Factors affecting wind power generation.
\r\n\tThis book chapter’s main theme will be focused on transmission dynamics, pathogenesis, mechanisms of host interaction and response, epigenetics and markers, molecular diagnosis, RNA interacting proteins, RNA binding proteins, advanced development of tools for diagnosis, possible development of concepts for vaccines and anti drugs for RNA viruses, immunological mechanisms, treatment, prevention and control.
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Electricity sector especially in supply industry over the last various years across the world has underwent through numerous structural and systematic changes due to two main reasons: orientation of industry towards privatizations (reforms) and movement of electricity generation towards clean and pollution free renewable energy sources [1]. In this changing environment forecasting electricity becomes one of the most important exercises in managing the power systems. Forecasting plays a significant role in operation planning, scheduling and real time balancing of power system. Mainly, there are three forecasting issues in present day power systems namely electricity load, price and the renewable energy sources. Among the recently emerged renewable sources of energy (solar energy), the wind power industry has witnessed tremendous growth and has taken a leading role [2, 3].
Besides this, the electricity based on renewable energy sources perceived as an alternate source of energy and their penetration within the power system is rising at a very fast rate [4]. Among new sources of renewable energy, the wind energy has seen tremendous growth over recent years; in various countries, it is a true alternative to fossil fuels. Furthermore, wind power generation capacity varies constantly, stochastic, intermittent in nature and associated with generation of other ramp events. In spite of that, it is freely available & pollution free source of energy; so, it has gained an extensive interest and one of the most established renewable energy alternatives to the conventional energy resources. On approaching towards the end of 2016, 486.8 GW would be worldwide installed wind nameplate capacity due to growth rate of 12.5%. As per estimate, wind power towards the end of 2021 will approach to 817 GW with growth rate of 10.4%. These wind capacity installations are mainly utilized in electric power systems based on large grid and their interconnections [5, 6]. Now-a-days another fast growing eco-friendly electrical generation technologies are solar, geothermal and tidal energy.
The uncertainty associated with wind power originates from uncertainties in its derivatives such as: wind speed & direction forecasts. In coordination with fast deployment of wind farms establishes a demand for efficient forecasting methods related to wind power production. The high is forecast reliability, low will be reserve maintenance cost of the system, which will result technical and commercial implications for proper management and working of power systems. Wind power forecasting (WPF) depicts how much wind power is to be expected at particular instant of time in the days to come. WPF is one of the most critical aspects in wind power integration and operation [6, 7, 8]. As per time horizons, the WPF has been done on the basis of long, medium and short term.
The availability of wind power is largely influenced by the prevailing weather conditions, seasonal variations and time spam variation and therefore, it is characterized by strong fluctuations, uncertainty and intermittency. These characteristics of wind power create a great attention towards it. Consequently, power generation from wind cannot be matched easily to the electricity demand like power generated with conventional plants. The penetration (share of wind power to meet demand) level of wind power introduces new challenges for the power system, some of them include:
Therefore, the accurate forecasts of wind power generation is an essential factor for a successful integration of large amounts of wind power into the electricity supply system, aiming at precise information on timing and magnitude of power generation from these variable sources.
Among requirements of wind power forecasting over three different forecasting horizons, there are different framework for the forecasting which includes single step ahead, multiple lead hours ahead and probabilistic forecasting. Typically multiple step and probabilistic forecasting is more complicated because in multiple, the error is multiples at every lead hour prediction; whereas, in probabilistic several statistical factors contribute additional complexity and additional complicacy. Moreover, it also affects the profits of a utility directly.
The predicted values can be provided to end-users either in a deterministic or in probabilistic format, with the former, a specific value for energy production at a particular time step (15-minutes or one hour) is forecasted; whereas, in later, range of possible output is forecasted on the behalf of deterministic forecasted values using probability theory.
It is the estimation of any quantity today for the next coming day with utmost possible precision and reliability. We have at our disposal the past values of this quantity, the data of one or several time series along with other several factors on which these time series are produced.
With
By the Eq. (1), e, is the prediction error or noise present between present forecasting value and n previous observations. WP is the wind power, T is the target, for multiple step the target matrix is increased with respect to each step in advance as given below in Eq. (3, 4).
The multiple steps ahead or multiple lead hour prediction is forecasting a pattern of values for given time series. It is an approach that works step-by-step by using current prediction for deterministic next stage prediction. In case of multi-step ahead prediction various anomalies like error accumulation and complexity of data prevails when prediction period is long. It all occurs due to propagation of bias and variances form previous prediction of future prediction. Because of this large forecasting horizon & error present in forecasting this method is suffered from the low performance & higher inaccuracy that is because of use of approximated values rather than actual values. The main reason for this higher inaccuracy is that the error is multiplied in every step-ahead prediction. So, the selection of input parameter function to fit the time series can be a challenging task for the power system researchers.
The probabilistic forecast systems are designed to estimate the uncertainty of a forecast and used to produce the application of probabilistic forecasting. The verification is an essential part of probabilistic forecast systems. The correct and accurate use of probability forecasts means that, given a large sample, on average and event will occur at the same frequency as the forecast probability [11].
As far as literature is concerned, number of forecasting methods have been designed and analyzed over last few decades. Based on information in research papers, author has examined various developments in the field of wind power generation & its derivatives prediction such as speed or direction. The major emphasis is led on facilitation of a number of issues concerned with techniques involved in WPF, focuses on complexity reduction in forecasting issues with higher accuracy in forecasting for different time span. This research mainly focuses on motivating power system researchers to design highly efficient and accurate models whether online/offline considering various issues related to wind power which in twin result in reliable operation of power system models by utilizing energy resources economically. On carrying out comparative study and analysis of accuracy in forecasting models, hybrid models outperformed all other models.
The generation of wind power is highly influenced by nature and seasons. So, it has been a tedious task to design a sound prediction model by taking in account above two factors. But, AI and machine learning have come with an advantage for developing new models due to their higher efficiency and accuracy. After a deep insight of various research papers authors have observed that the NN is the most prevailing approach for wind power and its derivatives estimation. It has also been observed that, hybrid models have been found to be more accurate model and for getting more accuracy, the training data should be updated regularly with small time span. Although for real time operation of power system, researchers have to move towards online models. There are three main steps involved in WPF (i) Input Selection, (ii) Data Pre-processing, & (iii) Forecasting models (tool) used.
The higher uncertainty in wind nature is result of uncertainties in its derivatives that affect systems of reliability. If forecast reliability is higher than operational cost of wind power system is lowered, in turn benefitting wind farm owners as they will have more substantial saving as well as have better efficiency of the system [12]. Apart from all this, wind power prediction is still a tedious task because wind flow is an unpredictable natural phenomenon and wind speed time series possesses various characteristics like: high volatility, high complexity, non linearity and non-stationary due to prevent physical conditions of place [13, 14]. After an extensive study of various research papers more than 46 exogenous variables have been observed as given in Table 1Table 1.
The input variables selection is main task because the accurate prediction by a forecasting model is highly influenced by proper input variables and their past results in the field of wind speed & power prediction and estimation. Furthermore, the selection of input variables for a prediction model mainly depends on exogenous and without exogenous variables. The various input selection techniques are as discussed.
These are very common model in which wind is a function of exogenous variables and forecasting tool input is the output of NWP models. These physical models forecasting process depends on entire input corresponding to wind power derivatives and are deterministic one. Their implementation process is very complex to perform, take high computation time to carry out forecasting process and depends on physical variables concerned with wind farm location. The equation which is used to convert wind speed into power is as follows as:
In statistical models, wind remains a function that works using past captured values. These models are trained by providing data patterns that are measured statistically. They are based on historical data patterns generated by wind power and hence, they are not based on computation of any form of mathematical expression. These models outperform other short term forecasting horizon over prediction accuracy and these models are easy to implement & validate. They employed the statistics like: Cross Correlation (CC), Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) for input selection on the basis of standard deviation, variance, mean and slope of input curve etc. The Figure 1 shows ACF and PACF of hourly Wind Power time series based on these two parameters input time lag parameterization of both time series and Artificial Intelligence (AI) take place. The higher is the value of ACF more is correlation between two consecutive series. However, the selection of input variables is one of the most important part of NN based forecasting model on with the accuracy of the model depends and that also determines the input architecture of the model. During the training of NN model, there may be problem of overtraining or over fitting that leads to poor accuracy of model. Therefore, it is necessary to know the relation that exists between present time wind power series along with their past time lag series. The input time lag is given below in Table 2. The wind forecast problem aims to find an estimate
ACF & PACF for hourly wind power series.
Class | Input variable | Input data |
---|---|---|
1. Atmospheric Characteristics | (1) Temperature (2) Pressure, (3) Humidity (4) Rainfall, (5) Cloud formation, (6) Cloud cover, (7) Turbulance, (8) Radiations Effect, (9) Density | |
2. Topographic Characteristics | (10) Turbine position, (11) Turbine size, (12) Hub height, (13) Tower height, (14) Elevation, (15) Degree in Latitude | |
3. Wind Power Characteristics | (16) Wind speed, (17) Wind direction, (18) Radiation transmission, (19) Sine & Cosine of wind direction, (20) Air density, (21) Local wind profile | |
4. Behavior Indices | (22) Hydrological cycle, (23) cloud-radiation interaction, (24) spatial behavior, (25) Temporal behavior, (26) Spatial resolution | |
5. Other Stochastic Uncertainty | (27) Ocean-land interactions, (28) Regime switching, (29) Exchanges of momentum, (30) Load distribution among parallel turbines, (3) 1Thunders, (32) Storms, (33) Risk index, (34) Guest wind speed | |
6. Geographical Conditions | (35) Orography, (36) Surface roughness, (37) Obstacles, (38) Geographical height, (39) Mean sea level pressure, (40) Air temperature, (41) Soil wetness, (42) Atmosphere covering, (43) Snow covering, (44) Moisture with land surface, (45) Complex terrain, (46) Terrain roughness |
Factors affecting wind power generation.
S. No. | Time lag series | No. of time lag |
---|---|---|
1. | 1 | |
2. | 2 | |
3. | 3 | |
4. | 4 | |
5. | 5 | |
6. | 6 | |
7. | Approximate Series | |
8. | Detailed Series |
Inputs used.
It is the combination of NWP and statistical tools for input data selection. In this, on the bases of statistical analysis, the NWP variables are pre-processed to time lag for the prediction of next step.
The input data and wind data pattern is accumulated in raw form and does not possesses highly efficient forecasting capability with accurate precision. Raw data is unpredictable, irregular, seasonal and more complex due to changing weather. While prediction computation, over-fitting or over-training of NN is the main issue in time series variation leading to foot fall in accuracy of forecasted values. Data pre-processing means data cleaning data transformation and data reduction input data and converting it into useful information as per dimensions. Data must be classified based on seasonal and weather variable variation. Kalman filter is an appropriate solution to various problems such as: complexity in data, over-fitting and outliers of input data generated during learning process [15, 16]. As Unscented Kalman Filter (UKF) achieves higher efficiency in handling random fluctuations, so it is an economical and adequate choice for non-linear estimation of wind speed [17].
In presented work, in order to investigate the performance of different forecasting models, real wind generation data of Ontario Electricity Market (OEM) from 2011 to 2014 [18] has been considered. For obtaining more accuracy and over-training avoidance in learning process to achieve greater accuracy, large set of data values have not been considered, as generation of wind power is dependent function on numerous parameters such as: changing season, temperature and weather conditions. As time moves wind capacity (defined as actual energy produced in comparison to energy actually dissipated by turbines under favorable conditions) can fluctuate. The main concern of Wavelet Transform (WT) is to collect the meaningful information with removal of noise & irregularities from the original signal. From the available literature on forecasting and experimental analysis, it has been observed that Daubechies wavelet at different levels performs an appropriate smoothness of the signal with respect to wave-length, which results in an appropriate behavior of input data pattern for wind power prediction tool.
The WT implementation is done to decompose wind power series broadly into constitutive series set. This set of constitutive series help in reduction of input data and outperforms original wind series in behavior leading to prediction accuracy improvement. The WT divides wind series signal into two distinguishing signals having low and high frequency, then the decomposed signals are provided to the separate NN model for training. There are four filters (decomposition low pass & high pass filter, reconstruction low & high pass filter) used in Discrete Wavelet Transform (DWT) for scaling the input data pattern into approximate (A) and detailed (D) signals as given in Table 2 [19, 20, 21, 22, 23, 24]. Empirical Model Decomposition (EMD) has also been used to decompose the wind power series into high and low frequency signals [25]. The NN models train themselves better with the pre-processed data, as a result of this better prediction performance.
For the past two decades, models based on machine learning have captured attention & become more sophisticated and reliable contenders in spite of traditional statistical models in forecasting. These are non parametric & non-linear models also known as data driven or black box models having usage of historical data patterns to learn the stochastic dependency between past and future. These NN’s models always leave behind other traditional statistical models such as: linear regression and Box-Jenkins approaches. The NNs can be successfully used for modeling and forecasting non-linear time series [26].
The conventional statistical models (persistence, Moving Average & Gray Models) are identical to the direct random time-series model. Based on a number of historical data, pattern identification, parameter estimation, model checking are utilized to make a mathematical model for the prediction problem.
Traditional Models
Linear or Time Series (TS) Models
According to the methods which have been proposed by Jenkins, these models can be further divided as follows: autoregressive model (AR), moving average model (MA), autoregressive moving average model (ARMA), auto regressive integrated moving average model (ARIMA) [28]. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) has been used for interval forecasting to simulate the fluctuating characteristics of the residual series in Mictrogrid China. Fractional-ARIMA method has been proposed to overcome the disadvantage of ARIMA method, which has been characterized by a slow decay in its ACF [29]. The stochastic and seasonality pattern of wind power has been tackled by designing a combined Autoregressive Fractionally Integrated Moving Average (ARFIMA) and GARCH model [30]; whereas, for above said problem ref. [31] demonstrated ARMA with Vector Auto-regression and ref. [32] designed different ARMA models for wind speed and direction tuples prediction (above said problem).
The FFNN architecture, which is also called as Multi Layer Perceptron (MLP), along with back propagation (BP) as the learning algorithm is the most popular choice among researchers. The neural network (NN) and machine learning algorithms structures used by most of the researchers after 2000 in the leading journals are: Feed Forward Neural Network (FFNN), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), Support Vector Regression (SVR), Adaptive Neuro Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Adaptive Wavelet Neural Network (AWNN), General Regression Neural Network (GRNN), and Linear Neural Network with Time Delay (LNNTD).
In this, wind forecasting has been done by the three different models: (i) Benchmark, (ii) NN and (iii) WT based model. In the first category, only Naïve Predictor has been considered. This is the standard benchmark for wind forecasting applications, in which the previous values of input wind power series have been used for the next lead hour as forecasted values. In the second category, different ANN based models have been taken into consideration with different structure of network and learning algorithms. The NN along with gradient-based optimization techniques is most popular choice among all researchers and associated with the short comings of local minima and sensitivity to initial value persists as a result of poor accuracy. So, as to resolve above said problems, global evolutionary algorithms (EA) such as Genetic Algorithms (GA) [1, 23], Particle Swarm Optimization (PSO) [19, 33, 34] have been utilized. The main advantages of EA lie in its global convergence, inherent parallel search nature, and great robustness. These algorithms generate a high quality solution within a short computation time.
For proper input selection, there is need of complete experimental analysis on the basis of error rate. The input structures of WT based models are different from that of the non WT based models. In the WT based models, the input is the combination of Wind Power series and WT based approximated and detailed wind power series. Therefore, the number of input nodes is more as compared to non WT models. The structure of WT based FFNN for wind power prediction has been shown in Figure 2 & detailed prediction steps are:
Hourly curve for load, price & wind power from Ontario electricity market.
WT based FFNN for wind power forecasting.
The aim of forecast evaluation is to assess, the general quality of a forecast by comparing the forecasted system states to actual observed states. The forecast evaluation provides a forecaster with:
The ability of better improvement and understanding of forecast. The evaluation of forecast exposes all those sub-spaces whose forecasting error is more out of model state space. So, a forecaster can take advantage of analyzing sub-spaces & utilize it for improving forecasting model.
Justifying the cost associated with resources used in forecasting model. The forecast performance assessment in accuracy terms gives a measure that can be directly linked to the utility or forecast user. Then coast and utility are compared with each other.
The ability of performing model selection so that maximum certainty of results can be obtained with the comparison of others.
In most of the forecasting models accuracy is the criterion for selecting a particular method for the forecasting. For a consumer accuracy of forecasting is most important. The various methods for accuracy calculation given below:
Where,
The prediction performance of forecasting carried out by the different models used in this research is justified on the basis of forecasting accuracy indices. The methodology described above has been applied to predict the wind power of OEM for two years from November 2012 to October 2014 on MAPE & MAE accuracy criteria. The software used for training and testing of NN is MATLAB version R2011b. The extensive use of WT for data pre-processing makes the results more significant and effective. From the results Table 3, it is clear that the results achieved with the help of WT based models have been found to be better up to 40–60% as compare to non WT based models. The 24 hours actual and forecasted wind power curves with error curve have been shown in Figure 4.
Model | Naïve | FFNN | ERNN | GANN | PSONN | GAPSONN | GRNN | LNNTD | WT + FFNN |
---|---|---|---|---|---|---|---|---|---|
MAPE | 15.016 | 13.83 | 13.885 | 14.015 | 13.91 | 13.915 | 14.48 | 13.825 | 5.948 |
MAE | 65.073 | 58.415 | 58.145 | 58.413 | 58.4675 | 58.29209 | 62.285 | 58.0475 | 23.225 |
Overall prediction comparisons for all models used.
One day ahead actual & forecasted wind power curve during winter season.
The uncertainty of forecasts is mainly due to the noise of training data, the misspecification of NN model for regression and input data selection.
In this, probabilistic forecasting of wind power has been performed in coordination with single step ahead wind power point forecasts. The major emphasis of probabilistic forecasting is to take into account the uncertainty associated with the wind power with probabilistic forecasting attributes such as: sharpness, reliability, resolution and discrimination. It consists of a set of prediction intervals which works in coordination with the best forecasts of single step ahead of wind power for the next coming hour; the interval forecasting has been incorporated. With a pre-assumed probabilistic value, the basic aim of interval forecasting is to find out the range of prediction interval in which next hour wind power output lies. This framework has been consequently used for evaluating and analyzing the skill of the models for one lead hour point forecast. Thus, the overall results have been proving the reliability of results and show how the resolution may improve the forecasts skill.
The probabilistic forecasting has a wide range of statistical parameters on which the probabilistic outcomes of wind power lies. The prediction intervals (PI) stands for a wide range of possible probabilistic values within which the observed wind power values lies with a certain predefined probability. The basic idea behind the prediction intervals is to estimate the uncertainty associated with observed wind power
For a given sample size α has been a significant level which has been used to take into account the CI of the certain prediction intervals. The probabilistic stochastic interval (PSI) can be obtained by:
In the Eq. (12), the lower bound and upper bound can be expressed as:
In (13) and (14)
For the WT based model, the upper bound curve and lower bound curves obtained at 95% of the confidence and the actual measured wind power curve in 24 hours has been shown in Figure 5.
PI with nominal confidence 95% in 24 hours look ahead.
The uncertainty, complexity and seasonal aspects associated with the wind contribute high level of uncertainties in wind power generation. Because weather conditions and wind speeds vary very much in different seasons. Therefore, for a perfect efficient forecasting model it is necessary to take care of input variables and their proper selection in time series. Actually, the improper input cause improper training of NN model as a result of that poor accuracy of forecasts. In this chapter, in order to take care of models forecast performance, probabilistic parameters have been taken into consideration.
In order to evaluate the performance on probabilistic forecasting, on the basis of single step reliable Prediction Intervals (PI’s) need to be derived. In this, instead of exact values of forecast a range of forecasting interval need to be considered. If the predicted values lie in that range then, the performance of model is good otherwise model is poor one. Furthermore, power system operations require useful efficient forecast values with high level of reference confidence. Therefore, to fulfill the need of power system, more practical data based model should be required with high-confidence-level PI’s.
In industrialized countries, more than 20% of the population has symptoms of allergies. The commonness of childhood asthma increased by 50% in the USA from 1980 to 2000. The allergy mechanism is an immune response to the allergen, which is often mediated by the immunoglobulin E (IgE) antibody [1]. Allergies can be a serious risk for individuals. Allergens or pollens represent a small fraction of the proteins that humans are regularly exposed to. The importance of the topic in the uncertainty is the cause of the B and T cells’ responses to these proteins [2, 3]. Notably, some proteins that are structurally similar pollens may lead to immune response, known as cross-reactivity [4].
The human body microbiome has a diverse composition of bacteria, archaea, fungi, protozoa, and viruses, which are inhabited mainly in the different epidermal surfaces of the body—the skin and mucosal surface. Some of the species of these microbiotas are identified based on cultural techniques, but due to limitations of these techniques [5], it is suggested that the number of human microbiota exceeds 1000 species or 10 times the number of cells in the entire body with 30 times larger total genome than the human genome.
A majority of these microbiotas are in the gastrointestinal tract, the major source of microbial exposure, and live in symbiosis with their host cells [6, 7]. Given up genes necessary for the survival of the commensal microbiota in other microenvironments and retained genes beneficial for the host with no or little benefit to themselves [8] are the evidences of the symbiotic coevolution of the microbiota and human [9].
The interplay of the immune system with gut microbiota starts from the day of birth and even before that. Early exposure during plasticity and prenatal period seems to be beneficial to prevent the T helper cell type-2 (Th2)-mediated allergic disease [10]. Th2 phenotype is the dominant one in newborns [11] to prevent rejection in utero. Skewing to Th2 in the immune system leads to the stimulated secretion of IgE by B cells and hence to allergies as seen in germ-free mice with the same condition that results in greater IgE responses to food antigens and failure in producing the proper amount of regulatory T cell (Treg) responses [12, 13, 14]. On the other hand, upsurge in the amount of T helper cell type-1 (Th1) also mediates the autoimmune disease [12, 15, 16, 17, 18, 19].
Restoring Th1/Th2 is the significant role of the microbiota [20]. The association of microbiota and the immune system is mutual. This engagement results in different signaling pathways through the immune system’s molecules that increase immune responses [21]. These regulations are crucial for maintaining the homeostasis of the host and for the prevention of different diseases by inducing secretion of IgA and regulatory T cell (Treg) and stimulation of tolerance in face of common antigens [22]. So the formation, maintenance, and heterogeneity of microbiota are necessary during early life owing to their regulatory and tolerance properties in the immune system [23, 24], as it was confirmed that the lack of microflora during a short time in early life results in defection in immune regulation [15]. The mechanisms of oral tolerance which are necessary to suppress excessive immune reactions to antigens are mediated by Foxp3þ Treg [25] and IgA, which is known as the most abundant immunoglobulin and is vital in establishing the composition of microbiota [26] and strengthening the mucosal barrier function [27].
Although, it is observed that abnormal IgA responses lead to allergy development [28]. So the obligation of equilibrium of the allergy mediators is more sensible now.
Lack of genetic elements such as Toll-like receptors that cause enterocyte proliferation like TLR4 and CD14, which enhance the detection of bacterial LPS by TLR4, and TLR9, which identify the genetic molecules of the microorganisms, also increase susceptibility to allergies [29, 30].
As the priority of the microbiome is proven, some factors are mentioned as follows, to support their presence and diversity in the body. Mode of birth; surgical or natural delivery, the process of contacting microflora in the first moment of the presence. Breast or formula feeding; the extension of contact with microflora. Nutritional patterns; the habit of food, based on people’s patterns to eat fatty and fast foods or healthy ones like prebiotics which are considered beneficial for even the microbiota of the host. Antibiotics; the matter of using antibiotics at an early age or the trouble of overuse of them in all ages which impair normal flora. Locality; living in urban areas with all of the stresses, less interaction with nature in contrast with living in rural areas results in losing ancient commensal microbiota. Environmental factors; contacting people or animals. Hygiene; the obsession behaviors or normal ones. Lifestyle; the matter of activity or sedentariness in someone’s lifestyle.
Natural delivery and breastfeeding are the first two initial and essential exposures when the immune system is not still mature and needs antigens to active oral tolerance [18, 25, 31]. Contravention of these simple factors grounds reformed patterns of early settlement which may result in the incidence of allergy [32]. Food sensitization especially milk allergy and atopic eczema are examples of reduced gut microbial diversity [33, 34, 35, 36, 37].
The microbiome is considered as an active organ because of manufacturing intrinsic signals for shifting postnatal development, inspiration of tolerance mechanisms and immunogenicity reduction, and resistance against invasive pathogens [38, 39, 40, 41, 42].
Consuming substrates of the microbiota containing fibers and mucins provides additional energy for the host as fatty acids [43]. Amines, sulfides, and ammonia are the products of them, which are detrimental metabolites for the human.
The protective barrier function against the invasive microbes by their colonization in the intestine is another potential of the microbiota. Different mechanisms for the resistance colonization of the microbiota are considered, such as competition for nutrients and connection to the binding sites and secretion of the antimicrobial substances [44].
Stimulation of the innate signaling pathways through the straight cell-to-cell communications or secretion of short-chain fatty acids (SCFA) are the other regulatory actions of the microbiota. SCFAs produced by the microbiota can direct intestinal Treg cells and inhibit pro-inflammatory responses [45, 46, 47, 48].
The role of maternal microbiota in the process of preventing allergy has been proven. Infants from allergic parents are at least twice more likely to the risk of developing allergic diseases than nonallergic parents. Microbiota diversity exists between allergic and nonallergic persons. Reduction in the fecal diversity of the bacteroidetes in pregnancy is connected with the prevalence of atopic eczema in their young children [49]. The microbiota of healthy infants with nonallergic parents frequently consists of healthy lactobacilli, representing the role of maternal microbiota in preventing allergic disorders. A decrease in the number of lactobacilli and bifidobacteria and an increase in the colonization of
The intestine, the largest immune organ of the body, which is the source of the most antibody-producing cells [58] is the target of triggering maturation of the immune system or the restoration of the impaired commensal bacteria. Stimulation of the immune system is one of the most impressive functions of the resident microbiota of the intestine. Probiotic bacteria are considered as a safe solution for modulation of diminished commensal composition and also as influencer of the immune system in preventing allergic disorders [59]. Lactic acid bacteria and bifidobacteria are good candidates as probiotics with an appropriate life span, no toxic or pathogenic properties, and no inflammatory induction. The selection of the bacteria as probiotics is mainly based on no harmful side effects during the history of their use for a long time. Consumption of these probiotics aid in balancing the ratio of the intestinal flora, avoiding the inhabiting of the pathogens by preventing the binding of them to the host cells, and suppressing the inflammation, which all are as the result of immune system regulation [60]. The effects of probiotics vary with the dose, strain, and duration of consumption and timing.
But the problem of the probiotics is their longevity and residence in the body of the host, as it was seen that they only remain during the administration period and not after that, showing the transient colonization of the probiotics [61, 62, 63, 64, 65].
Long-term effects of probiotics in different periods of everyone’s life need to be more investigated in complementary studies.
As it was mentioned, immune tolerance is one of the necessary immune reactions to stop excessive inflammatory reactions. Preservation of this tolerance involves the integrity of the epithelial barrier that is heightened by commensal anaerobes, such as
Immune homeostasis develops in the gut as a relationship between the intestinal microbiota, the luminal antigens, and the epithelial barrier is established. Microbial intestinal colonization starts after conception. This happens when the newborn’s sterile gut is slowly colonized by environmental bacteria and by interaction with the mother’s intestinal flora and surroundings and probably by genetic factors [70, 71, 72]. Exposure to microbial flora early in life causes a transition in the T helper cell type-1 (Th1)/Th2 cytokine balance, promoting a Th1 cell response [73].
An infant’s immune system at birth is not completely formed and appears to be geared toward a Th2 phenotype to prevent in utero rejection [74]. Nevertheless, the Th2 phenotype results in a stimulated production of IgE by B cells and therefore raises the risk of allergic reactions by mast cells activation [75, 76]. Early in life microbial stimulation will reverse the Th2 bias and promote the expansion of the Th1 phenotype and promote Th3 cell activity [76]. In this way, their combined activity will lead to B-cells releasing IgA. IgA contributes to the elimination of allergens and hence would reduce the immune system’s response to antigens. Th1 phenotype-produced cytokines will also reduce inflammation and promote tolerance toward specific antigens [77].
The hygiene concept states that inadequate or aberrant exposure to environmental microbes is one of the triggers of allergy production and related diseases [78]. As mentioned before, allergic diseases are associated with a change in the Th1/Th2 cytokine balance leading to Th2 cytokine activation and interleukin-4 (IL-4), IL-5, and IL-13 activation as well as IgE production [79, 80]. Probiotics significantly alter the gut microenvironment by encouraging a shift in local microflora and cytokine secretion [81] and can potentially modulate enterocyte Toll-like receptors and proteoglycan recognition proteins, resulting in dendritic cell (DC) activation and a Th1 response. The resulting stimulation of Th1 cytokines can suppress reactions to Th2 [82].
Atopic dermatitis (AD) is a widespread chronic inflammatory skin condition with a prevalence of around 20% in children and 2–5% in adults worldwide [83]. In recent years, the function of the intestinal microbiota in the aetiopathogenesis of AD has become increasingly important. Atopic dermatitis probiotic therapy is widely studied, with contradictory outcomes [84]. Probiotics containing
Asthma, a chronic complex airway disease, is characterized by reversible airflow obstruction, bronchial hyper responsiveness, and underlying inflammation [87]. In recent decades, the prevalence of asthma has risen. One possible mechanism behind this high prevalence is the microbial hypothesis, which suggests that less microbial exposure upregulates T helper cell type-2 (Th2) cytokine development, leading to a rise in allergic diseases [75]. A meta-analysis found that while perinatal and early-life probiotic administration reduces children’s risk of atopic sensitization and total rates of immunoglobulin E (IgE), it may not reduce their risk of asthma [88]. However, in addition to routine treatment, several studies have documented the advantage of using probiotics for treating children with asthma. A randomized, placebo-controlled trial for 7-week treatment with
On these bases, probiotic bacteria are capable of altering immune responses through a range of mechanisms that could minimize allergic reactions to airborne allergens without the side effects of any current drugs, and these possible mechanisms, as shown in Figure 1, include regulatory T cells that dampen immune responses and suppress the production of IgE antibodies [92, 93]. There are contradictory studies about the effectiveness of probiotics in treating allergic rhinitis [94]. It is reported that
Probiotic mechanism against the allergen include increasing regulatory T cells that damp down immune responses and suppress the production of IgE.
Food allergy (FA) is one of the pediatric age’s most common allergic disorders and has been considered a global health issue, particularly in the developed world.
Naturally, many subjects with FA outgrow this over time. Cow’s milk allergy (CMA), hen’s egg allergy, and wheat allergy resolve by 5–10 years in 50% of children. Many FAs (including peanuts, tree nuts, and fish) have low-resolution levels and are seen as persistent [99]. Furthermore, certain types of FA may be correlated with the subsequent development of other allergic symptoms such as oculorhinitis, atopic dermatitis, asthma, and urticaria (the so-called “Atopic March”) [100] as well as other diseases such as functional gastrointestinal disorders (FGIDs), inflammatory intestinal diseases (IBD) [101], and psychiatric disorders such as attentive autistic spectrum disorders (ASD).
The gut microbiome-immune system axis that influences the frequency of FA may be modulated by several genetic, environmental, and dietary factors [102]. For example, increased family size, pet and/or rural exposure, balanced diet (full of fibers, fermented foods, antioxidants, omega-3), breastfeeding, and probiotic use are correlated with FA safety. Conversely, C-section, prenatal, and early-life exposure to antibiotics/gastric acid inhibitors/antiseptic agents, unhealthy diet (low fibers/high saturated fats, and junk foods) may increase the risk of developing FA. All these environmental factors mainly operate on a modulation of the structure and function of the gut microbiota, which may in effect be responsible for the epigenetic control of genes involved in immune tolerance.
The pathogenesis of these incidents also is largely unknown, but increasing evidence suggests the hypothesis that disturbance of intestinal microbiome, leading to alterations in the immune system and gut-brain axis, may affect the occurrence of FA and FA-related conditions later in life [103] (Figure 2).
Good microbiome as a target for food allergy intervention.
Mediterranean diet (MD) is described as a healthy, balanced diet. It is associated with a high intake of assorted cereals, legumes, fruit, vegetables, olive oil, and nuts; moderate consumption of red wine, poultry, and fish; and a lower intake of red meat and sweets. MD has been shown to have a protective role against allergic illnesses in children during pregnancy and early life [104].
Elevated adherence to MD was associated with increased levels of Prevotella and other Firmicutes and production of short-chain fatty acids (SCFAs) [102]. One of the strongest links between diet, gut microbiome, and allergic diseases is the immunomodulatory mechanisms elicited by SCFAs [105]. Common SCFAs contain acetate, butyrate, propionate, and valerate. SCFA-producing bacteria include
The results of many studies have demonstrated that there is a strong relationship between modifications within the microbiome and many diseases. Much evidence proves that healthy microbiota affects and improves the immune system. It seems that probiotics can have an important role in the prevention of many diseases such as allergy. Microbiota diversity exists between allergic and nonallergic persons. Different mechanisms are considered for the anti-allergic impact of probiotics, like detecting related molecular patterns, including DNA motifs or LPS of the bacteria by Toll-like receptors. Probiotic mechanism against the allergen includes increasing regulatory T cells that damp down immune responses and suppress the production of IgE.
The authors are thankful to the National Institute of Genetic Engineering and Biotechnology (NIGEB), Iran, for providing the facilities.
The authors declare no conflict of interest.
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Gurgel Pereira",coverURL:"https://cdn.intechopen.com/books/images_new/10929.jpg",editedByType:"Authored by",editors:[{id:"202246",title:"Prof.",name:"Helena",middleName:null,surname:"Trindade Lopes",slug:"helena-trindade-lopes",fullName:"Helena Trindade Lopes"}],equalEditorOne:{id:"416486",title:"Dr.",name:"Ronaldo G.",middleName:"Guilherme",surname:"Gurgel Pereira",slug:"ronaldo-g.-gurgel-pereira",fullName:"Ronaldo G. Gurgel Pereira",profilePictureURL:"https://mts.intechopen.com/storage/users/416486/images/system/416486.jpg",biography:"Ronaldo Guilherme Gurgel Pereira is a historian (Universidade Federal do Rio de Janeiro, Brazil) and archaeologist (Universidade Nova de Lisboa, Portugal). In 2010, he received a Ph.D. in Egyptology from the University of Basel, Switzerland.\nFrom 2012 to 2017, Dr. Pereira was a post-doctoral fellow at CHAM/FCSH – Universidade Nova de Lisboa.\nIn 2018, he became an Onassis Fellow, hosted by the Department of Mediterranean Studies, University of the Aegean, Greece. \nIn 2019, he became an auxiliary researcher at CHAM/FCSH – Universidade Nova de Lisboa. He teaches Middle Egyptian grammar, Hieratic, and disciplines regarding Egyptology, and the history of Phoenician and Greek expansion in the Mediterranean basin. \nIn 2021, he was awarded a CAARI Scholar in Residence Fellowship.",institutionString:"Universidade NOVA de Lisboa",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Universidade Nova de Lisboa",institutionURL:null,country:{name:"Portugal"}}},equalEditorTwo:null,equalEditorThree:null,productType:{id:"4",chapterContentType:"chapter",authoredCaption:"Authored by"}},{type:"book",id:"10342",title:"Ovarian Cancer",subtitle:"Updates in Tumour Biology and Therapeutics",isOpenForSubmission:!1,hash:"25a0adac7f6afa7bcd0b6daa3ef6b538",slug:"ovarian-cancer-updates-in-tumour-biology-and-therapeutics",bookSignature:"Gwo-Yaw Ho and Kate Webber",coverURL:"https://cdn.intechopen.com/books/images_new/10342.jpg",editedByType:"Edited by",editors:[{id:"297757",title:null,name:"Gwo-Yaw",middleName:null,surname:"Ho",slug:"gwo-yaw-ho",fullName:"Gwo-Yaw Ho"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10485",title:"Fibroids",subtitle:null,isOpenForSubmission:!1,hash:"64ad14b1aba83e47fb100fa63e21533e",slug:"fibroids",bookSignature:"Hassan Abduljabbar",coverURL:"https://cdn.intechopen.com/books/images_new/10485.jpg",editedByType:"Edited by",editors:[{id:"68175",title:"Prof.",name:"Hassan",middleName:"S",surname:"Abduljabbar",slug:"hassan-abduljabbar",fullName:"Hassan Abduljabbar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9785",title:"Endometriosis",subtitle:null,isOpenForSubmission:!1,hash:"f457ca61f29cf7e8bc191732c50bb0ce",slug:"endometriosis",bookSignature:"Courtney Marsh",coverURL:"https://cdn.intechopen.com/books/images_new/9785.jpg",editedByType:"Edited by",editors:[{id:"255491",title:"Dr.",name:"Courtney",middleName:null,surname:"Marsh",slug:"courtney-marsh",fullName:"Courtney Marsh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9507",title:"Family Planning and Reproductive Health",subtitle:null,isOpenForSubmission:!1,hash:"a51ae8a0488480238f4cbbbe425058f2",slug:"family-planning-and-reproductive-health",bookSignature:"Zouhair Amarin and Hassan Abduljabbar",coverURL:"https://cdn.intechopen.com/books/images_new/9507.jpg",editedByType:"Edited by",editors:[{id:"101551",title:"Prof.",name:"Zouhair",middleName:null,surname:"Amarin",slug:"zouhair-amarin",fullName:"Zouhair Amarin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:65,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"30747",doi:"10.5772/27200",title:"Cervical Cancer in Sub Sahara Africa",slug:"cervical-cancer-in-sub-sahara-africa",totalDownloads:8051,totalCrossrefCites:24,totalDimensionsCites:36,abstract:null,book:{id:"951",slug:"topics-on-cervical-cancer-with-an-advocacy-for-prevention",title:"Topics on Cervical Cancer With an Advocacy for Prevention",fullTitle:"Topics on Cervical Cancer With an Advocacy for Prevention"},signatures:"Atara Ntekim",authors:[{id:"69178",title:"Dr.",name:"Atara",middleName:"I",surname:"Ntekim",slug:"atara-ntekim",fullName:"Atara Ntekim"}]},{id:"43348",doi:"10.5772/55562",title:"Molecular Mechanisms of Platinum Resistance in Ovarian Cancer",slug:"molecular-mechanisms-of-platinum-resistance-in-ovarian-cancer",totalDownloads:4226,totalCrossrefCites:19,totalDimensionsCites:28,abstract:null,book:{id:"3449",slug:"ovarian-cancer-a-clinical-and-translational-update",title:"Ovarian Cancer",fullTitle:"Ovarian Cancer - A Clinical and Translational Update"},signatures:"Gonzalo Tapia and Ivan Diaz-Padilla",authors:[{id:"157073",title:"Dr.",name:"Ivan",middleName:null,surname:"Diaz-Padilla",slug:"ivan-diaz-padilla",fullName:"Ivan Diaz-Padilla"},{id:"166871",title:"Dr.",name:"Gonzalo",middleName:null,surname:"Tapia Rico",slug:"gonzalo-tapia-rico",fullName:"Gonzalo Tapia Rico"}]},{id:"37219",doi:"10.5772/47914",title:"Determining Factors of Cesarean Delivery Trends in Developing Countries: Lessons from Point G National Hospital (Bamako - Mali)",slug:"determining-factors-of-cesarean-delivery-trends-in-developing-countries-lessons-from-point-g-nat",totalDownloads:3033,totalCrossrefCites:7,totalDimensionsCites:21,abstract:null,book:{id:"952",slug:"cesarean-delivery",title:"Cesarean Delivery",fullTitle:"Cesarean Delivery"},signatures:"I. Teguete, Y. Traore, A. Sissoko, M. Y. Djire, A. Thera, T. Dolo, N. Mounkoro, M. Traore and A. Dolo",authors:[{id:"87496",title:"Dr.",name:"Ibrahima",middleName:null,surname:"Teguete",slug:"ibrahima-teguete",fullName:"Ibrahima Teguete"}]},{id:"31273",doi:"10.5772/31669",title:"Aqueous Extract of Human Placenta",slug:"aqueous-extract-of-human-placenta-as-a-therapeutic-agent",totalDownloads:5577,totalCrossrefCites:5,totalDimensionsCites:19,abstract:null,book:{id:"702",slug:"recent-advances-in-research-on-the-human-placenta",title:"Recent Advances in Research on the Human Placenta",fullTitle:"Recent Advances in Research on the Human Placenta"},signatures:"Piyali Datta Chakraborty and Debasish Bhattacharyya",authors:[{id:"88185",title:"Prof.",name:"Debasish",middleName:null,surname:"Bhattacharyya",slug:"debasish-bhattacharyya",fullName:"Debasish Bhattacharyya"},{id:"127848",title:"Dr.",name:"Piyali Datta",middleName:null,surname:"Chakraborty",slug:"piyali-datta-chakraborty",fullName:"Piyali Datta Chakraborty"}]},{id:"27121",doi:"10.5772/27439",title:"Clinical Risk Factors for Preterm Birth",slug:"clinical-risk-factors-for-preterm-birth",totalDownloads:8743,totalCrossrefCites:9,totalDimensionsCites:19,abstract:null,book:{id:"776",slug:"preterm-birth-mother-and-child",title:"Preterm Birth",fullTitle:"Preterm Birth - Mother and Child"},signatures:"Ifeoma Offiah, Keelin O’Donoghue and Louise Kenny",authors:[{id:"68552",title:"Dr.",name:"Ifeoma",middleName:null,surname:"Offiah",slug:"ifeoma-offiah",fullName:"Ifeoma Offiah"},{id:"70166",title:"Prof.",name:"Louise",middleName:null,surname:"Kenny",slug:"louise-kenny",fullName:"Louise Kenny"},{id:"74717",title:"Dr.",name:"Keelin",middleName:null,surname:"O'Donoghue",slug:"keelin-o'donoghue",fullName:"Keelin O'Donoghue"}]}],mostDownloadedChaptersLast30Days:[{id:"58219",title:"Congenital Abdominal Anomalies",slug:"congenital-abdominal-anomalies",totalDownloads:1402,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Introduction: Abdominal anomalies that appear during intrauterine life are complex due to many organs that are affected. In cases, the ultrasound appearance is a cystic image with different content and the differential diagnosis is often difficult. Body—research methods: the organs affected by abdominal congenital anomalies involve the gastrointestinal tract (stomach, duodenum, small bowel or colon, and gall bladder), the kidney and urinary tract, the peritoneal cavity (ascites), suprarenal glands, and tumors of the reproductive system (especially the ovaries). In order to identify the affected structures, it is mandatory to know the normal aspect of the abdominal content at different gestational ages. The diagnosis may be very difficult, but its accuracy is important, considering the need of further counseling the couple. In minor conditions, without chromosomal anomalies or associations, the outcome is usually good, and there are even possibilities of in utero treatment. In severe conditions, with poor outcome, the couple can choose to terminate the pregnancy, after counseling is provided. Conclusion: abdominal congenital anomalies are common findings in ultrasound screenings for anomalies in all the trimesters of pregnancy and their recognition is important for subsequent management.",book:{id:"6307",slug:"congenital-anomalies-from-the-embryo-to-the-neonate",title:"Congenital Anomalies",fullTitle:"Congenital Anomalies - From the Embryo to the Neonate"},signatures:"Ples Liana and Anca Lesnic",authors:[{id:"212333",title:"Associate Prof.",name:"Liana",middleName:null,surname:"Ples",slug:"liana-ples",fullName:"Liana Ples"}]},{id:"64417",title:"Introductory Chapter: A Comprehensive Approach to the Process of Breastfeeding",slug:"introductory-chapter-a-comprehensive-approach-to-the-process-of-breastfeeding",totalDownloads:1283,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"6191",slug:"selected-topics-in-breastfeeding",title:"Selected Topics in Breastfeeding",fullTitle:"Selected Topics in Breastfeeding"},signatures:"René Mauricio Barría P",authors:[{id:"88861",title:"Dr.",name:"R. Mauricio",middleName:null,surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. Mauricio Barría"}]},{id:"62854",title:"The Surgical Technique of Caesarean Section: What is Evidence Based?",slug:"the-surgical-technique-of-caesarean-section-what-is-evidence-based-",totalDownloads:2525,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Caesarean section is the most frequent obstetric operation which is associated with increased maternal morbidity and mortality. Although these risks are low, affected women may suffer from severe consequences and this may affect subsequent pregnancies and deliveries. A variety of surgical approaches have been described, however, on low evidence level. The objective of this chapter is therefore to systematically search the literature and analyse the available evidence including preoperative workup, prophylactic antibiotics, skin disinfection, preoperative bladder catheterization as well as details of the individual steps of the actual operation itself such as skin incision types, preparation of soft tissue and womb, removal of the placenta, cervical dilatation and stitching of the womb, peritoneum, rectus muscle, fascia, subcutaneous fat, and skin. We systematically searched for meta-analysis, systematic reviews, and big studies and evaluated the evidence for each individual step.",book:{id:"6707",slug:"caesarean-section",title:"Caesarean Section",fullTitle:"Caesarean Section"},signatures:"Jan-Simon Lanowski and Constantin S. von Kaisenberg",authors:[{id:"100660",title:"Prof.",name:"Constantin",middleName:"Sylvius",surname:"Von Kaisenberg",slug:"constantin-von-kaisenberg",fullName:"Constantin Von Kaisenberg"},{id:"240353",title:"Dr.",name:"Jan-Simon",middleName:null,surname:"Lanowski",slug:"jan-simon-lanowski",fullName:"Jan-Simon Lanowski"}]},{id:"18348",title:"Anaesthetic Considerations during Laparoscopic Surgery",slug:"anaesthetic-considerations-during-laparoscopic-surgery",totalDownloads:28934,totalCrossrefCites:1,totalDimensionsCites:5,abstract:null,book:{id:"916",slug:"advanced-gynecologic-endoscopy",title:"Advanced Gynecologic Endoscopy",fullTitle:"Advanced Gynecologic Endoscopy"},signatures:"Maria F. Martín-Cancho, Diego Celdrán, Juan R. Lima, Maria S. Carrasco-Jimenez, Francisco M. Sánchez-Margallo and Jesús Usón-Gargallo",authors:[{id:"14715",title:"Prof.",name:"Francisco M.",middleName:null,surname:"Sánchez-Margallo",slug:"francisco-m.-sanchez-margallo",fullName:"Francisco M. Sánchez-Margallo"},{id:"29449",title:"Dr.",name:"Maria Fernanda",middleName:null,surname:"Martín-Cancho",slug:"maria-fernanda-martin-cancho",fullName:"Maria Fernanda Martín-Cancho"},{id:"39772",title:"Dr.",name:"Juan R.",middleName:null,surname:"Lima",slug:"juan-r.-lima",fullName:"Juan R. Lima"},{id:"39773",title:"Mr.",name:"Diego",middleName:null,surname:"Celdran",slug:"diego-celdran",fullName:"Diego Celdran"},{id:"39774",title:"Prof.",name:"Jesus",middleName:null,surname:"Usón-Gargallo",slug:"jesus-uson-gargallo",fullName:"Jesus Usón-Gargallo"},{id:"62320",title:"Prof.",name:"Maria Sol",middleName:null,surname:"Carrasco-Jiménez",slug:"maria-sol-carrasco-jimenez",fullName:"Maria Sol Carrasco-Jiménez"}]},{id:"41721",title:"Artificial Insemination in Poultry",slug:"artificial-insemination-in-poultry",totalDownloads:9582,totalCrossrefCites:5,totalDimensionsCites:14,abstract:null,book:{id:"3206",slug:"success-in-artificial-insemination-quality-of-semen-and-diagnostics-employed",title:"Success in Artificial Insemination",fullTitle:"Success in Artificial Insemination - Quality of Semen and Diagnostics Employed"},signatures:"M.R. Bakst and J.S. Dymond",authors:[{id:"155683",title:"Dr.",name:"Murray R.",middleName:null,surname:"Bakst",slug:"murray-r.-bakst",fullName:"Murray R. Bakst"},{id:"167852",title:"Dr.",name:"Jessica",middleName:null,surname:"Dymond",slug:"jessica-dymond",fullName:"Jessica Dymond"}]}],onlineFirstChaptersFilter:{topicId:"189",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"80860",title:"From Open to Minimally Invasive: The Sacrocolpopexy",slug:"from-open-to-minimally-invasive-the-sacrocolpopexy",totalDownloads:40,totalDimensionsCites:0,doi:"10.5772/intechopen.101308",abstract:"With an increased demand for pelvic organ prolapse surgeries as the population ages, mesh-related osteomyelitis will become more prevalent. This case series enriches the paucity of data on management options for delayed osteomyelitis related to pelvic organ prolapse mesh. A literature review revealed no case reports of delayed onset osteomyelitis presenting up to a decade after colpopexy mesh placement. We present three cases of delayed osteomyelitis, their presentation, diagnosis and management at a tertiary academic referral center. Patients presented between 1 and 10 years after mesh colpopexy. Three different mesh materials were utilized during the initial procedures: Restorelle Y, Gynamesh and Gore-Tex mesh. The first case demonstrates failed expectant management with eventual surgical intervention on a medically compromised patient. The two subsequent cases describe elective complete mesh resection after several prior failed mesh revision attempts. This short case series and literature review illustrates that mesh-related osteomyelitis after a remote sacrocolpopexy carries significant morbidity. Mesh removal by means of minimally invasive surgery in the hands of an experienced surgical team utilizing DaVinci Robotic System is a good option and may lead to best patient outcomes.",book:{id:"11040",title:"Hysterectomy - Past, Present and Future",coverURL:"https://cdn.intechopen.com/books/images_new/11040.jpg"},signatures:"Adriana Fulginiti, Frank Borao, Martin Michalewski and Robert A. Graebe"},{id:"80782",title:"Cases of Postpartum Hemorrhage and Hysterectomy in Thailand’s Northern and Northeastern Provincial Hospitals",slug:"cases-of-postpartum-hemorrhage-and-hysterectomy-in-thailand-s-northern-and-northeastern-provincial-h",totalDownloads:38,totalDimensionsCites:0,doi:"10.5772/intechopen.102948",abstract:"PPH is a major cause of maternal death. Hysterectomy is safe to treat uncontrollable PPH. However, it may not be the best option for women who want to have children. The risk score tool to detect PPH earlier is needed in low-resource cities such as Chiang Rai and Sakon Nakhon province. This study aims to perform a risk score tool to prevent PPH in the northern and northeastern hospitals in Thailand; using mixed methods, identify risk factors for PPH from 20 articles globally and in Thailand using Med Calc, and develop the tool for prediction of PPH; and tool testing and a one-year follow-up on PPH-related hysterectomy cases. Results showed that this risk score tool can detect PPH earlier, reducing the number of PPH and hysterectomy cases. This risk score tool needs to be implemented in the same situations as hospitals to save pregnant women’s lives.",book:{id:"11040",title:"Hysterectomy - Past, Present and Future",coverURL:"https://cdn.intechopen.com/books/images_new/11040.jpg"},signatures:"Thawalsak Ratanasiri, Natakorn I. Tuporn, Somnuk Apiwantanagul, Thitima Nutrawong, Thawalrat Ratanasiri and Amornrat Ratanasiri"},{id:"80633",title:"Hysterectomy: Past, Present and Future",slug:"hysterectomy-past-present-and-future",totalDownloads:35,totalDimensionsCites:0,doi:"10.5772/intechopen.103086",abstract:"Hysterectomy is a major operation and is as old as time. This chapter touches briefly on the history of this procedure, its present aspects and general advice for these women who may need a hysterectomy, and finally the direction of new developments about it.",book:{id:"11040",title:"Hysterectomy - Past, Present and Future",coverURL:"https://cdn.intechopen.com/books/images_new/11040.jpg"},signatures:"Zouhair Odeh Amarin"},{id:"80589",title:"Perspective Chapter: Total Vaginal Hysterectomy for Unprolapsed Uterus",slug:"perspective-chapter-total-vaginal-hysterectomy-for-unprolapsed-uterus",totalDownloads:59,totalDimensionsCites:0,doi:"10.5772/intechopen.101383",abstract:"Vaginal hysterectomy was the first method to extract the uterus. Vaginal hysterectomy goes back a long way into the history of medicine. Although the first hysterectomy was carried out by Themison of Athens in the year 20 B.C., the idea of extracting the uterus through the vagina was first mentioned in 120 B.C. by Soranus of Ephesos, a distinguished obstetrician. The first elective vaginal hysterectomy was performed by J. Conrad Langenbeck in 1813. The patient was a 50-year-old multipara, who suffered from chronic pelvic pain attributed to a prolapsed uterus with a hard, bleeding tumor. The operation was carried out in challenging conditions, without anesthesia, proper instruments, or surgical assistants. Until the early 1950s, vaginal hysterectomy was the method of choice for removing the uterus. With the widespread introduction of general anesthesia and antibiotic therapy, the site of vaginal hysterectomy was taken over by abdominal hysterectomy. With the introduction of minimally invasive surgery in gynecology, vaginal hysterectomy has regained its place. Harry Reich performed the first total laparoscopic hysterectomy in 1989, being one of the most renowned vaginal surgeons, and he still claims at the beginning of the 21st century that … when the first choice of approach for hysterectomy is possible, is the vaginal route. This chapter presents the relevant anatomy from the point of view of the vaginal surgeon and the standard technique used by the author in over 5,000 vaginal hysterectomies. All intraoperative drawings and photographs are original.",book:{id:"11040",title:"Hysterectomy - Past, Present and Future",coverURL:"https://cdn.intechopen.com/books/images_new/11040.jpg"},signatures:"Petre Bratila"},{id:"80400",title:"Laparoscopic Hysterectomy in Morbidly Obese Patients",slug:"laparoscopic-hysterectomy-in-morbidly-obese-patients",totalDownloads:39,totalDimensionsCites:0,doi:"10.5772/intechopen.101307",abstract:"The following chapter will focus on laparoscopic hysterectomy in morbidly obese patients. The discussion reviews the physiological changes associated with morbid obesity and the potential implications on pneumoperitoneum during laparoscopic surgery. Important considerations such as perioperative care and operating room setup are discussed. Additionally, obtaining abdominal access, reviewing the surgical approach, and post-operative considerations are all highlighted within this chapter.",book:{id:"11040",title:"Hysterectomy - Past, Present and Future",coverURL:"https://cdn.intechopen.com/books/images_new/11040.jpg"},signatures:"Merima Ruhotina, Annemieke Wilcox, Shabnam Kashani and Masoud Azodi"},{id:"80238",title:"Surgical Site Infection after Hysterectomy",slug:"surgical-site-infection-after-hysterectomy",totalDownloads:80,totalDimensionsCites:0,doi:"10.5772/intechopen.101492",abstract:"Surgical site infections (SSIs) are associated with increased morbidity, mortality, and healthcare costs. SSIs are defined as an infection that occurs after surgery in the part of the body where the surgery took place. Approximately 1–4% of hysterectomies are complicated by SSIs, with higher rates reported for abdominal hysterectomy. Over the past decade, there has been an increasing number of minimally invasive hysterectomies, in conjunction with a decrease in abdominal hysterectomies. The reasons behind this trend are multifactorial but are mainly rooted in the well-documented advantages of minimally invasive surgery. Multiple studies have demonstrated a marked decrease in morbidity and mortality with minimally invasive surgeries. Specifically, evidence supports lower rates of SSIs after laparoscopic hysterectomy when compared to abdominal hysterectomy. In fact, the American College of Obstetricians and Gynecologist recommends minimally invasive approaches to hysterectomy whenever feasible. This chapter will review the current literature on surgical site infection (SSI) after hysterectomy for benign indications.",book:{id:"11040",title:"Hysterectomy - Past, Present and Future",coverURL:"https://cdn.intechopen.com/books/images_new/11040.jpg"},signatures:"Catherine W. Chan and Michael L. 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