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Kranjc and J. Peternelj",authors:[{id:"17832",title:"Dr.",name:"Tomaz",middleName:null,surname:"Kranjc",fullName:"Tomaz Kranjc",slug:"tomaz-kranjc"}]},{id:"13207",title:"Conjugate Flow and Heat Transfer of Turbine Cascades",slug:"conjugate-flow-and-heat-transfer-of-turbine-cascades",signatures:"Zeng Jun and Xiongjie Qing",authors:[{id:"17798",title:"Dr.",name:"Jun",middleName:null,surname:"Zeng",fullName:"Jun Zeng",slug:"jun-zeng"},{id:"18940",title:"Dr.",name:"Xiongjie",middleName:null,surname:"Qing",fullName:"Xiongjie Qing",slug:"xiongjie-qing"}]}]}],publishedBooks:[{type:"book",id:"36",title:"Heat Transfer",subtitle:"Mathematical Modelling, Numerical Methods and Information Technology",isOpenForSubmission:!1,hash:null,slug:"heat-transfer-mathematical-modelling-numerical-methods-and-information-technology",bookSignature:"Aziz Belmiloudi",coverURL:"https://cdn.intechopen.com/books/images_new/36.jpg",editedByType:"Edited by",editors:[{id:"17391",title:"Prof.",name:"Aziz",surname:"Belmiloudi",slug:"aziz-belmiloudi",fullName:"Aziz Belmiloudi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"61",title:"Mass Transfer in Multiphase Systems and its Applications",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"mass-transfer-in-multiphase-systems-and-its-applications",bookSignature:"Mohamed El-Amin",coverURL:"https://cdn.intechopen.com/books/images_new/61.jpg",editedByType:"Edited by",editors:[{id:"17141",title:"Prof.",name:"Mohamed",surname:"El-Amin",slug:"mohamed-el-amin",fullName:"Mohamed El-Amin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"300",title:"Mass Transfer in Chemical Engineering Processes",subtitle:null,isOpenForSubmission:!1,hash:"d8de07525defe8ef2ac6c4f1d680526e",slug:"mass-transfer-in-chemical-engineering-processes",bookSignature:"Jozef Marko",coverURL:"https://cdn.intechopen.com/books/images_new/300.jpg",editedByType:"Edited by",editors:[{id:"12119",title:"Dr.",name:"Jozef",surname:"Markoš",slug:"jozef-markos",fullName:"Jozef Markoš"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"925",title:"Mass Transfer",subtitle:"Advanced Aspects",isOpenForSubmission:!1,hash:"1025f8fc1d2c7a4769cca9205cfac6ae",slug:"mass-transfer-advanced-aspects",bookSignature:"Hironori Nakajima",coverURL:"https://cdn.intechopen.com/books/images_new/925.jpg",editedByType:"Edited by",editors:[{id:"45206",title:"Dr.",name:"Hironori",surname:"Nakajima",slug:"hironori-nakajima",fullName:"Hironori Nakajima"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1286",title:"Advanced Topics in Mass Transfer",subtitle:null,isOpenForSubmission:!1,hash:"4bfd4251cf260812c5a9e80f3a1c7a9a",slug:"advanced-topics-in-mass-transfer",bookSignature:"Mohamed El-Amin",coverURL:"https://cdn.intechopen.com/books/images_new/1286.jpg",editedByType:"Edited by",editors:[{id:"17141",title:"Prof.",name:"Mohamed",surname:"El-Amin",slug:"mohamed-el-amin",fullName:"Mohamed El-Amin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[{type:"book",id:"36",title:"Heat Transfer",subtitle:"Mathematical Modelling, Numerical Methods and Information Technology",isOpenForSubmission:!1,hash:null,slug:"heat-transfer-mathematical-modelling-numerical-methods-and-information-technology",bookSignature:"Aziz Belmiloudi",coverURL:"https://cdn.intechopen.com/books/images_new/36.jpg",editedByType:"Edited by",editors:[{id:"17391",title:"Prof.",name:"Aziz",surname:"Belmiloudi",slug:"aziz-belmiloudi",fullName:"Aziz Belmiloudi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},onlineFirst:{chapter:{type:"chapter",id:"62232",title:"Body Mass Index and Insulin Sensitivity/Resistance: Cross Talks in Gestational Diabetes, Normal Pregnancy and Beyond",doi:"10.5772/intechopen.78363",slug:"body-mass-index-and-insulin-sensitivity-resistance-cross-talks-in-gestational-diabetes-normal-pregna",body:'Normal pregnancy has typical significant changes in maternal insulin resistance and hyperinsulinemia together with progressively increasing insulin secretion during gestation. The glucose metabolism regulation during pregnancy has a complex characteristic. The placenta plays a critical role in the delivery of nutrients and the regulation of normal fetal growth. It has a metabolic and endocrine function, and produces cytokines that influence on the fetal growth. Gestational diabetes mellitus (GDM) is a serious complication of normal pregnancy. It is defined as “any degree of glucose intolerance with onset or first recognition during the current pregnancy.” The global prevalence ranges between 1 and 14%, depending on the population studied and the diagnostic tests applied. GDM represents nearly 90% of all pregnancies with diabetes [1] and is one of the most common complications with risks for the mother and fetus. GDM is not only associated with adverse pregnancy outcomes such as macrosomia, shoulder dystocia, stillbirth, hypertension, and other obstetric complications [2, 3], but is also a strong predictor of impaired glucose tolerance and transitioning to overt type 2 diabetes mellitus (T2DM) postpartum [4]. Although most of the women with previous GDM return to normal glucose tolerance after delivery, both GDM patients and their offspring are at a greater risk of developing T2DM later [5]. The exact cellular mechanisms involved in GDM development are not yet completely understood. Growing data provide evidence for common pathogenesis of different diabetes forms as a result of a progressive β-cell dysfunction, inadequacy to secrete insulin, and insulin resistance in peripheral tissues leading to hyperglycemia. Pancreatic β-cell dysfunction is one of the main pathogenetic GDM mechanisms [6, 7]. Although this defect likely precedes the pregnancy [8], first it is detected clinically as insufficient β-cell compensation of insulin resistance in late pregnancy. GDM occurs if pancreatic β-cells are unable to face the increased insulin demand during pregnancy with elevated glucagon-like peptide 1 (GLP-1) confirming the abnormal insulin secretion [9]. The β-cell defect in GDM women is still present in the postpartum period [10]. Pregnancy is a diabetogenic condition. Many causes are suggestive for insulin resistance or decreased maternal insulin sensitivity. Pregnancy is normally characterized by progressive insulin resistance with beginning near mid-pregnancy and progression during the third trimester to levels approximating the insulin resistance typical for type 2 diabetes mellitus (T2DM) [11]. Firstly, Ryan et al. [12], using the euglycemic clamp technique, demonstrate decrease in insulin sensitivity with a state of insulin resistance in pregnancy being more marked in gestational-onset diabetic women in comparison of nondiabetic control group in late pregnancy. These alterations could be due to placental factors, progesterone, and estrogen, having insulin-antagonistic effects [12]. It seems that gestational diabetes and T2DM are the faces of one and the same disease. Women who develop GDM probably have reduced insulin secretion and/or chronic insulin resistance before pregnancy [13, 14] with a substantially increased risk of developing T2DM later [15]. GDM is the most common pregnancy metabolic disorder with an increasing prevalence ranging from less than 1 to 28% [16, 17, 18] that parallels the worldwide epidemic of T2DM [19]. The frequency of occurrence depends on diagnostic methods, ethnicity, and body composition [20]. Some ethnic groups have been long associated with an increased risk of GDM, and the prevalence seems particularly higher among women from South Asia and South East Asia than from Caucasian, African-American, and Hispanic communities [21]. Several pathophysiological mechanisms for GDM development have been proposed as metabolic, inflammatory, autoimmune, and genetic ones with various biologic and molecular pathways for regulation of glucose levels involved. During pregnancy, fine balance between pro -and anti-inflammatory cytokines, necessary for the normal development, exists [22]. In particular, GDM seems to be linked to downregulation of adiponectin and anti-inflammatory cytokines, and to upregulation of adipokines as leptin and pro-inflammatory cytokines, implicated in insulin resistance [22].
The etiology of GDM is multifactorial and not sufficiently elucidated. The overweight, obesity during prepregnancy and pregnancy, excessive gestational weight gain, excessive central body fat deposition, are among the main modifiable risk factors of GDM and contribute significantly to risk of pregnancy complications. Obesity and diabetes constitute worldwide threats to the public health [23] and health care systems and economies [24]. Obesity is a chronic inflammatory state. Pregnancy and especially GDM are associated with elevation in inflammatory markers thus the heightened inflammatory response may play a substantial role in pregnancy complications [22]. Obesity prevalence has been continuously grown, particularly in lower and middle-income countries, but in both, developed and developing countries, more women are obese at conception, and young women at fertile age are at high risk of excess weight gain driving obesity and related reproductive and metabolic complications [25]. The obesity in worldwide is epidemic. The number of individuals with obesity doubled between 1980 and 2014. Moreover, in 2014, over 1.9 billion adults (18+ years) were overweight, with over 600 million being obese [26]. Accumulating epidemiological data confirm that maternal obesity has short- and long-term implications for women and babies, with a threefold increased risk of GDM [27], large for gestational age babies [28], also increased probability of macrosomia and childhood obesity [29, 30, 31], and even of fetal death, stillbirth, and infant death [32]. GDM brings a sevenfold higher risk for future development of T2DM [15]. Excessive adiposity and weight gain are well-documented risk factors of type 2 diabetes in the general population [33, 34, 35]. Women who develop GDM are more likely to be overweight or obese at the time of the diagnosis in comparison to the general population. A larger part of them develop incident of overweight or obesity in later life. Women with a history of GDM are usually advised to control their weight after delivery [36].
An excess body weight is a major health issue worldwide as the sixth significant risk factor contributing to disease, and the increased obesity level may result in a decline of life expectancy in the future [37]. The body mass index (BMI), or Quetelet index, is used to assess the degree of obesity/human body fat based on an individual’s weight and height [38]. However, BMI values may have different connotations in individuals with diverse ethnic background, short/tall stature, or varied muscle mass, and do not reflect the regional distribution of fat in the body, i.e., subcutaneous versus visceral/central [39]. Both prepregnancy BMI and weight gain during pregnancy are positively associated with gestational insulin resistance [40, 41], with obesity being a risk factor for GDM [42] and increased risk of adverse maternal and perinatal outcomes [43]. In addition to high risk of GDM, excessive gestational weight gain (EGWG) and obesity in prepregnancy have further adverse risks of preeclampsia, eclampsia, cesarean delivery, macrosomia, etc. [44, 45, 46, 47, 48]. Because of increasing living standards, EGWG prevalence is higher than ever before with approximately 40% of pregnant women gaining more weight than is recommended [48]. These two factors—high prepregnancy BMI and EGWG—have been reported as well-established risk for adverse pregnancy outcomes [49, 50, 51, 52, 53, 54]. Large studies, including different ethnic women in western countries, determine increased risk for macrosomia in parallel with increasing EGWG in all prepregnancy BMI categories, and the risk varies in relation to degree of BMI [55, 56, 57, 58]. Moreover, more underlined risk of macrosomia in overweight and obese before pregnancy women and in those who gain excessive weight during pregnancy has been proved [59]. Women with previous pregnancies complicated by GDM are at an increased risk of developing T2DM in the postpartum [15]. A meta-analysis evaluates 28 studies including women with previous GDM, with follow-up ranging between 6 weeks and 28 years after the end of pregnancy, and it reveals rates of T2DM between 2.6 and 70%, depending on ethnicity, diagnostic criteria, and the follow-up period [60]. Prepregnancy obesity and excessive weight gain from prepregnancy to postpartum increase postpartum diabetes and prediabetes risks among GDM women [61]. Women, failing to lose weight postpartum, are with a higher risk of subsequent long-term obesity [62]. The recent meta-analysis shows 18% increase in risk of diabetes per unit increase in BMI [63], and every kilogram of weight gain increases by 7% the risk of diabetes [64]. Several studies have indicated that body fat distribution, dependent on ethnicity, has a larger effect than general obesity in predicting the risk of diabetes [65, 66]. Asians are with smaller frames and lower body fat distribution than white Europeans for the same BMI [67]. In comparison to Europeans, Chinese, and South Asians have more abdominal adipose tissue, especially visceral adipose tissue [68]. In this regard, waist circumference (WC) is a simple and valid index to assess abdominal fat and has been proved to be an independent predictor of T2DM [69, 70]. In Caucasian women, WC is also an important predictor of GDM [71].
In the last decade, abundant data have indicated that adipose tissue is not just an energy storage depot but rather a metabolically active tissue [72]. Adipose tissue is considered to be an important and active organ for maintenance of systemic homeostasis through a complex network of auto-, para-, and endocrine cross talks to other tissues and organs [73] mediating the development of obesity and related diseases. During obesity, the number and size of adipocytes are increased [74]. Studies of adipocytes from women in different trimesters reveal alterations in lipolytic activity that promote maternal fat accumulation in early pregnancy and enhance fat mobilization in late pregnancy [75]. Hypertrophy of adipocytes can impair the functions of adipose tissue in association with excess amount of adiposity and leading to a dysregulated secretory profile [76]. Obesity in pregnancy has intense effects, causing systemic inflammation. Maternal obesity and GDM may be associated with a state of chronic, low-grade inflammation, referred as “meta-inflammation,” opposite to an acute inflammatory response [77], or metabolically induced inflammation. Meta-inflammation is distinct from an acute pro-inflammatory response and is triggered primarily by metabolites and nutrients, leading to systemic insulin resistance [78]. The base of this chronic low-grade inflammation is a production of pro-inflammatory cytokines by adipocytes in obesity [79]. This elevation of circulating pro-inflammatory cytokines, originated from adipose tissue, may induce increased inflammatory cytokine secretion by the placenta and alter placental function [80]. During pregnancy, similar to gestational age, the size of the placenta is also in progress. The levels of pregnancy-associated hormones estrogen, progesterone, cortisol, and placental lactogen in the maternal circulation are elevated [81, 82] accompanied by an increasing insulin resistance. A healthy pregnancy outcome is highly reliant on tight physiological regulation largely orchestrated by the placenta, an extremely complex and multifunctional materno-fetal organ [83]. The placenta like a transient endocrine organ with a secretion of various hormones and cytokines, affecting both maternal and fetal metabolism, plays a major role in the initiation and preservation of pregnancy. Maternal obesity significantly impacts the endocrine function of the placenta. Obese pregnancies have a dysregulated maternal cytokine profile with considerable rise in pro-inflammatory cytokines [84, 85]. Furthermore, such over expression of pro-inflammatory cytokines is also observable in GDM placenta. This alteration in normal secretion of adipocytokines is involved as an essential factor in GDM development [86, 87, 88, 89].
Adipokines, secreted from adipose tissue, are involved in a wide spectrum of biological processes, including regulation of energy homeostasis, adipocyte proliferation and differentiation, inflammation, angiogenesis and regulation of coagulation, and vascular function [90, 91, 92]. Adipokines act locally in adipose tissue (auto- and paracrine manners), but they also mediate via the circulation the cross talks between adipose tissue and other key metabolic organs (endocrine manner). Some adipokines, such as leptin and adiponectin, are adipocyte specific, while others, like pro-inflammatory cytokines, to a higher degree are secreted by the nonfat cells in adipose tissue [76]. In obesity, dysregulation of pro- and anti-inflammatory cytokines released from adipose tissue is in the base of the chronic low-grade systemic inflammation as that leads to development of metabolic and cardiovascular disorders [93, 94] and promotes insulin resistance or GDM. Adipose tissue produces adipocytokines, including leptin, adiponectin, tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6), as well as the recently discovered resistin, visfatin, and apelin [95, 96]. A study finds the circulatory levels of IL-6, interleukin-8 (IL-8), interleukin-1β (IL-1β), TNFα, and C-reactive protein (CRP) are higher in overweight and obese pregnant women, relatively to normal weight women during pregnancy and postpartum [97]. The expression of pro-inflammatory cytokines has also been reported to be dysregulated in the development of GDM introducing an altered cytokine profile in hyperglycemic pregnancies [98, 99]. These effects are all related to regulation of insulin resistance. Higher circulatory levels of CRP, IL-6, monocyte chemoattractant protein-1 (MCP-1), and interleukin-1 (IL-1) receptor antagonist (IL-1Ra) are significantly associated with maternal adiposity [100]. Increased circulation levels of pro-inflammatory cytokines, IL-6, TNFα, leptin, and decreased levels of adiponectin and anti-inflammatory markers such as interleukin-4 (IL-4) and interleukin-10 (IL-10) are seen in GDM pregnancies in comparison to normal pregnancies, regardless of BMI [99]. The elevated circulating levels of IL-6 and TNFα in maternal blood are consistently observed in maternal obesity as well as in GDM, in the presence or absence of obesity [101, 102, 103]. TNFα and leptin have been suggested as the strongest predictors of pregnancy-associated insulin resistance [104, 105]. Together with increased levels of serum cortisol, interleukins, and other factors, they can interrupt the insulin signaling pathway and lead to insulin resistance during normal pregnancy [104]. Additionally, TNFα has been established as the most significant predictor of pregnancy-induced insulin resistance, with higher synthesis and releasing by the placenta in comparison to IL-6 or IL-8 [106]. Hence, TNF-α is more likely to exert crucial effects on IR during pregnancy. Although the leptin is produced mainly by adipocytes, there is strong evidence that the placenta, rather than maternal adipose tissue, contributes to the rise in maternal leptin concentrations during pregnancy [107]. Pregnancy is considered as a leptin-resistant state, but the results on circulating leptin levels in GDM are controversial. However, most studies have shown increased leptin in GDM [108, 109, 110]. Adiponectin, anti-inflammatory factor, is considered to have beneficial effects on insulin sensitivity and anti-inflammatory activities [99]. TNF-α, leptin, and adiponectin are produced by placenta [111, 112] and releasing into the maternal circulation contributes to the rise in maternal TNF-α and leptin concentrations during pregnancy [104], more pronounced in GDM than in normal pregnancy [99]. Increased circulating concentrations of TNF-α enhance leptin production, opposite, leptin increases the production of TNF-α and IL-6 by monocytes [113] and stimulates the production of CC chemokine ligands (CCL) [114]. Except this, TNF-α and other pro-inflammatory mediators suppress the production of adiponectin by adipocytes [115]. Something more, some studies find a significant positive correlation between BMI values and levels of TNF-α and leptin, and an inverse correlation between BMI and adiponectin levels in GDM [108, 116, 117, 118]. The increased secretion of pro-inflammatory cytokines, the relative hypoxia, and cell death due to hypertrophic adipocytes promote a high infiltration rate of monocytes into visceral adipose tissue and activation of macrophages [119]. In general, the increase in release of pro-inflammatory cytokines, infiltration of macrophages, as well as relationship between hypertrophic growth of adipose tissue and inflammation lead to the development of insulin resistance [120] and β-cell failure [121, 122].
Several recent studies have attempted to illuminate the effect of iron overload on adipocyte function. Although inflammatory cytokines can influence iron storage in various cell types, studies have shown that the link between elevated iron and obesity/diabetes is independent of inflammation [123, 124]. No central mechanism for the impact of iron on adipocytes is known; however, iron is known to influence adipocytes’ mitochondrial function and adiponectin production [125]. Alterations in adipocyte mitochondrial iron content affect adipocyte differentiation and insulin sensitivity [126, 127]. Some studies have suggested that adipose tissue may be a primary target organ for the metabolic effects of iron. The results propose that stores of body iron and/or iron metabolism may be involved in the development of insulin resistance not only in liver or muscle but also in adipocytes [128]. Adipocytes require iron for normal function and differentiation. They also express specialized proteins involved in iron metabolism and this fact is well suited to possible adipocyte action as an iron sensor. Evidence that adipocyte iron levels regulate adiponectin transcription and serum protein levels is present. These data further highlight the role of the adipocyte as a key regulator of metabolism in all tissues, based on integrated sensing of nutritional stores and iron availability [129]. The hypothesis that adiponectin links iron and insulin resistance is attractive as decreased adiponectin levels are associated with insulin resistance during GDM, a relationship between its reduced concentration and β-cell dysfunction in GDM women [130]. Moreover, studies in mice, human, and cell culture have demonstrated that iron lowers adiponectin production and increases diabetes risk [129]. Serum ferritin levels, as indicator for tissue iron stores, reflect insulin resistance during diabetic pregnancy [131], with a higher level in GDM women in comparison to normal pregnant [132], and also with a risk of subsequent development of postpartum impaired glucose tolerance and overt T2DM [131]. Furthermore, intracellular iron excess catalyzes the formation of reactive oxygen species (ROS), promoting oxidative stress [133, 134] thus leading to increased β-cells apoptosis, hepatic dysfunction, and insulin resistance, and in consequence, promoting the T2DM progression [135]. Research data verify that serum ferritin concentrations are among the best predictors of serum leptin under physiological conditions. More importantly, the relationship is causal, reflecting regulation of leptin transcription by iron [136]. Studies on relationship between ferritin and leptin have suggested a possible link which is independent of relationship with BMI and inflammation. Iron overload may lead to a decrease in leptin serum level [137] along with the destruction of the fat cell membrane and the dysfunction in adipose tissue [138]. Opposite to this suggestion—leptin with other stimuli, such as pro-inflammatory cytokines, can be added to the list of adipose-derived factors that may contribute to hypoferremia observed in the overweight and obese population [139]. The functional significance of iron accumulation in adipocytes and the reduced leptin level is not yet clear. One possible explanation is that while iron regulates the serum leptin level, at the same time, it could have an effect on leptin signaling to a change in leptin sensitivity [140]. This interplay between iron, leptin, and adiponectin is an intriguing subject for study in various population groups, including pregnant women with gestational diabetes.
Deficiency of vitamin D is associated with impaired glucose homeostasis during pregnancy [141]. New studies underline the key role of vitamin D in glucose homeostasis and insulin resistance: 1,25(OH)2D3, the active form of vitamin D, regulates circulating glucose levels by binding to vitamin D receptor of pancreatic β-cell and modulating insulin secretion [142, 143]; it promotes insulin sensitivity by stimulating the expression of insulin receptors and enhancing insulin responsiveness for glucose transport [144]; regulates the balance between the extracellular and intracellular calcium pools in pancreatic β-cell, [145]; it is responsible for the presence of vitamin response element in the human insulin gene promoter with stimulation of the expression of insulin receptor and for the effects on systemic inflammation by modulating the effects of cytokines on β-cell function [146], since insulin resistance and β-cell apoptosis could be induced by systemic inflammation. Vitamin D has a direct effect on pancreatic β-cells and is a prerequisite for the normal insulin secretion function of the endocrine pancreas [147, 148]. Probably, the active form of vitamin D decreases expression of pro-inflammatory cytokines such as IL-6, IL-1, and TNF-α involved in insulin resistance [149]. Some studies report a negative relationship between serum 25(OH) D levels, BMI [150, 151, 152], and HOMA-IR [147, 152]. Maternal overweight and obesity are among the highest modifiable risk factors. The prevalence of obesity is increasing, especially in women at reproductive age. In America, according to the data from Pregnancy Risk Assessment Monitoring System (PRAMS), one in five women is obese when they become pregnant, which presents the increase of the obesity prevalence by 70% compared to the previous decade [153]. Obesity is a risk factor for the development of GDM [154], and increased BMI is associated with a greater frequency of complications in pregnancy, at birth and postpartum [155, 156]. The most commonly studied index, body mass index, calculated by formula BMI = weight (kg)/height (m2) [37], is for measure of total body fat [157]. BMI is derived from easy measurements of height and weight and it is not expensive. Usually, women are classified as underweight (BMI less than 18.5), normal weight (BMI 18.5–24.9), overweight (BMI 25.0–29.9), class I obese (BMI 30.0–34.9), class II obese (BMI 35–39.9), or class III obese (BMI 40.0 or greater), according to Institute of Medicine (IOM) [158]. There are some limitations maybe even more important when attempting to compare individuals from different ethnic groups. The proposed BMI cut-off points ranging from 18·3 to 29·7 kg/m2 for children and adolescents aged 5–19 years, which correspond to the adult obesity threshold of 30 kg/m2.These cut-offs are on the base of data from the USA population [159]. The use of these global cut-off points to define overweight and obesity remains contentious. Given the marked variations in different world regions, countries, and populations within countries, the use of these values may underestimate the health hazards of adult obesity [160]. Current studies show that maybe visceral fat mass is a novel risk factor for predicting gestational diabetes in obese pregnant women [161]. Central obesity as assessed by early pregnancy waist-hip ratio (WHR) and visceral fat mass (VFM) measured by bioimpedance is an independent predictor of GDM in addition to classical risk factors [162]. In a prospective study of 485 women cohort in Canada, elevated first trimester visceral and total adipose tissue depth independently predict the risk of subsequent dysglycemia in pregnancy [163]. Measures of central/abdominal obesity such as WC and WHR have been compared to BMI for their association with adverse cardiovascular and metabolic consequences [164]. BMI and WHR are significant risk factors for development of gestational diabetes and IR, but this association varies among different ethnicities [165]. Results of meta-analysis of 20 studies show that the risk of developing GDM is about two, four, and eight times higher among overweight, obese, or severely obese compared with normal-weight women at the beginning of their pregnancies [166].
For every 1 kg/m2 increase in BMI, the prevalence of GDM increases by 0.92% [42]. The increasing BMI index with 1 kg/m2 increased the risk of GDM developing with 9.9% [167] Increasing trend in the risk of severe adverse obstetric outcomes, rising along with increasing maternal BMI, exists [168]. Maternal overweight and obesity, diabetes, and excessive gestational weight gain are associated with fetal overgrowth and large for gestational age (LGA), which then can lead to an increased risk in the offspring for later obesity and diabetes [169, 170]. It has been found that in Finnish obstetric population, the maternal morbidity rises markedly when comparing overweight (BMI ≥26–29 kg/m2) vs. obese (BMI ≥ 30 kg/m2) women: the incidence of maternal diabetes, hypertension, and other chronic diseases [171].
Pregnancy is a normal physiological state of insulin resistance, and it presents a physiological stress model of pancreatic β-cells [172, 173]. It is associated with a decrease in insulin sensitivity of an approximate 50–60% by the latter half of pregnancy and a 200–250% increase in insulin secretion with purpose to maintain euglycemia in the mother [10]. The increased resistance is caused by post-insulin receptor events and is brought about by the cellular effects of the increased levels of some pregnancy-associated hormones [174]. In gestational diabetes, insulin resistance is not adequately compensated by insulin hypersecretion because of defective β-cell function. Insulin resistance during pregnancy reveals limitations in insulin secretion; on the other hand, increasing insulin resistance and subsequent insulin hypersecretion may worsen the level of β-cell failure [174]. As a result, pregnant women with GDM have a higher level of insulin resistance compared to healthy pregnant women.
Some studies demonstrate that the insulin secretion and sensitivity capacities of Asian women are different from those of women in Western countries. Since even in Asians, the pancreatic β-cell mass is relatively smaller than in Westerners, and insulin secretion capacity is also lower on the background of abdominal obesity is more common in Asians than in Westerners with similar body weights [175]. A study assesses the change in insulin resistance and β-cell function in a multiethnic population-based cohort of pregnant women. Pregnant women from East Asia and South Asia are more insulin resistant and show poorer β-cell function (HOMA-β) than Western Europeans [176]. The mechanisms leading to increased insulin secretion in pregnancy, primary or compensatory to resistance, are not entirely elucidated yet. They are partly related to metabolic effects of several hormones and cytokines which are elevated in maternal circulation during pregnancy [177]. Decreased insulin sensitivity or increased insulin resistance is defined as the decreased biological response of a nutrient to a given concentration of insulin at the target tissue, e.g., liver, muscle, or adipose tissue. Obesity is the most common risk factor related to decreased insulin sensitivity. During the pregnancy, it is related with maternal energy metabolism, and visceral fat accumulation has important biological meaning. In this relation, the influence of visceral fat, respectfully BMI, and insulin sensitivity are too important [178].
In healthy pregnant women, pancreatic β-cells increase their insulin production through hyperplasia, hypertrophy, and hyperfunction to compensate for the pregnancy-induced insulin resistance [176]. Maternal islets adapt to this increased demand mainly through enhanced insulin secretion per β-cell and increased β-cell proliferation [179]. Like other forms of hyperglycemia, GDM is characterized by pancreatic β-cell dysfunction that is insufficient to meet the body’s insulin needs. Available data suggest that β-cell defects in GDM are a result from the same spectrum of causes that underlie hyperglycemia in general, including autoimmune disease, monogenic causes, and insulin resistance [180]. In normal pregnancies, the dynamic changes in glucose homeostasis and insulin sensitivity are in connection with alterations in lipid and protein metabolism. Longitudinal studies of glucose tolerance during gestation demonstrate an increased insulin response to oral glucose in the first trimester relative to prepregnancy values [10], with a subsequent progressive increased insulin responses in consistent with progressive IR [10]. Remarkably, there is an independent effect of pregnancy on β-cell function independent of the observed changes in insulin; but the etiology of this effect is at present unknown, although may include the role of incretins [181, 182]. The impact of obesity on these changes is significant; in particular, the decline in fasting glucose at early gestation is reduced, but not reduced at all in severely obese women [183]. In late gestation, the normal reduction in peripheral insulin sensitivity of 50% is reduced in obese women [10]. In addition to significant peripheral and hepatic insulin resistance, which manifests as reduced insulin-mediated glucose disposal, there is a large reduction in insulin-stimulated carbohydrate oxidation and a reduction in insulin suppression of endogenous glucose production, all of which are reversed in the postpartum period [184]. Importantly, the overall effects of this impaired insulin resistance are not influenced only on the glucose. In the postprandial state, this obesity-related insulin resistance overacts the normal circulatory increases in metabolic fuels, i.e., glucose, lipids, and amino acids. The fasting, postprandial, and integrated 24-h plasma concentrations of all basic macronutrients are affected by enhanced insulin resistance in obese pregnant women [185].
The homeostatic model assessment (HOMA) is a method used to quantify insulin resistance and β-cell function, based on a single measurement of fasting glucose and insulin or C-peptide concentrations in the blood [186]. The easiest and most popular assessment of β-cell function is the homeostatic index HOMA-B. It is widely used because of its simplicity and it reflects the release of insulin under nonstimulated conditions [187]. HOMA model is considered as a structural model of the underlying physiological basis for the feedback loop between the liver and the β-cell in fasting [188]. HOMA-IR has been observed to have a linear correlation with the glucose clamp and considered as minimal model for estimations of insulin sensitivity/resistance in various studies [188, 189]. HOMA-IR determines the relationship between the liver and pancreas. This index reflects more the liver insulin resistance in comparison to peripheral insulin resistance [190], and it is a good indicator of overall insulin sensitivity during pregnancy. Although surrogate marker HOMA-B is less evaluated as an index, it provides high reliability in the measurement of β-cell function. Both indices, HOMA-B and HOMA-IR, submit better overall picture of the essential metabolic disorder [191]. Disadvantage of HOMA model is related to the fact that it underlines the lack of linearity at deepening of insulin resistance [192]. This model is a widely used and well correlates with the insulin sensitivity, as measured by the venous clamp technique in various studies [188, 189].
The quantitative insulin sensitivity check index (QUICKI) is an empirically derived mathematical transformation of fasting blood glucose and plasma insulin concentrations [193, 194]. QUICKI is a simple, robust, accurate, and reproducible method that appropriately predicts changes in insulin sensitivity after therapeutic interventions as well as the onset of diabetes [195]. QUICKI has been seen to have a significantly better linear correlation with glucose clamp determinations of insulin sensitivity than minimal-model estimates [196]. Its calculation is used to evaluate the insulin sensitivity [197] including during early and late pregnancy [190]. The index assumes that the circulating glucose and insulin are determined by a feedback loop between the liver and pancreatic β-cells [198]. Insulin sensitivity has been modeled by proportionately decreasing the effect of plasma insulin concentrations at both the liver and the periphery [199]. Other parameter to assess insulin sensitivity is HOMA-S%. The computer model can be used to determine insulin sensitivity (HOMA–S%) from paired fasting plasma glucose and insulin concentrations. The data from individual subjects determine unique combinations of insulin sensitivity (HOMA %S) and beta cell function (HOMA %B) from steady-state conditions [200]. HOMA can be used to track changes in insulin sensitivity and β-cell function in individuals. Also, it can be used in individuals to indicate whether reduced insulin sensitivity or β-cell failure predominates. Determination of HOMA-%S is used to establish the prevailing normal over a normoglycemic population in each comparative group [188]. Maternal obesity is associated with higher maternal glucose and GDM risk; its association with newborn size at birth is, in part, independent of maternal glycemia [201, 202, 203, 204]. BMI is an indicator of the tissue quantity (weight) over the skeletal frame (height), including adipose tissue and muscle. BMI is known to increase blood volume and to reduce the concentration of serum metal ions, such as, iron and zinc [205]. Maternal overweight (BMI ≥ 25 kg/m2) has been shown to be the strongest risk factor for GDM. Two meta-regression analyses show that the odds ratios for developing GDM are 1.97–2.14 in overweight (BMI ≥ 25 kg/m2), 3.01–3.56 in obese (most studies BMI ≥ 30 kg/m2), and 5.55–8.56 in severely obese (BMI ≥ 35–45 kg/m2) women compared with normal weight women [154].
In late gestation, the normal reduction in peripheral insulin sensitivity of 50% [206] is reduced in obese women as determined by the quantitative insulin sensitivity check index and that insulin sensitivity in women with GDM worsened as gestation progressed [207]. The indexes of insulin sensitivity QUICKI and HOMA significantly correlated with a direct measurement of insulin sensitivity using the euglycemic-hyperinsulinemic clamp during pregnancy [208]. The mechanism for the decrease in insulin sensitivity in pregnancy is not fully understood and is part of the natural process during pregnancy, although the insulin signaling pathway can be interrupted by several factors, such as increased levels of serum cortisol, TNF α, and some interleukin cytokines, leading to insulin resistance, during normal pregnancy [104]. In this connection, it would appear that preconceptual fat mass is one of a major determinant, because lean women exhibit an inverse correlation between changes in insulin sensitivity and fat mass, which is not seen in obese women [209]. Obese women exhibit a negative relationship between the decrease in insulin sensitivity and accretion of fat mass from prepregnancy to late gestation [210]. Visceral fat volume in human body has important biological meaning, which is well expressed during the pregnancy. In this relation, the influence of visceral fat, respectfully BMI, on the insulin sensitivity is too important. A study has announced diminished insulin sensitivity in pregnant women with GDM compared to healthy pregnant women for BMI (P > 0.05) with significantly higher body fat percentage, expressed by connection QUICKI index-BMI (r = −0.384, P < 0.01) [211]. These results are similar to other author′s results—lower level of insulin sensitivity index QUICKI in pregnant women with GDM in comparison to NGT Р = 0.001, a reverse correlation between QUICKI index and BMI in the both of group (r = −0.458 for NGT and r = −0.603 for GDM) [167]. Insulin sensitivity measured during the clamp was higher during pregnancy in the NGT group than in the GDM group (P < 0.05) [208]. Values of QUICKI index in overweight women with normal glucose tolerance (NGT) and in women with GDM have been significantly lower (P < 0.01) than those in normal-weight women with NGT, and QUICKI in women with GDM has been decreased significantly (P < 0.05) during pregnancy, according to Endo et al. [207]. Furthermore, other authors have reported significant interaction between race and BMI (under/normal weight, overweight/obese) for glucose, insulin, and HOMA-IR at or above the 75th percentile and QUICKI less than the 25th percentile in mid-trimester [212]. Other authors have detected lower levels of QUICKI index in overweight compared to normal-weight women at third trimester of pregnancy [199]. Changes in insulin sensitivity are a hallmark of pregnancy and contribute to the metabolic changes, while nutrient transfer to the fetus impacts maternal metabolite levels [213, 214]. Studies show that values for HOMA-S% between pregnant with GDM and matched control NGT subject are highly significant different (P < 0.001) [215, 216]. Some authors found lower level for HOMA S% in GDM pregnant with prepregnancy BMI ≥ 25 kg/m2 in comparison to GDM with prepregnancy BMI <25 kg/m2 (P < 0.001) [217]. These values are not markedly different from those obtained in the other study [167]. In this study, there are statistically significant differences in HOMA-S% between the NGT and GDM groups (Р = 0.002). It is found a reverse correlation between HOMA-S% and BMI in the both NGT and GDM patient groups (r = −0.467 and r = −0.679, respectively). The authors’ hypothesis is that as higher is a BMI, stronger is its influence on insulin sensitivity, expressed by HOMA-S% index [167]. The current studies confirm that GDM is associated with increased insulin resistance and β-cell dysfunction, as well as reduced insulin sensitivity and secretion.
BMI, glucose, and insulin sensitivity are interrelated and alter maternal metabolism. A novel aspect of studies is identification of metabolic signatures uniquely associated with maternal BMI and glycemia, including differences in metabolites most strongly associated with these phenotypes [218]. The association of several plasma metabolites with maternal prepregnancy BMI across gestation in a cohort of 167 non-Hispanic and Hispanic ancestry women was reported [219]. Some of these metabolites have been found to have a role in aspects of metabolism such as insulin sensitivity and pancreatic β-cell function. A limited number of GDM metabolomics studies have been performed, evidence suggests that the metabolic signatures of T2D and GDM overlap [220]. Metabolomic studies of maternal metabolism during pregnancy are focused largely on normal pregnancy and GDM [221, 222, 223, 224, 225, 226, 227]. It is important to examine the associations of maternal BMI on the maternal metabolome, to consider estimated maternal insulin sensitivity as a predictor of the maternal metabolome. Furthermore, maternal BMI and insulin sensitivity impact a broad array of metabolites and have shared independent associations with the maternal metabolome [228].
Insulin resistance is, by definition, a disorder in the signal transduction of several known hormones [229]. Insulin resistance in peripheral tissues in women with GDM is exacerbated, but few studies have examined the extent of insulin resistance in placenta in this disease. It is possible that this insulin resistance could contribute to alter the placental transport of nutrients [230, 231, 232]. The degree of maternal insulin resistance manifested during pregnancy is theoretically associated with the degree of glucose flux from mother to fetus. Excessive insulin resistance during pregnancy is also observed in obese subjects without abnormal glucose tolerance [10]. Different studies found HOMA-IR values in the GDM group are significantly higher than in NGT patients, which indicated a significant insulin resistance [167, 215, 233, 234, 235, 236, 237, 238, 239]. Some studies report controversial results. They found that the HOMA-IR values are similar in GDM patients and healthy NGT controls [240, 241, 242, 243]. Women with GDM in early pregnancy had significantly higher HOMA-IR values than those with GDM in later pregnancy or those with NGT [244] and results are similar to other from prior work [245]. Probably, higher BMIs among women with early-onset GDM are detected to at least partially explain this phenomenon [246]. An important goal is to identifying women with GDM during early pregnancy to minimize maternal and neonatal morbidity. One study reported that first trimester HOMA-IR values are independent predictors for the development of GDM in logistic regression analysis, and the HOMA-IR value is found to be a better marker (AUC ¼ 0.75; 95% CI, 0.67e0.83) than the other factors [247]. Another study detects borderline significance for risk of subsequent GDM for increased HOMA-IR values at gestational weeks 16–18, independent of other variables that are associated with GDM [248]. Some researchers determined the predictability of GDM with a 90% sensitivity and 61% specificity by ROC analysis in patients whose HOMA-IR scores are >2.08 in the first trimester [249]. A study reports that HOMA-IR at 21–28 gestational weeks is reliable risky factor to development of IR (OR = 0.677, 95% CI = 0.573–0.781, P = 0.002, sensitivity 54.7%, and specificity 24.5%). HOMA-IR is found with statistically significant impact on developing of GDM-OR = 2.039 (95% CI = 1.427–2.914, P < 0.0001). The increasing HOMA-IR index with unit increases the risk of GDM developing about two times. The predictive threshold values for developing insulin resistance in gestational pregnant at 21–28 gestational weeks are HOMA–IR > 1.8 [250]. According to the International Diabetes Federation (IDF) criteria, the HOMA-IR cut-off point to differentiate low and high value of insulin resistance is 2.38. Several previous studies performed on smaller populations have demonstrated that HOMA-IR index assessed at diagnosis of GDM is ranged from 1.6 to 25 [130, 176, 251, 252]. HOMA-IR values of ≥1.29 at diagnosis may indicate insulin resistance in the studied population of women and are associated with a higher value of the prepregnancy BMI [177]. Maternal obesity-prepregnancy at the time of GDM diagnosis is in connection to enhance insulin resistance. A positive correlation between BMI and HOMA-IR in NGT group r = 0.485 and in GDM pregnant r = 0.594 has been established without statistical difference between two pregnant groups in second to third trimester [250]. The results are similar to those of others studies [253, 254, 255]. Other study obtains no significant correlations between BMI and markers of insulin resistance, indicating that BMI is not a confounder in the elevated insulin resistance among the enrolled GDM subjects [256]. The correctness requires to be noted some authors refer to BMI, especially in pregnancy, to be a poor index of fat mass, and it could be superseded in the statistical models by other anthropometric measures, three of which were independent predictors of GDM. These simple measures (age, fasting blood glucose, and subcutaneous fat), while are recognized in a few earlier reports, they are largely ignored in assessment of GDM risk [257, 258, 259]. Other study finds trimester-specific strongly positive association between HOMA-IR and prepregnancy BMI in each trimester (P < 0.001 in trimester 1 and 2, P = 0.004 in trimester 3). Also, the results from these analyses support the notion that the maternal metabolome is predominantly influenced by obesity and less by dietary intake during pregnancy [219]. However, it appears that beginning of the pregnancy in the obese state disturbs normal anabolic activity through early-gestational insulin resistance [260]. This may suggest that the obesity induces various metabolic and hormone fluctuations, rather than insulin resistance alone. This study demonstrates for the first time an association between prepregnancy BMI and a pattern of metabolites related to obesity, which differs from nonpregnant cohorts [219].
Undoubtedly, in recent years, the frequency of GDM is increasing in tandem with the dramatic increase in the prevalence of overweight and obesity in women of childbearing age, assessing by BMI. Another risk factor for GDM is the excessive weight gain during the pregnancy, assessing by use of BMI. The optimal weight increase in pregnancy is well established on the base of studies, and is different depending on BMI prior to pregnancy. Some studies show, that excessive weight gain is a significant risk factor for GDM in all categories of BMI, but the relationship is more stringent in obese individuals. Most of studies observed that higher BMI decreases the insulin sensitivity, increases the IR and contributes to development of GDM. New guidelines into the mechanisms underlying maternal metabolism during pregnancy are being gained through the use of new technologies. Future studies on the base of integrated data from multiple technologies will allow a systems biology approach to maternal metabolism during pregnancy.
This chapter is done by support of Medical University - Sofia, grant D-63/2018.
The authors have declared that no conflict of interest exists.
IntechOpen - where academia and industry create content with global impact
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After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. 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A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation"},{id:"18",title:"Proteomics",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/13418",hash:"",query:{},params:{id:"13418"},fullPath:"/chapters/13418",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()