential media for disease diagnostics and monitoring. Oral Oncology. 2012;48(7):569-577\n'},{id:"B89",body:'Khor GH, Froemming GRA, Zain RB, Abraham MT, Omar E, Tan SK, et al. DNA methylation profiling revealed promoter hypermethylation-induced silencing of p16, DDAH2 and DUSP1 in primary oral squamous cell carcinoma. International Journal of Medical Sciences. 2013;10(12):1727\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Manohar Bhat",address:"manoharpangala@gmail.com",affiliation:'
Department of Dentistry, Mysore Medical College and Research Institute, India
Department of Dentistry, S.D.M. College of Dental Sciences and Hospital, India
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
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