Reversible and irreversible cardiovascular risk factors.
\r\n\t1. Role and utility of animal models to understand the pathophysiology of PD and screening therapeutic molecules of dopaminergic neuroprotection
\r\n\t2. Importance of epidemiological studies and available therapeutic strategies for PD
\r\n\t3. Mechanistic insights into dopaminergic neuroprotection/neurodegeneration relating to PD
\r\n\tThe present project concerns “reducing the burden of disease” which is the prime aim of biomedical research.
",isbn:"978-1-80356-489-0",printIsbn:"978-1-80356-488-3",pdfIsbn:"978-1-80356-490-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"99788a4a7f9ee0b4de55de293a2ed3d0",bookSignature:"Prof. Sarat Chandra Yenisetti",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11583.jpg",keywords:"Cell Culture, Nutraceuticals, L-dopa, Deep Brain Stimulation, Paraquat, Rotenone, MPTP, Oxidative Stress, SOD, JNK, Catalase, ToR",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 4th 2022",dateEndSecondStepPublish:"May 5th 2022",dateEndThirdStepPublish:"July 4th 2022",dateEndFourthStepPublish:"September 22nd 2022",dateEndFifthStepPublish:"November 21st 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Sarat obtained post-doctoral training in neurogenetics from the University of Regensburg, Germany, and the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH), Bethesda, USA. 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His laboratory, funded through multiple research grants from Department of Biotechnology (DBT), India, University of Grants Commission (UGC), India and Department of Science and Technology (DST), India, focuses on Drosophila approach to understand Parkinson's Disease associated neurodegeneration as well as identification of novel therapeutic targets which may help to reduce the burden of PD in human.",institutionString:"Nagaland University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Nagaland University",institutionURL:null,country:{name:"India"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453624",firstName:"Martina",lastName:"Scerbe",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/453624/images/20399_n.jpg",email:"martina.s@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"7256",title:"Dopamine",subtitle:"Health and Disease",isOpenForSubmission:!1,hash:"e46d08f526c35d787be15bcb17126fb8",slug:"dopamine-health-and-disease",bookSignature:"Sarat Chandra Yenisetti",coverURL:"https://cdn.intechopen.com/books/images_new/7256.jpg",editedByType:"Edited by",editors:[{id:"181774",title:"Prof.",name:"Sarat Chandra",surname:"Yenisetti",slug:"sarat-chandra-yenisetti",fullName:"Sarat Chandra Yenisetti"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6406",title:"Parkinson's Disease",subtitle:"Understanding Pathophysiology and Developing Therapeutic Strategies",isOpenForSubmission:!1,hash:"0038453d1272466535c41e37d94ee52f",slug:"parkinson-s-disease-understanding-pathophysiology-and-developing-therapeutic-strategies",bookSignature:"Sarat Chandra Yenisetti",coverURL:"https://cdn.intechopen.com/books/images_new/6406.jpg",editedByType:"Edited by",editors:[{id:"181774",title:"Prof.",name:"Sarat Chandra",surname:"Yenisetti",slug:"sarat-chandra-yenisetti",fullName:"Sarat Chandra Yenisetti"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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A flood event occurs when stream flows exceed the natural or artificial confines at any point along a stream [1]. This is often due to heavy rainfall, ocean waves coming on shore, rapid snow melting, or failure of manmade structures such as dams or levees [2]. From 1998–2017, flood events affected more than two billion people globally [3]. Disasters of this frequency and magnitude are typified by extreme costs to governments. In 2019, historic flooding across Missouri, Arkansas, and the Mississippi River basin resulted in an estimated cost of 20 billion dollars [4]. These estimates typically do not reflect indirect costs such as added travel-miles and the subsequent loss of time. Further, floods are among the deadliest natural disasters. From 2010–2020, floods resulted in the fatalities of 1089 people in the United States [5]. A majority of these deaths were comprised of motorists. Therefore, urban planners such as traffic decision makers are tasked with proactively deploying resources that minimize motorist risk exposure. At present, traffic decision makers rely on static flash flood inundation profiles related to discrete rainfall events. These profiles are often created through multiagency cooperation efforts such as [6]. Some studies have begun to generate dynamic flood inundation data visualizations based on these profiles [7]. Additionally, integrated approaches that use machine learning and geographic information systems (GIS) to track changes in critical infrastructure over time are emerging as powerful decision support tools [8]. However, there is limited use of state-of-the-art time series prediction models to generate dynamic data visualizations in a GIS setting for improved flood management. This book chapter explores the integration of publicly available data and machine learning models to address this gap in the literature.
Precise determination of when and where to deploy re-routing measures is a complex task. One approach that improves planning effectiveness is to integrate time series characteristics of river behavior and corresponding spatial flood profile. In this chapter, a univariate time series prediction of river stage is conducted that improves the temporal resolution and accuracy of publicly available forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a GIS setting. The resulting geospatial deep learning model provides a data visualization tool that traffic decision makers can use to proactively manage road closures in the event that a flood is likely to occur. The first section provides an overview of relevant river behavior that causes flooding. State-of-the-art trend extraction and prediction techniques are then presented and tied to geospatial use cases. The methodology section presents the data used, time series prediction model selected, and geoprocessing procedures required for data visualization using GIS software. Next, an illustrative example is provided for a frequently flooded intersection in Missouri. A discussion section is provided that positions the findings in the context of improving traffic management in the event of a flood. Lastly, a conclusion is given that summarizes the key findings and outlines model limitations and future work.
Two key characteristics of streams that relate to flood events are stream stage and streamflow. Stream stage refers to height (ft) of the stream and streamflow corresponds to discharge (ft3/s) or alternatively, volumetric flowrate. Typically, governmental organization such as the United States Geological Survey maintain a network of sensors that monitor these characteristics over time for various stream segments. The National Weather Service classifies flood categories into four groups based on stream stage: Action Stage, Flood Stage, Moderate flood Stage, and Major Flood Stage [9]. These values vary for a given segment of stream based on analysis of previous floods, local topography, and underlying geological properties.
Given that stage is monitored over time, the use of time series forecasting methods to predict stage values is appropriate. There are two modeling approaches that are useful in this context: statistical and computational intelligence. Statistical models use historical data to identify underlying patterns to predict future values [10]. Some commonly used techniques for flood forecasting include simple exponential smoothing [11], autoregressive moving average [12], and autoregressive integrated moving average [13]. However, one shortcoming of these approaches is lack of scalability as the quantity and complexity of data increases [14]. An alternative approach that addresses these issues is computational intelligence. A key feature of computational intelligence approaches is the capacity to manage complexity and non-linearity without needing to understand underlying processes [15]. In summary, statistical methods rely on precise underlying relationships and exhibit decreased performance as the number of variables increases whereas computational intelligence approaches identify patterns using large amounts of training data to establish a model capable of accurate predictions [16]. Some commonly used flood forecasting computational intelligence models include support vector machines [17], artificial neural networks [18], and deep learning [19]. Further, they have demonstrated superior performance when compared to conventional statistical modeling approaches for flood prediction studies. LSTM models have explicitly shown promising results in time series contexts. Therefore, LSTM models provide a state-of-the-art trend extraction and prediction technique regarding stream stage values.
Stream stage values are categorized based on resulting flood severity. The physical reality of these categories is the spatial extent of the flooding event often referred to as a flood inundation map [20]. These maps provide decision makers with a useful visual reference to determine what specifically has been affected by a flood event. An area of research, data visualization, and practical application that has not been fully investigated is the integration of computational intelligence stream stage predictions with geospatial flood inundation maps. The methodology provided in the following section addresses this gap.
This section consists of three parts: LSTM prediction of stream stage, data required, and geoprocessing procedures. First, a brief overview of LSTM will be given. This will include explanatory figures and relevant mathematical formulas. Second, data required to conduct the LSTM prediction of stream stage will be procured. Flood inundation imagery and road network data will also be obtained. Lastly, data will be uploaded to a GIS software and processed for end use by traffic decision makers. An illustrative example is presented in the next section.
Stream stage prediction is a time series forecasting procedure that is dependent on previous data to predict future values. As the quantity and quality of data continues to increase, more powerful computational approaches can be applied to prediction problems. The results of the literature review demonstrated that deep learning approaches, namely LSTM networks, are increasingly being applied to these problems.
Deep learning is an extension of the conventional neural network by adding additional layers and layer types. Figure 1 provides a visual comparison of the two approaches [21]. The simple neural network (left) consists of a single input layer, hidden layer, and output layer. Alternatively, the deep learning neural network (right) has one input layer followed by three successive hidden layers that ultimately feed into a final output layer. This configuration has generated superior performance in capturing complex relationships.
Simple neural network vs. deep learning neural network.
However, neither approach retains previous time step information. Recurrent neural networks (RNNs) were introduced to address this limitation. LSTM networks are the deep learning variant of RNNs. All figures and mathematical formulation are borrowed from [15]. The primary benefit of LSTM networks is the capacity to retain longer term information. This is accomplished by removing and adding information determined by a series of ‘gates’ and vector operations. Figure 2 provides a visual representation of an LSTM cell. The first gate, illustrated in yellow, generates a value between 0 and 1 using the current input (xt) and output from the previous step (yt-1) that determines how much information is passed on (forget gate). A zero corresponds to no information transfer whereas a one represents a complete transfer.
LSTM network cell.
The result of this procedure (
Next, a decision must be made regarding what information needs to be stored. This is accomplished by applying an additional sigmoid layer (red,
The line at the top of the cell is known as the cell state (
Lastly, the output layer of the LSTM cell determines the forecast for the current time step. A sigmoid layer (blue) and tanh layer are multiplied to generate an output (
The result of this computational procedure is a time series forecast of future values. However, a large amount of data must be gathered to use as a model input. This data is presented in the next section.
Historic stream stage height for the location further explained in Section 4 must first be gathered. 113,994 data points were procured that correspond to 15-minute intervals from May 19, 2016 (5 PM) – September 1, 2019 (4 PM). Stage height is herein referred to as ‘gauge height’ to account for the source of the data. This data is represented graphically in Figure 3 [22].
Stream stage height for example locations.
Using USGS’ flood inundation mapper (FIM), these gauge heights can be tied to a specific flood inundation profile [23]. The FIM is a publicly available tool that provides resulting flood inundation maps for one-foot gauge height increments in image format (.tif). A sliding bar that accomplishes this is available on the online user interface and is presented in Figure 4.
FIM sliding gauge height tool.
An example of a flash flood inundation profile being uploaded to a GIS software is provided in Figure 5. Purple lines correspond to road network data derived from the National Transportation Dataset [24]. Blue raster (grids of pixels) imagery denote the depth of water at discrete locations where darker blue reflects deeper water. Useful geoprocessing techniques that generate actionable decision support tools are presented in the next section.
Flood inundation profile example.
Traffic decisions makers are tasked with identifying flood affected road segments. In Figure 5, it can be observed that the flood inundation profile does overlap certain road segments. Relying on visual inspection alone is time consuming and prone to inaccuracies due to human error. A solution to this issue is the application of a set of straightforward geoprocessing tools that are built-in to most GIS softwares: conversion and intersection.
Some tools do not allow raster and vector data layer interoperability. Therefore, it is necessary to convert one of the data layers to establish a consistent data type. One approach is to convert the raster layer into a vector layer using the conversion tool within ArcGIS. Figure 6 illustrates the result of this operation. The flood inundation profile has been converted into several points at 1-m increments. This spatial resolution can be modified by the user. The road network has been changed from its previous color to improve readability.
Raster layer conversion example.
Once the raster layer has been converted into vector format, it is eligible for use as an input layer for the intersection tool. The intersection tool generates a point at every location where there is an intersection between the input layers. In the next section, an illustrative example is provided to demonstrate the effectiveness of the methodology presented.
Valley Park, Missouri is located at the intersection of I-44 and State Route 141. This location is the setting for the example figures presented previously. The Meramec River winds through this area and has regularly flooded in recent years. In 2017, the river exceeded its banks and caused significant damage to the surrounding area as seen in Figure 7. This location provides a suitable candidate to test the methodology presented given the extent of the flood event and data availability.
Meramec River flood in 2017 [
First, data is gathered from a nearby stream gauge. Figure 8 provides a geographical point of reference for the gauge denoted by a green square with respect to I-44 and State Route 141. The data presented in Figure 5 is then procured and used as an input for the LSTM network. Figure 9 presents the prediction results of the LSTM model superimposed on the actual data for May 19, 2016-September 1, 2019.
Gauge location [
LSTM training and testing results.
The actual data (blue) can be observed deviating from the prediction results for the training (orange) and testing (green) results of the LSTM network. A lack of discrepancy between the actual data and predictions demonstrates the model’s effectiveness. Further, it is useful to determine how the prediction compares with publicly available forecasts for the same location. USGS provides a forecast every six hours. Alternatively, the LSTM network provides 24 predictions in the same period. Figure 10 provides a comparison of the prediction provided by USGS and the LSTM model for September 1, 2019 (6 PM) – September 3, 2019 (6 AM).
USGS and LSTM prediction comparison.
The red line represents the original data. Gauge height is initially observed at just above six feet. From there, it trends in a downwardly direction until it reaches the end of the dataset at less than 3.5 feet. The green line corresponds to the USGS prediction. This prediction initially overshoots the original data before briefly correcting and then diverging significantly from the observed trend. Lastly, the blue line represents the LSTM prediction. At first, this prediction captures the downward trend missed by the USGS prediction. Ultimately, the prediction flattens out and diverges from the original observations but to a lesser extent when compared to the USGS prediction. Root Mean Squared Error (RMSE) values for each of the predictions are provided to further demonstrate the difference in model performance. The RMSE value of 0.453 reported by the LSTM model represents superior accuracy compared to the 1.065 value reported by the USGS prediction. Therefore, the LSTM model presented here improves on the accuracy of publicly available forecasts and can be used as an input for the flood inundation tool.
Valley Park has 43 flood inundation profiles available in one-foot increments from 11–54 feet. The highest stage value recorded at this location is 44.11 feet on December 31, 2015. Figure 11 provides the flood inundation profile for 45 feet to approximate this event. Note that 45 feet is used instead of 44. This is due to the flood inundation profile incremental limitation and opting for a rounding approach that provides a more conservative risk assessment. The inundation profile is then converted to point format and intersected with the road network as illustrated by Figure 12.
Flood inundation profile for 45 ft. stage value for Valley Park, Missouri.
Flood affected road segments for flood inundation profile corresponding to 45 ft. stage value for Valley Park, Missouri.
At present, urban planners such as traffic decision makers rely on static flood inundation maps and post hoc planning to reroute traffic in the event that a flood occurs. This approach puts motorists already in-transit at risk to rapidly changing road conditions. To address these risks, a field of research has emerged to provide decision makers with real-time decision-making tools. However, using time series prediction models that capture river characteristics and integrating them with flood inundation profiles has received limited attention. The methodology provided here addresses this gap.
Traffic decision makers can use the data visualization presented in Figure 12 as a powerful decision support tool. The flood affected road segments can be easily identified (orange) and rerouting measures can be promptly dispatched. With the improved temporal resolution and accuracy of the LSTM prediction of stage height, traffic decision makers can deploy resources proactively to avoid unnecessary risk to motorists and improve traffic flow. Concluding remarks, limitations, and future work are presented in the next section.
Flash floods are a frequent and devastating natural disaster. The impetus to manage these events belongs to local decision makers that work in a resource constrained environment. To improve their decision-making effectiveness, a framework was presented that integrates machine learning and geospatial data to extract spatial and temporal trends using publicly available data. An illustrative example was provided to demonstrate the effectiveness of the framework provided. Valley Park, Missouri is located near the intersection I-44 and State Route 141. These roads represent major traffic throughputs and persistent flooding of the Meramec River has jeopardized the safety of motorists and the flow of commercial goods. Using 113, 994 river stage observations procured from a nearby sensor, an LSTM network was developed to improve the accuracy of publicly available forecasts. The result was an improvement in both the frequency and accuracy of forecasts provided. Once the stage value is predicted it can be tied to a spatial flood inundation profile using the publicly available FIM. Using the flood inundation profile for 45 feet observed at Valley Park as a proxy for the historic crest at this location, data visualization of flood affected road segments was generated in a GIS setting. The key benefit of this output is the ease with which traffic decision makers can use the results presented to inform urban planning and decision making. Traffic decision makers can use the resulting data visualization presented here to guide real-time decision making in the event that a river stage value is predicted to reach a flood event stage for a specified river segment. Despite the usefulness of the findings, there remain a number of model limitations that represent areas of future work.
Model limitations can be divided into two categories: data gathering and model extension. Deep learning models are dependent on large amounts of data. Therefore, sensors that collect data need to be installed and active for an extended period. The cost to install and maintain an enlarged sensor network might be prohibitive for some locations. Due to this fact, model implementation is limited to river locations where sensors are already installed. Additionally, FIM coverage is confined to a small number of locations nationwide. Similarly, to sensor coverage, if there are not already-available flood inundation maps, then the model cannot be applied to those locations. Model extension includes options to improve the model in a material way. One recommendation would be to determine the best locations for road signage that will provide optimal re-routing to motorists given a finite amount of signage. Another approach would involve working with local decision makers to determine re-routing effectiveness based on how quickly resources are deployed given model predictions. Areas of future work not related to model extensions include alternative prediction approaches in river networks with no sensors and refinement of the model to account for flash floods. Each of these components represent considerable opportunity for model enrichment that further improve the decision-making effectiveness for traffic management professionals.
The results presented here demonstrate the utility of using machine learning models and geospatial data to generate data visualization tools that key stakeholders can use to improve planning effectiveness. As data becomes increasingly available, use of comparably sophisticated methods can be applied to a suite of natural disaster phenomena. The outcome of such an undertaking will be the widespread use of data visualization tools that will reduce the risk motorists are exposed to and mitigate the accompanying economic fallout.
This work was partially funded by the Missouri Department of Transportation, Award Number TR201912 and the Mid-America Transportation Center, Award Number 25-1121-0005-130.
The authors declare no conflict of interest.
South Africa (SA) is a middle-income country that is characterised by contrasting living conditions ranging from wealthy urban suburbs to lower-income, underdeveloped areas. [1] SA has faced many socio-economic challenges such as high levels of poverty, inequality and unemployment [1, 2] despite having the second largest economy on the African continent. Since the country’s transition to democracy in 1994, progress has been made, but unemployment rates and poverty levels remain high. [1] Poverty is the main underlying factor contributing to food insecurity. [3, 4] The food insecure often use strategies to cope with the inability to access food. One of these include reducing the quality and quantity of food consumed, thus consuming poor diversity diets which can have detrimental consequences such as hunger, malnutrition [5, 6] and increased prevalence of metabolic and cardiovascular diseases (CVDs) [7] due to it hindering individuals’ ability to choose the most appropriate foods and beverages for an adequate diet. [8] A disadvantage of food insecurity is thus monotonous diets with consumption of more affordable energy-dense staples and foods that may have detrimental health outcomes such as obesity and its chronic disease comorbidities. [9] Food insecurity thus does not only cause under-nutrition, but also in over-nutrition such as obesity and its comorbidities, especially in low-income communities. [10] SA is a country in health transition and suffers from a quadruple burden of (a) poverty and nutrition-related chronic diseases of lifestyle [CDL], (b) communicable diseases, (c) peri-natal, maternal and injury-related disorders, [11] and (d) a nutrition transition. A recent study has found that this quadruple burden of disease is predominantly present in the black African population. [1] Urbanisation and westernisation of the Black African population of SA is marked not only by demographic transition, but also by increased animal protein, total dietary fat and added sugar intakes [11] and a health transition resulting in an increased prevalence of obesity [6] and CDL such as CVD. [11, 12]
The South African population of approximately 59 million people consists of 81% black Africans. [13] In 2017, it was reported that 56% of the SA population lived in poverty [14] with 28% living in extreme poverty, thus not having enough money to purchase enough food to consume around 2,100 calories per day for a month (food poverty). The most vulnerable to food poverty are women, children (66.8%), those with low education (79.2%) and people from the black population group (64.2%). [15, 16]
CVD incidence is increasing rapidly among all population groups in SA. [11] CDLs contribute 51% to the mortality rate, with CVD and diabetes accounting for 19% and 8% of the total deaths. Many people in SA have poor living conditions and limited resources to maintain health and well-being. [15] In spite of cultural background, people that has been subjected to urbanisation, has adopted a more Western lifestyle with lower dietary fibre and higher dietary fat and added sugar intakes, as well as lower physical activity levels. These dietary changes have led to higher prevalence of CDL, [17] specifically an increased risk and susceptibility of CVD among the black population, [18] and not only in adults, but also among children. [19] The face of CVD has thus changed in recent years. Initially it was a disease of the white population group, the affluent and older generations, but since the 2000s, it was also observed that the prevalence of CVD risk factors, such as dyslipidaemia and obesity, has increased among black Africans [20] as well as children and adolescents. [21, 22, 23, 24]
The aim of this chapter was thus to investigate the prevalence of the various cardiovascular risk factors, specifically those that are irreversible, among children (6–18 years old) in peri-urban Free State (FS), [25] rural Eastern Cape (EC), [24, 26, 27, 28] peri-urban [29] and urban [30, 31, 32, 33] Gauteng; adults (19–59 years old) in urban Gauteng [30, 31, 34, 35, 36, 37] and peri-urban FS; [38, 39, 40] and elderly (≥60 years) in urban Gauteng, [41, 42, 43] including both genders, living in poverty in SA. Gauteng was chosen as the authors both resided in Gauteng and it was the focus of the university for funding. No data had been available for the cardiovascular risk factors in the above-mentioned communities and a valuable research opportunity was created to address the paucity of information in these communities. For this reason, the FS and EC provinces were chosen because of funding opportunities and gap in the knowledge base on the areas included in these studies.
A search of electronic databases focusing on poverty, food insecurity and cardiovascular risk factors was carried out between 2010 and 2020. Databases used included: MEDLINE (PubMed), Web of Science, ScienceDirect, Scopus, EBSCOHost, Springer Link, and Sabinet. The keywords used included: “poverty”, “food security”, “nutrition security”, “food and nutrition security”, “cardiovascular disease”, “CVD”, “cardiovascular risk”, “CVR”, “cholesterol”, “triglycerides”, “HDL”, “LDL”, “C-reactive protein”, “CRP”, “fibrinogen”, “homocysteiene”, “vitamin B6”, “vitamin B9”, “folate”, “folic acid”, “vitamin B12”, “glucose”, “insulin”, “obesity”, “overweight”, “nutritional status”, “hypertension”, “high blood pressure”, “dietary diversity”, “dietary intake”, “children”, “adults”, “elderly”, “older people”, “aged”, “double burden”, and “South Africa”.
The data used for this chapter included all the databases and articles published for the various studies undertaken by the authors between 2000 and 2020 among black children in the EC, FS and Gauteng, [24, 25, 26, 27, 28, 30, 31, 32, 36] adults in Gauteng and the FS [25, 30, 35, 37, 40] and the elderly in Gauteng [37, 41, 43] in various urban, peri-urban and rural areas of SA. For the purpose of this book chapter, urban areas include cities and towns that are developed, thus having a density of human structures such as houses, commercial buildings, roads, and public transport. Peri-urban areas are underdeveloped areas on the outskirts of the towns and cities where people live, but no public transportation or commercial buildings are present. Rural areas refer to areas with low population density and large areas of undeveloped land where people mainly live far apart from their neighbours.
Comparative tables were drawn up using the published articles and, where data were not published, descriptive statistical analyses (frequencies) were calculated using IBM SPSS Statistics, version 26, from the study databases that had not been destroyed. The ethical and scientific procedures for the sampling strategy and data collection methods were the same for the published and unpublished data.
Poverty and food insecurity were observed in all seven study communities. A large majority of the adults (75.7%–78.0%) [35, 44] and child caregivers (53.0%–94.0%) [27, 29, 30, 44] were unemployed, had either no or only primary education (39.9%–78.8%), [27, 29, 30, 34, 36, 43, 44] and lived in poverty (67.7–100%). [27, 29, 35, 36, 44] The poverty rates of all the communities were more than double the 25.2% national food poverty rate. [15] This may have been due to the high unemployment rate and low education levels found among the adults in all the communities. A chronic money shortage to buy food was also reported in large percentages of the study population.
Many risk factors for CVD have been identified in the scientific literature and can be reversible or irreversible (Table 1). In 2016, 20% of the South African adults (15+ years) were smoking. [45] Risk factors present in the South African adult (18+ years) population are obesity (68% women; 31% men) hypertension (46% in women and 44% in men), [46] physical inactivity (37%), high blood pressure (24%) and hyperglycaemia (10%). [45] There is a paucity of national data for other CVD risk factors in adults, and very little CVD national data are available for children, except for the prevalence of overweight and obesity.
The history of CVD of an individual is directly proportional to the risk of CVD (the earlier the age of onset and the more family members affected the greater is the risk of CVD). [47] It is known that men are at greater risk of developing CVD than women [47, 48] maybe because oestrogen has an inhibiting effect on low density lipoprotein-cholesterol (LDL-C) oxidation and increasing the production of large very low-density lipoproteins (VLDL) and therefore has a protective effect against atherogenesis. [50] Low levels of education in middle-income countries like SA had a significantly higher risk of major CVD events compared to those with high incomes. [49] The majority (>70%) of our communities showed low education (no or primary school education), [29, 31, 34, 35, 43] except for the peri-urban adults in the FS (44.2%); [44] and caregivers of the peri-urban children in Gauteng (39.9%), [29] however, these percentages are still high. High unemployment rates (53.0–94.0%) [29, 30, 31, 35, 44] for the majority of all the communities were also observed. The low education and high unemployment rates of the communities could be some of the main reasons for the high poverty rates in the study communities (67.7–100%). [26, 35, 36, 44] Research has found that people with low education may not have access to health care that may prevent detecting and treating disease and thus compromise their health even further. [50]
Cigarette smoking doubles the risk of coronary artery disease and contributes seven-fold to the increase in risk for peripheral arterial disease. [51] Cigarette smoking and increases blood pressure and increases the heart’s workload. It deprives the heart muscle of oxygen and damages the platelets that increase coagulation and clot formation. Toxins in cigarettes may also damage the blood vessels and increase atherosclerosis. [48, 52] In SA, the proportion of adult (15+ years) women that smoke (37%) daily is higher than in men (8%). [46] Smoking patterns among children were not measured in our studies, but we previously reported 11.7%, 15.2% and 23.6% smoking among urban elderly, [43] peri-urban adults in Gauteng [31] and rural adults in the FS. [37]
Obesity is considered a multi-factorial condition [20, 53, 54] associated with an increased risk for comorbidities such as type 2 diabetes, insulin resistance, cancer, stroke, [53] hypertension, dyslipidaemia, [53, 55] and hypertriglyceridaemia. [55] Obesity is also considered an independent risk factor for CVD. [40] For every 1% increase above ideal body mass index (BMI), the cardiovascular risk (CVR) increases by 3.3% for females and 3.6% for males. [56] In our studies, the majority of the adults and elderly were overweight/obese. [44, 57] Although we did not report gender differences in this chapter, previous published results confirmed a higher prevalence among women in rural FS [37] and urban elderly [41] than in men. Our results further showed that the urban women in Gauteng had the highest prevalence (82.3%) of overweight/obesity, but cannot be compared to the peri-urban adults and urban elderly that included both men and women. However, the overweight/obesity prevalence among the urban elderly in Gauteng [57] and the peri-urban adults in the FS [44] was consistent with the national prevalence.
There is usually a higher prevalence of overweight/obesity in urban than rural. [20] We did not have any rural adult communities to compare our results, but the urban elderly in Gauteng (61.0%) [57] had lower prevalence of overweight/obesity than the peri-urban adults in the FS (67.9%). [44] This was inconsistent with research from sub-Saharan Africa (SSA) [54] and SA where it was found that age is positively correlated with overweight and obesity. [58, 59] In all three the adult communities, the prevalence of obesity was higher than the prevalence of overweight. (Table 2). The increasing prevalence of childhood overweight/obesity in SA [11] is presenting a major public health problem. Childhood overweight/obesity is associated with early onset of hypertension and hyperglycaemia, both risk factors for CVD, [71] as well as adult obesity, [54] premature death and disability. [54] Similar to adults, a higher prevalence of overweight/obesity among children is found in urban areas. [54, 72, 73] (Table 3) However, our results showed higher prevalence among the rural children (4.3%) [24] compared to the urban children (1.0%). [32] In addition, the rural [28] and urban [32] children had the lowest prevalence of overweight and no obesity prevalence. Both peri-urban areas showed a prevalence of 21.0% in the FS [25] and 18.3% in Gauteng. [32, 33] This was higher than the national prevalence. In our studies among resource-poor communities, the prevalence of obesity was much lower than the prevalence of overweight. Our studies have found significantly higher prevalence of overweight/obesity in girls when compared to the boys. [24, 26] These results were consistent with national data [77] and for SSA, [54] but inconsistent with a recent systematic review and meta-analysis investigating overweight/obesity among 5–19 year old children in 15 countries in Africa where the boys and girls were equally affected by overweight/obesity. [71].
Irreversible | Gender (male) |
Ageing | |
Genetically inherited factors | |
Potentially reversible factors | Cigarette smoking |
Obesity | |
Hypertension Physical activity | |
Hyperglycaemia, diabetes | |
Increased haemostatic factors, decreased fibrinolysis, increased platelet aggregration | |
Increased levels of homocysteine | |
Increased inflammatory response (HS-CRP) | |
Dyslipidaemia (increased cholestrol, LDL, Triglyseride, decreased LDL) | |
Diet and dietary diversity | |
Psyschosocial | Low socio-economic class |
Stressful environment | |
Personality types | |
Geographic | Climate and season (cold weather increased risk) |
Soft drinking water | |
Environmental pollution |
Reversible and irreversible cardiovascular risk factors.
Variable | Reference values | Urban women Gauteng (n = 628) % | Peri-urban adults Free State (n = 271) % | Urban elderly Gauteng (n = 170) % |
---|---|---|---|---|
Overweight | BMI ≥ 25 < 30 [60] | 39.3 [37] | 26.0 [44] | 29.5 [57] |
Obese | BMI ≥ 30 [60] | 43.0 [37] | 41.9 [44] | 31.5 [57] |
High serum TC levels | ≥6.2 mmol/L [61] | 0.5 [37] | 16.7 | 22.3 [57] |
Low HDL-C levels | <1 mmol/L (adult men) <1.3 mmol/L (adult women) [61, 62] | 43.0 [37] | 62.7 [39] | 76.2 [57] |
High LDL-C levels | >4.1 mmol/L [60, 63] | 0.5 [37] | 16.7 | 14.6 [57] |
Hypertriglyceridaemia (High TRG levels) | ≥2.3 mmol/L [60, 63] | 24.7 [37] | 12.7 [39] | 13.8 [57] |
High normal BP | 130–139 mm Hg/85–89 mm Hg (systolic/diastolic blood pressure) [64] | 11.6 [37] | 12.7 | 10.8 [57] |
Hypertensive | ≥140/≥90 mm Hg (systolic/diastolic blood pressure) [64] | 36.4 [37] | 53.2 [39] | 68.0 [57] |
Hyperhomocysteienemia | >15 umol/L [61](serum homocysteiene) | — | — | 66.4 [57] |
Fibrinogen | >3.5 g/L [65] | — | — | 68.0 [57] |
Inflammation (HS-CRP) | ≥3 mg/dL [62] | — | 56.9 | 68.3 [57] |
Hyperglycaemia (serum glucose) | >5.5 mmol/L [66] | — | 16.0 [39] | 38.5 [57] |
Serum vitamin B6 | <8.6 mcg/L [67] | — | — | 98.0 [57] |
Serum vitamin B12 | <156 pmol/L [68, 69] | — | — | 4.8 [57] |
Serum folate | <5.9 nmol/dL [70] | — | — | 9.6 [57] |
Cardiovascular risk factors in adults and elderly.
Variable | Reference values | Rural children Eastern Cape (n = 232) % | Peri-urban children Free State (n = 98) % | Peri-urban children Gauteng (n = 203) % | Urban children Gauteng (n = 152) % |
---|---|---|---|---|---|
Overweight | BMI:A ≥ 2 < 3 [74] | 4.3 [24] | 17.0 [25] | 15.8 [29] | 1.0 [32] |
Obese | BMI:A ≥ 3 [74] | 0.0 [24] | 4.0 [25] | 2.5 [29] | 0.0 [32] |
High serum TC levels | ≥5.18 mmol/L [75] | 1.3 [24] | 19.4 | 3.0 | 10.2 [32] |
Low HDL-C levels | <1.04 mmol/L [75] | 42.5 [24] | 30.6 | 19.2 | 95.9 [32] |
High LDL-C levels | ≤3.37 mmol/L [75] | 2.12 [24] | 12.2 | 2.5 | 28.6 [32] |
Hypertriglyceridaemia (High TRG levels) | ≥1.12 mmol/L (0–9 years old) ≥1.47 mmol/L (10–19 years old)[75] | 12.4 [24] | 35.7 | 4.4 | 1.0 [32] |
Hyperhomocysteienaemia | >15 umol/L [61] | 1.6 | — | — | — |
Fibrinogen | >3.5 g/L [65] | 14.8 | — | — | — |
Inflammation (HS-CRP) | ≥3 mg/dL [62] | 19.0 [24] | 7.8 | — | — |
Hyperglycaemia (serum glucose) | >6.1 mmol/L [76] | 10.3[24] | 6.5 | 6.9 | — |
Serum vitamin B12 | <156 pmol/L [69] | 7.6 | — | — | — |
Serum folate | <5.9 nmol/dL [70] | 4.6 | — | — | — |
Cardiovascular risk factors of children.
To summarise, overweight/obesity is common among the poor-resource adults and elderly in our study population. The high prevalence observed among the adults, specifically women, and elderly may be due to poor nutrition (Table 2). Although the prevalence of obesity is not yet high among the children in our study communities, the results highlight the increasing burden of overweight among children (Table 3). The high prevalence of overweight and obesity in our study communities is a concern as the comorbidities associated with overweight/obesity have negative effects on health across the life cycle. [71]
Hypertension (blood pressure ≥ 140/90 mm Hg) [64] is considered one of the most important risk factors for developing CVD [50, 78] due to organ injury to the heart and kidneys. [79] Sharp increases in childhood hypertension have been reported in SA recently. [11] In childhood, hypertension treatment does not reverse the target organ injury and although hypertension treatment will significantly reduce event rates, the burden of CVD event rates will remain high though adulthood. [79] SA has a high hypertension burden with 44% and 41% of adult (≥15 years) black African women and men respectively. [46] In our adult populations the urban women in Gauteng had lower prevalence of 36.4% compared to the peri-urban adults in the FS (53.2%). [39] The elderly in urban Gauteng had the highest prevalence (68.%). [57] (Table 2) This was consistent with the national prevalence (84% among both genders aged ≥65 years), indicating that the hypertension burden increases with age. [46, 80] A recent national survey has found an overall prevalence of 43%, of which 58% were unaware of the condition and thus did not receive treatment. [80] Similar results were observed where only 36.8% of the hypertensive urban elderly in Gauteng used hypertensive medication. [43] No hypertension data were available for the children.
In summary, our results showed high levels of hypertension in adults and the elderly in both urban and peri-urban areas. A recent national survey has found older age, obesity and lower education levels as the main risk factors for hypertension in SA. [80] High prevalence of obesity and poor education levels have been identified in all our adult communities.
Diabetes mellitus is the most common, but also the most complex CDL. [81, 82] Hyperglycaemia affects multiple organs and can lead to arterial hypertension. [83] It is estimated that the cause of death in 80% of individuals suffering from type 2 diabetes will be due to thrombotic complications of which 75% will result from a cardiovascular event. [84] Data on the incident rates of children with diabetes are available for only 6% of African countries and may be due to lack of screening tests available in the poor and low income communities. [84] Results in Tables 2 and 3 indicated that 38.5% urban elderly (Gauteng), [57] 16.0% peri-urban adults (FS), [39] 10.3% rural children (EC), [24] 6.5% peri-urban children (FS), and 6.9% peri-urban children (Gauteng) had high serum glucose levels. An increased prevalence of diabetes was reported for developing countries, [85] and it can be concluded that the prevalence of hyperglycaemia in all age groups in urban, peri-urban and rural areas in SA is concerning.
The development of coronary artery disease and myocardial infarction has both atheromatous and thrombotic components. Haemostasis is a finely balanced system of clot formation and fibrinolysis. [86, 87, 88] Fibrinogen is recognised as an independent risk marker of CVD. Fibrinogen, because of its mass, also has a direct effect on the blood viscosity and a physical functional effect on platelet aggregation. [65, 89] Studies have indicated an increased level of plasma fibrinogen in black South Africans. [12, 57, 90] An increase of one gram per litre in plasma fibrinogen doubles the risk of CVD. [89] The fibrinogen levels were measured in two of the communities. High fibrinogen levels were observed in 68.0% of the elderly [57] (Table 2) and 14.8% of the rural children (Table 3), indicating an increased risk for CVD.
Several mechanisms have been proposed to clarify the link between homocysteine and pro-thrombotic state. The oxidative damage to the endothelium, combined with inhibition of the vasculo-protective function of nitric oxide, enhances thrombogenecity. [91] Homocysteine is metabolised by (a) the trans-sulphuration pathway which results in the production of cystathionine - a process that requires vitamin B6 and the main route of metabolism is via a methionine-conserving pathway - a process that requires methyltetrahydrofolate (from folic acid) and vitamin B12 as co-factor or alternatively (b) by the remethylation pathway taking place in the kidney and liver (where betaine is utilised instead of folate). [92, 93, 94, 95] An association between elevated plasma homocysteine and the development of atherosclerosis has been confirmed. [96] Studies in animal models have shown that elevated homocysteine promoted atherosclerosis by increased oxidative stress impaired endothelial function and increased thrombogenecity. [92, 93, 95, 96, 97, 98, 99] Epidemiological retrospective and prospective clinical studies established homocysteine as a potent independent risk factor for atherothrombotic vascular disease. [91, 92, 100] Additionally, homocysteine increase superoxide (O2—) levels resulting in increased oxidative stress, causing an inflammatory state and increased atherosclerosis and ischemia reperfusion. Oxidative stress in return inhibits the cobalamine metabolism and enhances the cycle. [101, 102] The frequency of hyperhomocysteienaemia as an independent risk factor for atherothrombotic vascular disease [91, 92, 100] was found in 66.4% and 1.6% of the urban elderly [57] and rural children respectively. Thus, although homocysteine measurement did not form part of the objectives in all our communities, prevalence of hyperhomocysteienaemia in the urban elderly (Gauteng) (Table 2) and the rural children (EC) (Table 3) is an additional confirmation of an increased risk for CVD in the low income South African population.
Vitamin B6 acts as coenzyme in the irreversible trans-sulfuration of homocysteine to cysteine. Higher vitamin B6 level are associated with lower homocysteine levels. Fat metabolism requires carnitine, obtained either directly [103] through diet or via synthesis requiring lysine and vitamin B6. Vitamin B6 deficiency was also found to be associated with decreased plasma PUFAs (n-6 and n-3) which may be associated with elevated cardiovascular risk and a contributing factor to the anti-inflammatory response. [104, 105] Low circulating vitamin B6 levels have been found inversely related to inflammatory markers (HS-CRP, fibrinogen, IL-6 and TNF-α) and are related to the incidence of inflammatory diseases (rheumatoid arthritis, CVD, and diabetes). [106, 107] Vitamin B6 levels were only available for the urban elderly and 98% of the respondents had low serum vitamin B6 levels (Table 2). Vitamin B6 levels were not available for any of the children, but pre-school children in Zambia indicated a suboptimal vitamin B6 in the studied group. [108] It would, therefore, be beneficial to include vitamin B6 serum levels in their analytical profile in future.
Low serum folate levels is a cardiovascular risk marker independently from homocysteine level. [109] Folate, as a donor of one-carbon units, is essential for methylation and affects numerous metabolisms involved in CVD [110] and accurate replication of deoxyribonucleic acid (DNA) and its repair. If DNA repair capacity of the cell is exceeded by the rate of damage to the genome, serious defects in cellular and tissue physiology occur, resulting in degenerative diseases including CVD. [111] The four mechanisms by which folate is involved in reducing atherosclerosis are: (1) Optimising methylation cycle and thereby directly reducing the homocysteine levels; (2) Acting directly as an antioxidant; (3) Interacting with enzyme endothelial nitric oxide synthase; (4) Affecting cofactor bioavailability of nitric oxide. Apart from being an independent cardiovascular risk marker, decreased serum folate levels also Indicate a decreased cell regeneration. [112] The serum folate levels were only available for two of the communities and 4.8% and 7.6% had low folate levels in the elderly [57] (Table 2) and rural children (EC) (Table 3) respectively. Study communities included in this study are therefore at risk for CVD and the general effect of ineffective cell recovery.
The cofactor cobalamin is required for the optimal function of the enzymes methionine synthase and L-methylmalonyl-CoA. [113, 114] During methionine synthase, homocysteine is converted to methionine, when the methyl group is transferred from 5-methylene tetrahydrofolate to cobalamin to form methylcobalamin and tetrahydrofolate while methylcobalamin donates its methyl group that binds to homocysteine to form methionin (required for the synthesis of S-adenosylmethionine [SAM]). [87, 115] SAM is required in many cellular methylation reactions, including the methylation ribonucleic acid (RNA) and DNA. [116, 117] Reduced synthesis of methionine as a result of insufficient cobalamine results in increased homocysteine levels. [104] Vitamin B12 is also the coenzyme required to remove the methyl group from folate, thereby activating folate. [117, 118] Serum vitamin B12 was only available for the elderly in Gauteng (Table 2) and the rural children (EC) (Table 3) and 4.8% [57] and 7.6% had low folate levels respectively, thus at risk of impaired homocysteine metabolism and CVD.
An inflammatory response is initiated by damage to the vascular cell lining resulting in a series of mechanisms (acute-phase response) including haemodynamic (vasodilatation) activation of endothelial cells (increased adhesion molecule expression), increased permeability (enhanced protein movement) and an increase in acute-phase proteins. [119, 120] Vessel injury can also be caused by high LDL-C, hypertension, cigarette toxins and elevated homocysteine levels. During the inflammatory response that aims to repair the damage to the artery wall, LDL-C becomes trapped in the lesion that is engulfed by the macrophages and the free radicals oxidise the LDL trapped in the macrophage and eventually become plaque. [48] CRP is a β-globulin which is bound strongly to phospholipids and increases twentyfold to thirtyfold during an infectious or inflammatory response and is, therefore, considered a credible marker for systemic inflammation. [47] The prevalence of systemic inflammation was found in 56.9% of the adult respondents (peri-urban FS) and in 68.3% of the elderly, [57] (Table 2) as well as 19% and 7.8% of the rural (EC) [26] and peri-urban children (FS) respectively. (Table 3) Elevated CRP is a strong independent predictor of risk of future cardiovascular events. [121, 122] Thus, the results from our studies indicate an increased risk for CVD.
The prevalence of dyslipidaemia varies across the regions in SSA due to increased urbanisation and change of lifestyle factors (epidemiological transition). [123] A similar variation was observed in SA where a significant difference in the prevalence of dyslipidaemia occurs in different ethnic groups. [124] Although, studies indicated that people from an African decent showed an athero-protective lipid profile (lower total cholesterol) compared to their white European or Indian fellow countrymen, widespread low High density Lipoprotein (HDL-C) was present. [125] The use of antiretroviral therapy (ARV) also leads to an increase in dyslipidaemia. With the high prevalence of HIV/AIDS in SA [45, 46], is ARV treatment (the largest health programme internationally) is considered as a contributing factor to dyslipidaemia in SA. [126]. Previous studies indicated that prevalence of dyslipidaemia among black South Africans (independent of rural or urban) varies between 30% and 63%. [125, 127, 128]
The high protein component of high-density lipoprotein-cholesterol (HDL-C) accounts for its metabolic function of removing cholesterol from tissue back to the liver, and is considered as an important anti-atherogenic pathway modulating inflammation. The inverse correlation between serum HDL-C and cardiovascular risk (CVR) is well known and widely accepted. [129] Studies showed that improving poor lifestyle habits to have a positive effect on the HDL-C levels. [130]
With reference to the reported results in Tables 2 and 3, the prevalence of increased total serum cholesterol (TC) with the lowest in the urban adult women in Gauteng where 0.5% of participants had an increased TC. The highest prevalence was observed among the urban elderly population of Gauteng, where 22.3% of participants had an increased TC. The prevalence of low serum HDL-C levels was significantly decreased in all the study communities with the lowest prevalence in the peri-urban children where 19.2% had a HDL-C of less than 130 mg/dl. The highest prevalence was in the urban children where 95.6% had a decreased HDL-C level. The percentage of participants with the highest prevalence of abnormal LDL-C levels was found in the urban children in Gauteng (28.6%) and the lowest prevalence was found in the urban women of Gauteng (0.5%). In contrast, the lowest prevalence of participants with increased serum triglyceride levels were found in the urban children of Gauteng and the highest prevalence was in the urban (Gauteng) women (24.7%). Results obtained from our studies are in line with results obtained from other studies in SA [125, 126, 127, 128], confirming that prevalence of dyslipidaemia (mainly decreased HDL-C) is becoming an increasing concern that needs to be consciously addressed in planning for Health care interventions.
Dyslipidaemia is regarded as an independent CVR marker. [124] As indicated in Figure 1, a total of 4.1% Peri-urban children from the FS had four elevated lipid risk factors, additionally, of the urban elderly from Gauteng 3.8% had four, 13.1% had three, 12.3% had two and 47.7% had one elevated lipid parameter. Interestingly, more than one lipid risk factor were present in almost all the communities (>10% of adults and elderly), even in the children (>5%).
Dyslipidaemic factors present in study groups.
Dietary diversity has a significant positive association with health. [34] An inverse relationship between dietary diversity and CVD risk factors, namely hypertension, hypercholesterolaemia and high HDL-C has been observed. [131] Although we did not measure dietary diversity in all the communities, poor to moderate dietary diversity were observed in all of the communities. [26, 29, 33, 35, 38, 57]. This may have been due to their socio-economic status and food insecurity and may be a risk factor for CVD.
An association between higher dietary sugar intakes and overweight/obesity and CDLs such as CVD exists. Increased dietary glycaemic load, caused by high sugar consumption, results in increased hepatic lipogenesis, dyslipidaemia,[132] and CVD. [133] Childhood overweight/obesity risk and morbidity were associated with consumption of sugar-sweetened beverages (SSBs) and highly processed foods and snacks. [54] The World Health Organisation recommends the intake of added sugar to be <10% of total energy intake. [134] More than 20% of the adults, elderly [57] and children in rural EC had high added sugar intakes whereas the children in peri-urban FS and urban Gauteng [33] had no added sugar intakes. Although SSB consumption has not been investigated in our studies, during the past 50 years, SBB consumption has increased [132] and SA is in the top 10 countries with the highest consumption of SSBs globally. [135]
Vegetables, legumes, whole grains and fruit all contribute to dietary fibre intake. Dietary fibre is differentiated as soluble (dissolves in water and forms a gel) and insoluble fibres. Good sources of soluble fibre are oats, citrus fruit, barley and legumes. It lowers LDL-C and glucose levels and, therefore, has a protective effect against CVD. [136] Lowering of cholesterol is achieved by the binding of fibre to bile acids, thereby escalating its excretion. This inhibits the production of cholesterol by the liver, resulting in lower blood cholesterol. [137] Food sources of insoluble dietary fibre include whole grains and vegetables. It cannot be fermented and promotes bowel movement and alleviates constipation. [138] A large majority (0–100%) of the children and adults in all our communities had low dietary fibre intakes. [24, 26, 29, 33, 34, 35, 38, 43, 44, 57] This may be due to the mainly refined carbohydrate-rich diet consumed by all these communities.
Dietary fats consist mainly of cholesterol and fatty acids. Total dietary fat (% of total energy [TE]) intakes were higher than recommended for all the communities, ranging from 13.7% to 32.7% in urban Gauteng women and elderly respectively. [24, 26, 29, 32, 35, 36, 38, 39, 42, 43, 44, 57] High-fat diets cause an increase in postprandial triglyceride levels that are associated with risk of coronary heart disease (CHD). [139, 140] Fatty acids can be either protective against the development of CVD or can be risk factors for CVD. Saturated fatty acids (SFAs) and trans fatty acids (TFAs) have the greatest adverse effect on atherogenic cholesterol levels and are both associated with risk of CVD. [136, 141] Increased SFA intakes increase LDL-C levels. [142] TFAs have a HDL-C lowering effect and also increase LDL-C levels and, therefore, increase the risk of CVD.[47]. The contribution of TFA to CVD is a multiple pathway mechanism affecting lipid metabolism, increased inflammatory response and adiposity, and decreased endothelial function and insulin sensitivity. [143]
Dietary SFA intakes of <10% and TFAs of <1% of total energy intakes are recommended. [144] High TFA intakes were observed in less than 10% of our communities, except for the children in rural EC where the proportion of respondents with high intakes of TFAs was 36.7%. The proportion of the respondents that had high SFA intakes ranged from 18.6% to 42.9%. [24, 26, 32, 34, 37, 38, 42] The elderly (40.0%) [57] and peri-urban children (41.6% in Gauteng and 42.9% in the FS) [25, 29] had the highest prevalence of high TFA intakes (40.0%). Low-cost processed meats such as polony and Russians as well as chicken feet and heads were frequently consumed by our communities and may have contributed to the large intakes. Although there has been controversy about SFA intake and CVD risk, sufficient evidence exists that high SFA intakes cause increased LDL-C level by downregulating LDL receptors. [136]
PUFAs have a protective effect against CVD, specifically omega-6 PUFAs that significantly reduces total cholesterol and LDL-C levels as well as inflammatory markers. [145] High intakes of omega-3 PUFAs lowers the risk for myocardial infarction, CHD and CVD mortality and CVD events. [136] In addition, a diet rich in PUFA reduces the TC:HDL-C ratio and CHD incidence. [146] Linolenic acid (omega-3 fatty acid) and linoleic acid (omega-6 fatty acid) are essential fatty acids that cannot be physiologically produced and, therefore, need to be supplied by food sources. [147] Omega-3 decreases the risk of CVD by preventing thrombus formation, lowering blood pressure and protecting against irregular heart beat. [142] Replacing dietary carbohydrates and SFAs by an increased intake of omega-6 PUFAs lower LDL-C and increase HDL-C levels [148]. A large proportion of all of our communities had low PUFA intakes (33.0–100%), particularly for both omega-3 (93.1–100%) and omega-6 (2.4–29.7%) fatty acids. MUFA intakes were low in a large proportion of the participants (29.0–77.6%), except for the peri-urban children in Gauteng where only 4.8% of the children had low MUFA intakes. The majority of these children also showed high dietary cholesterol intakes (57.6%) whereas the rest of the study communities had relatively low prevalence (<20%) of high dietary cholesterol intakes. Because most of our communities live in poverty, it is questionable if they can afford oily fish, olive oil and the other MUFA and PUFA dietary sources, however, they do consume mostly sunflower oil, but in small quantities. [26, 29, 33, 34, 38, 57]
Dietary sources of vitamin B6 include meat, fish, potatoes and bananas which are good sources. However, it is also present in nuts, whole grain, fortified cereal and leafy vegetables, chicken, legumes, non-citrus fruit, liver and soy products. [149, 150, 151, 152] The bioavailability differs according to food type, with pyridoxine glycoside as the least bioavailable. Vitamin B6 (5–75%) obtained from plant sources is in the form of glycosylated pyridoxine. [153, 154] Owing to the abundance of vitamin B6 in a variety of food sources, deficiency is not very common, however, in our communities, large proportions of the adults (79.1% in peri-urban FS Province and 85.7% urban women in Gauteng) and elderly (91.0%) [57] had low vitamin B6 intakes. Among the children, 36.7% of the rural and 24.8% of the peri-urban children in Gauteng showed low intakes of vitamin B6. Vitamin B6 deficiency often occurs in conjunction with other nutritional disorders and is associated with an increased risk of CVD. [155] Vitamin B6 not only has a homocysteine lowering effect, but is also needed for the metabolism of omega-3 PUFAs. [96]
Folate is the major determinant of homocysteiene [96] and thus has homocysteiene lowering effect. A recent meta-analysis showed that folic acid supplementation resulted in a 4% reduced risk for CVD events and the benefit was even greater among participants without pre-existing CVD or low folate levels. [156] Because folate cannot be physiologically synthesised, concentration depends on consumption. [65] All of our communities showed large proportions of participants (>40.0% ≤ 95.0%) with low dietary folate intakes. Green leafy vegetables, citrus fruit, legumes, yeast, liver and organ meats contain the highest concentration of folate. [155] Low intakes of these food items have been found in our studies. [25, 26, 29, 30, 31, 33, 35, 38, 43, 44, 57] Folate is omnipresent in nature, but heat and oxidation during food preparation and storage have a destructive effect and can destroy up to 50% of the original concentration. [157]
Vitamin B12, together with folate, plays a key role for the enzyme methionine synthase needed for the re-methylation of homocysteine to methionine. [96] Dietary sources of vitamin B12 are animal products (meat, fish, chicken, milk and cheese) and rarely found in plants or yeast. [158] Vitamin B12 is stored in large quantities in the liver and a deficiency is developed over years. [65] The majority of our communities showed large proportions of participants (≥60% ≤ 95.2%) with low intakes of vitamin B12, except for the peri-urban children in Gauteng where 14.4% of the participants had low vitamin B12 intakes. This may be mainly due to the mainly carbohydrate-based diet with low meat and cheese intakes.
Sodium is an essential nutrient that is required for many physiological functions. [146] The daily physiological requirement for sodium is estimated at 0.1–1.0 gram. [159] High sodium intakes have been established as the major cause of hypertension in many epidemiological, experimental, controlled clinical and population trials. [160, 161] Sodium is mainly consumed as (a) salt (sodium-chloride) which is added during food preparation and cooking or at meal time, and (b) from sodium used in processed foods in SA. [162] Unfortunately we did not measure dietary sodium intakes in all our communities. Bread was identified as the largest contributor to salt intakes and that 41.0% of the South African population has a high salt intake. [162] Bread also consistently appeared in the top 20 most commonly consumed foods among our study communities [26, 29, 30, 31, 33, 34, 38, 43, 44, 57]. Another contributor to sodium intake in SA is sodium glutamate that is used as a condiment, [163] as well as salt in soup, gravy and spice mixes and powders, margarine and atchar, a spicy condiment, [163] biscuits/cookies, and breakfast cereals [164]. Stock cubes are regularly used for flavouring meat and vegetable dishes in SA [165, 166]. High stock cube consumption has also been observed in these communities by the authors.
In the studies reported among various communities, low education and employment status were observed as well as poverty in a large majority of the respondents. The scientific literature shows a strong association between poverty and CVD [163]. Poverty is an underlying factor of food insecurity that often results in poor dietary intakes that were observed in our communities. Many of the dietary CVD risk factors were present in large proportions of the communities. The literature is clear that these dietary factors may be associated with some of the risk factors for CVD, such as obesity, hypertension and the biochemical risk factors for CVD. Irreversible and potentially reversible and physiological (low income) risk markers were found to prevail. A summary of the elevated cardiovascular risk markers in our study is schematically presented in Figure 2. Multiple preventable CVR markers were present among the children, adults and elderly in rural, peri-urban and urban areas. It can thus be concluded that a double burden of poverty and risk of CVD exists across the different age groups and geographical locations in these resource-poor communities. Prevention of CVD can be achieved through nutrition education and awareness programs. It is recommended that policy makers give serious attention to CVR and screening should be done from an early age to identify those at risk and implement appropriate interventions.
Cardiovascular risk factors prevalent among children, adults and the elderly.
We thank the National Research Foundation (NRF) and Vaal University of Technology (VUT) for financial support. Replamed (Cornel Pretorius) provided technical support, Prof A Egal, Centre of Sustainable Livelihoods (VUT) staff member and team members from the CARE research group for their operational support.
The authors have no conflict of interest to declare.
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It manifests as a stable quasiperiodic interwell hopping between both stable states with a small random perturbation. Classical definition and basic features of SR are regarded. The most important methods of investigation outlined are: analytical, semi-analytical, and numerical procedures of governing physical systems or relevant Fokker-Planck equation. Stochastic simulation is mentioned and experimental way of results verification is recommended. Some areas in Engineering Dynamics related with SR are presented together with a particular demonstration observed in the aeroelastic stability. Interaction of stationary and quasiperiodic parts of the response is discussed. Some nonconventional definitions are outlined concerning alternative operators and driving processes are highlighted. The chapter shows a large potential of specific basic, applied and industrial research in SR. This strategy enables to formulate new ideas for both development of nonconventional measures for vibration damping and employment of SR in branches, where it represents an operating mode of the system itself. Weaknesses and empty areas where the research effort of SR should be oriented are indicated.",book:{id:"6128",slug:"resonance",title:"Resonance",fullTitle:"Resonance"},signatures:"Jiří Náprstek and Cyril Fischer",authors:[{id:"207472",title:"Dr.",name:"Jiri",middleName:null,surname:"Naprstek",slug:"jiri-naprstek",fullName:"Jiri Naprstek"},{id:"213311",title:"Dr.",name:"Cyril",middleName:null,surname:"Fischer",slug:"cyril-fischer",fullName:"Cyril Fischer"}]},{id:"74032",title:"Wavelets for EEG Analysis",slug:"wavelets-for-eeg-analysis",totalDownloads:1208,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"This chapter introduces the applications of wavelet for Electroencephalogram (EEG) signal analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and widely used frequency bands in EEG studies. The chapter then progresses to discuss the common artefacts that contaminate EEG signal while recording. With a short overview of wavelet analysis techniques, namely; Continues Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD), the chapter demonstrates the richness of CWT over conventional time-frequency analysis technique e.g. Short-Time Fourier Transform. Lastly, artefact removal algorithms based on Independent Component Analysis (ICA) and wavelet are discussed and a comparative analysis is demonstrated. The techniques covered in this chapter show that wavelet analysis is well-suited for EEG signals for describing time-localised event. Due to similar nature, wavelet analysis is also suitable for other biomedical signals such as Electrocardiogram and Electromyogram.",book:{id:"10065",slug:"wavelet-theory",title:"Wavelet Theory",fullTitle:"Wavelet Theory"},signatures:"Nikesh Bajaj",authors:[{id:"326400",title:"Dr.",name:"Nikesh",middleName:null,surname:"Bajaj",slug:"nikesh-bajaj",fullName:"Nikesh Bajaj"}]},{id:"70067",title:"Analytic Prognostic in the Linear Damage Case Applied to Buried Petrochemical Pipelines and the Complex Probability Paradigm",slug:"analytic-prognostic-in-the-linear-damage-case-applied-to-buried-petrochemical-pipelines-and-the-comp",totalDownloads:2681,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"In 1933, Andrey Nikolaevich Kolmogorov established the system of five axioms that define the concept of mathematical probability. This system can be developed to include the set of imaginary numbers by adding a supplementary three original axioms. Therefore, any experiment can be performed in the set \n\nC\n\n of complex probabilities which is the summation of the set \n\nR\n\n of real probabilities and the set \n\nM\n\n of imaginary probabilities. The purpose here is to include additional imaginary dimensions to the experiment taking place in the “real” laboratory in \n\nR\n\n and hence to evaluate all the probabilities. Consequently, the probability in the entire set \n\nC\n=\nR\n+\nM\n\n is permanently equal to one no matter what the stochastic distribution of the input random variable in \n\nR\n\n is; therefore the outcome of the probabilistic experiment in \n\nC\n\n can be determined perfectly. This is due to the fact that the probability in \n\nC\n\n is calculated after subtracting from the degree of our knowledge the chaotic factor of the random experiment. Consequently, the purpose in this chapter is to join my complex probability paradigm to the analytic prognostic of buried petrochemical pipelines in the case of linear damage accumulation. Accordingly, after the calculation of the novel prognostic model parameters, we will be able to evaluate the degree of knowledge, the magnitude of the chaotic factor, the complex probability, the probabilities of the system failure and survival, and the probability of the remaining useful lifetime; after that a pressure time t has been applied to the pipeline, which are all functions of the system degradation subject to random and stochastic influences.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Abdo Abou Jaoude",authors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé"}]}],onlineFirstChaptersFilter:{topicId:"163",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81892",title:"Perspective Chapter: Lattice Solitons in a Nonlocal Nonlinear Medium with Self-Focusing and Self-Defocusing Quintic Nonlinearity",slug:"perspective-chapter-lattice-solitons-in-a-nonlocal-nonlinear-medium-with-self-focusing-and-self-defo",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.104824",abstract:"The fundamental lattice solitons are explored in a nonlocal nonlinear medium with self-focusing and self-defocusing quintic nonlinearity. The band-gap boundaries, soliton profiles, and stability domains of fundamental solitons are investigated comprehensively by the linear stability spectra and nonlinear evolution of the solitons. It is demonstrated that fundamental lattice solitons can stay stable for a wide range of parameters with the weak self-focusing and self-defocusing quintic nonlinearity, while strong self-focusing and self-defocusing quintic nonlinearities are shortened the propagation distance of evolved solitons. Furthermore, it is observed that when the instability emerges from strong quintic nonlinearity, increasing anisotropy of the medium and modification of lattice depth can be considered as a collapse arrest mechanism.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Mahmut Bağcı, Theodoros P. Horikis, İlkay Bakırtaş and Nalan Antar"},{id:"80350",title:"A Comparison of the Undetermined Coefficient Method and the Adomian Decomposition Method for the Solutions of the Sasa-Satsuma Equation",slug:"a-comparison-of-the-undetermined-coefficient-method-and-the-adomian-decomposition-method-for-the-sol",totalDownloads:41,totalDimensionsCites:0,doi:"10.5772/intechopen.101817",abstract:"This chapter will talk about the mathematical as well as numerical aspects of the Sasa-Satsuma equation that is the extended nontrivial version of nonlinear Schrödinger’s equation. The exact solution will be found out by the undetermined coefficient method. After that, the Adomian decomposition method is secure numerical simulations of computed analytical solutions. The error plots are given to see the accuracy of the results.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Mir Asma"},{id:"79127",title:"Soliton Like-Breather Induced by Modulational Instability in a Generalized Nonlinear Schrödinger Equation",slug:"soliton-like-breather-induced-by-modulational-instability-in-a-generalized-nonlinear-schr-dinger-equ",totalDownloads:96,totalDimensionsCites:0,doi:"10.5772/intechopen.100522",abstract:"We consider the nonlinear Schrödinger equation modified by a rational nonlinear term. The model appears in various studies often in the context of the Ginzburg-Landau equation. We investigate modulational instability by means of a linear stability analysis and show how the nonlinear terms affect the growth rate. This analytical result is confirmed by a numerical simulation. The latter analysis shows that breather-like solitons are generated from the instability, and the effects of the nonlinear terms are again clearly seen. Moreover, by employing an auxiliary-equation method we obtain kink and anti-kink soliton as analytical solutions. Our theoretical solution is in good agreement with our numerical investigation.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Saïdou Abdoulkary and Alidou Mohamadou"},{id:"79040",title:"Traveling Wave Solutions and Chaotic Motions for a Perturbed Nonlinear Schrödinger Equation with Power-Law Nonlinearity and Higher-Order Dispersions",slug:"traveling-wave-solutions-and-chaotic-motions-for-a-perturbed-nonlinear-schr-dinger-equation-with-pow",totalDownloads:92,totalDimensionsCites:0,doi:"10.5772/intechopen.100396",abstract:"This chapter aims to study and solve the perturbed nonlinear Schrödinger (NLS) equation with the power-law nonlinearity in a nano-optical fiber, based upon different methods such as the auxiliary equation method, the Stuart and DiPrima’s stability analysis method, and the bifurcation theory. The existence of the traveling wave solutions is discussed, and their stability properties are investigated through the modulational stability gain spectra. Moreover, the development of the chaotic motions for the systems is pointed out via the bifurcation theory. Taking into account an external periodic perturbation, we have analyzed the chaotic behavior of traveling waves through quasiperiodic route to chaos.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Mati Youssoufa, Ousmanou Dafounansou, Camus Gaston Latchio Tiofack and Alidou Mohamadou"},{id:"78957",title:"Resonant Optical Solitons in (3 + 1)-Dimensions Dominated by Kerr Law and Parabolic Law Nonlinearities",slug:"resonant-optical-solitons-in-3-1-dimensions-dominated-by-kerr-law-and-parabolic-law-nonlinearities",totalDownloads:96,totalDimensionsCites:0,doi:"10.5772/intechopen.100469",abstract:"This study investigates the optical solitons of of (3+1)-dimensional resonant nonlinear Schrödinger (3D-RNLS) equation with the two laws of nonlinearity. The two forms of nonlinearity are represented by Kerr law and parabolic law. Based on complex transformation, the traveling wave reduction of the governing model is derived. The projective Riccati equations technique is applied to obtain the exact solutions of 3D-RNLS equation. Various types of waves that represent different structures of optical solitons are extracted. These structures include bright, dark, singular, dark-singular and combined singular solitons. Additionally, the obliquity effect on resonant solitons is illustrated graphically and is found to cause dramatic variations in soliton behaviors.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Khalil S. Al-Ghafri"},{id:"78793",title:"Nonlinear Generalized Schrödinger’s Equations by Lifting Hamilton-Jacobi’s Formulation of Classical Mechanics",slug:"nonlinear-generalized-schr-dinger-s-equations-by-lifting-hamilton-jacobi-s-formulation-of-classical-",totalDownloads:97,totalDimensionsCites:0,doi:"10.5772/intechopen.100068",abstract:"It is well known that, by taking a limit of Schrödinger’s equation, we may recover Hamilton-Jacobi’s equation which governs one of the possible formulations of classical mechanics. Conversely, we may start from the Hamilton-Jacobi’s equation and, by using a lifting principle, we may reach a set of nonlinear generalized Schrödinger’s equations. The classical Schrödinger’s equation then occurs as the simplest equation among the set.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Gérard Gouesbet"}],onlineFirstChaptersTotal:7},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,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:18,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. 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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:"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:"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:"Univeristy 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:"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:"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:null},{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:"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.\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.\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:null},{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: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:"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:"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"}}},{id:"355660",title:"Dr.",name:"Anitha",middleName:null,surname:"Mani",slug:"anitha-mani",fullName:"Anitha Mani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"355612",title:"Dr.",name:"Janani",middleName:null,surname:"Karthikeyan",slug:"janani-karthikeyan",fullName:"Janani Karthikeyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334400",title:"Dr.",name:"Suvetha",middleName:null,surname:"Siva",slug:"suvetha-siva",fullName:"Suvetha Siva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334239",title:"Prof.",name:"Leung",middleName:null,surname:"Wai Keung",slug:"leung-wai-keung",fullName:"Leung Wai Keung",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Hong Kong",country:{name:"China"}}}]}},subseries:{item:{id:"10",type:"subseries",title:"Animal Physiology",keywords:"Physiology, Comparative, Evolution, Biomolecules, Organ, Homeostasis, Anatomy, Pathology, Medical, Cell Division, Cell Signaling, Cell Growth, Cell Metabolism, Endocrine, Neuroscience, Cardiovascular, Development, Aging, Development",scope:"Physiology, the scientific study of functions and mechanisms of living systems, is an essential area of research in its own right, but also in relation to medicine and health sciences. The scope of this topic will range from molecular, biochemical, cellular, and physiological processes in all animal species. Work pertaining to the whole organism, organ systems, individual organs and tissues, cells, and biomolecules will be included. Medical, animal, cell, and comparative physiology and allied fields such as anatomy, histology, and pathology with physiology links will be covered in this topic. Physiology research may be linked to development, aging, environment, regular and pathological processes, adaptation and evolution, exercise, or several other factors affecting, or involved with, animal physiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11406,editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,series:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261"},editorialBoard:[{id:"306970",title:"Mr.",name:"Amin",middleName:null,surname:"Tamadon",slug:"amin-tamadon",fullName:"Amin Tamadon",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002oHR5wQAG/Profile_Picture_1623910304139",institutionString:null,institution:{name:"Bushehr University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",institutionString:null,institution:{name:"Miguel Hernandez University",institutionURL:null,country:{name:"Spain"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",institutionString:null,institution:{name:"Alexandria University",institutionURL:null,country:{name:"Egypt"}}}]},onlineFirstChapters:{paginationCount:14,paginationItems:[{id:"82103",title:"The Role of Endoplasmic Reticulum Stress and Its Regulation in the Progression of Neurological and Infectious Diseases",doi:"10.5772/intechopen.105543",signatures:"Mary Dover, Michael Kishek, Miranda Eddins, Naneeta Desar, Ketema Paul and Milan Fiala",slug:"the-role-of-endoplasmic-reticulum-stress-and-its-regulation-in-the-progression-of-neurological-and-i",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"80954",title:"Ion Channels and Neurodegenerative Disease Aging Related",doi:"10.5772/intechopen.103074",signatures:"Marika Cordaro, Salvatore Cuzzocrea and Rosanna Di Paola",slug:"ion-channels-and-neurodegenerative-disease-aging-related",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Ion Channels - From Basic Properties to Medical Treatment",coverURL:"https://cdn.intechopen.com/books/images_new/10838.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"81647",title:"Diabetes and Epigenetics",doi:"10.5772/intechopen.104653",signatures:"Rasha A. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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