WHO classification of endometrial hyperplasia [33]
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"25 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). From 2015 he has been appointed chairman of World Conference of Innovative Animal Nutrition and Feeding (WIANF).\r\nHis main research areas are related to pig and poultry nutrition: elimination of harmful effects of heat stress by nutrition tools, energy- amino acid metabolism in livestock, relationship between animal nutrition and quality of animal food products (meat).",institutionString:"University of Debrecen",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Debrecen",institutionURL:null,country:{name:"Hungary"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"25",title:"Veterinary Medicine and Science",slug:"veterinary-medicine-and-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"7144",title:"Veterinary Anatomy and Physiology",subtitle:null,isOpenForSubmission:!1,hash:"75cdacb570e0e6d15a5f6e69640d87c9",slug:"veterinary-anatomy-and-physiology",bookSignature:"Catrin Sian Rutland and Valentina Kubale",coverURL:"https://cdn.intechopen.com/books/images_new/7144.jpg",editedByType:"Edited by",editors:[{id:"202192",title:"Dr.",name:"Catrin",surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"48812",title:"Antioxidant Status and Sex Hormones in Women with Simple Endometrial Hyperplasia",doi:"10.5772/60853",slug:"antioxidant-status-and-sex-hormones-in-women-with-simple-endometrial-hyperplasia",body:'The uterus (womb) is a pelvic organ with reproductive function, i.e., maintenance of pregnancy. The lower, narrow part, which builds on top of the vaginal opening, was marked as cervix, and the broader, upper part, as corpus. The corpus consists of two types of tissue. The smooth-muscular outer layer (myometrium) has the function of expanding during pregnancy, and it follows the development of the fetus. The inner layer (endometrium) is subjected to a series of so-called cyclic monthly changes known as the menstrual cycle. The endometrium consists of an outer layer, glandular epithelium, below which is an internal part, stroma. This tissue is hormonally regulated by the steroid hormones estrogens (Es) and progestogens (Ps).
The reproductive axis consists of the hypothalamus, pituitary, and ovaries. The gonadotropin-releasing hormone (GnRH) acts on the anterior pituitary by regulating the synthesis and storage of gonadotropins, follicle-stimulating hormone (FSH), and luteinizing hormone (LH). GnRH also regulates the movement of gonadotropins from the reserve pool to a readily released point and their secretion. This action requires pulsatile GnRH release [1]. The secretion of FSH and LH takes place in a coordinated manner so as to regulate the growth of ovarian follicles, ovulation, and the maintenance of the corpus luteum and requires constant pulsatile release of LHRH from the hypothalamus [2]. Both estrogens and progestins help regulate the release of gonadotropins, acting through both the hypothalamus and anterior pituitary. High/low levels of either progestins or a combination of progestins and estrogens, as well as the length of exposure to these hormones, inhibit/stimulate the release of GnRH, FSH, and LH from the anterior pituitary – a negative/positive feedback control, respectively [3].
In the reproductive age of women, 17β-estradiol (E2) is a major circulating estrogen that is produced by the granulosa cells of the ovary prior to ovulation and by corpus luteum following ovulation. Almost 95 % of circulating Es in premenopausal women consists of 17β-estradiol and the remaining 5 % originates from the peripheral conversion of the estrone (E1) to estradiol [4]. Although a small amount of estrone, the second most important estrogen, is secreted directly from the ovaries and adrenal glands, its main quantity derives from conversion of androstenedione in adipose tissue [5]. The estrogenic potency of estrone is lesser than estradiol, and both, E2 and E1, are biologically equivalent with subtle structural differences and metabolized by the same pathways. Once a woman has reached menopause and ovaries lose their function, estrone becomes a predominant form of estrogen [6]. Studies have shown the trend to higher mortality rate from coronary heart disease in women with lower estrone level, while patients with higher estrone level had lower body weight, less frequent hypertension and diabetes mellitus, and also a lower triglyceride level [7].
During normal ovulatory cycle, the level of E2 varies individually within the range defined for the follicular phase, mid-cycle, and luteal phase. Most of E2 in the circulation is bound to sex hormone-binding globulin (SHBG) and to a lesser extent, to other serum proteins, such as albumin. Only a very small fraction of this hormone is free and is located in the conjugated form [8,9].
During a normal menstrual cycle, E2 secretion is biphasic, and the highest concentration is recorded just prior to ovulation. This growth affects the pituitary gland secretion of FSH and LH by a positive feedback. After ovulation, E2 level rapidly decreases and luteal cells, by their activities, cause mild, subsequent rise and a plateau of E2 during the luteal phase [2]. During pregnancy, the level of E2 in serum increases to much higher values than recorded in the preovulatory peak, and it is maintained during pregnancy [10].
Progesterone (P) belongs to a group of steroid hormones called progestogens, and it is secreted by the corpus luteum in the ovary during the second half of the menstrual cycle. During pregnancy, the high levels of progesterone are provided by the secretion of placenta. Contrary to stimulating, proliferative effect of estradiol, progesterone induces secretory activity of the endometrium and has the role of accepting a fertilized egg and beginning pregnancy. In the circulation, progesterone is bound to the corticosteroid-binding globulin and albumin [2,11]. During normal ovulatory cycle, the increase of serum levels of P induces an increase of LH concentration and together with E2, regulates in this way the preovulatory peak of gonadotropins [12]. In vivo studies in humans suggest that P stimulates its own production during the periovulatory and middle luteal period through self-priming [13]. Besides steroids, the ovary secretes peptide hormones (the inhibins) under gonadotropin control. The inhibins belong to transforming growth factors and have the ability to inhibit gonadotropin (FSH) secretion and may also play an important role in ovarian carcinogenesis [14]. The concentrations of inhibin A and inhibin B in circulation fluctuate during normal menstrual cycle. During the follicular phase, inhibin B is dominant, and during the luteal phase, inhibin A dominates [15].
The transition from the reproductive period to the menopause is a gradual process that takes place over many years and is referred to as perimenopause. It starts with the first symptoms of changes in the cyclic occurrence of menstruation and/or bleeding, which may be accompanied by physical and psychological symptoms and ends with the last menstruation. In terms of morphology, this phase is characterized by a sudden drop in the number of primordial follicles in the ovaries, as well as extreme fluctuations in hormone levels [16], so that the frequency of normal ovulatory cycles decreases [2].
It has been shown that some women experience an increase in serum FSH concentrations before the age of 40, especially during the mid-follicular and early luteal phase [17]. Similar increase of FSH was also detected through regular cycles, although there were no clinical manifestations of approaching menopause [18].
Generally, a significant increase in the concentration of FSH is observed approximately 5 years before the onset of menopause and it is positively correlated with age [19,20]. With the onset of menopause, there is an additional increase in FSH levels in serum for about six months, and the peak concentration is detected 3–4 years after menopause. After this period, a slight decline in serum FSH was detected. However, compared with fertile women, levels of gonadotropins remained at elevated levels even 10 years after menopause [19]. Besides FSH, the LH concentration also changes during this period. It has been shown that serum LH increases slightly during 4–5 years of perimenopause in women who still regularly cycled [17]. During the first six months from the onset of menopause, there is an increase in serum concentrations of LH, and the highest level is recorded during the first year of menopause. Over the next 8 years, there is a continuous fall, but as in the case of FSH, the LH level remained elevated compared with fertile period [19]. These data represent the results which should not be generalized and considered as absolute parameters that apply to the period of perimenopause and menopause, since clear markers still have to be identified. In addition, they cannot be reliably interpreted since ovulatory (potentially fertile) cycles can normally take place immediately after the detection of postmenopausal levels of FSH. Both estradiol and inhibin are important regulators of the negative feedback loop of circulating FSH [21-23].
As a consequence of declined follicular function during menopause, the concentration of Es in circulation also decreases. The level of estradiol in the serum of postmenopausal women is less than 15 pg/ml, and the level of estrone is about 30 pg/ml, so that the ratio E1/E2 is 2:1 [11,24]. The main source of E1, which is the principal form of the postmenopausal estrogen, derives from androstenedione in peripheral adipose tissue and liver [2]. In this period, 95 % of the total synthesis of androstenedione occurs in the adrenal glands and only 5 % in the ovaries [25,26]. Increased conversion of androstenedione to estrone is proportional to the increase of body weight, and it consequently increases the amount of estrogen in the bloodstream [2,26]. The main source of E2 in postmenopausal women originates from the peripheral conversion of E1. During and after menopause, the concentration of E1 decreases as well as the concentration of E2, so that both forms of estrogen are strongly correlated [27,28]. The concentration of estrone sulfate, which is a metabolite of those estrogens, shows a similar trend of decline in menopausal women. Although it does not belong to the active Es, it can be activated by hydrolysis of the sulfate group [27]. Since premenopause leads to inadequate luteal function or anovulation, progesterone is also lowered in the serum. The level of P is further reduced during the aging process, so it is very low in postmenopausal women [19]. Statistically, approximately 2 % to 3 % of women will develop uterine cancer during lifetime. About 97 % of all uterine cancers originate from endometrial glands and represent endometrial carcinomas [29]. Endometrial carcinoma is the fourth common cancer after breast, bowel, and lung carcinoma [30].
Endometrial proliferation is a normal part of the menstrual cycle that occurs during the follicular/estrogen phase of the cycle [31]. If the endometrium is exposed to continuous endogenous or exogenous estrogen in the absence of progesterone, simple proliferation can advance to endometrial hyperplasia, which is the most common precursor of endometrioid adenocarcinoma. Generally, endometrial hyperplasia is the abnormal proliferation of the glands and the stroma characterized by the presence of architectural and cytological changes [32].
As an attempt to correlate morphological features with clinical outcome, the World Health Organization (WHO) classified endometrial hyperplasia:
Nonatypical hyperplasias (typical) | \n\t\t
Simple hyperplasia without atypia | \n\t\t
Complex hyperplasia without atypia (syn. adenomatous hyperplasia without atypia) | \n\t\t
Atypical hyperplasias | \n\t\t
Simple atypical hyperplasia | \n\t\t
Complex atypical hyperplasia (syn. atypical adenomatous hyperplasia) | \n\t\t
WHO classification of endometrial hyperplasia [33]
The normal proliferative endometrium is characterized by no crowding of glands within the stroma. Morphological features of all endometrial hyperplasia forms include an increase in the gland-stroma ratio, irregularities in gland shape, and variation in gland size. Regardless of the presence of atypia, simple and complex forms of hyperplasia are distinguished by architectural alterations characterized by glandular complexity and the amount of stroma separating the glands [34]. Hyperplasia generally involves much of the whole endometrium, but sometimes it may be present as a localized lesion and might be associated within an endometrial polyp. Most endometrial hyperplasias are estrogen driven and related to type 1 endometrial carcinoma, the endometrioid endometrial adenocarcinoma [35].
Simple hyperplasia, formerly cystic or mild hyperplasia, is a proliferative lesion with minimal glandular complexity and crowding. Histologically, glands are of irregular size from small to those with cystic appearance and shape, separated by abundant stroma. The glandular architectural changes are characterized by varying degrees of irregular branching. Cytologically, the glandular epithelium resembles to proliferative endometrium. It is considered as the least significant form which is not commonly associated with progression to endometrial carcinoma [36].
Complex hyperplasia, previously adenomatous hyperplasia or moderate hyperplasia, represents a proliferative lesion with severe glandular complexity and more densely crowded glands. The glands can vary in size and may demonstrate increased structural complexity. Usually, the glands are closely packed, frequently appearing almost back to back and with gland-stroma ratio of more than 2:1 [37,38]. As the severity of hyperplasia increases, the glands become more crowded and more structurally transformed. The complex hyperplasia is considered as the true intraepithelial neoplastic process. Occasionally, this form of hyperplasia may be found coexisting with areas of endometrial carcinoma [39]. Endometrial hyperplasia is further classified based on the presence of cytologic atypia and disordered maturation. Cytologic atypia refers to enlarged epithelial cells that are hyperchromatic with prominent nucleoli, an increased nuclear-to-cytoplasmic ratio, and loss of cellular polarity. Cytologic atypia is the most important prognostic factor for progression to endometrial carcinoma [40].
Thus, the WHO classification also includes lesions termed simple atypical hyperplasia and complex atypical hyperplasia. Simple atypical hyperplasia is rare, so the term atypical hyperplasia is widely used to refer to all women with simple or complex atypical hyperplasia. The glands in atypical hyperplasia are very closely packed, and endometrial stroma might be seen, separating them [41]. Less than 2 % of hyperplasias without atypia progress to carcinoma, and the mean duration of progression takes almost 10 years. Atypical hyperplasia progresses to carcinoma in 23 % of cases over a mean duration of 4 years [42].
There is a discussion to replace the WHO classification of type 1 endometrial carcinoma precursors with the endometrial intraepithelial neoplasia classification system. This system was proposed in 2000 by an international group of gynecologic pathologists, and it defines two classes of endometrial changes, endometrial hyperplasia (EH) and endometrial intraepithelial neoplasia (EIN) [43]. In this classification, endometrial hyperplasia refers to changes observed with anovulation or other etiologies of prolonged estrogen exposure. Morphologically, EH varies from proliferative endometrium with a few cysts to endometria with many dilated glands. This type is also known as cystic glandular hyperplasia, mild hyperplasia, or simple hyperplasia [44]. The term EIN represents monoclonal endometrial preinvasive glandular proliferation as the immediate precursor of endometrial type 1 adenocarcinoma. In EIN, the proliferation of endometrial glands exceeds the stroma (gland/stroma >1) [45]. EIN categories do not correspond directly to the WHO system of classification. Most simple and some complex hyperplasias fall into EH category and many complex hyperplasias with or without atypia are in the EIN category.
A well-documented study regarding the epidemiology of endometrial hyperplasia included women aged 18 to 90 over the 18-year period. The diagnosis was mostly made in women aged 50–54 years and rarely was found in women under the age of 30. The incidence of simple and complex hyperplasia was 142 and 213 per 100,000 women-years, respectively. The rate of atypical hyperplasia was highest in older women aged 60–64 years, and it was 56 per 100,000 women-years. This rate seems to correlate with age of peak incidence for endometrial cancer [46,47]. Age-specific cancer incidence was demonstrated for the pancreas, bladder, stomach, lung, prostate, ovary, colorectal, and uterine endometrium. One explanation for increased cancer incidence with age is the latency period required for damage to occur and cancer to develop, including the time necessary for accumulation of carcinogen-induced genetic mutations like in oncogenes and tumor suppressor genes but also as a maladaptive response to replicative senescence due to telomere shortening. Also, a deterioration of the innate and the adaptive immune response with aging, referred to as immunosenescence, must be considered [48].
Symptoms of endometrial hyperplasia include heavy or prolonged menstrual periods, intermenstrual bleeding, and prolonged amenorrhea. Postmenopausal women with hyperplasia may experience vaginal bleeding or spotting. However, only minority of women with abnormal uterine bleeding (AUB) are subsequently diagnosed with endometrial hyperplasia [49].
The risk factors for endometrial hyperplasia are the same as for endometrial carcinoma. Most of them include exposure of endometrium to continuous estrogen unopposed by progestin. Unopposed estrogen may be of various sources like early menarche (beginning menstruation before age 12), hormone replacement therapy (HRT) with exogenous estrogen, late menopause (after 52 years of age), estrogen-secreting tumor (some breast cancer types), and nulliparity or low parity. Medical conditions such as diabetes mellitus, polycystic ovary syndrome, or thyroid diseases also increase the risk for hyperplasia and cancer of the uterus. Endometrial hyperplasia is also more likely to occur in women with personal history of breast, colorectal, or ovarian cancer and in women of white race. Endometrial cancer and hyperplasia are more common in Caucasian women, while uterine sarcoma is more common in African American women [50,51].
Although the findings suggest that there are certain molecular characteristics which distinguish types and degrees of endometrial cancer, the molecular mechanisms that underlie the endometrial carcinogenesis are still unclear. Cell changes can begin with genetic aberrations and continue with uncontrolled growth stimulated by tumor promoters.
Endometrial tissue is the target tissue for steroid hormones produced by ovaries. Both epithelium and stroma contain receptors for Es and Ps, and ovarian steroids have a fundamental role in the regulation of growth and differentiation of endometrial cells [2]. It seems this influence is partly preserved in well-differentiated tumors of the lower grade, as suggested by data which showed that these tumors are frequently receptor positive than the advanced tumors [52]. Growth factors are, among other influences, regulated by steroid hormones, and they are involved in a paracrine and autocrine regulation of endometrial proliferation. The most often mentioned are the epidermal growth factor (EGF) and transforming growth factor-α (TGF-α). Both factors are single-chain peptides that exert their effect through the EGF receptor. They were shown to be expressed in normal endometrial tissue [53] and to stimulate growth of cultured endometrial cancer cells [54]. In addition to these two factors, it is considered that the transforming growth factor-β (TGF-β) is also involved in the carcinogenesis. This factor is expressed in normal human endometrium and certain endometrial cancer cell lines. In some of these cell lines, like RL95-2, SPEC-2, and KLE, the TGF-β inhibits their growth [55]. Among the other growth factors which affect endometrial carcinogenesis, the basic fibroblast growth factor (bFGF) and insulin-like growth factor I (IGF-I) should also be mentioned [56].
The most frequently altered oncogenes in endometrial cancer are the point-mutational activation of K-ras. Point mutations of K-ras were found in approximately 10–30 % of endometrial cancers [57]. Also, K-ras mutations have been identified in endometrial hyperplasia and more frequently in complex atypical hyperplasia, suggesting that K-ras mutations may be an early event in endometrial carcinogenesis [58].
In addition to this oncogene, the amplification and overexpressed HER-2/neu (c-erb B-2) was found in about 10–20 % of sporadic endometrial carcinoma cases [59-61]. HER-2/neu gene encodes a membrane receptor protein which is structurally similar to the receptor for epidermal growth factor (EGF-R). In some endometrial carcinomas, the overexpression of oncogenes Myb, Fos, Myc, and fms, as well as their correlation with advanced stages of carcinogenesis and poor prognosis of the outcome of survival, was recorded [57, 62]. Results of some endometrial carcinoma studies detected the overexpression of oncogenes Myb, Fos, Myc, and fms, as well as their correlation with advanced stages of carcinogenesis and poor prognosis of the outcome of survival [57, 62].
Until now, it is observed that mutations in PTEN (phosphatase and tenzin homologue deletion on chromosome 10) tumor suppressor gene, also known as MMAC1 and TEP1, are detected in approximately 50 % of endometrial cancers [63], as well as in 20 % of endometrial hyperplasias, suggesting that these mutations occur relatively early in pathogenesis of this cancer type [64, 65]. PTEN is a dual-specificity protein phosphatase which dephosphorylates tyrosine-, serine- and threonine-phosphorylated proteins. Acting as lipid phosphatase, which is critical for its tumor suppressor function, it removes the phosphate in the D3 position of the inositol ring from phosphatidylinositol 3,4,5-trisphosphate; phosphatidylinositol 3,4-diphosphate; phosphatidylinositol 3-phosphate; and inositol 1,3,4,5-tetrakisphosphate. PTEN is crucial in the control of PI3K-AKT/PKB signaling pathway by dephosphorylating phosphoinositides and thereby modulating cell cycle progression and cell survival [66, 67]. There is a wide spectrum of PTEN mutations in endometrial cancer, which occur in exons 3, 4, 5, 7, and 8 and targeting the phosphatase domain and regions that control the stability and localization of proteins. The consequence of these mutations is reduced or completely absent expression of PTEN [68]. It was shown that progesterone treatment of cultured endometrial stromal cells induces an increase in PTEN levels, while estradiol induces the PTEN phosphorylation. This indicates an outstanding role of PTEN in the development and/or progression of endometrial cancer [69]. Although loss of PTEN function was implicated in the pathogenesis of many different tumors [70], it is believed that the altered expression of PTEN can be a diagnostic marker for the early precancerous conditions of the endometrium [43].
Mutations of the p53 tumor suppressor gene have been found in approximately 10–20 % of all endometrial cancers, with the greatest frequency in the high-grade tumors. Approximately 50 % of grade III tumors type 1 and the rare tumors of type 2 contain mutations in p53, but they have not been reliably detected within the tumor of grade I or hyperplasia [68, 71, 72], so it is considered that they occur in the late stages of endometrial carcinogenesis [56, 68]. The partial role of the p53 in the cell cycle regulation is mediated through the transcriptional activation of other genes, such as p21, followed by inhibition of the cyclin-dependent kinases [73]. Thus, inactivation of p21 could potentially lead to tumor progression. Studies have shown that in approximately 15–40 % of endometrial cancer cases, a loss of p21 gene expression can be detected [74-76]. In addition to p53 and p21, the alterations of p16INK4a (CDKN2A) tumor suppressor gene were also observed. This gene encodes the p16 protein that specifically binds to CDK4 cyclin-dependent kinases, thereby inhibiting the catalytic activity of the CDK4-cyclin D complexes. Until now, it is observed that methylation, mutations, and deletions of p16INK4a gene are rare, and they were detected in approximately 2–6 % of endometrial cancer cases [56, 77], while the loss of expression was found in 20–70 % of cases [78-80].
Endothelins (ETs), ET-1, ET-2, and ET-3, are potent vasoconstricting peptides involved in the pathophysiology of many human malignancies by activating G protein-coupled receptor (GPCR) subtypes, ETA and ETB [81]. Expression of ET-1 was detected in normal human endometrium and in endometrial adenocarcinoma. Also, ETAR and ETBR expression was decreased in endometrial cancer tissue compared with that of normal endometrium [82].The ET-1-ETRA axis is frequently dysfunctional in numerous types of carcinomas and contributes to the promotion of cell growth and migration [83].
In addition to mutations of the PTEN gene, microsatellite instability (MSI) is often detected in type 1 of endometrial cancer. MSI was first demonstrated in patients with hereditary nonpolyposis colorectal carcinoma (HNPCC), in which endometrial cancer is often an associated phenomenon. Additional studies have shown that MSI is detected in approximately 25 % of sporadic cases of endometrial cancer [84] or by other studies in 9–45 % of cases [56]. Unlike hereditary forms of nonpolyposis colorectal carcinoma, where subjects with this type of cancer carry mutations of one of the DNA mismatch repair genes, hMLH1, hMSH2, and hMSH6 [85, 86], promoter hypermethylation of the gene hMLH1 represents the predominant cause of MSI only in sporadic cases [87]. There are also data on the hypermethylation of this gene promoter in hyperplasia and in the absence of cancer, which suggests that inactivation of mismatch repair genes precedes the formation of MSI [88].
Oxygen may be a source of reactive oxygen species (ROS) due to its incomplete reduction mostly by the oxidoreductase complex I and III of the mitochondrial respiratory chain [89], forming the superoxide anion radical (O2\n\t\t\t\t•-). ROS molecules are characterized by a higher reactivity than oxygen in its ground state. The ROS include free radicals (a term that refers to molecules with one unpaired electron in the outer orbital), like superoxide anion radical (O2\n\t\t\t\t•-), hydroxyl radical (•OH), peroxyl radical (ROO• ), as well as reactive nonradical molecules such as singlet oxygen (1O2), peroxynitrite (ONOO-), or hydrogen peroxide (H2O2). Their half-life varies from a few nanoseconds for the most reactive molecules up to a few seconds or hours for stable radicals [90].
There are a few major sources of O2\n\t\t\t\t•- in the cell: the respiratory chain in mitochondria, endoplasmic reticulum cytochromes (cytochrome P-450-dependent oxygenase, NADPH-cytochrome P-450 reductase), as well as the oxidase contained in the cell cytoplasm and membranes (NADPH oxidase of polymorphonuclear leucocytes, macrophages, and endothelial cells) [91, 92]. The resulting O2\n\t\t\t\t•- may be converted to H2O2 by spontaneous dismutation, as well as by the enzyme superoxide dismutase (SOD). In addition, the H2O2 may originate from the monoamine oxidase activity [93] or from the beta-oxidation of fatty acids in peroxisomes [94]. Its reduction is carried out by the enzyme catalase (CAT) and glutathione peroxidase (GPx), which can be considered as the main way of detoxification. H2O2 may also be reduced by the neutrophil myeloperoxidase which catalyzes the conversion of H2O2 and Cl- to hypochlorous acid (HOCl) and in the presence of transition metals (Fe2+ or Cu+), producing •OH [95]. The hydroxyl radical is a highly reactive oxidant that reacts almost instantaneously with the surrounding molecules abstracting the hydrogen atom (RH). The resulting free radical (R•) is more stable and therefore has usually longer half-life compared to the •OH [96]. Peroxyl radicals have a relatively long half-life, and they are formed in the process of lipid peroxidation, which begins with removal of the hydrogen atom of polyunsaturated fatty acids [97]. Lipid peroxidation in cell membranes can significantly damage their function due to the formation of irreversible disturbance of fluidity and elasticity, which can lead to impairment of cellular homeostasis.
ROS are constantly produced in the body as a result of normal metabolic processes, but there is also a significant influence of external factors. Many chemical and biological agents which are prooxidants under certain conditions can lead to increased production of free radicals. If their production exceeds the capacity of the antioxidant defense, the oxidative stress occurs [96]. ROS can react with any molecules in the cell, thus causing considerable damage which results in cellular dysfunction. These processes are increasingly studied today in the framework of the mechanisms of etiopathogenesis of various diseases. Also, their role in cell signaling, proliferation, differentiation, and programmed cell death – apoptosis – is intensively examined.
The term antioxidant refers to a substance that, when present in small amounts compared with the substrate to be oxidized, inhibits or prevents its oxidation. The antioxidant system can be divided into two categories: nonenzymatic antioxidants, which include various compounds of low molecular weight (vitamin E, vitamin C, carotenoids, polyphenols, ubiquinone, and glutathione), and the AO enzyme system [98].
Vitamin E (tocopherol-OH, vitamin E) is a generic name for a group of compounds known as the tocopherols and tocotrienols, and it includes all forms which exhibit biological activity of natural vitamin E (d-alpha-tocopherol) [98]. Vitamin C (ascorbic acid) is the most important hydrophilic antioxidant. Their main function is to prevent peroxidation of lipids in the membrane and, consequently, cell damage. The carotenoids are the vitamin A, which also possess antioxidant properties. Beta-carotene is one of the most studied forms, and its antioxidant function is based on its attribute to quench the singlet oxygen and remove free radicals, thus protecting the cell membrane lipids from oxidative degradation. Polyphenols are a group of compounds with antioxidant capacity to prevent formation of ROS production through inhibition of the enzyme, as well as trace elements, involved in their formation [99]. Ubiquinone prevents lipid peroxidation in liposomes, lipid emulsions, phospholipids, and LDL particles [100]. Glutathione (GSH) is a tripeptide consisting of L-glutamine, L-cysteine, and L-glycine. In addition to its role as a substrate of GSH redox cycles, it also removes the hydroxyl radicals and singlet oxygen and maintains the enzymes and other cellular components in a reduced state [98].
In mammals, three types of SODs have been identified, depending on the cellular localization and prosthetic groups. In the cytoplasm, the predominant form is copper-zinc-superoxide dismutase (CuZnSOD, SOD1), which represents a stable dimeric protein with molecular mass of 32 kDa. It contains copper and zinc in its active site. Copper is considered necessary for the catalytic activity of this enzyme, whereas zinc contributes to its stability [101]. CuZnSOD is also located in the extracellular matrix, and this form is known as the extracellular superoxide dismutase (EC-SOD, SOD3). This form of CuZnSOD is a tetrameric protein with molecular mass of 135 kDa, and it possesses a heparin-binding domain that affects its extracellular distribution [102]. Manganese superoxide dismutase (MnSOD SOD2) is a tetramer enzyme with molecular weight of 88 kDa, containing manganese atom in the active sites and it is located in the mitochondria.
CAT is homo-tetramer enzyme with molecular weight of 240 kDa, with each subunit containing the heme prosthetic group and also the attached NADPH that protects the enzyme from oxidative damage. CAT has a function to decompose H2O2 to O2 and H2O [103].
GPx family can be divided into two groups: selenium-independent peroxidase presented glutathione S-transferase (GST) and selenium-dependent peroxidases (GPx).
Glutathione S-transferase belongs to the so-called phase II detoxifying enzymes that are involved in conjugation reactions of a wide range of electrophilic xenobiotics (including carcinogens and mutagens). Several selenoprotein glutathione peroxidases are present in human tissues, cell GPx (GPx-1, CGP-x), gastrointestinal GPx (GPx-2, giGPx), plasma (extracellular) GPx (GPx-3, eGPx), and phospholipid hydroperoxide GPx (GPx-4, PHGPx) and GPx-6, which is only expressed in the epithelium of the olfactory system [104]. With the exception of PHGPx which is a monomer (19 kDa), other forms of GPx are composed of four identical subunits of a molecular weight of 19–25 kDa. Each subunit in its active site contains a selenocysteine (CysSe). The enzyme uses a reduced GSH as a source of reducing equivalents (electrons) to regenerate CysSe to the reduced state [105]. Glutathione reductase (GR) is an enzyme that catalyzes the reduction of oxidized glutathione GSSG to GSH and it is essential for the GSH redox cycle [106].
Because of their high reactivity, elevated ROS concentrations represent a great danger for biomolecules. At physiological concentrations, these molecules are often necessary for normal functioning of cells as second messengers in the transduction of the cell signaling [107]. They can be activated in such a way as to prevent or potentiate the cell death. Many signaling pathways in the cell can be activated in both directions (cell survival or apoptosis), which depends on the type and duration of oxidative stress or cell types. Also, some of these pathways can affect the activation or suppression of other signaling pathways in the cell.
It is difficult to determine which type of ROS activates signaling pathways, because of their extremely rapid conversion to other forms or due to the conversion of acid conjugates or complexes with transition metals [108]. It is believed that H2O2 is highly suitable as a secondary messenger because it does not randomly react with all of the molecules like other forms of ROS, but tends to oxidize the -SH group of cysteine (Cys), which is then reduced by GSH [109].
In this way, by redox cycling of Cys, many transcription factors are regulated, such as activating protein 1 (AP-1) [110], nuclear factor NF-IL6 [111], and proteins important in cell signaling and cancerogenesis: protein kinase C (PKC), Ca2+-ATPase, collagenases, and SRC tyrosine kinase [108]. It is known that ROS are critical molecules in regulation not only of the AP-1 but also AP-2 [112] and of nuclear factor NF-kappaB [113] transcription families, which have a decisive role in cell proliferation, differentiation, and morphogenesis.
Other processes induced by hydrogen peroxide included activation of the stress-activated protein kinase/c-Jun N-terminal kinases (SAPK/JNK), the increased c-Jun phosphorylation, activation of caspase 3 (CPP32), and decomposition of poly(ADP-ribose) polymerase (PARP), which are associated with the apoptosis process [114]. Besides regulating the activity of cell proteins, H2O2 also induces the expression of many genes [115]. In addition, these molecules are responsible for the disruption of cell signaling and regular patterns of gene expression [116], which can lead to a number of pathological processes including carcinogenesis. The process of carcinogenesis is complex and consists of a series of changes at the cellular and molecular levels and in at least three stages: initiation, promotion, and malignant conversion, i.e., progression [117].
In relation to carcinogenesis, it is known that the AO system has a role in preventing its occurrence and promotion. The studies AO status in tumor tissues have not yet yielded results that could lead to general conclusions about AO defense in tumor tissues. Since the carcinogenesis occurs in several stages, it is likely that the antioxidant defense depends on the type of cell and tissue [118]. Mammalian cells and tissues differ significantly in the generation of ROS. They also vary in antioxidant activity, induction capability, and cell repair capacities which altogether results in a different susceptibility of mammalian tissues for tumor induction [119-121].
Our earlier studies indicated a significant role of oxidative-induced injury in the breast carcinogenesis, particularly during the later stages of aging [122]. It was also observed that chemotherapy and radiotherapy promote further oxidative shift, which potentiates already existing chronic oxidative stress linked to breast cancer [123]. It is believed that the high antioxidant capacity protects DNA from oxidative damage and mutagenesis but also can protect the cells in the stage of initiation of increased oxidative toxicity, thus favoring their clonal expansion and tumor progression [124]. It has long been known that oxidizing agents may be cytotoxic, although under certain circumstances, can promote cell growth and facilitate the clonal expansion of the initiated cells in carcinogenesis [125].
Some previous studies have shown that compared to healthy people, women with benign and malignant changes in the genital tract have increased level of lipid peroxidation and altered activity of AO enzymes in peripheral blood and tissue. Chiou and Hu [126] have detected that the activity of SOD in plasma and erythrocytes of patients with cervicitis and uterine myoma was lower compared to that of healthy women. At the same time, patients with cervicitis had an increased level of CAT and GPx activity, while their activity in patients with uterine fibroids (leiomyoma) was reduced. Similar results regarding the activities of SOD, CAT, and GPx in erythrocytes of patients with cervicitis were obtained by Manoharan et al. [127]. These authors also found that the activity of these enzymes was lower in patients with cervical cancer. Research of Kolanjiappan et al. [128] and Manoharan et al. [127] showed that the level of lipid peroxidation increased and the concentration of the antioxidant GSH, vitamin E, and CAT decreased in erythrocytes of patients with cervical cancer. These patients had altered activity of Na+K+-ATPase in erythrocytes compared to healthy persons. Our previous results showed that AO status in blood of gynecological patients varies with diagnosis and the enzyme type. Generally, both reduction in antioxidants and elevation of lipid peroxidation were observed. Lipid hydroperoxide level was negatively correlated to SOD and GPx activities and concurrently positively correlated with CAT activity. In addition, the lipid hydroperoxides/glutathione peroxidase ratio increased, according to the type of uterine disorder [129-131]. The perturbation of antioxidant status was more pronounced in blood of patients with hyperplastic and adenocarcinoma lesions compared to those with benign uterine changes such as polypus and myoma. Our results of AO status in endometrial tissue showed significant decrease of SOD activity in women with hyperplasia and adenocarcinoma. In both types of hyperplasia, activities of GPx and GR were increased to 60 % and 100 % on average, while in adenocarcinoma patients, only GR activity was elevated to 100 %. CAT activity was significantly decreased in adenocarcinoma patients (47 %). Lipid hydroperoxides level was negatively correlated to SOD and CAT activities and positively correlated to GPx and GR activities [132]. Since association of different clinical risk factors and various types of gynecologic pathologies is still not fully known as well as their influence on AO status, in our latest study, we evaluated the influence of diagnostic categories, age, and reproductive factors on AO status in blood of gynecological patients [133].The obtained results showed that reproductive and other factors may be associated, at least partially, with AO capacity and ability to defend against the oxidative damage in gynecological patients.
The AO status and hormone influence were studied during the menstrual cycle and postmenopause in healthy women and those with gynecologic disorders. The SOD was found to have a role in maintaining luteal cell integrity and steroidogenic capacity in fertile women [134]. An increase in the GPx activity was observed during the menstrual cycle, from the late follicular to the early luteal phase. The rise in GPx activity is related to increased ovarian production of estrogen that occurs in that particular period of menstrual cycle [135]. Decrease in GPx activity has been noted in the endometrium and blood in late-menopausal women [136]. Menopause is accompanied by hormone imbalance. A significant fall of the estrogen serum level with rise of follicle-stimulating hormone (FSH) has been recorded in postmenopausal women compared to premenopausal women [137]. Hormone replacement therapy (HRT) shows protective antioxidant role by reduction of lipid peroxide (LOOH) serum levels [138]. It is also found that HRT positively correlates with SOD activity in postmenopausal women [139].
We have shown that AO enzyme activity and lipid hydroperoxide level in patients with endometrial polyps are influenced by the changes in sex hormones during the menstrual cycle and in menopause [140]. In this study, we aimed to examine the AO status in menstrual cycle and postmenopause of women with endometrial hyperplasia simplex as well as the relationship between sex hormones and AO parameters.
Subjects. The material used in this study consisted of 35 blood and tissue specimens of women admitted to the Department of Gynecology and Obstetrics for gynecological evaluation within routine checkups or for abnormal uterine bleeding (prolonged menstrual bleeding and postmenopausal bleeding). On the basis of diagnosis and histological examination, subjects were diagnosed with hyperplasia simplex endometrii, and the specimens were taken after obtaining the informed consent. The study was conducted prospectively and it was approved by the Human Studies Ethics Committee of the Clinical Center. The protocol was consistent with the World Medical Association Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects). None of them had undergone hormone therapy or any other medical treatment in the last six months. Patients were divided as follows: 10 in the proliferative (follicular phase, F) (age, 40–52 years; median 46 years), 15 in the secretory (luteal phase, L) (age, 27–53 years; median 44 years), and 10 in the postmenopause (PM) (age, 47–60 years; median 53 years).
Samples. Samples were collected and prepared for enzyme assays according to the procedures described previously [129,131]:
Venous blood samples were collected into heparinized tubes on the same day of uterine biopsy and aliquoted immediately. For SOD assay (OxisResearch™), blood was centrifuged at 2500 g for 5 min. Plasma was discarded and pellet was resuspended in 4 packed-cell volume of ice-cold demineralized ultrapure water (MilliQ reagent grade water system, Millipore Corp., Bedford, MA, USA). After addition of ethanol/chloroform extraction reagent (62.5/37.5 vol/vol) to remove hemoglobin interference, samples were centrifuged at 3000 g for 10 min (Eppendorf centrifuge 5417, Eppendorf AG, Hamburg, Germany). Upper aqueous layer was collected and kept at -70 ºC until assay.
Fresh endometrial tissue samples were washed in saline solution and homogenized in phosphate buffer containing 0.05M KH2PO4 and 1 mM EDTA, pH 7.8 (1 g tissue per 2 ml buffer) in a Teflon/glass homogenizer (Spindler & Hoyer, Göttingen, Germany) and frozen at -70 ºC for 20 h in order to disrupt cell membranes. For SOD assay (OxisResearch™), thawed homogenates were vortexed 1 min and centrifuged at 8600 g, for 20 min at 4 ºC (Eppendorf centrifuge 5417, Eppendorf AG, Hamburg, Germany). According to manufacturer’s recommendation, after addition of ethanol/chloroform extraction reagent (62.5/37.5 vol/vol) to completely remove hemoglobin interference, samples were centrifuged at 6000 g for 20 min, at 4 ºC (Beckman centrifuge J2-21, Beckman Instruments Inc., Palo Alto, CA, USA). Upper aqueous layer was collected and kept at -70 ºC until assay. The enzyme activities and lipid hydroperoxide (LOOH) concentration were monitored spectrophotometrically (Perkin Elmer Spectrophotometer, Lambda 25, Perkin Elmer Instruments, Norwalk, CT,USA).
The specific enzyme activities were expressed as Units (U) or mU per milligram of total cell protein (U or mU/mg protein), and LOOH concentration was expressed as nmol/mg protein. Protein concentration in tissue homogenates was performed by the method of Lowry et al. [141] and expressed as mg/ml. Plasma follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E), and progesterone (P) levels were analyzed using standard radioimmunoassay (RIA) methods by the hormone analysis laboratory.
Enzyme Assays. Enzyme assays were performed as described previously [132]:
Assay of SOD activity. Determination of SOD activity was performed using Oxis Bioxytech® SOD-525™ Assay (Oxis International, Inc., Portland, OR, USA). The method is based on SOD-mediated increase of autoxidation of 5,6,6a11b-tetrahydro-3,9,10-tryhydroxybenzo[c]fluorene in aqueous alkaline solution to yield a chromophore with maximum absorbance at 525 nm. The SOD activity is determined from the ratio of the autoxidation rates in the presence (Vs) and in the absence (Vc) of SOD. One SOD-525 activity unit is defined as the activity that doubles the autoxidation rate of the control blank.
Assay of CAT activity. CAT activity was determined by the method of Beutler [142].The reaction is based on the rate of H2O2 degradation by catalase contained in the examined samples. The reaction was performed in an incubation mixture containing 1 M Tris-HCl, 5 mM EDTA, pH 8.0, and monitored spectrophotometrically at 230 nm. One unit of CAT activity is defined as 1 µmol of H2O2 decomposed per minute under the assay conditions.
Assay of GPX activity. GPx activity was assessed using the Oxis Bioxytech® GPx-340™ Assay (Oxis International, Inc., Portland, OR, USA), based on the principle that oxidized glutathione (GSSG) produced upon reduction of an organic peroxide by GPx, is immediately recycled to its reduced form (GSH) with concomitant oxidation of NADPH to NADP+. The oxidation of NADPH was monitored spectrophotometrically as a decrease in absorbance at 340 nm. One GPx-340 unit is defined as 1 µmol of NADH oxidized per minute under the assay conditions.
Assay of GR activity. Activity of GR was measured using the Oxis Bioxytech® GR-340™ Assay (Oxis International, Inc., Portland, OR, USA). Assay is based on the oxidation of NADPH to NADP+ during the reduction of oxidized glutathione (GSSG), catalyzed by a limiting concentration of glutathione reductase. The oxidation of NADPH was monitored spectrophotometrically as a decrease in absorbance at 340 nm. One GR-340 unit is defined as 1 µmol of NADH oxidized per minute under the assay conditions.
Lipid hydroperoxides. Concentration of LOOH was measured by Oxis Bioxytech® LPO-560™ Assay (Oxis International, Inc., Portland, OR, USA), which is based on the oxidation of ferrous (Fe2+) ions to ferric (Fe3+) ions by hydroperoxides under acidic conditions. Ferric ions then bind with the indicator dye, xylenol orange, and form a colored complex. The absorbance of the complex was measured at 560 nm. Since hydrogen peroxide content in many biological samples is much higher than that of other hydroperoxides, samples were pretreated with catalase to decompose the existing H2O2 and eliminate the interference.
Statistics. Statistical analysis was carried out by the use of the Kruskal-Wallis test followed by the Dunn’s post hoc test, which considered the unequal and small sample sizes we used in this study. A linear regression model was used to evaluate associations between hormonal and antioxidant variables. Before plotting the data in the regression study, the normality test on the variables was performed, and the values of estradiol and progesterone were log transformed. The 95 % confidence intervals (CIs) for the regression lines were calculated. Two-tailed p values are given throughout. All data were analyzed using GraphPad Prism software.
The phase-related concentrations of gonadotropins and sex hormones are reported in Table 2. Significant changes were observed in FSH (H=12.75, p<0.01, Kruskal-Wallis), LH (H=8.98, p<0.01, Kruskal-Wallis), and estradiol (H=7.93, p<0.05, Kruskal-Wallis) concentrations.
\n\t\t\t | \n\t\t\t\tFollicular phase\n\t\t\t | \n\t\t\t\n\t\t\t\tLuteal phase\n\t\t\t | \n\t\t\t\n\t\t\t\tPostmenopause\n\t\t\t | \n\t\t
FSH (U/L)** Median (Min/max) | \n\t\t\t14.30±3.51 11.50 (7.50–31.50) | \n\t\t\t13.20±2.21 10.80 (0.1–31.50) | \n\t\t\t38.88±7.88 32.15 (14.30–75.00) | \n\t\t
LH (U/L)** Median (Min/max) | \n\t\t\t3.33±1.70 2.50 (0.60–9.40) | \n\t\t\t3.93±0.96 3.10 (0.60–11.20) | \n\t\t\t11.88±2.47 11.25 (1.30–24.00) | \n\t\t
Estradiol (pg/ml)* Median (Min/max) | \n\t\t\t39.30±7.59 48.80 (12.60–57.20) | \n\t\t\t71.41±14.07 56.50 (10.00–208.10) | \n\t\t\t5.16±1.16 3.80 (0.70–11.40) | \n\t\t
Progesterone (nmol/L) Median (Min/max) | \n\t\t\t7.40±1.09 6.30 (5.20–12.10) | \n\t\t\t8.83±2.68 5.30 (1.30–41.60) | \n\t\t\t5.16±1.16 3.80 (0.70–11.40) | \n\t\t
Changes in hormone levels during follicular phase, luteal phase, and in postmenopause (data are expressed as mean ± SEM; * p<0.05, **p<0.01)
Figure 1 shows the phase-related changes of LOOH concentrations and AO enzyme activities in the blood of examined patients. The significant change with respect to the phase was observed in LOOH concentrations (H=5.76, p<0.05, Kruskal-Wallis). In the follicular phase, it was significantly lower than in the postmenopause (p<0.05, Dunn test). There were no significant changes of AO enzymes in the examined phases.
The linear regression analysis of individual hormonal variables against antioxidant parameters in blood (Figure 2) showed a significant negative correlation between FSH concentrations and GR activity (r=-0.42, p<0.05), as well as a significant positive correlation between LH and LOOH concentrations (r=0.38, p<0.05). No significant correlations were found between other hormones and antioxidant variables.
Changes in blood LOOH concentrations and AO enzyme activities in follicular phase (F), luteal phase (L), and postmenopause (PM) in blood of patients with hyperplasia simplex. Data are shown as mean ± SEM. P values refer to the results of the Dunn test
Linear regression line and 95 % CI to study the relationship between log FSH and GR activity; log LH and LOOH concentration in the blood of patients with hyperplasia simplex
The phase-related changes of LOOH concentrations and AO enzyme activities in hyperplasia simplex tissue are shown in Figure 3. The LOOH concentration significantly differed with respect to the phase (H=7.74, p<0.05, Kruskal-Wallis), and it was significantly elevated in luteal phase and in postmenopause, in comparison to the follicular phase (p<0.05, Dunn test).
Unlike blood, where no changes in AO enzyme activities were recorded, we found significant phase-related changes of SOD (H=9.11, p=0.01, Kruskal-Wallis) and CAT activity H=7.60, p<0.05, Kruskal-Wallis). Both enzymes had similar activity pattern, which was higher in luteal phase and in postmenopause, compared to follicular phase (p<0.05, Dunn test). The phase-related activity of GPx and GR did not show any statistical difference.
The linear regression analysis of hormone levels on the examined AO parameters in hyperplasia simplex tissue showed a negative correlation between progesterone and GR activity (Figure 4) (r=-0.36, p<0.05).
Changes in endometrial LOOH concentrations and AO enzyme activities in follicular phase (F), luteal phase (L), and postmenopause (PM) in hyperplasia simplex tissue. Data are shown as mean ± SEM. P values refer to the results of the Dunn test
Linear regression line and 95 % CI to study the relationship between log Pr and GR activity in hyperplasia simplex tissue.
Studies have shown a different AO status and sex hormone influence during menstrual cycle and postmenopause in healthy women and those with ovarian disorders [143-146], but we found no data regarding that relation in patients with endometrial hyperplasia simplex.
In the blood of these patients, we detected a lower level of LOOH in the F phase in comparison to the postmenopause. In hyperplastic tissue, LOOH level was lower in the F phase than in L phase and postmenopause. The activities of SOD and CAT were also lower in F phase when compared to the L phase and postmenopause. There was a negative correlation between FSH/P concentrations and GR activity in the blood and hyperplastic tissue, respectively. Positive correlation between LH and LOOH concentrations was recorded in the blood.
Similar pattern of LOOH concentration and SOD activity in endometrium of healthy women throughout the menstrual cycle was also observed in [134]. They found that LOOH concentration increased from early proliferative phase to mid-late proliferative phase and further increased in the late secretory phase. The SOD activity increased from early proliferative phase to mid-late proliferative phase, further increased in the mid-secretory phase, and then decreased in the late secretory phase. Previous investigations of immunohistochemical distribution of SOD in human endometrium during menstrual cycle also showed that surface and glandular epithelia contain SOD during proliferative and secretory phases except just prior to the menstruation [147].
The study of Ota et al. [148] regarding SOD expression in endometrium during the menstrual cycle of healthy fertile women and women with diagnosed endometriosis and adenomyosis have shown the phase-dependent changes of SODs in glandular and surface epithelia in healthy women. Specifically, the expression of copper, zinc SOD was lowest during the early and mid-proliferative phases and then gradually increased and was most pronounced in the early and mid-secretory phases. The expression of manganese SOD reached a peak in the late secretory phase. In women with endometriosis and adenomyosis, the expression of both SODs was constantly elevated compared to healthy women throughout the menstrual cycle, which suggested a key role of superoxide in infertility caused by endometriosis and adenomyosis [148]. Our recent findings in women with endometrial polyp showed the opposite pattern of LOOH concentration and SOD activity in blood and polyp tissue than in women with hyperplasia simplex. Both parameters were higher in the proliferative phase compared to the secretory phase or postmenopause in blood and endometrium of the examined women [140].
Regarding CAT, in [149], it was found that CAT expression in healthy women fluctuated greatly during the menstrual cycle and the surface epithelium showed a similar pattern to that in the glandular epithelium. The expression was the lowest during the early proliferative phase, increased during the mid-proliferative phase, and peaked in the late secretory phase. In patients with endometriosis, the CAT expression did not fluctuate during the cycle, but it was consistently elevated throughout the menstrual cycle when compared to healthy women. Likewise, in women with adenomyosis, the CAT expression did not vary during the cycle in comparison to healthy ones, and it was significantly higher than in patients with endometriosis [149]. In women with endometrial polyp, we found no significant change of CAT activity in different phases [140]. In this study, however, the CAT activity in endometrium of patients with simple hyperplasia was also similar to the healthy women.
Studies in women with gynecologic disorders indicate a different AO status, as one of the possible factors contributing to the development of oxidative stress [150]. There are also studies which investigated the role of oxidative stress and hormones in development of gynecologic pathologies. For example, in [151], it was found that FSH, LH, and estrogen could induce ROS production at different levels in ovarian epithelial carcinoma and may therefore participate in cancer development process. FSH was found to increase cell proliferation in ovarian epithelial carcinoma (OEC) [152], and LH may also be involved in OEC development under pathological conditions [151].
Simple hyperplasia is the most common type of endometrial hyperplasia and the type most likely to spontaneously regress, and it rarely progresses to endometrial cancer [42, 51, 153]. The LOOH concentrations and AO enzyme activities in this study which were similar to the healthy women point to the preserved cellular AO status in these patients. Endometrial hyperplasias are generally considered as precancerous lesions and are treated either conservatively or surgically. The regression of hyperplastic to normal endometrium is the main purpose of any conservative treatment. It is based on the administration of agents, like progestogens [154], which have an indirect antiestrogenic action and also a direct antiproliferative effect on the endometrium [155]. Also, therapeutic application of gonadotropin-releasing hormone analogue (GnRHa) in women with hyperplasia was associated with high regression rates. The regression to normal endometrium is considered to be due to decreased gonadotropin levels as a result of pituitary downregulation or to the decreased ovarian steroidogenesis following low gonadotropin levels [156, 157]. The results of this study also showed that gonadotropins and progesterone influenced oxidant/antioxidant parameters in hyperplastic patients. Although we found no significant changes of GR activity among the menstrual cycle phases, FSH/P was negatively correlated with GR activity in the blood and hyperplastic tissue, while positive correlation between LH and LOOH concentrations was recorded in the blood. Our previous study in women with endometrial polyp also showed the influence of gonadotropins on AO status. In these patients, we observed a negative correlation between FSH/LH and GPx activity and also between LH and SOD activity [140].
The role of gonadotropins in gynecological diseases in not fully clarified. In ovarian epithelial cancer (OEC), gonadotropin theory proposes that elevated serum FSH and LH levels contribute significantly to its development [158]. FSH generally acts through its membrane-bound receptor which activates the intracellular signaling cascade, starting with cyclic AMP/protein kinase A (cAMP/PKA) that is followed by phosphorylation of specific transcriptional factors, like cAMP-response element-binding protein (CRE), or p38 MAPK, which controls other kinase cascades. The FSH receptor can also activate extracellular signal-regulated protein kinases (ERK-s) [159].
It was shown that synthesis of antioxidants, such as glutathione in the ovary, is regulated by gonadotropins, but exact mechanisms are still unknown [160]. One of the mechanisms behind FSH and antioxidants interaction is through activation of transcriptional factors, like Nrf2. The induced Nrf2 binds to the antioxidant-response element (ARE), thus coordinately regulates the expression of AO genes [161].
The pathogenesis of endometrial hyperplasia is still not fully understood. Prolonged estrogen stimulation is considered as one of the factors related to the etiology. This study showed that patients with endometrial hyperplasia simplex have similar AO status like healthy women, and it also demonstrated the relation of hormones and prooxidant/antioxidant parameters in this gynecologic disorder. Since simple hyperplasia may spontaneously regress, these results point to the preserved AO capacity as a potentially important factor in the regression mechanisms. However, the role of ROS production as a risk factor for endometrial hyperplasia still needs to be clarified as well as the role of AO status in response to gonadotropins and sex steroids.
This work was financially supported by the Ministry of Education, Science and Technological Development, Republic of Serbia (Grants 41027, 41022, 173041).
The bus schedule is one of the operations planning process in bus transport that deals with the proper assignment of busses to routes to serve the expected passenger demand. The planning process in public transportation consists of different recurrent and complex tasks. It starts at a strategic level by collecting or forecasting the number of passengers at each transfer point, which is most of the time fully unknown and adds to the complexity of the planning process [1, 2, 3].
The decision-making process of the bus assignment is, however, a trade-off between service quality and operating costs for the bus operating companies [4]. It is because using too many busses incurs more operating costs while resulting in good service quality, whereas too few busses have the opposite effect. Based on the information collected from Anbessa City Bus Service Enterprise (ACBSE) currently, the enterprise serves more than 125 routes (as of 2019) that connect different parts of the city using 759 operational busses. The number of passengers shows high variability during each period which requires fluctuating the number of assigned busses in each route. But the enterprise uses mainly a fixed number of busses scheduled per route in its operation throughout the day. This resulted in some busses moving empty while others are being overcrowded, which subsequently results in poor performance and service quality. Moreover, the transportation service in ACBSE has many challenges such as low bus utilization, unsatisfied passengers’ demand, and higher operating costs.
To address the challenges of bus assignment and scheduling problems in ACBSE, this paper first focuses to develop a demand-oriented Linear Programming (LP). Linear Programming is a well-accepted technique within the field of Operations Research, a specialty area within the broader field of Industrial Engineering. Then the LP-model is used to solve and optimally satisfy the existing passengers’ demand in four operating periods in a day using 93 selected routes. For simplicity purpose, in this paper, the four operating periods are named as shifts.
The concept of planning a minimum cost set of transporting routes to serve a group of customers is a fundamental constraint in the field of transport and logistics [5, 6, 7]. It is because, in the total cost of the product, transportation accounts for about 20% of the total costs of a product [8]. Therefore, the need for developing a better route plan that can reduce the cost of transportation is the concern of various industries in the field.
To address the above issues, the basic and well-studied routing model is the Traveling Salesman Problem (TSP), in which a salesman is to visit a set of cities and return to the city he started in [2, 9, 10]. The objective of the TSP is to minimize the total distance traveled by the salesman. Vehicle Routing Problem (VRP) is a generalization of the TSP in that the VRP consists of determining m vehicle, where a route is a tour that begins at the depot, visits a subset of the customers in a given order, and returns to the depot [9, 10, 11].
The activity of planning and designing a delivery or a pickup service to customers in the logistics and supply chain is known as a Vehicle Routing Problem [6]. The first time it was proposed by [12] under the title “Truck dispatching problem” to design the optimum routing of a fleet of gasoline delivery trucks between a bulk terminal and a large number of service stations supplied by the terminal. Often the context is that of delivering goods located at a central depot to customers who have placed orders for such goods, but the area of application of VRP is also so versatile and is used in many areas in real-world life.
“The VRP is defined by a depot, as a set of geographically scattered customers with known demands, and a set of vehicles with fixed capacity” [7, 13]. All depts must be visited just once and the total demand of a route must not exceed the total vehicle capacity. The VRP aims to minimize the overall distribution costs. In most real-life distribution contexts many side constraints complicate the VRP model. These side constraints can be time, that is the total route time and windows time within which the service must begin.
In the literature, VRP was also known as the “vehicle scheduling” (VSP) [6], or “Vehicle dispatching” or simply as the “delivery problem” [14]. It appears very frequently in real-world situations not directly related to the physical delivery of goods.
The VRP problem is a combination of the two well-known optimization problems: the Bin Packing Problem (BPP) and the Traveling Salesman Problem (TSP) [15, 16, 17]. The BPP is a problem given a finite set of numbers (the item sizes) and a constant
Logically, all the items have to be inside exactly in one bin and the total capacity of items in each bin has to be within the capacity limits of the bin. This is known as the best packing version of BPP. The TSP [3] is about a traveling salesman who needs to visit several cities. The salesman has to visit each city exactly once, start and end location, commonly called depot in VRP. The issue is to search the shortest tour within all the cities [17]. Connecting this to the VRP, customers can be allotted to vehicles by solving BPP and the order in which they are visited can be found by solving TSP. A VRP with a single vehicle and infinite capacity is a TSP.
VRP is a common name given to a problem in which a set of routes for a fleet of vehicles based at one or several locations called a depot, must be determined for several dispersed cities or customers [18]. The motive is to service a set of customers with a minimum-cost [16, 19]. Vehicle routes originate and terminate at a depot. It is one of the most challenging combinatorial optimization problems in distribution, and logistics [7]. Customers may be in a dispersed location and a fleet of vehicles need to serve them from a depot and return to the depot [16]. The decision here is to determine the assignment of the vehicle (s) and route (s) that a vehicle will serve them best. The commonly used illustration of the input and output of VRP is given in Figures 1 and 2.
VRP inputs.
VRP outputs.
Average daily bus utilization; current vs improved system.
Since both BPP and TSP are the so-called NP-hard problems and since VRP is a combination of the two, it is also NP-hard [12, 16, 20, 21]. Since the last decades, VRP has got much interest from many scholars. Even in recent years, with the rapid advancements of globalization and supply chain systems, VRP is becoming one of the important research topics in the fields [4, 22, 23].
Moreover, the complexity and its application importance immense literature have devoted to the study and analysis of Bus Scheduling Problem (BSP) and many optimization models have been proposed [23]. The different models developed have tried to accomplish near-optimal solutions with an acceptable amount of computational effort and time [6]. There are many extensions for the Vehicle Schedule Problem (VSP) or VRP with several requirements in the literature over the last 50 years [16, 24]. Among many others, some of the examples are the existence of one depot [18] or more than one depots [4, 16], a heterogeneous fleet with multiple vehicle types [18] the permission of variable departure times of trips, VRP with deterministic demand which is commonly called classical VRP [13, 18].
Vehicle Routing Problem has been used in different applications and practices but with many constraints. The major constraints according to [16] are the network [25], demand and customers, depot locations, the type of vehicle used, and sometimes drivers. it is not possible to satisfy all the constraints in one model. In this case, some of the constraints can be reduced without loss of generality. When some customers left unserved due to this reason, in VRP, it called route failure [7]. In some cases, by introducing different penalties or priorities, the situations can be handled [6, 16].
The routing operations are performed to serve customers to start and end at one or more locations, located at the road [5]. Each location or depot is identified by the number and types of vehicles associated with it and by the number of goods it can deal with [23]. Transportation of goods is carried out by using a fleet of vehicles with fixed composition and size according to the necessities of the customers [25, 26].
The vehicle used in VRP is also one constraint in the model assumptions. According to [16], the vehicles may be characterized by the capacity of the vehicle, expressed as the maximum weight, or volume, or the number of pallets, the vehicle can carry. In most application areas, the least practice constraint is the drivers [16].
VRP is one of the most studied combinatorial optimization problems and is concerned with the optimal design of routes to be used by a fleet of vehicles to serve a set of customers [26]. VRP is not only focusing on the delivery and collection of goods but involves also in different application areas arising from the transportation and logistics system [16].
Different VRP models have been developed for different applications to study the Routing Problem [1, 22]. The most recent is the one that has been devoted to more complex variants of the VRP occasionally called “rich” VRPs. These are closer to the VRP models [23] and are used to design an optimum allocation system that can improve the level of their services [27]. Toth Paolo and Vigo Daniele [16] have reported that the use of computerized methods in the VRP problems has significantly saved the computational effort ranging from 5 to 20%.
VRP can be designed as a directed or undirected graph subject to the problem environment [21, 28]. In the case of classical VRP where customers or customer’s demands are known in advance and the driving time between the customer and the service time at each customer are also known, it can be defined and formulated in this section as a general VRP model [28].
It is designated in graph theory. To define the general model of VRP, let
The common VRP comprises a set of at most
Subject to
Where:
In many realistic cases, the cost or the distance matrix satisfies the triangular inequality such that Eq. (3):
In the VRP models, a differentiation has to be made between symmetric Eq. (4) and asymmetric Eq. (5). Solution approaches can vary significantly between these two cases [30].
In the real world, however, the general VRP model is enhanced by various constraints or side-constraints, [5]. The constraints can be such as vehicle capacity or time interval in which each customer has to be served [16], revealing the Capacitated Vehicle Routing Problem (CVRP) [20] and the Vehicle Routing Problem with Time Windows (VRPTW) [18, 21].
VRP models, whether they are used for public transport or transit, as well as distribution and logistics, they share certain mutual features. That is, they focus on the optimization of cost (working cost), distance covered, waiting time, etc.
The current operations schedule of ACBSE has faced many challenges in its schedule busses assignment for each route. The enterprise uses a scheduling system that operates from 6:30 to 20:30. As shown in Table 1 below, The enterprise has a fixed scheduling system, which means, the number of busses allowed on each route remains constant irrespective of the commuters’ demand. In reality, however, the number of busses in each route should have been varied and can be determined based on the commuters’ demand distribution. These approaches have made some busses moving empty while others being congested and resulting in capacity loss to the enterprise and bad service to passengers.
Average bus utilization for peak and off-peak shifts; current vs improved system.
Route No. | Origin(local Name) | Destination (local Name) | No. of busses | Kms |
---|---|---|---|---|
1 | Megenagna | Kara | 2.00 | 7.70 |
2 | Kore Mekanisa | Addis Ketema | 4.00 | 11.10 |
3 | Ayertena | Minilik Square | 8.00 | 10.80 |
4 | Haliti | Addis Ketema | 5.00 | 19.40 |
93 | Bole Bulbula | Megenagna | 2.00 | 15.20 |
Total | 36.00 | 132.80 |
The fixed number of busses assigned on selected routes (as of 2011).
The enterprise occasionally involves additional busses on few routes during peak hours to overcome the challenges. Due to the absence of records for such decisions, therefore, in this study, the fixed number of busses assigned on each route is only considered as a basis for benchmarking.
The data were collected only on the 93 routes that have been used by the enterprise for more than a decade. The data includes route performances, number of passengers served per route, the total trips made, revenue generated, running cost, and distance traveled. Moreover, data were also collected concerning the number of busses, bus capacity, average bus travel time, the length of each route, and working hours. The data were analyzed and organized per shift per route basis for validation purposes. The operating shifts, the time interval for each shift, and the passengers demand proportion per shift are reported in Table 2.
Shift | Time interval | Duration (minute) | Demand proportion (%) |
---|---|---|---|
Morning peak hours | 6:15–9:30 | 195 | 40% |
First off-peak hours | 9:30–15:30 | 360 | 20% |
Evening peak hours | 15:30–19:30 | 240 | 35% |
Second off-peak hours | 19:30–21:00 | 90 | 5% |
Total | 870 | 100 |
Demand proportion and duration of each shift.
As per the report of the company, the enterprise has 534 busses and transported about 640, 000 passengers per day in 125 routes. In addition to these, the enterprise covers about 138 km and makes 61.50 trips per day [32]. In this study, it is also possible to classify the enterprise schedules in four shifts, i.e. peak hour and off-peak-hour schedules. These are two peaks and two off-peak periods during the services time (from 6:30 to 21:00). The morning peaks are from 6:30–9:30 and 15:30–19:30, while the off-peaks are also in the morning and afternoon shift. The details are reported in Table 2.
The demand proportion has also distributed within the four shifts. In the morning peak hour, the enterprises serve about 40% of the daily passengers. In the second peak-passengers’ demand occurs in the evening shift shares about 35% of total passengers. The other periods are the first and the second off-peak shift which occurs after the morning peak and the evening peak shift in which 20% and 5% for the demand proportion is allocated respectively.
The researchers have investigated the existing operating systems of the ACBSE, and a linear programming (LP) model was developed. The model helps to achieve a solution for the scheduling problems of the enterprise. The LP model developed in this study is a new approach in the literature of VRP that considers the trips made by two different types of busses to address the demand distribution of passengers in 93 routes in four working shifts.
The LP model was fitted with data. To achieve its object, the model was coded and run using the General Algebraic Modeling System (GAMS) optimization software. The GAMS code was running within 0.15 seconds on 3.10 GHz, Window7 Home Premium, 4GB RAM, and core (i5) Dell Personal computer (Optiplex 790-Model). The resulting solution of the LP-model was first the number of trips per route per shift for each type of bus. After obtaining the solution, then, the number of trips was translated into the number of busses per route per shift for each type of bus.
The LP model was investigated and validated to identify potential areas of improvement in the scheduling and assignment problem of ACBSE. It is also used to determine the number of busses to be assigned in a given route at a given shift that can address the demand distribution of passengers each shift for the 93 routes. The results of the LP-model were validated by comparing the current schedule and performances of the enterprise. The validation was made using four performance measuring parameters namely bus utilization, the total number of trips made, the total distance traveled, and the different operating costs.
In order to develop the LP mode, let
Definition of terms:
j = Shifts,
The objective is to minimize the total number of trips made by bus type-I and bust type-II. This objective is represented by
The first constraint is to set the overall bus capacity of the Enterprise. This is the capacity of the two types of busses assigned in route
The fourth constraint will determine the total minimum number of trips required in every 30 minutes for each of the 93 routes. This constraint is given b,
After compiling all the above constraints, the overall LP-model that determines the optimum number of trips i required per route per shift j is formulated as follows.
Subject to
The objective function 6 will minimize the total number of busses required to serve the total demand. Constraint 7 represents the combined capacity of busses assigned to each route i at a given shift j. Eq. 8 and 9 represent the total number of busses that need to be assigned for each route. The total required number of busses must be equal or less than the available number of busses the enterprise has; Eq. 10 shows that the total number of busses to be allotted on each route per shift has to fulfill the minimum number of busses required per route per shift. Eq. 11 and 12 are the number of busses to be assigned to a given route. Moreover, Eq. 13 warrants that the total sum of a probability is always one; last Eq. 14 is nonnegativity constraints.
This model guarantees at least one round trip for each route every 30 minutes to maintain a quality service level. It also contributes to the scientific body of knowledge by introducing different bus types in a single LP-model as a new constraint.
In the model, the travel time of bus on a given route was considered as the total sum of passenger boarding and alighting time (dwell time), acceleration and deceleration at bus stops, traffic light, and transfer time between each stop. However, little attention was also given to consider the functionality of the model and its output. In particular, the model was run once for all of the four shifts. The number of busses will be checked to make sure,
For clarity and understanding, moreover, some of the parameters were defined. Some of the parameters are,
Thus using Eq. 15 and the data reported in Table 2 above, the value of
Route No. | Demand | Demani (Dij) per Shift | Trip factor (Tij) per Shift | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Pi | D1 (m1 = 7) | D2 (m2 = 12) | D3 (m3 = 8) | D4 (m4 = 3) | 1 | 2 | 3 | 4 | ||
1 | 4126 | 0.014 | 1650 | 825 | 1444 | 206 | 7 | 12 | 8 | 3 |
2 | 3497 | 0.012 | 1399 | 699 | 1224 | 175 | 4 | 7 | 5 | 2 |
3 | 11,030 | 0.038 | 4412 | 2206 | 3860 | 551 | 4 | 7 | 5 | 2 |
4 | 3029 | 0.010 | 1212 | 606 | 1060 | 151 | 3 | 5 | 3 | 1 |
93 | 778 | 0.003 | 311 | 156 | 272 | 39 | 2 | 4 | 3 | 1 |
Input parameters for the LP model for some routes.
The other parameter that needs to be defined and explained is the trip factor,
The trip factor is the maximum number of trips a bus can make on route
By multiplying the trip factor by the available number of busses, that is
The other parameter that needs explanation is the trip proportion,
The last parameter the requires explanation is
The next step is to run the model to obtain feasible solutions. The LP model is solved based on the data of the average daily passengers that have been transported for the last 19 months in four shifts. The daily passengers’ that demand for transport for the last 19 months was collected and then the average daily passenger’ demand of each month was computed per route per shift based on the trip proportion (
In the process of running and solving the model, first, the input data has to be fitted. In this regard, to fit the LP-model with the input parameters that are involved in the model, first it the parameters needs to be determined. These parameters are either computed or collected from the enterprise. The sample input parametric values are shown in Table 3.
These inputs parameters are standard carrying capacity of busses, the operational number of busses, the passenger that demand transport services per route per shift (
There are four types of busses used by the enterprise (namely DAF, Mercedes, Single, and rigged Articulated busses) but they can be categorized in two based on their seat capacity. These are one bus with seat capacities of 30 passengers (DAF, Mercedes, Single, and rigged Articulated) and busses with seat capacity 50 passengers (the Articulated one).
While fitting to the LP-model busses with a seating capacity of 30 are classified as bus type-I (but can transport 60 passengers) and busses with a seating capacity of 50 are classified as bus type-II (but can transport 90 passengers). The maximum number of capacity, 60 passengers and 90 passengers are based on the standard capacity of public bus transportation [33]. The total capacity of each bus type is equal to the seating capacity plus the standing capacity. The enterprise has a total number of type-I and type-II is 600 and 90, respectively. Thus, the objective function of the research is used to compute the optimum trips and mixes of the two types of busses per route per shift.
The total operational busses in bus type-I are 600 and that of bus type-II is 90 busses. The numbers of operational busses are not only 690, but the rest of the operational busses are kept for backups during failure and other services such as contract and employee service. Also, the 93 routes which are under analysis serve more than 90% of the demand during a day and thus the operational bus assignment is based on this proportion. After substituting the values of input parameters and constants into the LP model, the model can be re-written as:
Subject to
The output of the model indicates the total number of trips by the two types of busses needed to serve the average demand of each route on a given shift. The sample outputs of the LP model are shown in Table 4.
Route No. | Shift 1 | Shift 2 | Shift 3 | Shift 4 | ||||
---|---|---|---|---|---|---|---|---|
Bus type-I | Bus type-II | Bus type-I | Bus type-II | Bus type-I | Bus type-II | Bus type-I | Bus type-II | |
L | 19 | 9 | 0 | 10 | 14 | 11 | 0 | 3 |
2 | 19 | 5 | 5 | 8 | 15 | 6 | 0 | 2 |
3 | 61 | 14 | 14 | 24 | 48 | 17 | 0 | 7 |
4 | 18 | 3 | 6 | 5 | 15 | 3 | 2 | 1 |
5 | 14 | 3 | 4 | 6 | 12 | 3 | 0 | 2 |
6 | 51 | 12 | 0 | 21 | 41 | 14 | 0 | 6 |
· | · | · | · | · | · | · | · | · |
· | · | · | · | · | · | · | · | · |
· | · | · | · | · | · | · | · | · |
91 | 4 | 1 | 2 | 2 | 4 | 1 | 1 | 1 |
92 | 21 | 5 | 0 | 9 | 17 | 6 | 0 | 3 |
93 | 11 | 4 | 0 | 5 | 9 | 4 | 0 | 2 |
Total | 1541 | 402 | 174 | 618 | 1231 | 490 | 18 | 156 |
Number of trips required per route per shift.
The GAMS build system has different solvers such as BARON, BDMLP, and BENCH. Replace this text with your section Heading CNOPT, CPLEX, LGO, etc. that are capable of solving different varieties of problems. After trying each solver, for reporting purposes, CPLEX solver is chosen to solve the LP model developed above. The LP model is coded and programmed using the GAMS. This build system and the piece of the GAMS code are reported in the Appendix.
The outputs of the model are reported by taking the upper integer value. As shown in Table 4, for example for route 3, 61 trips by bus type-I and 14 trips by bus type-II were required for shift one. There are also routes where no trips are required by bus type-I in the off-peak shifts. Since the LP model produces the number of trips required, the output has to be converted into the number of busses required for each route in a given shift. This has to be done by dividing the number of trips from the model output by the trip factor (
Based on the results of the output of the LP-model, there are 4627 total trips required to serve the average daily passenger demand. The actual number of busses required for a given route i during a given shift
For some routes, the output of the LP is small for some route, so adjusting the actual number of busses is required for such routes to have at least two busses on a given route per shift to allocate them on both ends of the route; that is one on the going trip and the other on the returning trip. This is because the demand during a given shift
Shift 1 | Shift 2 | Shift 3 | Shift 4 | |||||
---|---|---|---|---|---|---|---|---|
Route No. | Bus type-I | Bus type-II | Bus type-I | Bus type-II | Bus type-I | Bus type-II | Bus type-I | Bus type-II |
1 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 |
2 | 5 | 1 | 1 | 1 | 3 | 1 | 1 | 1 |
3 | 16 | 3 | 2 | 3 | 10 | 4 | 0 | 3 |
4 | 6 | 1 | 2 | 1 | 4 | 1 | 2 | 0 |
5 | 4 | 1 | 1 | 1 | 3 | 1 | 1 | 1 |
6 | 12 | 3 | 0 | 3 | 8 | 3 | ||
· | · | · | · | · | · | · | · | · |
· | · | · | · | · | · | · | · | · |
· | · | · | · | · | · | · | · | · |
91 | 3 | 0 | 2 | 0 | 3 | 0 | 3 | 0 |
92 | 5 | 1 | 1 | 1 | 3 | 1 | 1 | 1 |
93 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 0 |
Total | 396 | 94 | 114 | 88 | 269 | 94.8 | 121 | 79 |
Number of busses per route per shift.
The results showed that the actual number of busses required during peak periods is higher than that of off-peak periods. Thus, some of the busses that operate during the morning peak period have to wait on bus stops until they are required for the evening peak.
Similarly, the actual number of busses required for each shift varies and the number of busses required during peak periods is higher than that of off-peak periods. Thus, some of the busses that operate during the morning peak period have to wait on bus stops until they are required for the evening peak.
The outputs of the LP-model were evaluated using different performance measuring parameters. For the validation purpose, various assessments were made between the existing performances and the LP improved systems. The comparisons made, in this research, were based on bus utilization, distance and trips covered and the different operating costs of the enterprise [32]. Each of them is discussed in the following sections.
After converting and assigning busses to each route and each shift, then the improvements achieved by the LP model were compared with the existing bus utilization of ACBSE [32]. The bus utilization was calculated as the ratio of the number of passengers served by the bus to the number of passengers carrying capacity. The average daily bus utilization of the existing and the improved systems are presented in Figure 3. The results of the study show that the improved system has better bus utilization than the existing one. The existing system has a maximum bus utilization of about 125% on daily basis, which is very congested and crowded, while the improved system by this study has shown that a maximum bus utilization of 97% [32]. This shows how passenger congestion and service quality were improved by the new system. The average bus utilization for the improved systems is about 66.33% which is better than the existing systems that are 64.26%. Though the average utilization of both of the systems seems close to each other, in the improved most of the utilization lies between 60% and 80% whereas in the existing system had unbalanced bus utilization which is sometimes failed below 20% and other times above 120%.
As presented in Figure 4, an in-depth analysis of bus utilization based on shift was carried out. The improved system exhibited significant improvement as compared to the existing one and reported by shift. The improved bus utilization by shift is 89.8% during the morning peak, 51.19% during the first off-peak, 82.24% during the evening peak, and 42.1% during the second off-peak periods. The improvement as compared to the existing systems is 19.75% during first off-peak and 12.15% during the second off-peak. The bus utilization of the improved system also reduced the passengers’ congestions in peak hours, i.e. bus utilization is decreased from 116.1% to 89.8%. Thus, the improved system has a relatively stable and consistent bus utilization with improved service quality. The new model also indirectly improves the service quality for passengers by reducing over crowdedness while during peak periods and reduces the operating cost during off-peak periods to the enterprise.
In this section, the distance covered by the existing and the new system are compared. To compare this, the total kilometer covered by busses for route i per day is computed. It is the sum of the Kilometer covered during all the four shifts. Figure 5 shows that the total distance coverage of the current and improved systems. The distance covered on each shift was computed by multiplying the number of busses allocated to a given route at that shift by the number of trips that can be made by a single bus and by the route length. The total distance covered per day for the improved system is 70,964 kilometers, while for the existing system, it is about 78,963.7 kilometers per day. This shows a reduction of 10.13% in the daily distance coverage to serve the same number of passengers.
Distance coverage; current vs improved system.
In this case, the total daily trips made for the improved system is also computed for each shift by multiplying the number of busses assigned and the number of trips a single bus can make during that shift. The total trips covered for the existing systems were 5504 trips per day while 5584 for improved systems. This also shows an improvement of 80 trips per day compared with the existing system. The increased number of trips was achieved with a 10.13% reduction on the daily Kilometer. This also improves service availability in addition to saving on the operating costs.
Using the different operating costs of the enterprise, the other performance measuring parameters were also assessed in this research. The improvements made by the new model were also considerable. The total daily operating cost of the enterprise for each route is the sum of operating costs for all the shifts. From the comparison made, the results of the study show that the average daily operating costs of the enterprise for the existing systems are about 1,101,283.68 ETB (ETB = Ethiopian Birr and 1USD = 34.33ETB as of Jun 4, 2020) while for the improved systems is 949,991.49 ETB. The saving of the new system in this read is about 13.74% per day compared to the current system. Figure 6 shows the improvements made by the new systems are achieved nearly in all the operating costs of the enterprise compared to the existing one.
Current vs improved operating cost.
As compared to all the operating costs, larger saving is observed on gas oil. This reduction has also a strong relationship with the total Kilometer covered by each bus. This is due to the fact that the total kilometer covered is improvement resulted in a reduction in the cost of gas oil consumed.
The motivation of this research was to develop a model that can optimize the operational performances of ACBSE. Based on the major findings, it can be concluded that the existing scheduling systems of ACBSE have shown low performances on the bus utilization, operating cost, and daily trips and distance covered. These improvements have been achieved because the existing system has fixed numbers of busses assigned to routes without considering the variability of passenger demands. This had cost more the enterprise. However, the operational performance improvements of the LP-model have shown better performances over the existing one nearly in all of the above performance measuring parameters. Besides, it can be concluded that the existing bus scheduling and operations system has a lower average utilization of busses compared with the new system by 2.1%. The bus utilization per route per shift also shows significant improvement over the existing system. With regard to the cost-saving, the new model has resulted in a saving of 13.74% (151,292.19 ETB) in the operating costs of the enterprise. Moreover, the new model also resulted in a 10.13% saving on the total km covered with 80 additional available trips per day for the enterprise. In addition to these and the saving in all the parameters, the improved system has also reduced the waiting time, improve service quality, and reduce passenger congestion by scheduling busses based on the international standard bus capacity. The new system has also a significant reduction in the total kilometer covered while improving the total trips made daily. All these improvements of the new system of the LP-Model were exhibited without altering the existing routes used by the enterprise. But rerouting the existing route’s design may also bring radical improvement to the performances of the enterprise.
***************
Defining Route i and Shift j
Sets i Routes /1 ∗ 93/ This sets routes from route 1 to route 93.
j Time periods /1 ∗ 4/; Sets shifts from shift 1 to shift 4.
Sample Demand distribution per route per shift
Table d(i, j) demand distribution on route
1 2 3 4
1 2249 1124 1968 281
2 2249 1124 1968 281
3 2249 1124 1968 281
. . . . .
. . . . .
. . . . .
92 1848 924 1617 231
93 1362 681 1192 170
Sample Trip Factors (Tij)
Table T(
Minimum Trip Required (M
Parameters M(
Trip Proportion (Pi)
1 2 3 4
1 7 12 8 3
2 4 7 5 2
3 4 7 5 2
. . . . .
. . . . .
. . . . .
92 8 5 2
93 5 9 6 2
/1 7
2 12
3 8
4 3/
P(i) demand proportion of route
1 0.014
2 0.012
3 0.040
. .
. .
. .
92 0.012
93 0.009/;
Decision Variables
X(i
Y (i
Variable Z; For the objective function
Model Equation
obj.. Z=e=sum((i,j), X(i,j))+ sum((i,j), Y(i,j));
const1.. sum((i,j), X(i,j))*60 + sum((i,j), Y(i,j))*90 =g= sum((i,j),d(i,j));
const2.. sum((i,j), X(i,j))=l= sum((i,j), T(i,j))*600;
const3.. sum((i,j), Y(i,j))=l= sum((i,j), T(i,j))*90;
const4.. sum((i,j), X(i,j))+ sum((i,j), Y(i,j)) =g= sum(j, M(j));
const5.. sum((i,j), X(i,j))+ sum((i,j), Y(i,j))=l=sum((i,j),T(i,j))*690;
Since bus type-II has greater capacity, priority is given so that demand is to be served by the available capacity of Y for that route to reduce number of buses required and if it is beyond the available number of Y for that route then the rest is to be served by the available number of X. Thus, based on this assumption the following equation sets the lower bound to values of X and Y, if not since it is minimization it starts from zero and can’t display the appropriate mix of X and Y.
Y.lo(i, j)$[(d(i, j)/90) ≤ (T(i, j) * P(i) * 90)] = d(i, j)/90; lower bound for Y
Y.lo(i, j)$[(d(i, j)/90) ≥ (P(i) * T(i, j) * 90)] = P(i) ∗*T(i, j) * 90;
X.lo(i, j)$[(d(i, j)/90) ≥ (P(i) * T(i, j) * 90)] = (d(i, j)/60) − (P(i) * T(i, j) * 90);
*lower bound for X*
X.lo(i, j)$[((d(i, j)/60)−(P(i)*T(i, j)*90)) ≥ (P(i)*T(i, j)*600)] = (P(i)*T(i, j)* 600);
Thus from this, the assignment of bus type X depends on the assignment of Y and X is
assigned for a given route if the expected demand during that time period is beyond
the capacity and availability of bus type Y
Model eq / All / ;
Solve eq using lp minimizing Z;
display X.l;
display Y.l;
*****************
Authors are listed below with their open access chapters linked via author name:
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\\n\\nMyung-Haing Cho 2016, 2018
\\n\\nMark Connors 2015-18
\\n\\nCyrus Cooper 2017, 2018
\\n\\nLiming Dai 2015-18
\\n\\nWeihua Deng 2017, 2018
\\n\\nVincenzo Fogliano 2017, 2018
\\n\\nRon de Graaf 2014-18
\\n\\nHarald Haas 2017, 2018
\\n\\nFrancisco Herrera 2017, 2018
\\n\\nJaakko Kangasjärvi 2015-18
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\\n\\nJose Luiszamorano 2015-18
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\\n\\nAndrea Natale 2017, 2018
\\n\\nAlberto Mantovani 2014-18
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\\n\\nSandra Orchard 2014, 2016-18
\\n\\nMohamed Oukka 2016-18
\\n\\nBiswajeet Pradhan 2016-18
\\n\\nDirk Raes 2017, 2018
\\n\\nUlrike Ravens-Sieberer 2016-18
\\n\\nYexiang Tong 2017, 2018
\\n\\nJim Van Os 2015-18
\\n\\nLong Wang 2017, 2018
\\n\\nFei Wei 2016-18
\\n\\nIoannis Xenarios 2017, 2018
\\n\\nQi Xie 2016-18
\\n\\nXin-She Yang 2017, 2018
\\n\\nYulong Yin 2015, 2017, 2018
\\n"}]'},components:[{type:"htmlEditorComponent",content:'New for 2018 (alphabetically by surname).
\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYuekun Lai
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\n\nAbdul Latif Ahmad 2016-18
\n\nKhalil Amine 2017, 2018
\n\nEwan Birney 2015-18
\n\nFrede Blaabjerg 2015-18
\n\nGang Chen 2016-18
\n\nJunhong Chen 2017, 2018
\n\nZhigang Chen 2016, 2018
\n\nMyung-Haing Cho 2016, 2018
\n\nMark Connors 2015-18
\n\nCyrus Cooper 2017, 2018
\n\nLiming Dai 2015-18
\n\nWeihua Deng 2017, 2018
\n\nVincenzo Fogliano 2017, 2018
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\n\nFei Wei 2016-18
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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