\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state-of-the-art on Theileriosis, Babesiosis, and Anaplasmosis, both in humans and domestic animals. Particularly this book aims to permit the researchers to enter into a critical focus on the biology of the parasites, eco-epidemiology of the diseases, clinical manifestations, risk factors, immunology, surveillance, diagnosis, identification, and management of risks as well as the potential economic impact on animal production.
",isbn:"978-1-80356-384-8",printIsbn:"978-1-80356-383-1",pdfIsbn:"978-1-80356-385-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"3d72ae651ee2a04b2368bf798a3183ca",bookSignature:"Prof. Elisa Pieragostini",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11577.jpg",keywords:"Zoonosis, Global Climate Change, Epidemiology, Risk Factors, Haemoparasites, Prevention & Control, Piroplasmosis, Anaplasmosis, Babesiosis, Theileriosis, Tick-Borne Diseases, Ticks",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 23rd 2022",dateEndSecondStepPublish:"April 29th 2022",dateEndThirdStepPublish:"June 28th 2022",dateEndFourthStepPublish:"September 16th 2022",dateEndFifthStepPublish:"November 15th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor of Animal genetics and breeding at Bari University, a researcher in animal genetics related to the resilience of Apulian livestock to enzootic tick-borne haemoparasites and to the involved functional effect of hemoglobin variants. She is a member of national and international scientific associations.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"51521",title:"Prof.",name:"Elisa",middleName:null,surname:"Pieragostini",slug:"elisa-pieragostini",fullName:"Elisa Pieragostini",profilePictureURL:"https://mts.intechopen.com/storage/users/51521/images/system/51521.jpg",biography:"Dr. Pieragostini has a Degree in Biological Sciences, DPhil in Biochemistry received from Bologna University, Italy.\r\nShe was an Assistant professor of “Genetics” at the Faculty of Science of Bologna University, a Full professor of “Animal genetics and breeding” Bari University, Italy, and Elected President of course on “Agricultural Science and Technology” (2001-2010) Bari University.\r\nShe retired in 2015.\r\nFrom 1977 to 1984 she carried out intensive work on Drosophila investigating quantitative characters associated with biochemical markers. From the1985 onwards, she had been studying Apulian farm animals focusing on blood-based markers as well as on the structure-function relationship of the different hemoglobin variants come across and their relationship with the Apulian livestock resilience to the enzootic tick-borne diseases. \r\nShe was Project Director\r\nShe is a member of national and international scientific associations.",institutionString:"Independent Researcher",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Bari Aldo Moro",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@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, copy-editing 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:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/6550.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. 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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:"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:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],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:"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"}}]},chapter:{item:{type:"chapter",id:"61097",title:"Molecular Docking Studies of Enzyme Inhibitors and Cytotoxic Chemical Entities",doi:"10.5772/intechopen.76891",slug:"molecular-docking-studies-of-enzyme-inhibitors-and-cytotoxic-chemical-entities",body:'\n
\n
1. Introduction
\n
Due to the current problems and complicated challenges faced by medicinal chemists docking is a most demanding and efficient discipline in order to rational design new therapeutic agents for treating the human disease. Previously, the strategy for discovering new drugs consisted of taking a lead structure and developing a chemical program for finding analog molecules exhibiting the desired biological properties, the whole process involved several trial and error cycles patiently developed and analyzed by medicinal chemists utilizing their experience to ultimately select a candidate analog for further development. The entire process when looked at today, conceptually inelegant. These days picture are quite reverse after the emergence of computational chemistry discipline in science world. The concepts used in three-dimensional (3D) drug design are quite simple. New molecules are conceived either on the basis of similarities with known reference structures or on the basis of their complementarity with the 3D structure of known active sites. Molecular modeling is a discipline that contributes to the understanding of these processes in a qualitative and sometimes quantitative way [1, 2].
\n
In this chapter we have presented a brief introduction of the available molecular docking methods, and their development and applications in drug discovery especially for synthetic and bio-transformed derivatives.
\n
\n
\n
2. Quantum mechanical calculations and molecular docking studies of α-glucosidase inhibitors
\n
Inhibitors of a-glucosidase regarded as a convincing therapeutic target in the development of drugs against diseases such as obesity, diabetes, HIV, and cancer [3, 4]. In this connection, few synthetic a-glucosidase inhibitors (AGI’s), such as acarbose, miglitol, and voglibose are in use since last two decades. Among the six drug classes for the management of diabetes mellitus (DM), α-glucosidase inhibitors are one of them. These inhibitors are quite target specific as they act in the intestine locally, in contrast to other oral anti-hyperglycemic drugs, which in addition, alter certain biochemical processes in the human body [5]. Therefore, discovery and development of novel α-glucosidase inhibitors are urgently needed.
Development of novel α-glucosidase inhibitors requires screening of a large number of compounds. Cedryl acetate (1) and cedrol (2) are examples of newly identified α-glucosidase inhibitors that exhibit potent inhibitory activity. The most potent compound one was selected for microbial transformation and the transformed products were screened for the same activity. We successfully identified several α-glucosidase inhibitors that are more potent than acarbose [6]. However, this was the first report describing the α-glucosidase inhibitory activity of cedrol (2), cedryl acetate (1), [7] and some of the transformed products of cedryl acetate including 10β-hydroxycedryl acetate (3), 2α, 10β-dihydroxycedryl acetate (4), 2α-hydroxy-10-oxocedryl acetate (5), 3α,10β-dihydroxycedryl acetate (6), 3α,10α-dihydroxycedryl acetate (7), 10β,14α–dihydroxy cedryl acetate (8), 3β,10β-cedr-8(15)-ene-3,10-diol (9), and 3α, 8β, 10β -dihydroxycedrol (10) as mentioned in Figure 1. Compounds one, two, and four showed α-glucosidase inhibitory activity whereby one was more potent than the standard inhibitor, acarbose, against yeast α-glucosidase.
\n
Figure 1.
Structure of cedryl acetate and its microbial derivatives.
\n
The structures have been also optimized computationally at Hartree-Fock (HF) level of theory using valence triple-zeta plus diffuse and polarization functions (6–311++G*) basis sets for H, C, N, and O atoms to get insight into the 3D structure of these metabolites. GAMESS package [8] has been used for all quantum chemical calculations. Molecular docking studies have been also performed to delineate the ligand-protein interactions at molecular level using autodock vina programs [9]. Avogadro [10], Gabedit [11], VMD [12], and Chimera [13] have been used for the structure building, analysis, and visualization for our calculations.
\n
\n
\n
2.2. α-Glucosidase inhibitory activity
\n
Compounds one, two, four, and six were tested for inhibition of the α-glucosidase enzyme. For the first time, the cedrol (2) and cedryl acetate (1) demonstrated α-glucosidase inhibitory with the latter being more potent than the former. This is possibly due to the presence of an Ac group at C (8). Overall compounds one, two, and four showed more than or comparable activity to the standard inhibitors (Table 1). Apparently, the polar OH group lowers the inhibitory activity toward the enzyme, as observed in compounds four and six (inactive) in comparison to one.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Compound
\n
IC50* (in mM ± S.E.M)
\n
Binding energy in kcal/mol (Yeast a-glucosidase)
\n
Binding energy in kcal/mol (Human maltase glucoamylase)
\n
Binding energy in kcal/mol (Human pancreatic amylase; 1 U33.pdb)
\n
\n
\n
C-terminal domain (3TOP.pdb)
\n
N-terminal domain (3L4T.pdb)
\n
\n\n\n
\n
1
\n
94 ± 15
\n
−8.4
\n
−6.9
\n
−6.5
\n
−7.9
\n
\n
\n
2
\n
130 ± 15
\n
−7.4
\n
−6.6
\n
−6.2
\n
−7.9
\n
\n
\n
4
\n
690 ± 16
\n
−7.9
\n
−6.3
\n
−7.1
\n
−7.6
\n
\n
\n
6
\n
Inactive
\n
−8.2
\n
−6.4
\n
−6.5
\n
−7.6
\n
\n
\n
Acarbose
\n
780 ± 20
\n
—
\n
—
\n
—
\n
—
\n
\n
\n
Deoxynojirimycin
\n
425.6 ± 8.14
\n
—
\n
—
\n
—
\n
—
\n
\n\n
Table 1.
α-Glucosidase inhibitory activity of compounds 1, 2, 4 and 6 with their predicted binding energies in the active sites of yeast and mammalian α-glucosidases.
\n
\n
\n
2.3. Geometry optimization
\n
The biological activity of ligands is a function of their 3D structures. Thus, it is crucial to have an accurate description of the ligand in 3D space. Hartree-Fock (HF) approach have been used to obtain the structural details of all metabolites that were probed through the geometry optimization in the gaseous-phase with valence triple-zeta plus diffuse and polarization functions (6–311++G*) basis sets. We found in all the compounds studied, the distance of the bond between C and OH is 1.421 Å. The optimized geometry of these compounds also, showed a short length of carbonyl groups (C=O and COC=OCH3) distance of 1.208 Å. However, the bond order was slightly higher by a value of 0.11 in the case of C=O as expected. The carbon–oxygen bond in C-OCOCH3 was slightly larger as compared to that in CO-COCH3 (1.402 and 1.338 Å, respectively) due to a lower bond order by a value of 0.233. The presence of acetate group (-O-CO-CH3) in the molecule was lowered the dipole moment of the molecule as could be seen in Table 2. These compounds with a low dipole moment seem to be most active. However, due to limited experimental inhibitory assay data, it was difficult to make a generalize conclusion.
\n
\n
\n
\n\n
\n
Compound
\n
Dipole (Debye)
\n
\n\n\n
\n
1
\n
2.03
\n
\n
\n
2
\n
3.03
\n
\n
\n
3
\n
2.87
\n
\n
\n
4
\n
3.87
\n
\n
\n
5
\n
5.07
\n
\n
\n
6
\n
3.90
\n
\n
\n
7
\n
4.09
\n
\n
\n
8
\n
3.93
\n
\n
\n
9
\n
2.65
\n
\n
\n
10
\n
6.01
\n
\n\n
Table 2.
Dipole moment of metabolites calculated at HF/6–311++G* level of theory and basis sets.
\n
\n
\n
2.4. Molecular docking studies
\n
The most ideal is to obtain the orientation of ligand in 3D space into the protein binding site for determination of ligand activity. The ligand-protein binding mode and interaction are a very crucial to understand the catalytic activity. This modeled protein has been used as our target protein. In Addition, to elucidate their binding activity with mammalian α-glucosidase, we performed molecular docking studies of the human intestinal and pancreatic maltase glucoamylase with the active compounds. We found no significant difference in the binding affinity of active ligands with yeast α-glucosidase and the human pancreatic maltase glucoamylase. However, some differences in the binding energy were observed when ligands bind with the human intestinal maltase (Table 1). The structural changes in the binding sites of these proteins are postulated to be the cause of this less affinity of ligands toward intestinal maltase as compared to the yeast α-glucosidase. Figure 2a shows the homology model of the yeast α-glucosidase with the ligand cluster into the binding site. Figure 2b displays the close view of the binding site with the best predicted orientation of ligands 1–15, obtained from the molecular docking studies, almost overlapping with each other to form a cluster. The amino acid residues forming the binding site cavity have been labeled in white. The cavity can be clearly visualized when the protein is shown with the surface model as depicted in Figure 2c.
\n
Figure 2.
(a) Homology model of the yeast α-glucosidase (yellow color) showing the ligand cluster (variable color; licorice) into the binding site. The red color indicates the amino acid residues (labeled in white) surrounding the binding site (b). The lower picture (c) displays the binding site cavity with the ligand cluster.
\n
Figure 3 displays the interactions of individual metabolites one, two, four, and six with the yeast α-glucosidase protein. Polar amino acid residues, that is, Asp349 and Arg439 have strong H-bonding with the acetate group of the ligand. Cedryl acetate (1) exhibits the strongest binding affinity with the protein as inferred by its lowest binding energy (−8.4 kcal/mol), the values are given in Table 1. Compound one had the lowest IC50 of 94 ± 15 μM, which makes it in agreement with the enzymatic assay. The metabolite 2, showed no interaction with the residues. The acetate group of metabolite two has been hydrolyzed to form hydroxyl group that may play a partial role in its low activity (Figure 3b) as compared to the compound one. Metabolites four and six are acetylated and they do form H-bonds with Asp349 and Arg439, thereby showing a good ligand-protein binding energy, however, their activity is dramatically lowered or diminished as compared to compound one. This attenuate activity of metabolites four and six may be associated with their high-polarity arising from the introduction of two hydroxyl groups into the rings, partially due to the fact that the neighboring residues around -OH are slightly hydrophobic in nature.
\n
Figure 3.
Ligand-protein interaction studies of compounds (a) 1, (b) 2, (c) 4, and (d) 6. The hydrogen bonds are shown as black dotted lines. The H-bond distances in Å are given in boxes. The amino acid residues in the binding pockets are indicated as red.
\n
\n
\n
\n
3. Molecular docking studies of novel quinoline derivatives as potent β-glucuronidase inhibitors
\n
Glucuronidase has been used in numerous biotechnology and research applications. Glucuronidase as a gene has been studied as a positive selection marker for transformed plants, bacteria, and fungi carrying glucuronidase gene [14, 15]. It is also widely has been used for the structural investigations of proteoglycans and for research purposes in many diagnostic research laboratories [16].
\n
\n
3.1. Novel quinoline derivatives as potent β-glucuronidase inhibitors
\n
Quinoline is an aromatic compound having an aza-heterocyclic ring. It possesses a weak tertiary base that can undergo both nucleophilic and electrophilic substitution reactions. The quinoline moiety is present in several pharmacologically active compounds as it does not harm humans, when it is orally absorbed or inhaled.
\n
Various classes of compounds that showed considerable potential as β-glucuronidase inhibitors involved benzothiazole, bisindolylmethanes, bisindolylmethane-hydrazone, benzimidazole, unsymmetrical heterocyclic thioureas, 2,5-disubtituted-1,3,4-oxadiazoles with benzimidazole backbone, and benzohydrazone–oxadiazole [17]. In continuation of this work our study identified novel series of potent β-glucuronidase inhibitors of quinoline for further investigation [18].
\n
\n
\n
3.2. β-Glucorinadase inhibitory activity
\n
Thirty analogs of quinoline were synthesized, which have varied degree of β-glucorinadase inhibition ranging in between 2.11 ± 0.05 and 80.10 ± 1.80 μM, when compared with the standard inhibitor D-saccharic acid 1,4 lactone having IC50 value 48.4 ± 1.25 μM. Out of these thirty analogs, twenty four analogs 1–30 showed outstanding β-glucorinadase inhibitory potential with IC50 values (Table 3) analogs 17, 20, 21, and 27–29 showed good β-glucorinadase inhibitory potential. The structure–activity relationship studies suggested that the β-glucuronidase inhibitory activities of this class of compounds are mainly dependent upon the substitutions on the phenyl ring.
\n
Table 3.
Different quinoline derivatives and their β-glucuronidase activity.
IC50 values are expressed as mean ? standard error of mean.
\n
The most potent inhibition was noted in analog 13 that have hydroxy groups at 3, 4-positions on the phenyl part. Making comparison of analog 13 having IC50 value 2.11 ± 0.05 μM with other dihydroxy analogs such as 12, 14, and 15 having IC50 values 3.10 ± 0.10, 5.01 ± 0.20, and 2.60 ± 0.05 μM, respectively, analog 13 was found to be superior than other. In analog 13 the two hydroxy groups are present at meta-para position while in analog 12 the two hydroxy groups are present at ortho-para positions, in analog 14 the two hydroxy groups are present at ortho-meta positions, and in analog 15 the two hydroxy groups are present at ortho-meta positions. The little bit difference in the activity of these analogs may be due to the difference in position of the substituents on the phenyl part.
\n
Similarly, effect of substituent position was also observed in other analogs such as 4, 5, and 6 having fluoro group. If we compare analog four, a ortho analog, having IC50 value 9.20 ± 0.30 μM with analog five, a meta analog, and six, a para analog having IC50 values 37.01 ± 0.70 and 26.30 ± 0.50 μM, respectively. In analog four the fluoro group is present at ortho position while in analog five the floro group is present at meta position and in analog six the floro group is present at para position. These three analogs demonstrated minute differences in their activity possibly due to the difference in the position of the substituents of the phenyl section. This was also observed in monohydroxy analogs. From these findings, we concluded that the factors that influence the inhibitory potentials of these analogs include the nature, position, and the number of substituents.
\n
\n
\n
3.3. Docking studies
\n
Molecular docking is a useful tool to obtain data on binding mode and to validate experimental results of active derivatives within the active site of β-D-glucuronidase. By using X-ray crystal structure of the human β-glucuronidase enzyme at 2.6 Å resolution (PDB ID: 1BHG) [19], it can be used to identify predict the binding modes involved in the inhibition activity.
\n
Utilizing docking approach, we identified the stable binding mode of six most active compounds (8, 12–15, and 23) that was further used in characterizing their inhibitory activity. Compounds with the most stable binding conformation suggest to strongly alignment to the core of β-glucuronidase. In Figure 4 shows that the quinolone moiety of these active compounds are oriented toward the active pocket and share some common interaction with catalytically important amino acids such as Glu450, Glu541, and Tyr504.
\n
Figure 4.
Active compounds aligned well into the binding cavity of β-glucuronidase enzyme.
\n
We predict that the hydrogen bonding interaction between the hydroxyl at C-4 of quinoline moiety and Glu451 plays a vital role. According to the docking result compound 13 (Figure 5), was found to be to most active compound in this series, because of the hydroxyl (OH) at C-4 involved in hydrogen bonding with Oε2 of Glu451 side chain (1.99 Å). The complex is stabilized by π-donor hydrogen bond formation between the benzene ring on quinoline moiety and with hydroxyl (OH) of Tyr508 (3.73 Å). Two interactions were detected in hydrazone linkage between carboxamide and the surrounding residues. The hydrazone carbonyl (C=O) oxygen linked by a hydrogen bonding with the nitrogen on the backbone of Tyr504 (2.77 Å), another hydrogen bond forms between the NH group and the oxygen on side chain of Asn484 with a bond length of 3.10 Å. The two hydroxyls on the benzylidene moiety at meta positions also, involved in hydrogen bonds with indole nitrogen at Trp528 backbone having a distance of 2.11 and 1.99 Å, respectively.
\n
Figure 5.
Best binding position of compound 13 in active pocket of β-glucuronidase enzyme.
\n
Compound 15 showed that hydroxyl (OH) at C-4 of quinoline moiety for compound formed hydrogen bonding with Oε2 of Glu451 side chain at a longer distance (2.24 Å) as compared to previous compound (Figure 6). In this compound the quinoline benzene rings on forms a π-donor hydrogen bond with hydroxyl (OH) of Tyr508 at (3.96 Å). It was also observed that hydrazone linkage was oxygen of carbonyl (C=O) interacts with side chain of Tyr504 through a hydrogen bond at a distance of 2.80 Å. Both form hydrogen bonds were formed between hydroxyls at ortho and meta position on the benzylidene moiety and nitrogen of indole backbone of Tyr508 at a distance of 2.19 and 1.99 Å, respectively. Compound 15 was found to be a second most active inhibitor.
\n
Figure 6.
Binding positions of compound 15 in an active pocket of the β-glucuronidase enzyme.
\n
In third most active compound 12 (Figure 7), it was observed that hydroxyl (OH) at Carbon no 4 exhibited hydrogen bonding with Oε2 of Glu451 side chain with a distance of 2.11 Å. On the other hand we noted that a more stable π-donor hydrogen bond with hydroxyl (OH) of Tyr508 at (3.77 Å) and benzene ring on quinoline moiety when compared with derivative 14. Docking studies also showed the hydrazone linkage interaction of oxygen of carbonyl (C=O) with side chain of Tyr504 through a hydrogen bond with length of 2.99 Å. There is also a hydrogen bonding of hydroxyl at ortho position on the benzylidene with the oxygen of Asn502 (1.87 Å), while the other another hydrogen bonding of hydroxyl at para position on the benzylidene with the nitrogen of indole backbone of Trp528 (1.89 Å).
\n
Figure 7.
Binding position of compound 12 in an active pocket of the β-glucuronidase enzyme.
\n
\n
\n
\n
4. Morpholine hydrazone scaffold: synthesis, anticancer activity, and docking studies
\n
Cancer is a broad term to describe a disease that characterized by the uncontrolled proliferation of cells resulting from the disruption or dysfunction of regulatory signaling pathways that are normally under tight control [20, 21]. In modern life, cancer is one of the big health killers. According to the American Association for cancer research (AACR) cancer progress report 2013, it expected that 580,350 Americans would die from the various type of cancer in the same year. Luckily, ultimate evolution has made against cancer. Approximately, from 1990 to 2012 almost 1,024,400 lives saved [22].
\n
Currently chemotherapy is an ultimate clinic treatment to repel cancer [23]. Cisplatin drug has been commonly used in cancer treatment for decades [24, 25]. Though, its clinical value tends to be inadequate by the abrupt increase of drug resistance or new side effects [26]. Consequently, the exploration of unusual chemotherapeutic agents has sparked the great attention of scientists from varied disciplines.
\n
The morpholine scaffold has been found to be an outstanding pharmacophore in medicinal chemistry and a number of molecules having morpholine skeleton are the clinically approved drugs [27]. N-substituted morpholines are used in the treatment of inflammatory diseases, such as migraine and asthma [28]. Morpholines derivatives have reported to possess activity such as platelet aggregation inhibitors, anti-eme-tics, and bronchodilators [29]. Morpholine analogs establish a new antifungal chemical entity not allied with other presently available medications with anti-fungal potential. The benefit in synthesizing morpholine analogs resides in the fact that these molecules offer chlorohydrates that are water soluble for pharmacological assays [30, 31].
\n
Recently, we have reported synthesis, characterization, anti-cancer activity, and molecular docking studies of morpholine derivatives [32]. A small series of morpholine hydrazones synthesized by treating 5-morpholinothiophene-2-carbaldehyde with different aryl hydrazides to form morpholine hydrazones scaffold (1–17) (Table 4). The in vitro anti-cancer potential of all these compounds were checked against human cancer cell lines such as HepG2 (Human hepatocellular liver carcinoma) and MCF-7 (Human breast adenocarcinoma). Analogs 13 had similar substantial cytotoxic effects toward HepG2 with IC50 value 6.31 ± 1.03 μmol/L when compared with the standard doxorubicin (IC50 value 6.00 ± 0.80 μmol/L); while compounds five, eight, and nine showed potent cytotoxicity against MCF-7 with IC50 value 7.08 ± 0.42, 1.26 ± 0.34, and 11.22 ± 0.22 μmol/L, respectively, when compared with the standard Tamoxifen (IC50 = 11.00 ± 0.40 μmol/L). Molecular docking studies also performed to understand the binding interaction.
\n
Table 4.
Various analogs of morpholine.
\n
\n
4.1. In vitro anti-cancer activity
\n
All synthesized analogs (1–17) were screened against two human cancer cell lines, human breast carcinoma (MCF-7) and human liver carcinoma (HepG2). The potentials of these analogs calculated in IC50 value shown in Table 5. Among the series 10 compounds showed potential against HepG2 and six compounds showed potential against MCF-7.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
S. No.
\n
HepG2
\n
MCF-7
\n
S. No.
\n
HepG2
\n
MCF-7
\n
\n\n\n
\n
2
\n
—
\n
30.0 ± 1.00
\n
9
\n
40.0 ± 0.93
\n
11.22 ± 0.22
\n
\n
\n
4
\n
7.94 ± 7.94
\n
—
\n
11
\n
19.95 ± 1.31
\n
41.67 ± 1.62
\n
\n
\n
5
\n
19.95 ± 0.63
\n
7.08 ± 0.42
\n
12
\n
31.0 ± 2.26
\n
—
\n
\n
\n
6
\n
12.59 ± 1.22
\n
—
\n
13
\n
6.31 ± 1.03
\n
—
\n
\n
\n
7
\n
20.0 ± 0.32
\n
14.13 ± 1.42
\n
14
\n
56.23 ± 0.56
\n
—
\n
\n
\n
8
\n
—
\n
1.26 ± 0.34
\n
15
\n
15.85 ± 0.82
\n
—
\n
\n
\n
Doxorubicin
\n
6.00 ± 0.80
\n
—
\n
\n
\n
\n
\n
\n
Tamoxifen
\n
—
\n
11.00 ± 0.40
\n
\n
\n
\n
\n
\n
Cisplatin
\n
12.00 ± 0.33
\n
15.00 ± 0.80
\n
\n
\n
\n
\n\n
Table 5.
Anti-cancer activity data (IC50 values in μmol/L) of morpholine derivatives (1–17).
\n
Among them compound eight was found to be the excellent inhibitor against MCF-7 with IC50 value 1.26 ± 0.34 μmol/L, which is more potent than the standard inhibitor Tamoxifen (IC50 = 11.00 ± 0.40 μmol/L). Secondly, the compound five was found to be more potent with IC50 value 7.08 ± 0.42 μmol/L almost two fold better than the standard. The analogs such as two, seven, nine, and 11 also showed potent inhibition for this cell line, while remaining analogs found to be completely in active.
\n
Compound 13 showed potent inhibition against HepG2 with IC50 value 6.31 ± 1.03 μmol/L when compared with the standard Doxorubicin (IC50 value 6.00 ± 0.80 μmol/L). Compound four and six were found second and third most active analogs among the series with IC50 value 7.94 ± 7.94 and 12.59 ± 1.22 μM, respectively. Other analogs such as five, seven, nine, 11, 12, 14, and 15 also showed good to moderate potential.
\n
Molecular docking studies were performed to investigate the binding mode of the active compounds.
\n
\n
4.1.1. Molecular docking analysis of morpholinothiophene hydrazone compounds
\n
The molecular docking procedure was widely used to predict the binding interaction of the compound in the binding pocket of the enzyme. The 3D crystal structure of the topoisomerase II enzyme (PDB id: 4FM9) was retrieved from the protein data bank. All the ions and water molecules removed and the hydrogen atoms added to the enzyme by the 3D protonation using the Molecular Operating Environment (MOE) software. The target enzymes were then energy minimized by the default parameters of the MOE for the stability and further assessment of the enzyme. The structures of the analogs of the morpholinothiophene hydrazone compounds built in MOE and energy minimized using the MMFF94x force field and gradient 0.05. The active site pocket of the enzyme found out by the site-finder implemented in the MOE software. The synthesized compounds docked into the active site of the target enzyme in MOE by the default parameters, that is, placement: Triangle matcher, Rescoring, and London dG. For each ligand, 10 conformations generated. The top-ranked conformation of each compound used for further analysis.
\n
Molecular docking studies predicted the proper orientation of the compound five inside the binding pocket of topoisomerase II enzyme. From the docking conformation of this active compound, we have observed a docking score of (−11.4975), which correlates well to the biological activities (IC50 = 19.95 ± 0.63 μmol/L in HepG2 and 7.08 ± 0.42 μmol/L in MCF-7 cell lines). The compound was observed making two interactions with active residues of the active site pocket of the enzyme. The oxygen atom of the morpholine moiety of the compound formed side chain acceptor interaction with the Lys 990 residue of the binding pocket. Arg 929 was observed making the hydrogen bond with the –NH group of the hydrazine moiety of the ligand as shown in the Figure 8. The electro-negative nature of Cl, O, and S of the substituent moiety may increase the polarizability of the ligand by electrons withdrawing inductive effect resulting in the enhanced potency and interactions.
\n
Figure 8.
Docking conformations of compound five in the active site of topoisomerase II enzyme.
\n
\n
\n
\n
\n
5. Conclusion
\n
The molecular docking is now fully recognized and integrated in the research process. In the past the emergence of this new discipline had occasionally encountered some opposition here and there. At presents, the science is mature and there are a growing number of success stories that continuously expand the armory of drug research. Several considerations that can greatly improve the success and enrichment of true bioactive hit compounds are commonly overlooked at the initial stages of a molecular docking study. In this chapter, we tried to cover several of these considerations, including few examples, of molecular docking studies of natural and synthetic analogs of potent α-glucosidase inhibitors, β-glucuronidase inhibitors, and cytotoxicity from our own findings. These molecular studies were performed for different classes of bioactive compounds in order to understand the binding interaction of the active compounds. It was concluded that the nature, position as well as the number of substituents affects the inhibitory potential of these analogs.
\n
\n
Acknowledgments
\n
Sadia Sultan would like to acknowledge Universiti Teknologi MARA for the financial support under the reference number UiTM 600-IRMI/FRGS 5/3 (0119/2016), Ministry of Higher Education Malaysia. One of our author Gurmeet Kaur Surindar Singh would also like to acknowledge Universiti Teknologi MARA for the financial support under the reference number UiTM 600-IRMI/FRGS 5/3 (28/2015), Ministry of Higher Education Malaysia. We also would like to highlight and acknowledge our previous published research under reference 7, 18, and 32.
\n
\n',keywords:"docking studies, α-glucosidase inhibitors, cedryl acetate, quinoline, β-glucuronidase inhibitors, morpholine hydrazone",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/61097.pdf",chapterXML:"https://mts.intechopen.com/source/xml/61097.xml",downloadPdfUrl:"/chapter/pdf-download/61097",previewPdfUrl:"/chapter/pdf-preview/61097",totalDownloads:1583,totalViews:671,totalCrossrefCites:1,totalDimensionsCites:2,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:65,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"May 26th 2017",dateReviewed:"March 29th 2018",datePrePublished:null,datePublished:"July 11th 2018",dateFinished:"April 28th 2018",readingETA:"0",abstract:"Docking is a powerful approach to perform virtual screening on large library of compounds, rank the conformations using a scoring function, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. Using experimentally proven active compounds, detailed docking studies were performed to determine the mechanism of molecular interaction and its binding mode in the active site of the modeled yeast α-glucosidase and human intestinal maltase-glucoamylase. All active ligands were found to have greater binding affinity with the yeast α-glucosidase as compared to that of human homologs, intestinal, and pancreatic maltase, by an average value of ~−1.3 and ~−0.8 kcal/mol, respectively. Thirty quinoline derivatives have been synthesized and evaluated against β-glucuronidase inhibitory potential. Twenty-four analogs, which showed outstanding β-glucuronidase activity, have IC50 values ranging between 2.11 ± 0.05 and 46.14 ± 0.95 μM than standard D-saccharic acid 1,4-lactone (IC50 = 48.4 ± 1.25 μM). Structure activity relationship and the interaction of the active compounds and enzyme active site with the help of docking studies were established. In addition, Small series of morpholine hydrazones synthesized to form morpholine hydrazones scaffold. The in vitro anti-cancer potential of all these compounds were checked against human cancer cell lines such as HepG2 (Human hepatocellular liver carcinoma) and MCF-7 (Human breast adenocarcinoma). Molecular docking studies were also performed to understand the binding interaction.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/61097",risUrl:"/chapter/ris/61097",book:{id:"6365",slug:"molecular-docking"},signatures:"Sadia Sultan, Gurmeet Kaur Surindar Singh, Kamran Ashraf and\nMuhammad Ashraf",authors:[{id:"176737",title:"Dr.",name:"Sadia",middleName:null,surname:"Sultan",fullName:"Sadia Sultan",slug:"sadia-sultan",email:"nasajid12@gmail.com",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/176737/images/system/176737.png",institution:{name:"Universiti Teknologi MARA",institutionURL:null,country:{name:"Malaysia"}}},{id:"222831",title:"Dr.",name:"Kamran",middleName:null,surname:"Ashraf",fullName:"Kamran Ashraf",slug:"kamran-ashraf",email:"kamranashraf2@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"256881",title:"Dr.",name:"Gurmeet Kaur Surindar",middleName:null,surname:"Singh",fullName:"Gurmeet Kaur Surindar Singh",slug:"gurmeet-kaur-surindar-singh",email:"gurmeet9952@uitm.edu.my",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256881/images/system/256881.png",institution:{name:"Universiti Teknologi MARA",institutionURL:null,country:{name:"Malaysia"}}},{id:"398577",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ashraf",fullName:"Muhammad Ashraf",slug:"muhammad-ashraf",email:"muhammadashraf@bzu.edu.pk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Quantum mechanical calculations and molecular docking studies of α-glucosidase inhibitors",level:"1"},{id:"sec_2_2",title:"2.1. Cedrol, cedryl acetate: microbial transformed metabolites",level:"2"},{id:"sec_3_2",title:"2.2. α-Glucosidase inhibitory activity",level:"2"},{id:"sec_4_2",title:"2.3. Geometry optimization",level:"2"},{id:"sec_5_2",title:"2.4. Molecular docking studies",level:"2"},{id:"sec_7",title:"3. Molecular docking studies of novel quinoline derivatives as potent β-glucuronidase inhibitors",level:"1"},{id:"sec_7_2",title:"3.1. Novel quinoline derivatives as potent β-glucuronidase inhibitors",level:"2"},{id:"sec_8_2",title:"3.2. β-Glucorinadase inhibitory activity",level:"2"},{id:"sec_9_2",title:"3.3. Docking studies",level:"2"},{id:"sec_11",title:"4. Morpholine hydrazone scaffold: synthesis, anticancer activity, and docking studies",level:"1"},{id:"sec_11_2",title:"4.1. In vitro anti-cancer activity",level:"2"},{id:"sec_11_3",title:"4.1.1. Molecular docking analysis of morpholinothiophene hydrazone compounds",level:"3"},{id:"sec_14",title:"5. Conclusion",level:"1"},{id:"sec_15",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Meyer EF, Swanson MS, Williams JA. Pharmacology & Therapeutics. 2000;85:113-121\n'},{id:"B2",body:'Abraham DJ. Burger’s Medicinal Chemistry Drug Discovery. 6th ed. Vol. 12003. pp. 847-900\n'},{id:"B3",body:'Pili R, Chang J, Partis RA, Mueller RA, Chrest FJ. A. Passaniti Cancer. Research. 1995;55:2920\n'},{id:"B4",body:'Zitzmann N, Mehta AS, Carrouée S, et al. Proceedings of the National Academy of Sciences of the United States of America. 1999;96:11878\n'},{id:"B5",body:'Hitoshi S, Nagao M, Harada T, Nakajima Y, Tanimura-Inagaki K, et al. Journal of Diabetes Investigation. 2014;23:206-212\n'},{id:"B6",body:'Choudhary MI, Atif M, Shah SAA, Sultan S, Erum S, Khan SN, Atta-ur-Rahman. International Journal of Pharmaceutical Science. 2014;6:375-378\n'},{id:"B7",body:'Sultan S, Choudhary MI, Nahar Khan S, Fatima U, Ali RA, Atif M, Atta-ur-Rahman, Fatmi MQ. European Journal of Medicinal Chemistry. 2013;62:764-770\n'},{id:"B8",body:'Trott O, Olson AJ. Journal of Computational Chemistry. 2010;31:455-461\n'},{id:"B9",body:'Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Journal of Cheminformatics. 2012;4:17\n'},{id:"B10",body:'Allouche AR. Journal of Computational Chemistry. 2011;32:174-182\n'},{id:"B11",body:'Humphrey W, Dalke A, Schulten K. Journal of Molecular Graphics. 1996;14:33-38\n'},{id:"B12",body:'Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. Journal of Computational Chemistry. 2004;25:1605-1612\n'},{id:"B13",body:'Smith RV, Rosazza J. Journal of Pharmaceutical Sciences. 1975;64:1737-1759\n'},{id:"B14",body:'Wenzl P, Wong L, Kwang-won K, Jefferson RA. Molecular Biology and Evolution. 2005;22:308-316\n'},{id:"B15",body:'Basu C, Kausch AP, Chandlee JM. Biochemical and Biophysical Research Communications. 2004;320:7-10\n'},{id:"B16",body:'Kuroyama H, Tsutsui N, Hashimoto Y, Tsumuraya Y. Carbohydrate Research. 2001;333:27-39\n'},{id:"B17",body:'Khan KM, Rahim F, Halim SA, Taha M, Khan M, Perveen S, Haq Z, Mesaik MA, Choudhary MI. Bioorganic & Medicinal Chemistry. 2011;19:4288\n'},{id:"B18",body:'Taha M, Sultan S, Nuzar HA, Imran S, Ismail NH, Rahim F, Ullah H. Bioorganic and Medicinal Chemistry. 2016;4:3596\n'},{id:"B19",body:'Taha M, Ismail NH, Imran S, Ali M, Jamil W, Uddin N, Kashif SM. RSC Advances. 2016;6:3276\n'},{id:"B20",body:'Chen WS, Ou WZ, Wang LQ, et al. Dalton Transactions. 2013;42:15678-15686\n'},{id:"B21",body:'Rahman FU, Ali A, Guo R, et al. Dalton Transactions. 2015;44:2166-2175\n'},{id:"B22",body:'Sawyers CL, Abate-Shen C, Anderson KC, et al. AACR cancer progress report 2013. Clinical Cancer Research. 2013;19:S1-S98\n'},{id:"B23",body:'Robert NJ, Diéras V, Glaspy J, et al. Journal of Clinical Oncology. 2011;29:1252-1260\n'},{id:"B24",body:'Rosenberg B, Van Camp L, Krigas T. Nature. 1965;205:698-699\n'},{id:"B25",body:'Sak A, Grehl S, Engelhard M, et al. Clinical Cancer Research. 2009;15:2927-2934\n'},{id:"B26",body:'Galluzzi L, Senovilla L, Vitale I, et al. Oncogene. 2012;31:1869-1883\n'},{id:"B27",body:'Andrs M, Korabecny J, Jun D, et al. Journal of Medicinal Chemistry. 2015;58:41-71\n'},{id:"B28",body:'Dugave C, Demange L. Chemical Reviews. 2003;103:2475-2532\n'},{id:"B29",body:'Arrieta A, Oteagui D, Zubia A, et al. The Journal of Organic Chemistry. 2007;72:4313-4322\n'},{id:"B30",body:'Polak A. Annals of New York Academy of Sciences. 1988;554:221-228\n'},{id:"B31",body:'Kerkenaar A. Prous Science Publ., 1987;1:523-542. R.G.M.P\n'},{id:"B32",body:'Taha M, Shah SAA, Afifi M, Sultan S, Ismail NH. Chinese Chemical Letters. 2017;28:607-611\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Sadia Sultan",address:"sadiasultan301@yahoo.com, drsadia@puncakalam.uitm.edu.my",affiliation:'
Faculty of Pharmacy, Universiti Teknologi MARA, Puncak Alam Campus, Malaysia
Atta-ur-Rahman Institute for Natural Products Discovery (AuRIns), Universiti Teknologi MARA, Puncak Alam Campus, Malaysia
Pharmaceutical and Life Sciences Core, Universiti Teknologi MARA, Shah Alam, Malaysia
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1. Introduction
Gender disparities in STEM enrollment in college continue to receive a significant amount of attention [1]. Research investigating reasons for this disparity in enrollment by gender highlights three consistent themes: a) females who are less academically prepared in math and sciences than males are less likely to enroll in STEM; b) despite comparable academic preparation, females, on average, enroll in STEM majors in smaller percentages than males, and c) females’ participation varies by major, with some majors, such as biology seeing a higher proportion of females than in math intensive fields (MI STEM) such as engineering or computer science [2, 3, 4, 5]. Despite the abundance of research on this topic, there lacks a more detailed yet cohesive look at the interrelationships among high school academic preparation, ACT scores, and STEM enrollment.
This study utilized hierarchical agglomerative clustering to analyze high school and college data from a cohort (n = 3104) of students at a large, public research institution in the Midwest. Students entering this institution tend to arrive with a broad academic background and varying levels of readiness to pursue a STEM degree. Students receive guidance from professional academic advising staff and faculty about course enrollment and academic trajectories. Frequently these decisions are made by considering one or two individual data points such as a students’ GPA or ACT score, yet research in this area consistently demonstrates that it is a combination of several of these factors that more accurately represents students’ preparedness and ability to succeed [6, 7, 8]. By clustering, our data analysis will provide more nuanced insights than traditional statistical analyses. Traditional analyses tend to focus on averages and distributions of individual student characteristics across all students. Clustering algorithms seek to identify natural groupings (clusters) of students such that students within a cluster are academically more similar to each other regarding their pre-collegiate training than they are to students from any other cluster. Thus, clustering better accounts for the interrelationships of several factors and offers much more robust information than the more common approach to examining individual variables.
As it relates to our study, we were interested in the potential for this technique to provide a more in-depth understanding of female students’ enrollment in STEM based on several factors of academic preparation. This approach has been used to study gender inequality in the STEM workforce (see, for example, [9]); we apply a similar method to examine gender disparities in STEM choice.
We begin by investigating the potential of identifying unique groups of students using hierarchical agglomerative clustering. We then examine enrollment in STEM by gender and clusters. The following questions guide our analyses:
What are pre-collegiate grades, academic rank, and academic mathematical and science courses, of males and females in NonSTEM, math-intensive STEM (MISTEM), and other STEM (OSTEM)?
Can hierarchical agglomerative clustering meaningfully identify unique groups of students based on precollege student characteristics? If yes, do gender differences in academic background exist?
Given cluster membership, are there gender differences in enrollment into STEM, specifically into MISTEM and OSTEM majors?
The results of our study have implications for secondary and postsecondary education. A more robust understanding of the interrelationships among variables that contribute to enrollment in STEM areas can be used to develop strategies that can enhance STEM enrollment. Postsecondary institutions can gain a better and more sophisticated understanding of the academic readiness of a cohort of incoming students. This information can be used strategically by institutions and departments to tailor course offerings to the immediate academic needs of students allowing for possibly critical adjustments in the present time. This approach can also be used to better understand not only enrollment, but also retention and completion of STEM degrees. Further, using the clusters to measure the pursuit of STEM degrees, retention, and completion of STEM degrees permits a novel use of clusters as a statistical predictive model in STEM education. Our data analysis approach is innovative and differs from traditional statistical analyses by using hierarchical agglomerative clustering that allows students with similar characteristics to be grouped together. Through the use of clustering, this study more closely examines gender differences in academic preparation, STEM interest, and enrollment in STEM.
2. Literature review
Females have been narrowing the gaps in math and science achievement and have seen more participation in STEM enrollment and careers [10]. However, the rate of participation is still drastically disproportionate, with only 27% of all STEM careers being occupied by women [11]. These statistics also vary based on major. For example, women represent over half of all bachelor’s degree recipients in biology but are significantly underrepresented in physical sciences, engineering, mathematics, and computer sciences (MISTEM) [12, 13, 14]. Even at selective institutions with a large pool of interested students willing to enroll, females represent between 15 and 28% for Bachelor’s degrees and only 13–20% for Ph.D.s in math departments [15]. We drew upon past literature to better understand differences in STEM and guide our selection of variables.
2.1 Pre-collegiate preparation and STEM enrollment
According to Weeden, Gelbgiser, and Morgan [16], between 19% and 32% of the gender gap for STEM degree completion can be attributed to the gender gap in STEM career interest in high school. Additionally, only 13% of female high school graduates expressed an intent to pursue a STEM career compared to 26% of their male counterparts [16]. This gap in interest as early as high school indicates that the STEM career gap is not solely caused by attrition in college or women exiting STEM careers post-graduation alone.
In high school, females consistently outperform males in their core classes, including math and science [17, 18, 19]. Despite earning high grades, females do not perform quite as well on high stakes standardized tests in math and science, scoring 0.7, 0.2, and 0.4 fewer points than males on the math, science, and STEM portions of the ACTs, respectively [20]. In spite of earning better grades in math and science, course selection among males and females shows some discrepancies. For example, female graduates took roughly the same number of advanced math courses as their male counterparts, experiencing an overrepresentation only in Algebra II. Science, however, shows more variations, with ten percent more females taking Advanced Biology, about six percent more females taking Chemistry, about 6 percent more males taking Physics, and males enrolling in engineering courses five times higher than females [21]. Furthermore, correlational research supports that participation in Advanced Placement (AP) STEM courses and STEM career interest are associated [22] and that students with high math abilities and exposure to rigorous courses were more likely to enroll in STEM majors [23].
GPA is positively correlated with the pursuit of a STEM major [24], and it is a better predictor for college success than the ACTs [25]. The rigor of math and science courses is a better predictor for enrollment in a STEM major than the number of courses alone [26]. Class rank, on the other hand, is more complex. When ranks are calculated by subject (i.e., math and reading) and communicated to students, ranking can have statistically significant effects on students’ career choices. For example, a study conducted in Ireland found that students ranked highly in math had a positive association with STEM career choice and a negative association with careers in the arts and social sciences, while those who were highly ranked in English had a positive association with arts and social sciences and a negative association with STEM careers [27]. A study performed in Florida found that high school class rank and GPA, which are higher for females, were the best predictors of collegiate GPA and the number of credits earned in college [28]. But, as detailed above, males and females experience similar pre-collegiate STEM preparation in many respects with small differences in math, science, and STEM test scores and some discrepancy in enrollment of advanced science courses. Despite these similarities, males are twice as likely to intend to declare a STEM major than females. A closer look at STEM enrollment is required.
2.2 STEM enrollment
In high school, females are less likely to be interested in STEM and more likely to lose interest over time [29]. Controlling for math achievement and aptitude, females are still less likely than males to be interested in STEM [30]. In fact, one of the best predictors for enrollment and persistence through a STEM major is an individual’s desire to pursue a STEM career in high school, with those expressing interest in high school completing degrees at three times the rate of those who do not express this interest in high school [31]. Among females who intend to major in a STEM field in college, nearly half of them switch majors to non-STEM fields compared with only a third of males [21].
That is not to say women are not enrolling in STEM majors; in fact, women earned 53% of STEM degrees (short of their 58% share of all degrees that would be proportional to their overall makeup of the workforce) [32]. However, there are significant disparities among the types of STEM degrees women choose to pursue. For example, women are overrepresented in health-related STEM careers with, 85% of Bachelor’s degrees being awarded to women, but they are awarded less than 45% in Mathematics and Physical Science and less than 25% in Engineering and Computer Science [32]. Because interest in STEM begins before students enroll in postsecondary education and gender gaps in STEM still persist, we consider new ways to understand reasons for these gaps based on high school preparation.
3. Methods
3.1 Data source and sample
Data for this study were provided by the institution’s Office of the Registrar at one large public, research-intensive institution. To ensure our research conformed to standards and guidelines of ethical research practice, we received approval from the Institutional Review Board at the study’s institution. Per written agreement with the Office of the Registrar, all students enrolled in an introductory level mathematics or statistics course in the Spring 12, Fall 12, or Spring 13 semester were eligible for the parent study. Students were given the opportunity to opt out of the study, and of 16,401 eligible students, 32 chose to opt out. We focused on these courses as they often serve as gatekeeper courses to a STEM degree.
Because we were interested in the relationship between high school preparation and enrollment in a STEM major, we focused only on first-semester, degree-seeking students who entered the institution directly from high school and were enrolled in an introductory level math course. We excluded students who transferred from another post-secondary institution because we did not have access to pre-college data for many of these students. Students who were classified as non-degree-seeking or international were also removed prior to the analysis as these students are likely to differ in their academic background or degree goal. Of the initial 3219 eligible students, we had complete data on the variables of primary interest for 3104 students.
Using the STEM Designated Degree Program List (2012 revised list) provided by the Department of Homeland Security, we categorized students into STEM and NonSTEM majors. We further split STEM majors into math-intensive STEM (MISTEM) and Other STEM (OSTEM) majors. For the purpose of this study, a STEM major was considered math-intensive if it required at least one semester of science or engineering calculus. This definition is similar to the definitions used by Ceci and Williams [33] and by Bressoud [34]. This differentiation served distinct purposes: (a) the gender gap has historically been more pronounced in MISTEM majors such as engineering, computer science, or physics, whereas fields like biology or chemistry (OSTEM) have increasingly grown the proportion of women to the extent that women are now in the majority of degree earners [35]; (b) definitions of STEM vary greatly and can range from more inclusive by considering fields such as psychology, dietetics majors or kinesiology as STEM fields to less inclusive lists, which consider mainly engineering, mathematics, physics, natural sciences, and computer sciences. Distinguishing MISTEM majors define majors represented in most STEM field definitions.
Our analysis included the following variables: gender, major, high school rank (HS Rank), grade point average (GPA), number of high school credits earned in mathematics courses, including algebra, geometry, trigonometry, and calculus, and credits earned in biology, chemistry, and physics, ACT composite, ACT English, and ACT Math scores. The ACT is a national standardized test commonly used in college admissions decisions.
3.2 Statistical methodology
Using student demographic characteristics and pre-college academic background variables, we conducted an agglomerative hierarchical cluster analysis. “Cluster analysis is a data-mining technique that allows researchers to cluster a set of observations into similar (homogeneous) groupings based on a set of features” [36]. It accounts for the different high school experiences and preparation with which students enter their first year of college and can provide a more complex description of students than a comparison on a variable-by-variable basis. Clustering students reduces the focus on mean comparisons, which captures a population’s average behavior, but less on how students compare at the individual level. Although cluster analysis has been used in a variety of academic settings, its use to investigate female enrollment discrepancies in STEM vs. non-STEM fields is novel. Cluster analysis has been used to develop classroom observation tools [36], reveal different learner profiles based on motivation, achievement, needs satisfaction, etc. [37], and the differences between females and males who succeed within higher technical education [38]. Using similar methods as these studies, we will compare clusters of similar students to determine any trends among factors such as gender, preparation, and STEM enrollment.
3.3 Data analysis
All statistical analyses were run in SAS/STAT software, Version 9.4 of the SAS system and RStudio Version 1.3.1073. To address the first research question, we calculated the means and standard deviations of each of the variables used in the cluster analysis by type of major (NonSTEM, MISTEM, OSTEM) and by gender. To address the second research question, we utilized PROC Cluster and Ward’s minimum-variance method [39]. Ward’s minimum-variance method is based on the total error sum of squares that arises by grouping observations into distinct clusters where the total sum of squares corresponds to the sum of the within-cluster sum of squares [40]. Merging a set of observations into a cluster can be considered a loss of information. Ward’s method seeks to minimize the loss of information from merging any two clusters at a given step in the clustering algorithm. That is, the two clusters whose merging will lead to the smallest increase in the total error sum of squares will be combined into a new cluster [40]. Initially, each student represents a single cluster. At each step of the algorithm, two existing clusters are merged until only one cluster remains. The number of clusters is unknown prior to the analysis and an appropriate cluster solution is typically based on a set of clustering criteria such as the cubic clustering criterion (CCC), the Pseudo-F, and Pseudo T2 statistic [41, 42].
In order to see if the gender disparity in STEM enrollment is associated with gender or merely high school preparation, we clustered students according to their high school science, mathematics, and standardized test score data. Taking calculus in high school is a strong predictor of STEM interest and success [43]. Therefore, we separated students into two groups prior to clustering: students with calculus in high school (Calc group) and students without (NonCalc group). We then ran a separate cluster analysis based on high school rank, ACT English, ACT Math, and the sum of high school science credits in biology, physics, and chemistry. We decided on an initial number of clusters in each group based on the CCC, Pseudo-F, and Pseudo-T2 clustering criteria. To address the final research question, we examined the proportion of males and females in each cluster that chose a major in NonSTEM and STEM. We then limited our sample only to those who chose STEM and examined the proportion of males and females in each cluster who chose MISTEM or OSTEM.
3.4 Limitations
We wish to acknowledge some methodological limitations. To be included in the sample, a student had to take a mathematics or statistics course during their first semester in college. We are therefore missing students who may have transferred credits into college or postponed taking a mathematics or statistics course in their first semester. Our sample also only included students who chose to major in STEM in their first semester. Additional research that examines students who may decide to major in STEM after their first semester would provide additional insights.
A cluster analysis using different variables would likely result in different clusters. For example, if we were to treat the numbers of biology credits, chemistry credits, and physics credits as separable variables rather than use their sum, clusters would likely form around differences between students with respect to the individual variables such as students with many biology credits versus students with few biology credits. We chose the sum of all science credits for two reasons. From a methodology point of view, the sum of science credits is more preferable as a variable because it has a greater range of values. Secondly, the choice of variables depends on the characteristics deemed meaningful to identify differences between students.
Additionally, we wish to acknowledge the limitations and ethical considerations of using quantitatively techniques to group students and subsequent interpretations of these efforts. Quantitatively analysis affords an opportunity to see patterns that may otherwise be unclear, yet this approach can also minimize nuances within clusters and overlook significant implications of variables that were not included. For example, our study focused on gender but did not consider variables such as socioeconomic status, nationality or race, or secondary school quality. Our results also should not be used to imply causality or judgment [44, 45]– we seek to understand possible associations between variables but cannot conclude that one set of patterns causes a specific outcome or that one is qualitatively better than others.
4. Results
4.1 Research question 1. what are advanced placement scores, pre-collegiate grades, academic rank, and academic mathematics and science courses, of males and females in NonSTEM, math-intensive stem (MISTEM), and other stem (OSTEM)
Across all fields, NonSTEM, MISTEM, and OSTEM, female students are equally prepared as men in the mathematics and sciences courses (see Table 1). Females have consistently higher high school ranks and GPA scores compared to their male peers, which is consistent with the results of the American Association of University Women Educational Foundation [17], Degol et al., [18], and Voyer and Voyer [19]. Females who enroll in MISTEM also score on average as well as their male peers and slightly outperform them on the English ACT placement test. Men enrolling in NonSTEM and OSTEM majors show a slight advantage on the Math ACT placement test, which is consistent with prior research [20].
Variable
NonSTEM NF=537, NM=542
MISTEM NF=273, NM=1385
OSTEM NF=216, NM=151
High School
Female
Male
Female
Male
Female
Male
GPA
3.62 (0.36)
3.38 (0.42)
3.81 (0.38)
3.63 (0.4)
3.65 (0.34)
3.5 (0.4)
Rank
77 (14.89)
68.1 (16.23)
83.9 (14.36)
76.6 (16.17)
77.9 (14.46)
72.3 (17.17)
Calculus Cr
0.4 (0.8)
0.5 (0.91)
1.3 (1.17)
1.2 (1.18)
0.6 (0.98)
0.5 (0.89)
Algebra Cr
4 (0.26)
4 (0.28)
4 (0.35)
4 (0.21)
4 (0.26)
4 (0.27)
Geometry Cr
2.6 (0.51)
2.6 (0.5)
2.7 (0.46)
2.8 (0.45)
2.6 (0.49)
2.6 (0.51)
Trigonometry Cr
1 (0.68)
1 (0.66)
1.2 (0.57)
1.2 (0.6)
1 (0.63)
1 (0.67)
Statistics Cr
0.3 (0.64)
0.4 (0.78)
0.4 (0.77)
0.3 (0.69)
0.3 (0.67)
0.2 (0.57)
Adv. Math Cr
1.3 (1.05)
1.2 (0.99)
1.8 (0.95)
1.7 (0.90)
1.3 (1.31)
1.3 (1.12)
Physics Cr
1 (1.03)
1.3 (1.05)
1.9 (1.09)
2 (1.05)
1.1 (0.99)
1.5 (1.01)
Biology Cr
2.7 (1.07)
2.5 (0.95)
2.6 (1.19)
2.4 (0.91)
3.1 (1.36)
2.8 (1.32)
Chemistry Cr
2.1 (0.63)
2 (0.71)
2.4 (1.03)
2.3 (0.95)
2.2 (0.69)
2.1 (0.94)
Science Cr
5.8 (1.6)
5.9 (1.47)
6.9 (2.01)
6.7 (1.66)
6.4 (1.83)
6.4 (1.92)
ACT
Math
23.3 (3.71)
24.3 (3.68)
27.3 (4.27)
28.1 (3.89)
23.3 (4.1)
24.8 (4.12)
English
24 (4.56)
22.9 (4.28)
26.7 (5.06)
25.4 (4.6)
23.6 (4.57)
23.4 (4.8)
Composite
23.8 (3.48)
24.1 (3.47)
26.9 (4.01)
26.9 (3.63)
23.9 (3.77)
24.6 (3.62)
Table 1.
High School mathematical and science background of incoming students by enrollment into MISTEM, OSTEM and NonSTEM. All values are rounded to the nearest decimal place.
4.2 Research question 2: can hierarchical agglomerative clustering meaningfully identify unique groups of students based on pre-college student characteristics? If yes, do gender differences in academic background exist?
Based on the clustering criteria, four or five clusters were reasonable choices for students with and without calculus. To arrive at the most meaningful number of clusters for each group, we plotted each clustering variable using side-by-side boxplots (see Figure 1). Each boxplot shows the distribution of the variables for all students in the respective cluster, while the horizontal line inside the box represents the median value observed for these students. We based our decision on the final number of clusters for each group of students on what we considered to be meaningful differences in the distribution and median value for each cluster in the context of our research questions [46]. Due to the agglomerative nature of the clustering procedure, a solution consisting of four clusters arises from the merging of the two closest clusters in the solution consisting of five clusters while the remaining clusters remain unchanged. Thus, we will decide, for example, on four clusters if merging the two closest clusters in the five-cluster solution does not result in a sufficiently large loss of information but changing from four to three clusters would. In essence, we are looking for a solution that is both inclusive and parsimonious. For this reason, we included the three- and six-cluster solutions in the decision-making process. For simplicity, we discuss the different cluster solutions in terms of one cluster being split as opposed to two clusters being merged.
Figure 1.
Finalized cluster solution.
4.2.1 Clustering for students with calculus
We begin with the interpretation of the three-cluster solution and describe which of the three clusters is divided in the transition from three clusters to four.
For students with calculus (N = 1280), a three-cluster solution identifies three types of students. A first cluster consists of 627 students with noticeably lower HS ranks, ACT English and ACT Math scores compared to the students in the remaining two clusters. Among the latter, the more academically prepared students, two groups emerge; a cluster with students who have substantially more science credits and a slightly better but noticeable ACT Math score (N = 335) compared to the students in the second group (N = 318). Both groups have comparable ACT English scores and HS Ranks.
The four-cluster solution arises from splitting the lower performing group of 627 students into two distinct groups based on differences in ACT scores and HS Ranks. Students in both groups have the lowest HS ranks out of all calculus students but the first group (N = 213) performs substantially better on the ACT English and ACT Math exams than the second group (N = 414). We deemed this split meaningful. A plausible interpretation could be that the second group (N = 414) consists of students who tend to underperform on standardized tests for a variety of reasons. Alternatively, the smaller group of students (N = 213) excels on standardized exams relative to their overall high school performance as reflected by high school rank. Due to the hierarchical nature of the clustering algorithm, the other two clusters remained unchanged.
In a five-cluster solution, the group of 414 students is broken up into students with slightly higher ACT scores, noticeably more science credits, and better HS ranks (N = 99) compared to the second group (N = 315). Although this distinction might be relevant, it was not relevant for our research questions, and we decided against this additional split; both groups of students maintained their relative, below-average performance on the ACT exams. Consequently, we determined four clusters to be the most appropriate number of clusters for students with calculus. For this reason, we did not consider the six-cluster solution.
4.2.2 Clustering students without calculus
For students without calculus (N = 1284), a three-cluster solution distinguishes three groups. Students in group 1 (N = 663) have overall the lowest high school ranks but perform otherwise similar to students in a second group (N = 423) with respect to the number of science credits taken in high school and scores on the ACT exams. The third group (N = 738) outperforms the first and second group in the number of science credits and on the ACT components but shares HS ranks similar to those in the group of 423 students.
In a four-cluster solution, the third group of 738 students is divided into two separate groups. The first group (N = 603) includes the best students with respect to HS rank and ACT English and Math scores, but students in this cluster tend to have taken fewer science credits. The remaining students (N = 135) have the most science credits among all students without calculus. Their ACT scores and HS ranks are, however, much lower compared to the top students, and they also tend to have lower HS ranks compared to the second group (N = 423) in the three-cluster solution.
To move from four clusters to five the group of students with below average ACT scores and lower HS ranks (N = 663) are divided. This split reflects the same pattern we saw in the group of students with calculus. The 663 students are separated into a group of students (N = 258) who have overall the low high school ranks but who score better on the ACT English and Math placement tests and a second group, whose students have higher high school ranks but do not perform below average on the placement tests. Because we already saw a similar distinction among the calculus students, we consider the five-cluster solution meaningful and retain it over the four-cluster solution.
The six-cluster solution focused on the cluster previously consisting of the overall most prepared students (N = 603). These students are broken up into two clusters. The smaller of the two clusters retains the best students overall (N = 228), while students in the second cluster (N = 375) perform slightly worse than the top students, they still do better than the cluster of N = 423 students. Because both groups still outperform any of the remaining clusters overall, we decided against this additional split and retain the 5-cluster solution as the final number of clusters. A description of the finalized cluster solution is given in Table 2.
Cluster
Name
N
Cluster description
1
Calc. Strong, Less Science
318
Highest ACT English and HS Rank, Second highest ACT Math scores, below average (less than 6) science credits
2
Calc, Average
414
Typically, about average, showing slightly above average HS Rank and science credits but tend to fall short of average ACT English and Math scores
3
Calc, Strong Overall
335
Best students overall with all students having taken above average number of science credits
4
Calc, Low HS Rank
213
Students with far below average HS Ranks that have above average ACT English and Math scores
5
No Calc, Average
423
Above average HS Rank, very few science credits, below average ACT English and Math scores
6
No Calc, Low ACTs
405
About 50% of students have HS rank 1 standard deviation below average, lowest ACT English and Math scores
7
No Calc, Strong Overall
603
Strongest performers out of all non-calculus groups but students do not score as high as two top calculus clusters
8
No Calc, Low HS Rank
258
Lowest HS Rank, below average science credits but almost all students have ACT English and Math scores within 1 standard deviation below average
9
No Calc, Average, More Science
135
Worst performers on ACT English and Math, low HS Rank but take many science credits, credits comparable to the top cluster (Cluster 3)
Table 2.
Description of clusters and respective sample sizes.
4.2.3 Gender distribution across clusters
Our analysis revealed that we could use clustering to find meaningful differences in groups. We then examined the proportion of female students in each cluster. Out of 3104 students, 1026 are female representing 33% of the students in the sample. Assuming that there are no systematic differences in the academic background between females and males, we expect to see about 33% of each cluster to be female students. Figure 2 shows a visualization of the proportion of females in each cluster. In Cluster 7 (No Calculus, Low ACTs), the proportion is close to the target value of 33% with 34.3%; females are slightly below 33% in Clusters 1–3 (Calculus, Strong Less Science – Strong Overall). On the other hand, females are overrepresented in three out of the five non-calculus clusters and underrepresented in Clusters 4 (Calculus, low HS rank) and 8 (No Calculus, low HS rank). Although these students had lower HS ranks they still scored well on the ACT English and Math tests relative to students in clusters that proportionally contain more female students (Clusters 5, 6, and 9). Students in Cluster 8 (No Calculus, Strong Overall) are very similar to students in Clusters 1–3 when it comes to high school rank and performance on the ACT, except they did not have calculus in high school. The proportion of female students in a calculus cluster being average or below average shows that proportionally, fewer female students take calculus in high school (33%) than their male peers (45%).
Figure 2.
Distribution of gender across clusters.
4.3 Research question 3. Given cluster membership, are there gender differences in enrollment into STEM, specifically into MISTEM, and OSTEM majors?
In the overall sample, 48% of females chose to major in STEM and 74% of males chose to major in STEM. Of those who majored in STEM, 90% of males and 56% of females enrolled in MISTEM.
Using the cluster solution identified in Table 2, we examine the proportion of female students by cluster. As mentioned previously, if differences in enrollment by gender are within natural variation, we can expect about one-third of the students to be female in each cluster.
Even though the proportion of females in Clusters 1–3 and 7 are similar and close to average (see Figure 2), the enrollment in STEM majors is strikingly different. For example, females with a calculus background are consistently more likely than females without a calculus background to choose a STEM major. This is evident when comparing Cluster 7 to Clusters 1–4: female students with no calculus background are less likely than male students to enter STEM.
Within the same cluster, thus with similar academic background, a smaller proportion of female students enroll in STEM than male students (see Figure 3). When we compare across gender and clusters, we see differences in this gap. For example, for Cluster 3, there is less than a 13% difference between males and females (94% vs. 81%); however, in Cluster 7, this gap increases to 31%.
Figure 3.
Enrollment into STEM by gender.
The lower enrollment rate for females is especially evident for students in the NoCalc, Strong Overall cluster. We mentioned before that this NoCalc cluster is similar in background to the calculus clusters, the only difference being that the students did not have calculus in high school. Males in this cluster, however, enroll in STEM at a similar proportion to their female peers who did have calculus.
4.3.1 MISTEM and OSTEM
We then restricted ourselves to students enrolled in STEM in each cluster. Among those, Figure 4 shows that a higher proportion of male STEM students choose MISTEM than female STEM students. The trend is very apparent in the NoCalc clusters but is also present in the calculus clusters. Another way of saying this is that more female STEM students choose OSTEM than male students, especially the NoCalc Students. There are two interesting clusters to contrast: the NoCalc, Strong Overall and NoCalc, Average More Science. They have the same proportion of females enrolling into STEM; however, a much higher proportion of females in NoCalc, Strong Overall choose MISTEM than the NoCalc, Average More Science cluster.
Figure 4.
Proportion of STEM students enrolled in MISTEM by gender.
Most male students in STEM, independently of their cluster membership, chose to go into MISTEM majors. Female students with a calculus background are more likely to go into MISTEM than female students without calculus in high school. The percentage of females in MISTEM is high for the NoCalc, Low ACT cluster, but because we limited ourselves to females who chose STEM within the cluster and the females are underrepresented in this cluster, we find an even smaller proportion of females chose STEM.
5. Discussion and implications
Despite significant efforts to minimize the gender gap in STEM, differences in interest and enrollment between men and women still exist. Ensuring a globally skilled workforce that meets the needs of the 21st century requires a post-secondary education in STEM fields, yet the interest to pursue STEM begins prior to enrolling in college. Therefore, efforts examining pre-collegiate preparation and STEM enrollment are critical.
We first examined the individual pre-collegiate variables of males and females. Similar to other research in this area [17, 18, 19], we found that overall, females and males have similar preparation although males are more likely to take calculus than females [16]. Examining these individual variables may lead to an assumption that these similarities in preparation are consistent across individuals and groups. However, by employing a more advanced statistical technique such as cluster analysis, we notice that when accounting for several pre-collegiate factors simultaneously uncovers (or reveals) marked differences in enrollment patterns within and across gender. For example, if we were to investigate just one variable, such as ACT scores, we get a different picture than when we combine variables such as standardized test scores with rank and/or GPA. For example, in our findings the combination of other factors such as enrollment in science courses, standardized test scores and high school rank results in variations in enrollment in STEM. Relatedly, the grouping of variables reveals differences in STEM participation more broadly and in the types of STEM, i.e., MISTEM and OSTEM.
Females consisted of 33% of our overall sample, yet, their representation in each cluster varies from 12% in Cluster 4 to 49% in Cluster 5 (Figure 2). Females never reached 33% in any of the calculus clusters. Enrollment in STEM and type of STEM (MISTEM, OSTEM) also varies by cluster. The results in Figure 3 add/continue the pattern of female underrepresentation in STEM. Although males and females follow overall a similar trend across clusters, female enrollment in STEM is consistently below male enrollment, confirming previous results that females chose STEM at lower rates than their male peers even when they are equally prepared [3]. The consistency across clusters also confirms that this holds true for all levels of preparedness in terms of academic high school background. Patterns for males and females, however, were not entirely parallel and equal. While Cluster 3 shows the highest, and Clusters 5 and 8 the lowest enrollment in STEM for both genders differences can be found in Clusters 6 and 7. For men there is little difference between Clusters 5 and 6 and larger differences between Clusters 6 and 7. For females, these percentages increase steadily from Cluster 5 to Cluster 7. Cluster 7 tells us that, despite being strong students, females who do not have Calculus in high school are much less likely to choose STEM than male students with the same background.
Further differences exist in enrollment by type of STEM. Figure 4 shows percentages for enrollment into MISTEM above 60% for both genders in all calculus clusters. But females in non-calculus clusters show much more variation. Although females in Clusters 5 and 8 are equally unlikely to enroll into STEM having the lowest enrollment rate overall, there is a clear difference in the rate at which females in Cluster 8 enroll into MISTEM compared to Cluster 5. Interestingly, females in Cluster 8 enroll into MISTEM in rates comparable to females in any of the calculus clusters. Female students in Clusters 5, 6 and 9 are more likely to enroll into OSTEM than MISTEM while 75% or more of male students choosing STEM still enroll into MISTEM in those clusters. Overall, male students in STEM overwhelmingly choose MISTEM ranging between 73% in Cluster 6 to 94% in Cluster 8.
Our analysis limits our ability to understand why these differences occur. However, past research may lend some insights. For example, females are more likely to possess both high verbal and high math skills, whereas males are more likely to possess solely high math skills [47]. Due to the discrepancy between math and verbal skills, males tend to choose STEM careers, whereas females, who have a choice between verbal and math-centric careers, tend to choose non-STEM-related fields, opting instead for challenging fields that are more applied and practical rather than theoretical [46]. Of course, there are also work and lifestyle factors to consider; women are looking for work-family balance and value it more highly than men [48]. In addition to lifestyle values, there are also differences between social and moral career preferences with women tending toward occupations with a social, community, or altruistic component and men tending toward careers that require working with objects [49].
Although our study cannot account for the reasons differences exist, the results have implications for research and practice. Our study illustrates a method that can be adopted by institutional leaders for use on their own campuses. Although this study was limited to one institution, we utilized commonly collected pre-collegiate data. Because of the availability of this data on most campuses, this study can be replicated in a variety of campus contexts. Institutions vary in their enrollment criteria and student populations; cluster analysis techniques afford the ability to select relevant variables and determine if unique groups emerge. Researchers have noted the importance of variables such as students’ race, ethnicity, and nationality and non-cognitive variables such as self-efficacy [50]. Future research could incorporate these additional variables.
Identifying these clusters is an important first step in a more comprehensive understanding of STEM interest and success. Once established, future efforts could examine the persistence and graduation rates of students in these clusters. The research on the relationship between individual measures of academic preparation and persistence and graduation in STEM has produced mixed results. Examining the combination of these measures through cluster analysis would lead to a more nuanced understanding of the role of academic preparation in STEM enrollment. For example, if there was a consistent relationship found between completing calculus and graduation in STEM, regardless of other factors, the availability and enrollment in calculus courses in high school should be encouraged.
Qualitative research methodologies could help address the questions of “why?” Individual interviews or focus groups with students in each cluster could be conducted to understand student choices in academic preparation or what aspects of their academic preparation contributed to their enrollment and success in STEM.
6. Conclusion
Minimizing the gender gap in STEM fields continues to be necessary to meet the needs of the global workforce. Academic preparation prior to enrolling in a post-secondary institution influences students’ intent to pursue STEM; yet research efforts that investigate this relationship often are limited by focusing on individual variables. Our study uses an advanced statistical technique - hierarchical agglomerative clustering - that considers multiple factors simultaneously. This technique groups students into distinct categories based on a combination of academic preparation measures and by doing so, paints a different picture of the relationship between academic preparation and STEM enrollment than simply examining individual variables. Subsequently, these inconsistencies reaffirm that narrowing the gap requires a multi-faceted approach that consider academic preparation and non-cognitive factors. In addition to its research significance, there are valuable practical implications from this work. We demonstrate how this technique can be applied to institutional data; thus, we provide a valuable tool that can be utilized in postsecondary institutions for postsecondary leaders to utilize in understanding enrollment patterns within their institutions. Summarily, our study contributes to both research and practice through its use of a robust yet accessible technique that can be widely applied to quantitative data to uncover unique patterns largely overlooked by other approaches.
Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. HRD 1036791.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"STEM, gender, cluster analysis, female, enrollment",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81138.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81138.xml",downloadPdfUrl:"/chapter/pdf-download/81138",previewPdfUrl:"/chapter/pdf-preview/81138",totalDownloads:25,totalViews:0,totalCrossrefCites:0,dateSubmitted:"January 17th 2022",dateReviewed:"January 26th 2022",datePrePublished:"April 7th 2022",datePublished:null,dateFinished:"April 7th 2022",readingETA:"0",abstract:"Despite efforts to close the gender gap in science, technology, engineering, and math (STEM), disparities still exist, especially in math intensive STEM (MISTEM) majors. Females and males receive similar academic preparation and overall, perform similarly, yet females continue to enroll in STEM majors less frequently than men. In examining academic preparation, most research considers performance measures individually, ignoring the possible interrelationships between these measures. We address this problem by using hierarchical agglomerative clustering – a statistical technique which allows for identifying groups (i.e., clusters) of students who are similar in multiple factors. We first apply this technique to readily available institutional data to determine if we could identify distinct groups. Results illustrated that it was possible to identify nine unique groups. We then examined differences in STEM enrollment by group and by gender. We found that the proportion of females differed by group, and the gap between males and females also varied by group. Overall, males enrolled in STEM at a higher proportion than females and did so regardless of the strength of their academic preparation. Our results provide a novel yet feasible approach to examining gender differences in STEM enrollment in postsecondary education.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81138",risUrl:"/chapter/ris/81138",signatures:"Ann M. Gansemer-Topf, Ulrike Genschel, Xuan Hien Nguyen, Jasmine Sourwine and Yuchen Wang",book:{id:"11279",type:"book",title:"Advances in Research in STEM Education",subtitle:null,fullTitle:"Advances in Research in STEM Education",slug:null,publishedDate:null,bookSignature:"Associate Prof. Michail Kalogiannakis and Dr. Maria Ampartzaki",coverURL:"https://cdn.intechopen.com/books/images_new/11279.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-730-4",printIsbn:"978-1-80355-729-8",pdfIsbn:"978-1-80355-731-1",isAvailableForWebshopOrdering:!0,editors:[{id:"260066",title:"Associate Prof.",name:"Michail",middleName:null,surname:"Kalogiannakis",slug:"michail-kalogiannakis",fullName:"Michail Kalogiannakis"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Literature review",level:"1"},{id:"sec_2_2",title:"2.1 Pre-collegiate preparation and STEM enrollment",level:"2"},{id:"sec_3_2",title:"2.2 STEM enrollment",level:"2"},{id:"sec_5",title:"3. Methods",level:"1"},{id:"sec_5_2",title:"3.1 Data source and sample",level:"2"},{id:"sec_6_2",title:"3.2 Statistical methodology",level:"2"},{id:"sec_7_2",title:"3.3 Data analysis",level:"2"},{id:"sec_8_2",title:"3.4 Limitations",level:"2"},{id:"sec_10",title:"4. Results",level:"1"},{id:"sec_10_2",title:"4.1 Research question 1. what are advanced placement scores, pre-collegiate grades, academic rank, and academic mathematics and science courses, of males and females in NonSTEM, math-intensive stem (MISTEM), and other stem (OSTEM)",level:"2"},{id:"sec_11_2",title:"4.2 Research question 2: can hierarchical agglomerative clustering meaningfully identify unique groups of students based on pre-college student characteristics? If yes, do gender differences in academic background exist?",level:"2"},{id:"sec_11_3",title:"4.2.1 Clustering for students with calculus",level:"3"},{id:"sec_12_3",title:"Table 2.",level:"3"},{id:"sec_13_3",title:"4.2.3 Gender distribution across clusters",level:"3"},{id:"sec_15_2",title:"4.3 Research question 3. Given cluster membership, are there gender differences in enrollment into STEM, specifically into MISTEM, and OSTEM majors?",level:"2"},{id:"sec_15_3",title:"4.3.1 MISTEM and OSTEM",level:"3"},{id:"sec_18",title:"5. Discussion and implications",level:"1"},{id:"sec_19",title:"6. Conclusion",level:"1"},{id:"sec_20",title:"Acknowledgments",level:"1"},{id:"sec_23",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'National Science Board. Science and Engineering Indicators 2020 [Internet]. 2020. Available from: https://ncses.nsf.gov/pubs/nsb20201'},{id:"B2",body:'Crisp G, Nora A, Taggart A. 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Available from: https://files.eric.ed.gov/fulltext/ED509653.pdf'},{id:"B44",body:'Petousi V, Sifaki E. Contextualising harm in the framework of research misconduct. Findings from discourse analysis of scientific publications. International Journal of Sustainable Development. 2020;23(3-4):149-174'},{id:"B45",body:'Murnane RJ, Willett JB. Methods Matter: Improving Causal Inference in Educational and Social Science Research. New York, NY: Oxford University Press; 2010'},{id:"B46",body:'Hennig C. What are the true clusters? Pattern Recognition Letters. 2015;64:53-62. DOI: 10.1016/j.patrec.2015.04.009'},{id:"B47",body:'Wang MT, Eccles JS, Kenny S. Not lack of ability but more choice: Individual and gender differences in STEM career choice. Psychological Science. 2013;24:770-775. DOI: 10.1177%2F0956797612458937'},{id:"B48",body:'Wang MT, Degol JL. Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review. 2017;229:119-140. DOI: 10.1007/s10648-015-9355-x'},{id:"B49",body:'Ceci SJ, Williams WM. Understanding current causes of women’s underrepresentation in science. Proceedings. National Academy of Sciences. United States of America. 2011;108:3157-3162. DOI: 10.1073/pnas.1014871108'},{id:"B50",body:'Zeldin AL, Britner SL, Pajares F. A comparative study of the self-efficacy beliefs of successful men and women in mathematics, science, and technology careers. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching. 2008;45(9):1036-1058. DOI: 10.1002/tea.20195'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ann M. Gansemer-Topf",address:"anngt@iastate.edu",affiliation:'
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OASPA
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STM
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COPE
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Creative Commons
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Digital Science is a technology company serving the needs of scientific and research communities at key points along the full cycle of research. They support innovative businesses and technologies that make all parts of the research process more open, efficient and effective. IntechOpen integrates tools such as Altmetric to enable our researchers to track and measure the activity around their academic research and Dimensions, to ease access to the most relevant information and better understand and analyze the global research landscape.
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CLOCKSS
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CLOCKSS preserves scholarly publications in original formats, ensuring that they always remain available and openly accessible to everyone.
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iThenticate
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Enago
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OASPA
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STM
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The International Association of Scientific, Technical and Medical Publishers (STM) is the leading global trade association for academic and professional publishers. As a member, IntechOpen has not only made a commitment to STM's Ethical Principles.
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COPE
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Creative Commons
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Crossref
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Crossref is the official Digital Object Identifier (DOI) Registration Agency for scholarly and professional publications with a goal of making scholarly communications more effective. IntechOpen deposits metadata and registers DOIs for all content using the Crossref System. IntechOpen also deposits its references and uses the Crossref Cited-by service that enables researchers to track citation statistics.
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Altmetric and Dimensions from Digital Science
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Digital Science is a technology company serving the needs of scientific and research communities at key points along the full cycle of research. They support innovative businesses and technologies that make all parts of the research process more open, efficient and effective. IntechOpen integrates tools such as Altmetric to enable our researchers to track and measure the activity around their academic research and Dimensions, to ease access to the most relevant information and better understand and analyze the global research landscape.
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CLOCKSS
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CLOCKSS preserves scholarly publications in original formats, ensuring that they always remain available and openly accessible to everyone.
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Counter
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COUNTER provides the Code of Practice that enables publishers and vendors to report usage of their electronic resources in a consistent way. This enables libraries to compare data received from different publishers and vendors.
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DORA
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DORA is a worldwide initiative covering all scholarly disciplines which recognizes the need to improve the ways in which the outputs of scholarly research are evaluated and seeks to develop and promote best practice. To date it has been signed by over 1500 organizations and around 14,700 individuals.
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iThenticate
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Enago
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IntechOpen collaborates with Enago, through its sister brand, Ulatus, one of the world’s leading providers of book translation services. Their services are designed to convey the essence of your work to readers from across the globe in the language they understand.
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Straive
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Straive is the market leader in technology-driven solutions for the extraction, enrichment and transformation of content assets. IntechOpen publishing services are designed to meet the unique needs of Authors. As part of our commitment to that objective, we have an ongoing partnership agreement for production solutions.
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Amazon
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Amazon is the world’s largest online retailer and cloud services provider. IntechOpen books have been available on Amazon since 2017, guaranteeing more visibility for our Authors and Academic Editors.
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DHL
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IntechOpen has partnered with DHL since 2011 to ensure the fastest delivery of Print on Demand books.
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United Nations Sustainable Development Goals Publishers Compact
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River Valley Technology
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Figshare
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However, agricultural malpractices like excessive use of chemical fertilizers and pesticides, as well as climate change have aggravated the effects of biotic and abiotic stresses on crop productivity. These led to the degradation of ecosystem, leaving bad impacts on the soil qualities and water body environment. As an alternative to the rising agricultural energy, the use of Vesicular– Arbuscular Mycorrhizae (AM) may be a better option. Being natural root symbionts, AM provide essential inorganic nutrients to host plants, thereby improving its growth and yield even under stressed conditions. AM fungi can also potentially strengthen the adaptability of a plant to the changing environment, as a bio-fertilizer. The chapter provides a comprehensive up-to-date knowledge on AM fungi as a tool for sustainable agricultural system. 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These two complementary techniques for investigating the structure of a virus and its parasitic life cycle are presented in this paper.",book:{id:"4692",slug:"microbiology-in-agriculture-and-human-health",title:"Microbiology in Agriculture and Human Health",fullTitle:"Microbiology in Agriculture and Human Health"},signatures:"Debora Ferreira Barreto-Vieira and Ortrud Monika Barth",authors:[{id:"174492",title:"Dr.",name:"Debora",middleName:"Ferreira",surname:"Barreto-Vieira",slug:"debora-barreto-vieira",fullName:"Debora Barreto-Vieira"},{id:"175200",title:"Dr.",name:"Ortrud Monika",middleName:null,surname:"Barth",slug:"ortrud-monika-barth",fullName:"Ortrud Monika Barth"}]},{id:"18412",title:"C4 Plants Adaptation to High Levels of CO2 and to Drought Environments",slug:"c4-plants-adaptation-to-high-levels-of-co2-and-to-drought-environments",totalDownloads:26513,totalCrossrefCites:6,totalDimensionsCites:31,abstract:null,book:{id:"371",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",title:"Abiotic Stress in Plants",fullTitle:"Abiotic Stress in Plants - Mechanisms and Adaptations"},signatures:"María Valeria Lara and Carlos Santiago Andreo",authors:[{id:"60504",title:"Dr.",name:null,middleName:null,surname:"Andreo",slug:"andreo",fullName:"Andreo"},{id:"62008",title:"Dr.",name:"María",middleName:"Valeria",surname:"Lara",slug:"maria-lara",fullName:"María Lara"}]},{id:"60831",title:"Introductory Chapter: Fusarium: Pathogenicity, Infections, Diseases, Mycotoxins and Management",slug:"introductory-chapter-fusarium-pathogenicity-infections-diseases-mycotoxins-and-management",totalDownloads:1872,totalCrossrefCites:2,totalDimensionsCites:4,abstract:null,book:{id:"6329",slug:"fusarium-plant-diseases-pathogen-diversity-genetic-diversity-resistance-and-molecular-markers",title:"Fusarium",fullTitle:"Fusarium - Plant Diseases, Pathogen Diversity, Genetic Diversity, Resistance and Molecular Markers"},signatures:"Tulin Askun",authors:[{id:"89795",title:"Dr.",name:"Tulin",middleName:null,surname:"Askun",slug:"tulin-askun",fullName:"Tulin Askun"}]},{id:"58344",title:"Fusarium Wilt: A Killer Disease of Lentil",slug:"fusarium-wilt-a-killer-disease-of-lentil",totalDownloads:1814,totalCrossrefCites:3,totalDimensionsCites:10,abstract:"Lentil (Lens culinaris Medikus subsp. culinaris) is an important dietary source of protein and other essential nutrients in South and West Asia, North and East Africa. Lentil crops are vulnerable to a number of diseases caused by fungi, viruses, nematodes, insect pests, parasitic plants and abiotic stresses. Among them, the most significant and serious soil-borne disease is Fusarium wilt (Fusarium oxysporum f.sp. lentis: Fol). Fusarium wilt causes yield loss up to 50% in farmers’ fields. The pathogen showed high levels of phenotypic and genotypic diversity in India, Algeria, Syria and Iran. The disease thrives at 22–25°C temperature and affect lentil either at seedling and vegetative or the reproductive stages of the crop. To minimize yield losses, an integrated management strategy comprising resistant/partial resistant cultivars, adjusting sowing time, bio-control and chemical seed treatments is the best approach to reduce the incidence of the Fusarium wilt of lentil. This review covers past achievements in managing the disease, pathogen diversity and identify gaps in managing Fusarium wilt to improve productivity and production of the crop.",book:{id:"6329",slug:"fusarium-plant-diseases-pathogen-diversity-genetic-diversity-resistance-and-molecular-markers",title:"Fusarium",fullTitle:"Fusarium - Plant Diseases, Pathogen Diversity, Genetic Diversity, Resistance and Molecular Markers"},signatures:"Neha Tiwari, Seid Ahmed, Shiv Kumar and Ashutosh Sarker",authors:[{id:"213094",title:"Dr.",name:"Neha",middleName:null,surname:"Tiwari",slug:"neha-tiwari",fullName:"Neha Tiwari"},{id:"213095",title:"Dr.",name:"Ashutosh",middleName:null,surname:"Sarker",slug:"ashutosh-sarker",fullName:"Ashutosh Sarker"},{id:"213176",title:"Dr.",name:"Seid Ahmed",middleName:null,surname:"Kemal",slug:"seid-ahmed-kemal",fullName:"Seid Ahmed Kemal"}]}],onlineFirstChaptersFilter:{topicId:"407",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:6,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:17,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems. \r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.
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Dr. Summers is a systems ecologist and began his career at the EPA in 1989 and has worked in various programs and capacities. This includes leading the National Coastal Assessment in collaboration with the Office of Water which culminated in the award-winning National Coastal Condition Report series (four volumes between 2001 and 2012), and which integrates water quality, sediment quality, habitat, and biological data to assess the ecosystem condition of the United States estuaries. He was acting National Program Director for Ecology for the EPA between 2004 and 2006. He has authored approximately 150 peer-reviewed journal articles, book chapters, and reports and has received many awards for technical accomplishments from the EPA and from outside of the agency. 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He also has an honorary appointment to serve as a Collaborative Professor at Kanazawa University, Japan, from Mar 2015 to the present. \nFormerly, Dr. Rahman was a faculty member of the University of Chittagong, Bangladesh, affiliated with the Department of Chemistry (Oct 2002 to Mar 2012) and the Department of Applied Chemistry and Chemical Engineering (Mar 2012 to Sep 2015). Dr. Rahman was also adjunctly attached with Kanazawa University, Japan (Visiting Research Professor, Dec 2014 to Mar 2015; JSPS Postdoctoral Research Fellow, Apr 2012 to Mar 2014), and Tokyo Institute of Technology, Japan (TokyoTech-UNESCO Research Fellow, Oct 2004–Sep 2005). \nHe received his Ph.D. degree in Environmental Analytical Chemistry from Kanazawa University, Japan (2011). He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. 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Graduate in Sciences (Chemist), graduate in Geography and History (Geography), master in Water Management, Treatment, master in Fertilizers and Environment and master in Environmental Management; Ph.D. in Environmental Sciences. His research is focused on soil-water and waste-environment relations, mainly on soil-water and soil-waste interactions under different management and waste reuse. His work is reflected in more than 230 communications presented in national and international conferences and congresses, 29 invited lectures from universities, associations and government agencies. 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He has developed his research activity in the fields of fauna and soil ecology, and in the treatment of organic waste, having been the founder and principal investigator of the Environmental Biotechnology Group of the University of Vigo.\r\nHis research activity in the field of Environmental Biotechnology has been focused on the development of novel organic waste treatment systems through composting. 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For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}}]},{type:"book",id:"7218",title:"OCT",subtitle:"Applications in Ophthalmology",coverURL:"https://cdn.intechopen.com/books/images_new/7218.jpg",slug:"oct-applications-in-ophthalmology",publishedDate:"September 19th 2018",editedByType:"Edited by",bookSignature:"Michele Lanza",hash:"e3a3430cdfd6999caccac933e4613885",volumeInSeries:2,fullTitle:"OCT - Applications in Ophthalmology",editors:[{id:"240088",title:"Prof.",name:"Michele",middleName:null,surname:"Lanza",slug:"michele-lanza",fullName:"Michele Lanza",profilePictureURL:"https://mts.intechopen.com/storage/users/240088/images/system/240088.png",biography:"Michele Lanza is Associate Professor of Ophthalmology at Università della Campania, Luigi Vanvitelli, Napoli, Italy. His fields of interest are anterior segment disease, keratoconus, glaucoma, corneal dystrophies, and cataracts. His research topics include\nintraocular lens power calculation, eye modification induced by refractive surgery, glaucoma progression, and validation of new diagnostic devices in ophthalmology. \nHe has published more than 100 papers in international and Italian scientific journals, more than 60 in journals with impact factors, and chapters in international and Italian books. He has also edited two international books and authored more than 150 communications or posters for the most important international and Italian ophthalmology conferences.",institutionString:'University of Campania "Luigi Vanvitelli"',institution:{name:'University of Campania "Luigi Vanvitelli"',institutionURL:null,country:{name:"Italy"}}}]},{type:"book",id:"7560",title:"Non-Invasive Diagnostic Methods",subtitle:"Image Processing",coverURL:"https://cdn.intechopen.com/books/images_new/7560.jpg",slug:"non-invasive-diagnostic-methods-image-processing",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Mariusz Marzec and Robert Koprowski",hash:"d92fd8cf5a90a47f2b8a310837a5600e",volumeInSeries:3,fullTitle:"Non-Invasive Diagnostic Methods - Image Processing",editors:[{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. 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He was also the recipient of the Australian Government Endeavour Research Fellowship for postdoctoral research as an adjunct senior researcher at the University of Tasmania, Australia. Dr. Hasanuzzaman’s current work is focused on the physiological and molecular mechanisms of environmental stress tolerance. Dr. Hasanuzzaman has published more than 150 articles in peer-reviewed journals. He has edited ten books and written more than forty book chapters on important aspects of plant physiology, plant stress tolerance, and crop production. According to Scopus, Dr. Hasanuzzaman’s publications have received more than 10,500 citations with an h-index of 53. He has been named a Highly Cited Researcher by Clarivate. He is an editor and reviewer for more than fifty peer-reviewed international journals and was a recipient of the “Publons Peer Review Award” in 2017, 2018, and 2019. He has been honored by different authorities for his outstanding performance in various fields like research and education, and he has received the World Academy of Science Young Scientist Award (2014) and the University Grants Commission (UGC) Award 2018. He is a fellow of the Bangladesh Academy of Sciences (BAS) and the Royal Society of Biology.",institutionString:"Sher-e-Bangla Agricultural University",institution:{name:"Sher-e-Bangla Agricultural University",country:{name:"Bangladesh"}}},{id:"187859",title:"Prof.",name:"Kusal",middleName:"K.",surname:"Das",slug:"kusal-das",fullName:"Kusal Das",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBDeQAO/Profile_Picture_1623411145568",biography:"Kusal K. Das is a Distinguished Chair Professor of Physiology, Shri B. M. Patil Medical College and Director, Centre for Advanced Medical Research (CAMR), BLDE (Deemed to be University), Vijayapur, Karnataka, India. Dr. Das did his M.S. and Ph.D. in Human Physiology from the University of Calcutta, Kolkata. His area of research is focused on understanding of molecular mechanisms of heavy metal activated low oxygen sensing pathways in vascular pathophysiology. He has invented a new method of estimation of serum vitamin E. His expertise in critical experimental protocols on vascular functions in experimental animals was well documented by his quality of publications. He was a Visiting Professor of Medicine at University of Leeds, United Kingdom (2014-2016) and Tulane University, New Orleans, USA (2017). For his immense contribution in medical research Ministry of Science and Technology, Government of India conferred him 'G.P. Chatterjee Memorial Research Prize-2019” and he is also the recipient of 'Dr.Raja Ramanna State Scientist Award 2015” by Government of Karnataka. He is a Fellow of the Royal Society of Biology (FRSB), London and Honorary Fellow of Karnataka Science and Technology Academy, Department of Science and Technology, Government of Karnataka.",institutionString:"BLDE (Deemed to be University), India",institution:null},{id:"243660",title:"Dr.",name:"Mallanagouda Shivanagouda",middleName:null,surname:"Biradar",slug:"mallanagouda-shivanagouda-biradar",fullName:"Mallanagouda Shivanagouda Biradar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243660/images/system/243660.jpeg",biography:"M. S. Biradar is Vice Chancellor and Professor of Medicine of\nBLDE (Deemed to be University), Vijayapura, Karnataka, India.\nHe obtained his MD with a gold medal in General Medicine and\nhas devoted himself to medical teaching, research, and administrations. He has also immensely contributed to medical research\non vascular medicine, which is reflected by his numerous publications including books and book chapters. Professor Biradar was\nalso Visiting Professor at Tulane University School of Medicine, New Orleans, USA.",institutionString:"BLDE (Deemed to be University)",institution:{name:"BLDE University",country:{name:"India"}}},{id:"289796",title:"Dr.",name:"Swastika",middleName:null,surname:"Das",slug:"swastika-das",fullName:"Swastika Das",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/289796/images/system/289796.jpeg",biography:"Swastika N. Das is Professor of Chemistry at the V. P. Dr. P. G.\nHalakatti College of Engineering and Technology, BLDE (Deemed\nto be University), Vijayapura, Karnataka, India. She obtained an\nMSc, MPhil, and PhD in Chemistry from Sambalpur University,\nOdisha, India. Her areas of research interest are medicinal chemistry, chemical kinetics, and free radical chemistry. She is a member\nof the investigators who invented a new modified method of estimation of serum vitamin E. She has authored numerous publications including book\nchapters and is a mentor of doctoral curriculum at her university.",institutionString:"BLDEA’s V.P.Dr.P.G.Halakatti College of Engineering & Technology",institution:{name:"BLDE University",country:{name:"India"}}},{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248459/images/system/248459.png",biography:"Akikazu Takada was born in Japan, 1935. After graduation from\nKeio University School of Medicine and finishing his post-graduate studies, he worked at Roswell Park Memorial Institute NY,\nUSA. He then took a professorship at Hamamatsu University\nSchool of Medicine. In thrombosis studies, he found the SK\npotentiator that enhances plasminogen activation by streptokinase. He is very much interested in simultaneous measurements\nof fatty acids, amino acids, and tryptophan degradation products. By using fatty\nacid analyses, he indicated that plasma levels of trans-fatty acids of old men were\nfar higher in the US than Japanese men. . He also showed that eicosapentaenoic acid\n(EPA) and docosahexaenoic acid (DHA) levels are higher, and arachidonic acid\nlevels are lower in Japanese than US people. By using simultaneous LC/MS analyses\nof plasma levels of tryptophan metabolites, he recently found that plasma levels of\nserotonin, kynurenine, or 5-HIAA were higher in patients of mono- and bipolar\ndepression, which are significantly different from observations reported before. In\nview of recent reports that plasma tryptophan metabolites are mainly produced by\nmicrobiota. He is now working on the relationships between microbiota and depression or autism.",institutionString:"Hamamatsu University School of Medicine",institution:{name:"Hamamatsu University School of Medicine",country:{name:"Japan"}}},{id:"137240",title:"Prof.",name:"Mohammed",middleName:null,surname:"Khalid",slug:"mohammed-khalid",fullName:"Mohammed Khalid",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/137240/images/system/137240.png",biography:"Mohammed Khalid received his B.S. in Chemistry in July 2000, and his Ph.D. in Physical Chemistry in 2007 from the University of Khartoum, Sudan. In 2009 he joined the Dr. Ron Clarke research group at the School of Chemistry, Faculty of Science, University of Sydney, Australia as a postdoctoral fellow where he worked on the Interaction of ATP with the phosphoenzyme of the Na+, K+-ATPase, and Dual mechanisms of allosteric acceleration of the Na+, K+-ATPase by ATP. He then worked as Assistant Professor at the Department of Chemistry, University of Khartoum, and in 2014 was promoted to Associate Professor ranking. In 2011 he joined the staff of the Chemistry Department at Taif University, Saudi Arabia, where he is currently active as an Assistant Professor. His research interests include:\r\n(1) P-type ATPase Enzyme Kinetics and Mechanisms; (2) Kinetics and Mechanism of Redox Reactions; (3) Autocatalytic reactions; (4) Computational enzyme kinetics; (5) Allosteric acceleration of P-type ATPases by ATP; (6) Exploring of allosteric sites of ATPases and interaction of ATP with ATPases located in the cell membranes.",institutionString:"Taif University",institution:{name:"Taif University",country:{name:"Saudi Arabia"}}},{id:"63810",title:"Prof.",name:"Jorge",middleName:null,surname:"Morales-Montor",slug:"jorge-morales-montor",fullName:"Jorge Morales-Montor",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/63810/images/system/63810.png",biography:"Dr. Jorge Morales-Montor was recognized with the Lola and Igo Flisser PUIS Award for best graduate thesis at the national level in the field of parasitology. He received a fellowship from the Fogarty Foundation to perform postdoctoral research stay at the University of Georgia. He has 153 journal articles to his credit. He has also edited several books and published more than fifty-five book chapters. He is a member of the Mexican Academy of Sciences, Latin American Academy of Sciences, and the National Academy of Medicine. He has received more than thirty-five awards and has supervised numerous bachelor’s, master’s, and Ph.D. students. Dr. Morales-Montor is the past president of the Mexican Society of Parasitology.",institutionString:"National Autonomous University of Mexico",institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"217215",title:"Dr.",name:"Palash",middleName:null,surname:"Mandal",slug:"palash-mandal",fullName:"Palash Mandal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217215/images/system/217215.jpeg",biography:null,institutionString:"Charusat University",institution:null},{id:"49739",title:"Dr.",name:"Leszek",middleName:null,surname:"Szablewski",slug:"leszek-szablewski",fullName:"Leszek Szablewski",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49739/images/system/49739.jpg",biography:"Leszek Szablewski is a professor of medical sciences. He received his M.S. in the Faculty of Biology from the University of Warsaw and his PhD degree from the Institute of Experimental Biology Polish Academy of Sciences. He habilitated in the Medical University of Warsaw, and he obtained his degree of Professor from the President of Poland. Professor Szablewski is the Head of Chair and Department of General Biology and Parasitology, Medical University of Warsaw. Professor Szablewski has published over 80 peer-reviewed papers in journals such as Journal of Alzheimer’s Disease, Biochim. Biophys. Acta Reviews of Cancer, Biol. Chem., J. Biomed. Sci., and Diabetes/Metabol. Res. Rev, Endocrine. He is the author of two books and four book chapters. He has edited four books, written 15 scripts for students, is the ad hoc reviewer of over 30 peer-reviewed journals, and editorial member of peer-reviewed journals. Prof. Szablewski’s research focuses on cell physiology, genetics, and pathophysiology. He works on the damage caused by lack of glucose homeostasis and changes in the expression and/or function of glucose transporters due to various diseases. He has given lectures, seminars, and exercises for students at the Medical University.",institutionString:"Medical University of Warsaw",institution:{name:"Medical University of Warsaw",country:{name:"Poland"}}},{id:"173123",title:"Dr.",name:"Maitham",middleName:null,surname:"Khajah",slug:"maitham-khajah",fullName:"Maitham Khajah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173123/images/system/173123.jpeg",biography:"Dr. Maitham A. Khajah received his degree in Pharmacy from Faculty of Pharmacy, Kuwait University, in 2003 and obtained his PhD degree in December 2009 from the University of Calgary, Canada (Gastrointestinal Science and Immunology). Since January 2010 he has been assistant professor in Kuwait University, Faculty of Pharmacy, Department of Pharmacology and Therapeutics. His research interest are molecular targets for the treatment of inflammatory bowel disease (IBD) and the mechanisms responsible for immune cell chemotaxis. He cosupervised many students for the MSc Molecular Biology Program, College of Graduate Studies, Kuwait University. Ever since joining Kuwait University in 2010, he got various grants as PI and Co-I. He was awarded the Best Young Researcher Award by Kuwait University, Research Sector, for the Year 2013–2014. He was a member in the organizing committee for three conferences organized by Kuwait University, Faculty of Pharmacy, as cochair and a member in the scientific committee (the 3rd, 4th, and 5th Kuwait International Pharmacy Conference).",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"195136",title:"Dr.",name:"Aya",middleName:null,surname:"Adel",slug:"aya-adel",fullName:"Aya Adel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/195136/images/system/195136.jpg",biography:"Dr. Adel works as an Assistant Lecturer in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. Dr. Adel is especially interested in joint attention and its impairment in autism spectrum disorder",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"94911",title:"Dr.",name:"Boulenouar",middleName:null,surname:"Mesraoua",slug:"boulenouar-mesraoua",fullName:"Boulenouar Mesraoua",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94911/images/system/94911.png",biography:"Dr Boulenouar Mesraoua is the Associate Professor of Clinical Neurology at Weill Cornell Medical College-Qatar and a Consultant Neurologist at Hamad Medical Corporation at the Neuroscience Department; He graduated as a Medical Doctor from the University of Oran, Algeria; he then moved to Belgium, the City of Liege, for a Residency in Internal Medicine and Neurology at Liege University; after getting the Belgian Board of Neurology (with high marks), he went to the National Hospital for Nervous Diseases, Queen Square, London, United Kingdom for a fellowship in Clinical Neurophysiology, under Pr Willison ; Dr Mesraoua had also further training in Epilepsy and Continuous EEG Monitoring for two years (from 2001-2003) in the Neurophysiology department of Zurich University, Switzerland, under late Pr Hans Gregor Wieser ,an internationally known epileptologist expert. \n\nDr B. Mesraoua is the Director of the Neurology Fellowship Program at the Neurology Section and an active member of the newly created Comprehensive Epilepsy Program at Hamad General Hospital, Doha, Qatar; he is also Assistant Director of the Residency Program at the Qatar Medical School. \nDr B. Mesraoua's main interests are Epilepsy, Multiple Sclerosis, and Clinical Neurology; He is the Chairman and the Organizer of the well known Qatar Epilepsy Symposium, he is running yearly for the past 14 years and which is considered a landmark in the Gulf region; He has also started last year , together with other epileptologists from Qatar, the region and elsewhere, a yearly International Epilepsy School Course, which was attended by many neurologists from the Area.\n\nInternationally, Dr Mesraoua is an active and elected member of the Commission on Eastern Mediterranean Region (EMR ) , a regional branch of the International League Against Epilepsy (ILAE), where he represents the Middle East and North Africa(MENA ) and where he holds the position of chief of the Epilepsy Epidemiology Section; Dr Mesraoua is a member of the American Academy of Neurology, the Europeen Academy of Neurology and the American Epilepsy Society.\n\nDr Mesraoua's main objectives are to encourage frequent gathering of the epileptologists/neurologists from the MENA region and the rest of the world, promote Epilepsy Teaching in the MENA Region, and encourage multicenter studies involving neurologists and epileptologists in the MENA region, particularly epilepsy epidemiological studies. \n\nDr. Mesraoua is the recipient of two research Grants, as the Lead Principal Investigator (750.000 USD and 250.000 USD) from the Qatar National Research Fund (QNRF) and the Hamad Hospital Internal Research Grant (IRGC), on the following topics : “Continuous EEG Monitoring in the ICU “ and on “Alpha-lactoalbumin , proof of concept in the treatment of epilepsy” .Dr Mesraoua is a reviewer for the journal \"seizures\" (Europeen Epilepsy Journal ) as well as dove journals ; Dr Mesraoua is the author and co-author of many peer reviewed publications and four book chapters in the field of Epilepsy and Clinical Neurology",institutionString:"Weill Cornell Medical College in Qatar",institution:{name:"Weill Cornell Medical College in Qatar",country:{name:"Qatar"}}},{id:"282429",title:"Prof.",name:"Covanis",middleName:null,surname:"Athanasios",slug:"covanis-athanasios",fullName:"Covanis Athanasios",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/282429/images/system/282429.jpg",biography:null,institutionString:"Neurology-Neurophysiology Department of the Children Hospital Agia Sophia",institution:null},{id:"190980",title:"Prof.",name:"Marwa",middleName:null,surname:"Mahmoud Saleh",slug:"marwa-mahmoud-saleh",fullName:"Marwa Mahmoud Saleh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/190980/images/system/190980.jpg",biography:"Professor Marwa Mahmoud Saleh is a doctor of medicine and currently works in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. She got her doctoral degree in 1991 and her doctoral thesis was accomplished in the University of Iowa, United States. Her publications covered a multitude of topics as videokymography, cochlear implants, stuttering, and dysphagia. She has lectured Egyptian phonology for many years. Her recent research interest is joint attention in autism.",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259190/images/system/259190.png",biography:"Dr. Naqvi is a radioanalytical chemist and is working as an associate professor of analytical chemistry in the Department of Chemistry, Government College University, Faisalabad, Pakistan. Advance separation techniques, nuclear analytical techniques and radiopharmaceutical analysis are the main courses that he is teaching to graduate and post-graduate students. In the research area, he is focusing on the development of organic- and biomolecule-based radiopharmaceuticals for diagnosis and therapy of infectious and cancerous diseases. Under the supervision of Dr. Naqvi, three students have completed their Ph.D. degrees and 41 students have completed their MS degrees. He has completed three research projects and is currently working on 2 projects entitled “Radiolabeling of fluoroquinolone derivatives for the diagnosis of deep-seated bacterial infections” and “Radiolabeled minigastrin peptides for diagnosis and therapy of NETs”. He has published about 100 research articles in international reputed journals and 7 book chapters. Pakistan Institute of Nuclear Science & Technology (PINSTECH) Islamabad, Punjab Institute of Nuclear Medicine (PINM), Faisalabad and Institute of Nuclear Medicine and Radiology (INOR) Abbottabad are the main collaborating institutes.",institutionString:"Government College University",institution:{name:"Government College University, Faisalabad",country:{name:"Pakistan"}}},{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.png",biography:"Gyula Mózsik MD, Ph.D., ScD (med), is an emeritus professor of Medicine at the First Department of Medicine, Univesity of Pécs, Hungary. He was head of this department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition, and dietetics. His research fields are biochemical pharmacological examinations in the human gastrointestinal (GI) mucosa, mechanisms of retinoids, drugs, capsaicin-sensitive afferent nerves, and innovative pharmacological, pharmaceutical, and nutritional (dietary) research in humans. He has published about 360 peer-reviewed papers, 197 book chapters, 692 abstracts, 19 monographs, and has edited 37 books. He has given about 1120 regular and review lectures. He has organized thirty-eight national and international congresses and symposia. He is the founder of the International Conference on Ulcer Research (ICUR); International Union of Pharmacology, Gastrointestinal Section (IUPHAR-GI); Brain-Gut Society symposiums, and gastrointestinal cytoprotective symposiums. He received the Andre Robert Award from IUPHAR-GI in 2014. Fifteen of his students have been appointed as full professors in Egypt, Cuba, and Hungary.",institutionString:"University of Pécs",institution:{name:"University of Pecs",country:{name:"Hungary"}}},{id:"277367",title:"M.Sc.",name:"Daniel",middleName:"Martin",surname:"Márquez López",slug:"daniel-marquez-lopez",fullName:"Daniel Márquez López",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/277367/images/7909_n.jpg",biography:"Msc Daniel Martin Márquez López has a bachelor degree in Industrial Chemical Engineering, a Master of science degree in the same área and he is a PhD candidate for the Instituto Politécnico Nacional. His Works are realted to the Green chemistry field, biolubricants, biodiesel, transesterification reactions for biodiesel production and the manipulation of oils for therapeutic purposes.",institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. He is the co-editor of The Open Biology Journal and associate editor for Oxidative Medicine and Cellular Longevity.",institutionString:"Universidad Nacional de La Plata",institution:{name:"National University of La Plata",country:{name:"Argentina"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",slug:"francisco-javier-martin-romero",fullName:"Francisco Javier Martin-Romero",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",biography:"Francisco Javier Martín-Romero (Javier) is a Professor of Biochemistry and Molecular Biology at the University of Extremadura, Spain. He is also a group leader at the Biomarkers Institute of Molecular Pathology. Javier received his Ph.D. in 1998 in Biochemistry and Biophysics. At the National Cancer Institute (National Institute of Health, Bethesda, MD) he worked as a research associate on the molecular biology of selenium and its role in health and disease. After postdoctoral collaborations with Carlos Gutierrez-Merino (University of Extremadura, Spain) and Dario Alessi (University of Dundee, UK), he established his own laboratory in 2008. The interest of Javier's lab is the study of cell signaling with a special focus on Ca2+ signaling, and how Ca2+ transport modulates the cytoskeleton, migration, differentiation, cell death, etc. He is especially interested in the study of Ca2+ channels, and the role of STIM1 in the initiation of pathological events.",institutionString:null,institution:{name:"University of Extremadura",country:{name:"Spain"}}},{id:"198499",title:"Dr.",name:"Daniel",middleName:null,surname:"Glossman-Mitnik",slug:"daniel-glossman-mitnik",fullName:"Daniel Glossman-Mitnik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/198499/images/system/198499.jpeg",biography:"Dr. Daniel Glossman-Mitnik is currently a Titular Researcher at the Centro de Investigación en Materiales Avanzados (CIMAV), Chihuahua, Mexico, as well as a National Researcher of Level III at the Consejo Nacional de Ciencia y Tecnología, México. His research interest focuses on computational chemistry and molecular modeling of diverse systems of pharmacological, food, and alternative energy interests by resorting to DFT and Conceptual DFT. He has authored a coauthored more than 270 peer-reviewed papers, 32 book chapters, and 4 edited books. He has delivered speeches at many international and domestic conferences. He serves as a reviewer for more than eighty international journals, books, and research proposals as well as an editor for special issues of renowned scientific journals.",institutionString:null,institution:null},{id:"217323",title:"Prof.",name:"Guang-Jer",middleName:null,surname:"Wu",slug:"guang-jer-wu",fullName:"Guang-Jer Wu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217323/images/8027_n.jpg",biography:null,institutionString:null,institution:null},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/148546/images/4640_n.jpg",biography:null,institutionString:null,institution:null},{id:"272889",title:"Dr.",name:"Narendra",middleName:null,surname:"Maddu",slug:"narendra-maddu",fullName:"Narendra Maddu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272889/images/10758_n.jpg",biography:null,institutionString:null,institution:null},{id:"242491",title:"Prof.",name:"Angelica",middleName:null,surname:"Rueda",slug:"angelica-rueda",fullName:"Angelica Rueda",position:"Investigador Cinvestav 3B",profilePictureURL:"https://mts.intechopen.com/storage/users/242491/images/6765_n.jpg",biography:null,institutionString:null,institution:null},{id:"88631",title:"Dr.",name:"Ivan",middleName:null,surname:"Petyaev",slug:"ivan-petyaev",fullName:"Ivan Petyaev",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Lycotec (United Kingdom)",country:{name:"United Kingdom"}}},{id:"428313",title:"Dr.",name:"Sambangi",middleName:null,surname:"Pratyusha",slug:"sambangi-pratyusha",fullName:"Sambangi Pratyusha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"CGIAR",country:{name:"France"}}},{id:"423869",title:"Ms.",name:"Smita",middleName:null,surname:"Rai",slug:"smita-rai",fullName:"Smita Rai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}},{id:"424024",title:"Prof.",name:"Swati",middleName:null,surname:"Sharma",slug:"swati-sharma",fullName:"Swati Sharma",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}},{id:"439112",title:"MSc.",name:"Touseef",middleName:null,surname:"Fatima",slug:"touseef-fatima",fullName:"Touseef Fatima",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}}]}},subseries:{item:{id:"15",type:"subseries",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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