Demographics of the pediatric study subjects.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"43",leadTitle:null,fullTitle:"Advances in Telemedicine: Technologies, Enabling Factors and Scenarios",title:"Advances in Telemedicine",subtitle:"Technologies, Enabling Factors and Scenarios",reviewType:"peer-reviewed",abstract:'Innovative developments in information and communication technologies (ICT) irrevocably change our lives and enable new possibilities for society. Telemedicine, which can be defined as novel ICT-enabled medical services that help to overcome classical barriers in space and time, definitely profits from this trend. Through Telemedicine patients can access medical expertise that may not be available at the patient\'s site. Telemedicine services can range from simply sending a fax message to a colleague to the use of broadband networks with multimodal video- and data streaming for second opinioning as well as medical telepresence. Telemedicine is more and more evolving into a multidisciplinary approach. This book project "Advances in Telemedicine" has been conceived to reflect this broad view and therefore has been split into two volumes, each covering specific themes: Volume 1: Technologies, Enabling Factors and Scenarios; Volume 2: Applications in Various Medical Disciplines and Geographical Regions. 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Following his research studies at the Department for Mathematics and Natural Sciences of the Technical University Dresden (Germany) he gained his Ph.D. there in 1974. After research appointments at various international sites he became in 1987 co-founder of the Research Unit OP 2000 at the German Cancer Research Center DKFZ in Heidelberg (Germany), where he acted as its Scientific Coordinator until its transfer to Berlin. He made major contribution to numerous leading national and international telemedicine projects: SICONET, PANORAMA, GALENOS, DELTASS, MEDASHIP, EMISPHER. He has co-authored more than 360 scientific publications and presentations and holds 18 patents in Germany, the European Union, Japan and USA.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Charité",institutionURL:null,country:{name:"Germany"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"19129",title:"Dr.",name:"Theo A.",middleName:null,surname:"Roelofs",slug:"theo-a.-roelofs",fullName:"Theo A. Roelofs",profilePictureURL:"https://mts.intechopen.com/storage/users/19129/images/1575_n.jpg",biography:"Dr. Theo A. Roelofs has been working since 1999 as Research Associate & Project Manager at the Surgical Research Unit OP 2000 of the Max-Delbrück-Center for Molecular Medicine and the Experimental and Clinical Research Center of Charité – University Medicine Berlin (Germany). 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The gut mucosal immune system has to maintain an intricate immune homeostasis by maintaining tolerance to macronutrients, commensal flora, and other harmless molecules in the gut lumen while exerting effective immune defense against pathogenic microbes. It takes the first few years of life to establish this intricate immune homeostasis and until then the gut mucosal immune system is rather error-prone. This is reflected in the fact that young infants and children often suffer from temporary intolerance to common food proteins (FP), e.g. food allergy (FA). During this period, most FA manifests as delayed type FA, mediated by cellular immune responses – often called FP induced enterocolitis syndrome (FPIES) (Jyonouchi, 2008; Nowak-Wegrzyn and Muraro, 2009; Sicherer et al., 1998). Although FPIES seldom cause fatal anaphylaxis, as opposed to IgE-mediated FA, this condition is often under-diagnosed and under-treated. This is, in part, due to the delayed onset of symptoms that renders clinical connection more difficult, as well as, the lack of commercially available diagnostic measures. This is in contrast to IgE mediated FA in which a causal relationship is apparent in many cases and reactivity to food allergens is easily detected with prick skin test (PST) and/or presence of FA specific IgE antibody. The delay in the onset of symptoms of FPIES makes it especially difficult to diagnose FPIES clinically in infants and young children, as well as those with limited expressive language including children with autism spectrum disorders (ASD). In general, FPIES has an excellent prognosis provided there is timely implementation of avoidance measures against offending food (Jyonouchi, 2008; Nowak-Wegrzyn and Muraro, 2009). However, delayed diagnosis/treatment of FPIES can lead to failure to thrive (FTT), protein losing enteropathy, and possibly other irreversible complications.
GI symptoms are frequently observed in ASD (Buie et al., 2010). A role of FPIES has been suspected to play a role in GI symptoms observed in ASD children, since many parents of ASD children with GI symptoms report favorable responses to the casein-free, gluten-free (cf/gf) diet. We have reported previously that FPIES accounts for the GI symptoms experienced in many young ASD children (Jyonouchi et al., 2005). We have also observed that resolution of GI symptoms in ASD/FPIES children following implementation of avoidance of offending food was associated with improvement of certain behavioral symptoms, (Jyonouchi H, 2007).
However, in our observation, even after recovering from FPIES, some ASD children continue to suffer from recurrent or persistent GI symptoms. We also observed that flare ups of GI symptoms in these ASD/FPIES children are usually triggered by GI insults such as microbial gastroenteritis and prolonged oral antibiosis. Such ‘treatment-resistant’ GI symptoms after implementation of appropriate avoidance measures are less frequently observed in non-ASD/FPIES children. Since diagnosis of FPIES in ASD children are typically delayed partly due to their limited expressive language, it may be argued that this is simply reflecting a longer period of GI inflammation in ASD/FPIES children than in non-ASD/FPIES children. However, ASD/FPIES children with persistent GI symptoms also often exhibit other co-morbid conditions such as recurrent respiratory infection, adverse reaction to multiple medications, and seizure disorders, in our observation. Thus we hypothesized that 1) oral tolerance to FP and commensal flora is fragile and easily broken in ASD/FPIES children with persistent GI symptoms, and 2) this is associated with altered innate immune and adaptive immune responses in these ‘treatment-resistant’ ASD/FPIES children. We also hypothesized that evidence of impaired oral tolerance can be detected by studying peripheral blood mononuclear cells (PBMCs). These hypotheses are based on the facts that 1) aberrant immune responses to commensal flora is implicated with onset of chronic inflammatory conditions in the gut (Schirbel and Fiocchi, 2010), 2) plasticity of T-helper (Th) cell differentiation is not as definite as initially thought and aberrant innate immune responses and altered gut microbiota can hinder development of gut immune homeostasis (Lee and Mazmanian, 2010; Zhou et al., 2009), and 3) FP specific Th cells circulate in the peripheral blood (PB) (Karlsson et al., 2004).
This study focused on ASD/FPIES children and non-ASD/FPIES children who have already been treated for FPIES. In these subjects, we assessed innate immune responses, Th cell polarization, and T cell functions in comparison with normal control children. Our results indicate that a subset of ASD/FPIES children with persistent GI symptoms revealed a different pattern of innate and adaptive immune responses as compared to both ASD/FPIES or non-ASD/FPIES children.
The study subjects were recruited following the study protocols approved by the Institutional Review Board, University of Medicine and Dentistry of New Jersey-New Jersey Medical School (UMDNJ-NJMS). Blood samples were collected after obtainment of signed parental consent forms. Signed assent forms were also obtained, if applicable, in children older than 7 years of age.
A total of 45 ASD/FPIES children who have been treated for FPIES were included in this study in addition to control ASD/non-FPIES (N=24), non-ASD/FPIES (N=26), and typically growing, healthy control children without FPIES (N=43). Among 45 ASD/FPIES children, 16 children revealed persistent or recurrent GI symptoms with suboptimal responses to dietary intervention measures (avoidance of offending food). These children are categorized as ASD-immune subtype (ASD-IS), since their behavioral and GI symptoms flared up repeatedly following immune insults such as viral infection. The demographics of the study subjects are summarized in Table 1. Non-ASD/FPIES children recruited to the study were also already treated for FPIES with implementation of appropriate avoidance measures. ASD and FPIES children were recruited in the Pediatric Allergy/Immunology Clinic at our institution. Normal healthy control and ASD/non-FPIES children were recruited in the Subspecialty Clinic at UMDNJ-NJMS where subspecialties include allergy/immunology, cardiology, developmental pediatrics, endocrinology, genetics, gastroenterology, nephrology, pulmonology, and general pediatrics. In most cases, blood samples were obtained when they were medically indicated to have venipuncture for routine blood work or general health screening. At the time of sample obtainment, all the subjects were examined to ensure absence of active infection.
Age (yr) median (range) | Sex (male: female) | Ethnicity | |
ASD-IS (N=16) | 9.7 (5-15.9) | 16 : 0 | 13 W, 1 AA, 1 mixed, 1 Asian |
ASD/FPIES (N=29) | 6.2 (3-17.3) | 25 : 4 | 24 W, 1 AA, 1 mixed, 3 Asians |
ASD/non-FPIES (N-24) | 6.5 (3-15.9) | 20 : 4 | 16 W, 1 AA, 2 mixed, 5 Asians |
Non-ASD/FPIES (N=26) | 4.2 (1.7-15.9) | 17 : 9 | 22 W, 2 mixed, 2 Asians |
Normal control (N=43) | 8.5 (1-17.8) | 29 : 14 | 33 W, 6 AA, 4 mixed |
Demographics of the pediatric study subjects.
ASD diagnosis was made or ascertained by DSM-IV (Diagnostic and Statistical Manual of Mental Disorders IV) criteria, ADI-R (Autism Diagnostic Interview-Revised), and/or ADOS (Autism Diagnostic Observational Schedules). All the ASD children recruited to this study were those with established autism diagnosis from established autism diagnostic centers including ours at UMDNJ.
Allergic rhinitis (AR), allergic conjunctivitis (AC) were diagnosed with positive prick skin test reactivity and/or presence of allergen-specific IgE accompanied by clinical features consistent with AR and AC (Butrus and Portela, 2005; Nassef et al., 2006). Asthma diagnosis was based on NIH guideline criteria (National Heart, Lung, Blood Institute, 2007). Asthma without prick skin test reactivity and/or allergen-specific IgE antibody was categorized as non-atopic asthma (Nassef et al., 2006)
FPIES to common food proteins (FPs; cow’s milk protein, wheat, and soy) was diagnosed using the following diagnostic criteria: 1) presence of objective GI symptoms (diarrhea, loose stool, and constipation) which resolved with avoidance of causative FPs (Sicherer and Sampson, 2006), 2) delayed (more than 6 h) onset of GI symptoms following exposure to offending FPs after resolution of GI symptoms, and 3) cellular immune reactivity to offending FPs defined as the production of more than 1 standard deviation (SD) + control mean value of TNF-α and/or IL-12 by PBMCs with stimuli of causative DPs (Jyonouchi et al., 2005).
Diagnoses of other GI conditions were ascertained by reviewing medical charts and previous laboratory findings. Persistent GI symptoms are defined as lack of complete resolution of GI symptoms following introduction of appropriate restricted diet (avoidance of offending food) with persistent unformed stools, loose stool, and constipation alternating with diarrhea or loose stool. It is of note that these ASD/FPIES children with persistent GI symptoms (e.g. ASD-IS children) often revealed beneficial effects from oral anti-fungal medications (fluconazole or nystatin) and antibiotics (usually metronidazole) with improvement of GI symptoms. However, the beneficial effects of anti-fungals and antibiotics are generally short-lived and repeated courses of these therapies are often required, along with the persistent use of probiotics to control GI symptoms.
PBMCs were isolated by Ficoll-Hypaque density gradient centrifugation. Innate immune responses were assessed by incubating PBMCs (106 cells/ml) overnight with TLR4 agonist (LPS; 0.1 µg/ml, GIBCO-BRL, Gaithersburg, MD), TLR2/6 agonist (zymosan; 50 µg/ml, Sigma-Aldrich, St. Luis, Mo), TLR3 agonist (Poly I:C, 0.1 µg/ml, Sigma-Aldrich), and TLR7/8 agonist (CL097, water-soluble derivative of imidazoquinoline, 20 µM, InvivoGen, San Diego, CA) in RPMI 1640 with additives as previously described (Jyonouchi et al., 2001). Overnight incubation was adequate to induce the optimal responses in this setting. Levels of proinflammatory [tumor necrosis factor-α (TNF-), interleukin (IL)-1, IL-6, IL-12p40, and IL-23] and counter-regulatory [IL-10, transforming growth factor-ß (TGF-ß) and soluble TNF receptor II (sTNFRII)] cytokines in culture supernatant were then measured by an enzyme-linked immunosorbent assay (ELISA).
Cellular reactivity to T cell stimulants was assessed by incubating PBMCs (106 cells/ml) with T cell mitogens [Con A (2 µg/ml) and PHA (5 µg/ml)], recall antigens (Ags)[soy protein (100 µg/ml), ß-lactoglobulin (ßLG; 10 µg/ml) Sigma-Aldrich, candida Ag (5 µg/ml), dust mite (5 µl/ml) Greer, Lenoir, NC, tetanus toxoid (1:5000)], and IFN-γ inducing cytokines [IL-12p70 (0.2 ng/ml, BD Biosciences, San Diego, CA), IL-18 (1 ng/ml, BD Biosciences) for 4 days and measuring levels of IFN-, TNF-α, IL-5, IL-10, IL-12p40, and IL-17 in the culture supernatant (Jyonouchi et al., 2005). Initial titration studies showed that a four day incubation period resulted in the optimal production of these cytokines, in this setting.
Cytokine levels were measured by ELISA, using OptEIA™ Reagent Sets (BD Biosciences) for IFN-γ, IL-1ß, IL-5, IL-6, IL-10, IL-12p40, and TNF-α, and ELISA reagent set (R & D, Minneapolis, MN) for sTNFRII, IL-17 (IL-17A), and TGF-ß. IL-23 ELISA kit was purchased from eBiosciences, San Diego, CA. Intra- and inter-variations of cytokine levels were less than 5%.
For intracellular cytokine staining in CD4+ T cells, the following fluorochrome-conjugated monoclonal antibodies were used: CD4-PerCp, IFN-γ-PE-Cy7, IL-17-PE, IL-4-FITC, IL-10-Pacific Blue (all from eBiosicences), and TGF-ß-APC (R & D, Minneapolis, MN). PBMCs were incubated at 37oC overnight (16 h) with medium alone, Staphylococcal enterotoxin B (5 µg/mL, Sigma-Aldrich), or candida Ag (5 µg/ml, Greer) in the presence of Brefeldin A (BFA; 5 µg/ml, Sigma-Aldrich), anti-CD28 (1 µg/ml, eBiosciences), and anti-CD49 (1 µg/ml, eBiosciences). The same culture medium used for the cytokine production assay was utilized. Then PBMCs were permeabilized (permeabilization buffer, BD Biosciences) and stained with the above described antibodies. All flow cytometry was conducted by using FACSVantage SE TM (BD Biosciences) and the data were analyzed with the CellQuest software (BD Biosciences) and FlowJo (TreeStar, Ashland, OR).
PB monocytes were purified using an immuno-affinity column following the company’s instructions (MACS monocytes isolation kit, Miltenyi Biotec, Auburn, CA). Total RNA were extracted by the RNA easy kit (Quiagen, Valencia, CA). RNA labelling and hybridizations on Agilent Human 4x44K arrays (Agilent, Lexington, MA) were done using the Agilent One-Color Microarray-Based Gene Expression Analysis Ver 5.5 protocol (Agilent). All slides were scanned by an Agilent Scanner and normalized numerical data were obtained by Agilent Feature extraction software 9.5.
For comparison of test values with control values, a Wilcoxon signed rank test was used. For comparison of values of multiple groups, a Kruskall-Wallis test was used. A Chi square (χ2) test was used to examine the difference in frequency. These tests were performed using R.2.10.1 (R-Development Core Team 2009). A p value of <0.05 was considered to be statistically significant. For the analysis of microarrays experiments, Gene Spring GX v11 software (Agilent) was used. After filtering for “present” calls in at least 20% of samples, fold change analysis were performed for group for comparisons on 26992 probes. Genes with at least two fold changes, as compared to controls, are determined to be either up-regulated or down-regulated. Using a specific module of GeneSpring software (Agilent), pathways enrichment analysis on those genes was performed to see if there is a statistically significant enrichment (p<0.05) for specific BioPax pathways.
Prevalence of common childhood disorders in the study groups is shown in Table 2. The prevalence of allergic rhinoconjunctivitis and asthma in ASD-IS, ASD/FPIES, ASD/non-FPIES, as well as in non-ASD/FPIES children was found to be similar to what is reported in general population (Table 2) (Akinbami et al., 2011; Singh et al., 2010). However, ASD-IS children revealed a higher prevalence of recurrent infection [recurrent otitis media (ROM) and chronic rhinosinusitis (CRS)] than other study groups (Table 2). It is of note, that 3 subjects out of these 6 ASD-IS children with recurrent infection were diagnosed with specific polysaccharide antibody deficiency (SPAD). In contrast, non-ASD/FPIES children seldom revealed chronic infection. Around 10% of ASD/FPIES and ASD/non-FPIES children had history of ROM but they did not suffer from CRS and they are responsive to the first-line antibiotics such as amoxicillin. It remains to be seen whether apparent higher prevalence of ROM in the ASD children in our study is associated with under-diagnosis or under-treatment, secondary to their limited expressive language.
AR+AC | Asthma | ROM/CRS | Seizure disorders | |
ASD-IS(N=16) | 4 (25.0%) | 3 (18.8%)4 | 62 (37.5%) | 2 (12.5%) |
ASD/FPIES(N=29) | 4 (13.8%) | 3 (10.3%) | 43 (13.8%) | 1 (3.4%) |
ASD/non-FPIES(N=24) | 5 (20.8%) | 2/24 (8.3%) | 2 (8.3%) | 0 |
Non-ASD/FPIES (N=26) | 6 (23.1%) | 3/26 (11.5%) | 1 (0.4%) | 0 |
Normal control (N=43) | 8 (18.6%) | 5 (11.6%) | 0 | 0 |
Prevalence of co-morbid condition in the study subjects.
In response to a TLR4 agonist (LPS), ASD/FPIES PBMCs revealed lower TNF-α and IL-12 production than normal controls (Fig. 1). Non-ASD/FPIES PBMCs also revealed similar tendencies, however, the ASD/non-FPIES and ASD-IS cells did not differ from normal controls in the production of these cytokines (Fig. 1). IL-23 production with a TLR4 agonist
Production of TNF-α and IL-12 by PBMCs (106 cells/ml) from the study groups following overnight incubation with a TLR4 agonist (LPS). *; lower than normal controls by Wilcoxon signed rank test.
was lower in both the ASD-IS and ASD/FPIES groups than normal controls (Fig. 2). We also observed lower production of IL-12 (with a TLR9 agonist) and IL-1ß (in the absence of stimuli and with a dectin 1 agonist) in the ASD-IS group. This was not observed in any other study groups (Figs. 2-3). IL-6 production without stimuli and in response to a TLR9 agonist were also the lowest in the ASD-IS group (Fig.3). ASD/FPIES but not non-ASD/FPIES PBMCs revealed a similar tendency. However, this was less evident than in the ASD-IS cells (Fig. 3). Non-ASD/FPIES PBMCs revealed higher TGF-ß production with a TLR2/6 agonist than normal controls (Fig. 3). Taken together, our results indicate that ASD-IS, ASD/FPIES, and non-ASD/FPIES children reveals different patterns of cytokine production in response to TLR agonists.
Production of IL-23, IL-12, and IL-1β by PBMCs (106 cells/ml) in responses to TLR agonists as shown in the figure. *; significantly lower than normal controls by Wilcoxon signed rank test. IL-1β production was obtained when cells were cultured in the medium without a stimulus.
Production of IL-6, IL-1β, and TGF-β by PBMCs (106 cells/ml) when incubated overnight with medium only (IL-6), TLR9 agonist (IL-6), Dectin-1 agonist (D1 – IL-1β), and TLR2/6 agonist (TGF-β). *; significantly lower as compared to normal controls by Wilcoxon signed rank test. **; significantly higher than normal controls by Wilcoxon signed rank test.
No significant differences were observed among the study groups in responses to stimuli of T cell mitogens or recall antigens via vaccination (tetanus toxoid) or respiratory tract (dust mite). However, when PBMC responses to gut luminal Ags were tested, we observed a higher IL-5 production (with candida Ag) in non-ASD/FPIES children, while IL-17 production with ß-LG and candida Ag were higher in the ASD-IS group as compared to the non-ASD/FPIES group (Fig. 4). Such increase in IL-17 production was not observed in ASD/FPIES children (Fig. 4). Moreover, IL-10 production was lower in the ASD-IS children without stimuli as well as in response to candida Ag (Fig. 5). We also assessed frequency of Th cell subsets following stimulation of PBMCs with a polyclonal T cell stimulant (SEB) overnight by measuring intracellular expression of Th-lineage specific cytokines in CD4+ T cells. Both the ASD-IS and ASD/FPIES groups revealed a lower frequency of IFN-γ+ Th1 cells than controls (Fig. 6). Frequency of IL-17+ Th17 cells were also lower in the ASD/FPIES group but not in ASD-IS or non-ASD/FPIES children. Our results also indicated differences in T cell responses in the study groups.
Production of IL-5 and IL-17 in response to gut luminal antigens as shown in Figure. βLG; β-lactoglobulin. PBMCs (106 cells/ml) were incubated for 4 days with these luminal antigens. **; significantly higher than normal controls by by Wilcoxon signed rank test. ***; significantly higher than non-ASD/FPIES, ASD/non-FPIES, ASD/FPIES controls by Wilcoxon signed rank test.
Transcript profiles of PB monocytes were tested in 16 ASD-IS, 14 ASD/FPIES, 16 ASD/non-FPIES, and 26 normal control children. The changes of transcript expression are summarized in Table 3. As compared to ASD/FPIES, ASD/non-FPIES, and normal control groups, ASD-IS PB monocytes revealed that a large numbers of genes are up- or down-regulated over 2-fold and the difference was most significant when compared to normal controls (Table 3). ASD/FPIES children also revealed changes in gene expression as compared to ASD/non-
IL-10 production when PBMCs (106 cells/ml) were cultured for 4 days with candida Ag or medium only in the study groups. *; significantly lower than normal controls by Wilcoxon signed rank test.
Percent of IFN-γ+ and IL-17+ CD4+ T-helper cells per total CD4+ cells in PBMCs obtained from the study groups. PBMCs were incubated overnight with SEB and intracellular cytokine expression was assessed by flow cytometry as detailed in the Materials and Methods section. *; lower than normal controls by Wilcoxon signed rank test.
FPIES and normal controls. However, the numbers of genes up- or down-regulated were not as many as seen in ASD-IS children (Table 3). ASD/non-FPIES children also revealed changes in gene expression in a large number of genes as compared to controls (Table 3). Notable findings are the down-regulated expression of cytokines and chemokines in ASD-IS, as well as ASD/FPIES children, as compared to ASD/non-FPIES children. This finding is consistent with those from the bioassays. CCL7 expression was down regulated in ASD/non-FPIES children as compared to all the study groups. The pathway analysis did not reveal any significant enrichment of genes in known signaling pathways. GO analysis revealed significant differences in the ASD/FPIES and ASD/non-FPIES groups; changes were evident in inflammatory processes and cytokine/chemokine signaling pathways. In comparison with normal controls, both ASD-IS and ASD/FPIES patients did not reveal significant changes.
ASD-IS Vs. ASD/FPIES Vs. ASD/non-FPIES Vs. control | 2311 784 1341 | 61 1482 617 |
ASD/FPIES Vs. ASD/non-FPIES Vs. control | 251 223 | 922 191 |
ASD/non-FPIES Vs. control | 7233 | 11523 |
Transcription profiling data in the study subjects.
FPIES was first reported in 1940’s and has been recognized as a relatively benign condition that shows good responses to avoidance measures. However, delayed onset of symptoms and lack of readily (commercially) available diagnostic measures hinders early diagnosis and early intervention. This is especially true for children with impaired expressive language, such as ASD children. For example, it has been our observation that behavioral changes associated with GI discomfort and pain are often attributed to be just ‘being autistic’, despite presence of objective GI symptoms. In previous years, we have attempted to sort out whether FPIES is prevalent in ASD children and if so, how FPIES affects their behavioral symptoms. Our previous results indicated a high prevalence of FPIES in ASD children, probably partly due to delayed diagnosis and treatment (Jyonouchi, 2010; Jyonouchi H, 2007). We also observed changes of behaviors after resolution of GI symptoms when behavioral changes were assessed using the Aberrant Behavior Check list (ABC) (Jyonouchi, 2010; Jyonouchi H, 2007).
Corrected p value | |||
ASD-IS vs. ASD/FPIES | 3290 3593 8215 8217 | Oxygen transporter activity Hemoglobin complex Gas transport Oxygen transport | 0.02578 0.02578 0.01368 0.02578 |
ASD-IS vs. ASD/non-FPIES | 4528 6431 19583 | Response to stress Response to wounding Positive regulation of cell adhesion | 0.06276 0.06276 0.02184 |
ASD-IS vs. controls | NS | ||
ASD/FPIES vs. ASD/non-FPIES | 801 936 1402 1465 3128 3145 3430 4518 4528 4529 4531 4532 5082 5098 5114 6425 6431 7346 10575 12106 13736 15920 16941 17002 17201 17250 19242 19243 19628 22266 22267 23421 23697 23761 23762 | G-protin-coupled receptor binding Positive regulation of cytokine production Immune system process Chronic inflammatory response to antigenic stimulus Receptor binding Cytokine activity Extracellular space Chemotaxis Response to stress Defense response Inflammatory response Immune response Behavior Locomotry behavior Chemokine activity Response to external stimulus Response to wounding Regulation of vascular endotheial growth factor production Cytokine and chemokine mediated signaling pathway Nitric oxide transport Positive regulation of synaptic plasticity Positive regulation of heterotype cell-cell adhesion Regulation of cytokine biosynthetic process Positive regulation of cytokine biosynthetic process Taxis Chemokine receptor binding Regulation of nitric oxide biosynthetic process Positive regulation of nitric oxide biosynthetic process Positive regulation of mitosis Regulation of smooth muscle cell proliferation Positive regulation of smooth muscle cell proliferation Response to stimulus Positive regulation of nitrogen compound metabolic process Regulation of muticellular organismal process Positive regulation of muticellular organismal process | 0.06068 0.06644 0.03348 0.05304 0.05304 3.75E-04 0.03299 0.00644 2.43E-05 2.45E-08 2.14E-08 3.75E-04 0.05386 0.03956 0.00822 1.25E-05 2.15E-08 0.06512 0.03138 0.05304 0.05304 0.05304 0.05304 0.05613 0.00644 0.00918 0.06068 0.03956 0.05304 0.03956 0.00822 0.07731 0.05304 0.05304 0.03348 |
GO analysis results in the study subjects
However, among ASD children with FPIES, there exists a subset of children who are also vulnerable to recurrent infection and have history of adverse reactions to multiple medications and other substances. These children are sensitive to a variety of food proteins and often require the intake of extremely hydrolyzed hypoallergic formulas to get sufficient nutrition, in our experience. Parents of these children also reports fluctuating behavioral symptoms and cognitive skills following immune insults, such as viral syndrome. Our previous studies indicated that these children, with fluctuating behaviors, often exhibit distinct innate immune abnormalities (Jyonouchi et al., 2008). Further analysis of this population led to the finding that ASD children with these clinical phenotypes, as well as ‘treatment-resistant’ FPIES, do reveal more significant changes in innate immunity which can be detected using bioassays as well as transcript profiles in PB monocytes (manuscript in press). We have categorized this group of ASD children as ASD-immune subtype (ASD-IS), since they appear to reveal different immune abnormalities than ASD/FPIES as well as ASD/non-FPIES children. These children also seem to be vulnerable to dysbiosis, since their GI symptoms, as well as behavioral symptoms, tend to improve following anti-fungal treatment and/or antibiosis targeting pathogenic microbes in the gut. Unfortunately, as reported by others (Sandler et al., 2000), these effects are generally transient.
It remains unclear as to how such clinical phenotypes are associated with innate and adaptive immune responses and how their immune responses are different from those observed in ASD/FPIES and non-ASD/FPIES children. To address this question, we conducted detailed studies of innate and adaptive immune responses in ASD-IS children in comparison with ASD/FPIES, non-ASD/FPIES, ASD/non-FPIES, and normal controls. Since the presence of active GI inflammation is likely to affect the bioassay results, we conducted these assays after FPIES subjects were appropriately treated with avoidance measures and nutritional supplements if required. Co-morbid conditions summarized in Table 2 in our study groups revealed that ASD-IS children do seem to have a higher prevalence of COM/CRS than other study groups, although prevalence of atopic disorders does not appear to be altered significantly among the study groups, indicating that atopy or Th2 deviated responses are unlikely to be associated with their clinical characteristics.
When we assessed innate immune responses in the study groups, our results revealed lower TNF-α and IL-12 production with a TLR4 agonist (LPS) than normal controls in the ASD/FPIES group. In the colon where microbes reside at the highest concentration, responses to endotoxin such as LPS are suppressed – so-called LPS desensitization (Abreu et al., 2005; Michalek et al., 1982; Smith and Nagler-Anderson, 2005; Wannemuehler et al., 1982). Tendency for lower responses to LPS in ASD/FPIES children may be beneficial for maintaining oral tolerance after recovering from FPIES. Although it was not significant, the same tendency was observed in non-ASD/FPIES children.
In ASD-IS children, while their LPS responses are equivalent to those of normal controls, spontaneous production of IL-1ß and IL-12 production with a TLR9 agonist was markedly lower. IL-1ß production with a dectin 1 agonist (heat killed candida) was also lower in the ASD-IS children. Lower production of IL-6 (without stimuli and with TLR9 agonist) and IL-23 (with a TLR4 agonist) were observed in both ASD/FPIES and ASD-IS children but this was more evident in ASD-IS children. On the other hand, non-ASD/FPIES children were noted to have higher TGF-ß with a TLR2/6 agonist (zymosan). IL-1ß, IL-6, and IL-23 are all important for differentiation and maintenance of Th17 cells as a part of their pleiotropic biological actions (Kimura and Kishimoto, 2010; Zhou et al., 2009). While IL-12 has a key role in Th1 cell differentiation. Given these findings, it may be questioned whether ASD-IS, as well as ASD/FPIES children, have impaired development of Th17 cells. When we tested frequency of IL-17+ Th cells after stimulation of PBMCs with SEB overnight, frequency of Th17 cells was lower in ASD/FPIES children than normal controls, but this was not evident in the ASD-IS children. Both ASD-IS and ASD/FPIES children revealed a little lower frequency of IFN-γ+ Th1 cells. However, to our surprise, IL-17 production in response to food proteins was higher in the ASD-IS children. Comparative frequency of Th17 cells, but higher IL-17 production in the ASD-IS children indicate that committed Th17 cells may keep producing a large amount of IL-17. These findings also indicate that excessive IL-17 responses in ASD-IS children may be associated with dysregulated development and maintenance of Th17 cells. In addition, persistent IL-17 responses will not aid in and may hinder establishment of gut immune homeostasis consistent with clinical phenotype of ‘treatment-resistant’ FPIES in the ASD-IS children.
In contrast to the ASD-IS group, non-ASD/FPIES children recovering from FPIES revealed higher TGF-ß with a TLR2/6 agonist (zymosan). These children did not reveal an increase in frequency of Th17 cells or IL-17 production. Since TGF-ß regulates (down-regulates) various immune responses and also serves as a key differentiation factor for regulatory T (Treg) cells, especially in the absence of inflammatory cytokines (Burgler et al., 2009; Zhou et al., 2009), it may be wondered whether ASD-IS children and in some-degree, ASD/FPIES children have impaired development or function of regulatory T cells. In the GI mucosa, IL-10 and TGF-ß producing Treg cells are believed to have a major role in the gut immune homeostasis (Lee and Mazmanian, 2010). In that regard, non-ASD/FPIES children and perhaps ASD/FPIES children who did not reveal excessive IL-17 responses may be in the process of establishing gut mucosal immune homeostasis, while recovering from FPIES.
In the gut mucosa, it became known that Th17 cell develop prior to antigen exposure, bearing an important role in mucosal immune defense. However, following antigen-exposure, regulatory T cells develops, developing immune homeostasis in the gut. During this process, the gut microbiota is believed to have an pivotal role in developing gut immune homeostasis affecting plasticity of Th cell and regulatory T cell development (Atarashi et al., 2011; Lee and Mazmanian, 2010; Lochner et al., 2011; Zhou et al., 2009). Consistent with this assumption, we also found that spontaneous IL-10 production, as well as that in response to luminal antigens (food proteins and candida Ag), were equivalent in non-ASD/FPIES children in comparison with normal controls. While ASD-IS children revealed lower IL-10 production, in the absence of stimuli and with candida Ag. These results again indicate that tolerance induction or establishment of immune homeostasis may be impaired in the ASD-IS children. However, it needs to be cautioned that further studies are necessary to explore this possibility, including testing gut mucosal expression of Th lineage cells in these patients. As previously stated, ASD-IS children are clinically characterized with markedly fluctuating behavioral and cognitive activity. Our findings also indicate the possibility that in addition to impairment of gut immune homeostasis, systemic immune homeostasis may also be dysregulated in the ASD-IS children. This may be the results of chronic gut inflammation or uncontrolled inflammatory responses.
To further assess changes in innate immunity, we also studies transcript profiles of PB monocytes in the study groups. However, partly due to the restriction of approved protocol, we were not able to conduct such a study in the non-ASD/FPIES children. Nevertheless, our results indicated a significantly altered gene expression in PB macrophages in the ASD-IS children. The numbers of genes with >2 fold up- or down-regulated expression was the highest when compared to normal controls and least when compared to ASD/FPIES children. This is not surprising, given the fact that ASD-IS and ASD/FPIES children both suffered from FPIES and gut inflammation. However, what was surprising is that the significant differences in transcript profiles of PB monocytes between ASD/non-FPIES and normal controls. These results also indicate that ASD/non-FPIES children may have altered immune responses or other changes that can be reflected in PB monocytes. This seems to be consistent with the results of the GO analysis, which revealed significant changes between ASD/FPIES and ASD/non-FPIES children. Involved pathways revealed differences with GO analysis are largely associated with immune/inflammatory responses as well as cytokines and chemokines, possibly reflecting fundamental differences in the 2 study groups. It should be noted that recent genetic studies have been accumulating evidence that ASD is a behavioral syndrome encompassing markedly heterogeneous populations (Bale et al., 2010; Rudan, 2010; Toro et al., 2010). Our findings also support results of previous genetic studies. However, we have to be cautious about the results given the low number of study subjects that underwent this analysis.
With transcription profiling, we also found that certain cytokines, notably IL-6, IL-1ß, and IL-23, were down regulated in ASD-IS children, as compared to ASD/non-FPIES children. ASD/FPIES children also revealed down-regulation of IL-6 and IL-23. Interestingly, down-regulation of IL-6 was more prominent in ASD/FPIES children, despite the fact that in vitro IL-6 protein production was lower in the ASD-IS children. In addition, GO analysis revealed significant differences between ASD/FPIES and ASD/non-FPIES groups, although given clinical features, one can expect that differences may be more prominent between ASD/IS and ASD/non-FPIES children. Our findings may indicate an importance of post-transcriptional regulation including ones exerted by microRNA (Baltimore et al., 2008).
The notable weakness of this study is lack of data of transcription profiling in the non-ASD/FPIES children. This was secondary to the restriction imposed by the IRB and lack of funding. In the future, it will be interesting to compare transcript profiles between ASD-IS/ASD-FPIES and non-ASD/FPIES children which may yield further important information.
In summary, our results indicate that altered adaptive and innate immune responses are observed in both ASD-IS and ASD/FPIES children but difference exist between the ASD-IS and ASD/FPIES children. In addition, these changes also differed from those observed in non-ASD/FPIES children. Our findings thus indicate that changes in innate and adaptive immunity observed in ASD-IS children and in some degree even in ASD/FPIES children, are not likely to be attributed to FPIES but may be associated with impaired establishment of immune homeostasis, perhaps, affected by aberrant innate immune responses.
This study was partly funded by Jonty Foundation, St. Paul, MN, and Autism Research Institute, San Diego, CA. We are also thankful for critical review by Dr. Lisa Huguienin.
Dairy products are beneficial to human health, especially for formula-fed newborns. India and the European Union both produced around 160 Mt milk in 2016 [1]. People are very concerned about the nutritional ingredients and illegal additives of the milk products. As we all know, bovine milk includes 80% caseins (CN) and 20% whey proteins. Caseins consist of αs1-CN, β-CN, αs2-CN, and κ-CN in an approximate 4:4:1:1 weight ratio. Whey proteins mainly consist of β-lactoglobulin (β-LgA, β-LgB) and α-lactalbumin (α-Lac) in a 3:1 weight ratio [2]. The quantity of milk fraction proteins is related to the development of the baby. As many literatures reported, preservatives and artificial sweeteners may be harmful to people’s health [3, 4, 5].
\nAccording to the regulation of FDA and China national food safety standard, food additives such as benzoic acid, sorbic acid, natamycin, lysozyme, saccharin sodium, and aspartame are not permitted to be added to milk powder. So, establishment of accurate and convenient methods for the analysis of these food additives in milk powder is critical to people’s health.
\nHere, I’ll introduce two works of our groups to readers: the comparison of six sample preparation methods for the analysis of four preservatives and two artificial sweeteners in milk powders [6] and two-dimensional liquid chromatography (2DLC) for determination of five major proteins and seven additives in milk powders.
\nIt is not very easy to determine the trace residues or contaminants in infant milk powder for its complex matrix [7]. So, the sample pretreatment is the key step in the whole analytical procedures. According to the literatures, there are two kinds of sample preparation methods for analysis of contaminants in dairy products. One is to extract the targeted analytes, and the other is to remove the interferents. Usually, solid-phase extraction had been widely used as a milk sample preparation method. Sometimes, liquid-liquid extraction followed by a SPE cleanup step was served to remove the macromolecular protein prior to determination of the target analytes [8]. Removal of the protein in milk could be done by precipitating them with heavy metallic salt [9] or sodium tungstate [10].
\nOur group developed six sample preparation methods based on the literatures, that is, liquid-liquid extraction, organic precipitation, heavy precipitation, and three different solid-phase extraction methods (C18, HLB, MAX). In order to obtain the higher recovery and reduce the time cost and organic solvent dosage, the six different sample preparation methods were compared.
\nThis method is based on the study of preservatives in cheeses [11]. Around 2.0 g milk powder was mixed with 4.0 mL of deionized water (60°C). After 10 min of ultrasonication, 5.0 mL of ethyl acetate and 1.0 mL 10 mmol L−1 of formic acid were added. The samples were extracted for 40 min on a rotary mixer at 400 rpm. After that, they were centrifuged for 5 min at 3200 rpm. The supernatant was transferred to another tube, and the sediment was extracted with 5.0 mL of ethyl acetate once again. The second supernatant obtained was combined with the one from the first extraction. Then they were filtered and evaporated to dryness at ambient temperature. The residues were dissolved in 500 μL mixture solution (0.1 M acetate buffer: methanol = 2:1, v/v) and vortexed for 20 s.
\nThis method is based on the study of five macrolide antibiotics in milk [10]. The sample dissolving steps were the same as method A (liquid-liquid extraction). After 10 min of ultrasonication, this solution was centrifuged at 6000 rpm for 15 min. The defatted milk was transferred to a new centrifuge tube, and then 1.0 mL 10% sulfuric acid and 5.0 mL 10% sodium tungstate solutions were added. The resulting solution was vigorously shaken for 2 min and diluted to 10 mL with water and centrifuged at 4000 rpm for 10 min. The supernatant was filtered and evaporated to dryness at ambient temperature. The residues were dissolved in 500 μL mixture solution (0.1 M acetate buffer:methanol = 2:1, v/v) and vortexed for 20 s.
\nThis method is based on the detection of adulteration of milk with soy milk [9]. The protocols of method C were the same as method B, only except the precipitants of 3.0 mL 0.085 mol L−1 K4[Fe(CN)6] and 3.0 mL 0.25 mol L−1 ZnSO4 solutions employed in this method.
\nThis method is based on the study of fluoroquinolones in milk [12] and determination of 20 pharmacologically active substances in various milk samples [13]. After 10 min of ultrasonication, 7.0 mL 1% trichloroacetic acid (TCA) and 3.0 mL acetonitrile were added. The resulting solution was vigorously shaken for 2 min and centrifuged at 4000 rpm for 10 min. The supernatant obtained extracted by SPE. Three kinds of cartridges (CNWBOND LC-C18 SPE, Poly-Sery HLB SPE, and Poly-Sery MAX SPE) were examined. The SPE protocol on three types of cartridges was consisted of the following steps: (1) activation of the cartridges with 3 mL methanol first and then conditioned with 3 mL deionized water, (2) sample loading, and (3) sample elution with 3 mL 20 mmol L−1 ammonium sulfate and 3 mL 80% acetonitrile. After the eluates were filtered, the steps of evaporation and residues dissolving were the same as method A (liquid-liquid extraction).
\nFor method A (liquid-liquid extraction), the extraction time of 10, 15, 20, 25, 30, 35, 40, 45, and 50 min was investigated. The results are illustrated in Figure 1, indicating 40 min was the best choice. The different extraction temperatures from 25 to 40°C were tested, and the results showed that there were no obvious differences in recoveries of each preservative. For method B (precipitation based on sodium tungstate) and method C (precipitation based on potassium ferrocyanide), the results showed that there were no significant differences between these two methods. The recoveries of benzoic acid, sorbic acid, and saccharin sodium were more than 80%. However, the recovery of lysozyme (<30%) indicated that both of the precipitate-based methods were not suitable for enzyme [14] and it might be coprecipitated with the proteins in milk powder. For method D-F (SPE), three kinds of cartridges (CNWBOND LC-C18 SPE, Poly-Sery HLB SPE, and Poly-Sery MAX SPE) and their elution solvents applied were investigated. Porous silica particles surface bonded with C18 was the most commonly used sorbents. Poly-Sery HLB is the kind of polymeric sorbents that was reported as being superior to the silica-based C18. The major difference between the HLB and MAX sorbents is the presence of the anion-exchange groups that provide high selectivity for acidic compounds. So, the three kinds of cartridges were tested to evaluate their applicability. Four elution solvents with different polarity were tested: 70% acetonitrile, 80% acetonitrile, 90% acetonitrile, and 100% acetonitrile. The recoveries of each food additive showed that Poly-Sery HLB with 80% acetonitrile provided the best results.
\nEffects of extraction time on the six food additives recovery obtained with method A in the two milk samples (n = 3). (A) Whole milk powder and (B) skimmed milk powder (SMP). The six food additives: benzoic acid, sorbic acid, natamycin, lysozyme, aspartame, and saccharin sodium.
Figure 2 shows the recovery results of each food additive obtained by the six different abovementioned methods, respectively. Each sample was analyzed in triplicate. Both method A (liquid-liquid extraction) and method D (Poly-Sery HLB SPE) have good recoveries (>80%) for all six analytes, but results of our study showed that the average RSD% for method A of the six analytes were all between 3.7 and 5.4%, which is more than that of HLB SPE method (2.3–3.8%). Considering the environmental and economic costs, method D (Poly-Sery HLB SPE) was employed in our further study.
\nRecovery results of the six different sample preparation methods. The left group in A–F represents the whole milk powder matrix, and the right group in A–F represents the skimmed milk powder matrix. The six food additives: benzoic acid, sorbic acid, natamycin, lysozyme, aspartame, and saccharin sodium. (A) Method A (liquid-liquid extraction). (B) Method B (precipitation based on sodium tungstate). (C) Method C (precipitation based on potassium ferrocyanide). (D–F) Method D-F (solid phase extraction): (D) Poly-Sery HLB SPE, (E) CNWBOND LC-C18 SPE, and (F) Poly-Sery MAX SPE.
The proposed SPE (Poly-Sery HLB)-HPLC-DAD method was validated in terms of linearity, limit of detection, limits of quantity (LOQ), within- and between-day precision, and accuracy. Figure 3 shows the chromatograms of the mixed standard solutions. We can see that the six preservatives and sweeteners were baseline separated and had a good resolution (R ≥ 2.6) under the chromatographic condition.
\nChromatogram of mixed standard solutions. (1) Benzoic acid, (2) sorbic acid, (3) saccharin sodium, (4) natamycin, (5) aspartame, and (6) lysozyme.
The linearity of the method was evaluated under 210 nm with the mixed standard solutions, pooled whole milk powder matrices,and pooled skimmed milk powder matrices. The LODs and LOQs in each case were estimated based on S/N = 3 and 10, respectively. The results including the regression equations, the linear ranges, and regression coefficients are summarized in Table 1.
\n\n | Amounts of chemical reagents used | \nTime coste | \n
---|---|---|
Method Aa | \nEthyl acetate: 5.0 + 5.0 mL Formic acid solution (10 mmol L−1): 1.0 mL | \n>60 min | \n
Method Bb | \nSulfuric acid solution (10%): 1.0 mL Sodium tungstate solution (10%): 5.0 mL | \n≈50 min | \n
Method Cc | \nK4[Fe(CN)6] solution (0.085 mol L−1): 3.0 mL ZnSO4 solution (0.25 mol L−1): 3.0 mL | \n≈50 min | \n
Method D–Fd | \nTCA solution: 7.0 mL Acetonitrile: 3.0 mL Methanol: 3.0 mL Ammonium sulfate solution: 3.0 mL Acetonitrile: 3.0 mL | \n≈25 min | \n
Comparison of the cost-effectiveness of six methods.
Method A (LLE).
Method B (precipitation based on sodium tungstate).
Method C (precipitation based on potassium ferrocyanide).
Method D–F (SPE).
Time cost is the average value of the three experiments.
The precision and accuracy were tested in two milk powder matrices, that is, the whole milk powder and the skimmed milk powder. Figures 4 and 5 showed the representative chromatograms. All chromatographic peaks were separated completely and had a good resolution (R ≥ 1.5). Recovery studies of benzoic acid, sorbic acid, natamycin, lysozyme, saccharin sodium, and aspartame were evaluated by analysis of blank pooled samples spiked with 10 μg g−1 (lower level), 50 μg g−1 (middle level), and 100 μg g−1 (upper level) of each analyte. And the data were calculated based on the matrix-matched regression curves and summarized in Table 2. The intraday precision was studied at lower, middle, and upper concentration levels (n = 5). The interday precision was analyzed with spiked samples at 50 μg g−1 for six-day determinations (Table 3).
\nChromatograms of the blank whole milk powder sample and spiked whole milk powder sample. (A) The spiked whole milk powder sample: (1) benzoic acid, (2) sorbic acid, (3) saccharin sodium, (4) natamycin, (5) aspartame, and (6) lysozyme (50 μg g−1 of each food additives). (B) The blank whole milk powder sample.
Chromatograms of the blank skimmed milk powder sample and spiked skimmed milk powder sample. (A) The spiked skimmed milk powder sample: (1) benzoic acid, (2) sorbic acid, (3) saccharin sodium, (4) natamycin, (5) aspartame, and (6) lysozyme (50 μg g−1 of each food additives). (B) The blank skimmed milk powder sample.
Analytes | \nSample matrix | \nRegression equationb y = ax ± b | \nRegression coefficient | \nLinear range (μg L−1) | \nLOD (μg L−1) | \nLOQ (μg L−1) | \n
---|---|---|---|---|---|---|
Benzoic acid | \nAa | \ny = 0.582x − 0.035 | \n0.9999 | \n100–20,000 | \n45 | \n95 | \n
Ba | \ny = 0.497x − 0.028 | \n0.9998 | \n250–20,000 | \n70 | \n200 | \n|
Ca | \ny = 0.501x + 0.037 | \n0.9998 | \n250–20,000 | \n60 | \n200 | \n|
Sorbic acid | \nAa | \ny = 0.362x + 0.017 | \n0.9998 | \n150–25,000 | \n60 | \n130 | \n
Ba | \ny = 0.297x + 0.028 | \n0.9994 | \n300–25,000 | \n90 | \n255 | \n|
Ca | \ny = 0.500x − 0.054 | \n0.9995 | \n300–25,000 | \n90 | \n240 | \n|
Natamycin | \nAa | \ny = 0.313x + 0.039 | \n0.9999 | \n200–30,000 | \n60 | \n135 | \n
Ba | \ny = 0.364x + 0.023 | \n0.9997 | \n500–30,000 | \n80 | \n215 | \n|
Ca | \ny = 0.288x + 0.060 | \n0.9997 | \n500–30,000 | \n90 | \n230 | \n|
Saccharin sodium | \nAa | \ny = 0.405x + 0.097 | \n0.9999 | \n100–25,000 | \n30 | \n75 | \n
Ba | \ny = 0.329x − 0.010 | \n0.9992 | \n200–25,000 | \n50 | \n120 | \n|
Ca | \ny = 0.525x − 0.091 | \n0.9995 | \n200–25,000 | \n70 | \n150 | \n|
Aspartame | \nAa | \ny = 0.617x − 0.043 | \n0.9999 | \n100–30,000 | \n50 | \n95 | \n
Ba | \ny = 0.577x + 0.033 | \n0.9996 | \n200–30,000 | \n60 | \n165 | \n|
Ca | \ny = 0.505x − 0.023 | \n0.9997 | \n200–30,000 | \n70 | \n180 | \n|
Lysozyme | \nAa | \ny = 0.405x + 0.097 | \n0.9997 | \n100–30,000 | \n45 | \n95 | \n
Ba | \ny = 0.381x + 0.059 | \n0.9995 | \n250–30,000 | \n60 | \n170 | \n|
Ca | \ny = 0.370x + 0.068 | \n0.9996 | \n250–30,000 | \n60 | \n170 | \n
Linearity and LOD of the developed method (n = 3).
(A) Aqueous, (B) pooled whole milk powder, and (C) pooled skimmed milk powder.
y is the average peak area of each analyte (n = 3), and x is the mass concentration of the analyte in μg L−1.
Analytes | \nAdded (μg g−1) | \nWhole milk powder | \nSkimmed milk powder | \n||
---|---|---|---|---|---|
Recovery (%) | \nRSD (%) | \nRecovery (%) | \nRSD (%) | \n||
Benzoic acid | \n10 | \n93 | \n4.8 | \n95 | \n3.3 | \n
50 | \n95 | \n3.5 | \n93 | \n2.9 | \n|
100 | \n93 | \n1.4 | \n95 | \n2.5 | \n|
Sorbic acid | \n10 | \n94 | \n3.7 | \n94 | \n4.1 | \n
50 | \n97 | \n1.5 | \n97 | \n3.0 | \n|
100 | \n92 | \n1.2 | \n99 | \n2.2 | \n|
Natamycin | \n10 | \n89 | \n4.9 | \n90 | \n3.9 | \n
50 | \n90 | \n2.0 | \n91 | \n3.5 | \n|
100 | \n96 | \n1.0 | \n99 | \n2.7 | \n|
Lysozyme | \n10 | \n91 | \n4.5 | \n92 | \n4.4 | \n
50 | \n92 | \n5.0 | \n95 | \n1.9 | \n|
100 | \n101 | \n2.8 | \n103 | \n2.3 | \n|
Saccharin sodium | \n10 | \n95 | \n3.3 | \n89 | \n4.6 | \n
50 | \n90 | \n4.5 | \n93 | \n4.1 | \n|
100 | \n97 | \n2.0 | \n98 | \n2.4 | \n|
Aspartame | \n10 | \n90 | \n2.9 | \n93 | \n3.6 | \n
50 | \n92 | \n4.5 | \n94 | \n1.1 | \n|
100 | \n94 | \n2.4 | \n94 | \n1.2 | \n
Precision and accuracy of the assay for whole milk powder and skimmed milk powder analysis (n = 3).
Among the six different sample extraction methods, two precipitate-based methods (method B and method C) were not suitable for the low recovery of lysozyme. Both method A and method D obtained good recoveries of six food additives simultaneously, but the major problem of method A is the lower reproducibility and much more time cost than method D. SPE was a simple and rapid method for the extraction of six food additives. From the results of method D–F, Poly-Sery HLB cartridge was confirmed as the most appropriate material for its high recovery.
\nTwo-dimensional liquid chromatography has been used in many aspects. Herein, a 2DLC method was introduced for the simultaneous determination of five major proteins and seven additives in milk powders. Considering the macromolecular proteins, C4 column was placed in the first dimension (1D), and C18 column was placed in the second dimension (1D) for analysis of the seven additives. Finally, the five proteins in milk powders were separated completely on the 1D column, and the seven additives can be simultaneously analyzed on the 2D column. In the middle of 1D and 2D, a trapping column and a ten-port switching valve was served. This method was compared with the conventional one-dimensional liquid chromatography (1DLC) in terms of sample preparation, limit of detection (LOD), and recovery.
\n0.5 g milk powder samples were diluted in 2.0 mL ultrapure water. After 10 min of ultrasonication, 0.5 mL of Carrez I solution (500 mM aqueous potassium ferrocyanide), 0.5 mL of Carrez II solution (500 mM aqueous zinc acetate), and 1 mL of ACN were added in order to precipitate the proteins.
\nMaltol, ethyl maltol, vanillin, ethyl vanillin, benzoic acid, sorbic acid, and saccharin sodium were separated on a 2010 AT chromatographic instrument from Shimadzu Corporation (Kyoto, Japan). A C18 analytical column (4.6 × 150 mm, 5 μm) was used. The mobile phases were ammonium acetate buffer (25 mM, pH 6.6) (solvent A) and ACN/water = 90/10 (v/v) (solvent B); the following gradient program was used: 0–2.8 min, 0% ACN; 2.8–5 min, progressing linearly to 45% ACN; and 5–15 min, maintaining at 45% ACN. The flow rate was 1.0 mL/min, and the column temperature was maintained at 40°C. The injection volume was 5 μL, and the detection wavelength was 214 nm. The peak area was calculated for quantification, and each sample or standard was injected in triplicate.
\nAround 0.2 g of the milk powder samples were dissolved for 10 min in 5 mL of buffer (6 M urea, 0.5% OG). The samples were then filtered through a 0.45 μm nylon membrane before injected into the 2DLC system for analysis.
\nThe 2DLC system consisted of two LC-20AB binary gradient pumps (Shimadzu Technologies), one six-port two-position switching valve (VICI Valco Instruments, Houston, TX, USA), a SIL-20A autosampler, two DGU-20A3 degassers, a CTO-20A column oven, and two SPD-M20A diode array detectors.
\nA scheme of the 2DLC system is shown in Figure 6. For the first dimension (1D), a Venusil XBP-C4 analytical column (4.6 mm × 100 mm, 5 μm) coupled with a C4 guard column was used. One aspect is for separation of proteins and additives, and the other is for five proteins analysis. The target fractions (polar substances) from 1D were enriched by a trapping column (ODS C18, 4.6 mm × 50 mm, 5 μm) and switched into the 2D through a six-port valve. A Hypersil ODS-2 C18 column (4.6 × 150 mm, 5 μm) was used to completely separate the seven food additives in 2D. The mobile phase consisted of ACN/water/TFA (10/90/0.1, v/v/v) (solvent A) and ACN/water/TFA (90/10/0.1, v/v/v) (solvent B) for 1D and ammonium acetate (25 mM, pH 6.6) (solvent A) and ACN/water (50/50, v/v, pH 7.2) with 25 mM ammonium acetate (solvent B) for 2D. The column temperature was 40°C. The detection wavelengths for 1D was 214 nm, for 2D were 254 nm and 278 nm. The eluted program is shown in Figure 6. The injection volume was 5 μL, and each sample or standard was injected in triplicate.
\nSchematic representation (a–c) and gradient, flow rates, and switching times (d and e) of the stop-flow heart-cutting 2DLC system.
In order to accurately quantify the five proteins and seven additives in milk powder samples using the 2DLC method, some important parameters were optimized, including the stationary phase, mobile phase, and switching time.
\nAccording to the literature, a shorter switching time in the 2DLC system means better shape of the peaks in the 2D chromatogram [15]. Because the milk powder matrix is so complex, the ideal 1D column would be able to separate the proteins and additives into two groups. The additives with higher polarity were concentrated within a short period of time and eluted rapidly, while the proteins were separated completely after the elution of additives by adjusting the mobile phase. In order to achieve this goal, two columns were tested: a Venusil XBP-C4 column (4.6 mm × 100 mm, 5 μm) and a Venusil XBP-C8 column (4.6 mm × 100 mm, 5 μm). These two types of columns could separate the seven additives and five proteins as two groups. The seven additives were concentrated at 2.0–5.0 min on the C4 column and 2.0–6.0 min on the C8 column. Therefore, the Venusil XBP-C4 column was chosen as the 1D column because of the shorter switching time. Figure 7A shows the chromatogram of the seven additives and five major proteins. The seven additives were focused at the first minutes, and the five major proteins could be separated later. For 2D separation, Hypersil ODS-2 C18 column showed better separation performance for the seven additives. A trapping column was used as the interface between 1D and 2D, which should result in better enrichment of the targets [16]. For online 2DLC, the choice of the mobile phase is very important. Because of protein separation, ACN was chosen as the organic mobile phase. 0.1% v/v TFA was added to all mobile phases to improve the protein separation effect.
\nThe 2DLC chromatogram of the 12 mixed standards substances. The 1D chromatogram of seven additives (2.0–5.0 min) and five proteins on the 1D C4 column (4.6 mm × 100 mm, 5 μm) (up) and the 2D chromatogram of the seven additives on the C18 analytical column (down). (1) Maltol, (2) saccharin sodium, (3) benzoic acid, (4) sorbic acid, (5) ethyl maltol, (6) ethyl vanillin, and (7) vanillin.
The mobile phases for 1D were A1, ACN/water (10/90 v/v, 0.1% TFA), and B1, ACN/water (90/10 v/v, 0.1% TFA). Solvent B1 was set at 25% from 0 to 5 min in order to elute the additives quickly. Due to the little polarity difference of proteins, a gentle gradient of 0.14% B min−1 was used to achieve good separation of the five proteins, which was consistent with the literature [17, 18, 19]. As shown in Figure 7, the proteins were eluted in the following order: αs2-CN, αs1-CN, α-Lac, β-CN, β-LgB, and β-LgA. It should be noted that there were no standards for αs1-CN and αs2-CN proteins, only for their mixture [10]. The chromatographic profiles showed no carryover effects of these proteins. A shoulder for the αs1-CN standard can be seen due to the presence of its two variants (αs1-CN and αs2-CN), which are very difficult to separate completely. From the different findings from previous reports, the monomorphic α-Lac was eluted firstly than β-CN [17, 18, 19]. The three shoulders of β-CN corresponded to its variants. As previously reported, γ-CN is the proteolytic product of β-CN, so they could be eluted together [18]. For β-Lg, variant B eluted before variant A, which is consistent with the literature [18]. During the process of quantitative analysis, αs1-CN and αs2-CN were quantified together, as for the three variants of β-CN.
\nAcetic ammonia is often used as the modifier in liquid chromatography separation. To obtain better separation of the seven additives in 2D, a series of acetic ammonia concentrations (15, 20, 25, 30 mM) were tested. When 25 mM acetic ammonia was added, the baseline was much more stable, and the peak shape was greatly improved. Therefore, the 2D mobile phase were as follows: A2, 25 mM acetic ammonia, and B2 ACN/water (50/50 v/v) with 25 mM acetic ammonia. The gradient program is shown in Figure 6. The initial mobile phase of 1D was optimized and set at 25% B1. If lower than 25% B1, elution of the seven additives would be taken too long in the 1D column, which could lead to sample loss in the trapping column before switching; if higher than 25% B1, maltol and saccharin sodium could be separated incompletely in 2D because of ACN in the trapping column.
\nThe switching time is a key parameter in this method. Three switching time (2.0–4.5, 2.0–5.0, and 2.0–5.5 min) were tested. When the switching time was between 2.0 and 4.5 min, maltol and saccharin sodium were separated incompletely, and the sorbic acid peak was less sharp than that for 2.0–5.0 min; when between 2.0 and 5.5 min, some analytes were lost in the trapping column. Therefore, 2.0–5.0 min was chosen as the final switching time for the experiment. Figure 7 shows the chromatogram of the 12 mixed standard substances using the optimized 2DLC method. In Figure 7A, the seven additives were eluted between 2.0 and 5.0 min due to their higher polarity, and the proteins were separated on the 1D column (8.0–30.0 min); Figure 7B shows the 2D chromatogram of the seven additives that were switched from the 1D column at 2.0–5.0 min. The whole analysis process was less than 30 min, which provide a highly efficient analysis method.
\nThe matrix effect, linearity, LOD, intra- and interday precision, and accuracy were validated under the optimized conditions for 1DLC and 2DLC.
\nThe method validation parameters of 1DLC and 2DLC were shown in Table 4. The correlation coefficient values (
Analytes | \nMethods | \nSample matrix | \nRegression equationa y = ax ± b | \nR2 | \nSlope ratio (matrix/blank) | \nLinear range (μg mL−1) | \nLOD (μg mL−1) | \nLOQ (μg mL−1) | \n
---|---|---|---|---|---|---|---|---|
MAL | \n1DLC | \nBlank | \ny = 34636x − 34306 | \n0.9996 | \n1.21 | \n0.28–28 | \n0.051 | \n0.21 | \n
Matrix | \ny = 41933x − 77788 | \n0.9995 | \n/ | \n1.0–100 | \n0.41 | \n1.02 | \n||
2DLC | \nBlank | \ny = 33548x + 367540 | \n0.9991 | \n1.09 | \n0.28–28 | \n0.065 | \n0.187 | \n|
Matrix | \ny = 36410x + 354210 | \n0.9990 | \n/ | \n0.28–28 | \n0.105 | \n0.25 | \n||
Saccharin sodium | \n1DLC | \nBlank | \ny = 24706x + 43056 | \n0.9998 | \n0.97 | \n0.22–22 | \n0.026 | \n0.10 | \n
Matrix | \ny = 23945x + 96114 | \n0.9998 | \n/ | \n0.5–50 | \n0.067 | \n0.21 | \n||
2DLC | \nBlank | \ny = 4328x + 7786 | \n0.9991 | \n0.94 | \n0.22–22 | \n0.044 | \n0.11 | \n|
Matrix | \ny = 4086x + 10206 | \n0.9991 | \n/ | \n0.22–22 | \n0.074 | \n0.19 | \n||
Benzoic acid | \n1DLC | \nBlank | \ny = 31513x − 244 | \n1.0000 | \n1.08 | \n0.25–25 | \n0.018 | \n0.051 | \n
Matrix | \ny = 34007x − 3087 | \n1.0000 | \n/ | \n0.5–50 | \n0.18 | \n0.42 | \n||
2DLC | \nBlank | \ny = 4497x + 318 | \n0.9999 | \n1.06 | \n0.25–25 | \n0.09 | \n0.25 | \n|
Matrix | \ny = 4757x – 485 | \n0.9999 | \n/ | \n0.3–30 | \n0.10 | \n0.28 | \n||
Sorbic acid | \n1DLC | \nBlank | \ny = 147925x − 1384 | \n1.0000 | \n0.89 | \n0.2–20 | \n0.01 | \n0.025 | \n
Matrix | \ny = 131050x − 3021 | \n1.0000 | \n/ | \n0.5–50 | \n0.056 | \n0.136 | \n||
2DLC | \nBlank | \ny = 57114x + 186559 | \n0.9992 | \n0.96 | \n0.2–15 | \n0.054 | \n0.133 | \n|
Matrix | \ny = 54623x + 228303 | \n0.9990 | \n/ | \n0.25–18 | \n0.075 | \n0.20 | \n||
EMA | \n1DLC | \nBlank | \ny = 34036x − 197820 | \n0.9977 | \n1.04 | \n2.5–50 | \n0.14 | \n0.42 | \n
Matrix | \ny = 35360x − 87395 | \n0.9985 | \n/ | \n5–100 | \n1.14 | \n3.33 | \n||
2DLC | \nBlank | \ny = 82117x + 167643 | \n0.9996 | \n1.07 | \n0.5–50 | \n0.165 | \n0.50 | \n|
Matrix | \ny = 87714x + 47480 | \n0.9995 | \n/ | \n1–100 | \n0.18 | \n0.56 | \n||
EVA | \n1DLC | \nBlank | \ny = 45172x + 459 | \n1.0000 | \n0.90 | \n0.2–20 | \n0.017 | \n0.055 | \n
Matrix | \ny = 40683x + 521 | \n1.0000 | \n/ | \n0.5–50 | \n0.043 | \n0.15 | \n||
2DLC | \nBlank | \ny = 42319x + 4981 | \n0.9996 | \n1.01 | \n0.2–20 | \n0.016 | \n0.051 | \n|
Matrix | \ny = 42898x − 1620 | \n0.9995 | \n/ | \n0.1–10 | \n0.019 | \n0.056 | \n||
VAN | \n1DLC | \nBlank | \ny = 65925x + 54232 | \n0.9999 | \n0.84 | \n0.2–20 | \n0.015 | \n0.048 | \n
Matrix | \ny = 55386x + 47261 | \n0.9998 | \n/ | \n0.5–50 | \n0.039 | \n0.12 | \n||
2DLC | \nBlank | \ny = 45492x + 4220 | \n0.9994 | \n1.06 | \n0.2–20 | \n0.018 | \n0.049 | \n|
Matrix | \ny = 48056x – 1552 | \n0.9997 | \n/ | \n0.1–10 | \n0.018 | \n0.054 | \n||
α-CN | \n1DLC | \nBlank | \ny = 1596833x + 58163 | \n0.9984 | \n/ | \n100–5000 | \n50 | \n92.7 | \n
α-Lac | \n1DLC | \nBlank | \ny = 4334341x – 13906 | \n0.9997 | \n/ | \n10–500 | \n3.0 | \n9.9 | \n
β-CN | \n1DLC | \nBlank | \ny = 2982340x + 12008 | \n1.0000 | \n/ | \n16–780 | \n4.0 | \n10.2 | \n
β-LgB | \n1DLC | \nBlank | \ny = 707669x + 1006 | \n0.9997 | \n/ | \n15–750 | \n5.1 | \n18.4 | \n
β-LgA | \n1DLC | \nBlank | \ny = 2393184x + 4246 | \n1.0000 | \n/ | \n15–750 | \n3.8 | \n12.4 | \n
Method validation parameters of 1DLC and 2DLC (n = 3).
y is the average peak area of each additive (n = 3), and x is the mass concentration of the additive in mg mL−1.
Considering the complexity of milk powder, the possibility of a matrix effect was investigated by comparing the slope ratio of the calibration curves for the seven additives obtained in the presence and absence of blank milk powder [20]. For example, the slope ratio is closer to 1.0, which means a lower matrix effect in the method. The results in Table 4 show that 2DLC (slope ratio: 0.94–1.09) had a lower matrix effect than 1DLC (slope ratio: 0.84–1.21). The sample matrix effect for the determination of the seven additives for both 1DLC and 2DLC can be seen in Figure 8. The milk powder sample matrix chromatogram of 2DLC (b′) is much clean and flat than that in 1DLC (a′), and there has no interference peak for the analytes. Although the matrix effect of 2DLC is low, we still chose the matrix-matched standard curve for the sample analysis [19]. The LOD values of the 2DLC method were higher than that of the 1DLC method, as the peak width obtained with the new method is broader than that with the conventional method. Those are the advantages and disadvantages of these two methods.
\n1DLC (A) and 2DLC (B) chromatograms for testing sample matrix effect. (a′ and b′) Sample matrix without standard substances. (a and b) Sample matrix with standard substances. Chromatographic peaks: (1) benzoic acid, (2) sorbic acid, (3) saccharin sodium, (4) maltol, (5) ethyl maltol, (6) vanillin, and (7) ethyl vanillin.
Table 5 shows the precision and recovery results of 1DLC and 2DLC. The intraday and interday data showed that the precision of the two methods is satisfactory. However, the recovery of the 2DLC method (89.6–103.5%) was much better than that for the 1DLC method (65.5–99.2%), which is mainly benefit from the “one-step” sample preparation method. Analytes may be lost during the processes of traditional sample pretreatment (such as solid-phase extraction, liquid–liquid extraction, and precipitation). In this method, the whole analysis time was less than 1 h. So, 2DLC is much more efficient than 1DLC. Overall considering the environmental protection and time saving, the automation offered by 2DLC possesses more advantages.
\n\n | Concentration (μg mL−1) | \nPrecision | \nSpiked (ng) | \nRecovery | \n||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Intraday RSD | \nInterday RSD | \nRecovery (%) | \nRSD | \n|||||||||
1D | \n2D | \n1D | \n2D | \n1D | \n2D | \n1D | \n2D | \n1D | \n2D | \n1D | \n2D | \n|
Maltol | \n1.25 | \n0.5 | \n2.93 | \n2.86 | \n3.85 | \n2.93 | \n3 | \n1.4 | \n73.6 | \n92.1 | \n0.55 | \n3.66 | \n
3 | \n6.25 | \n0.85 | \n0.19 | \n0.43 | \n2.17 | \n9 | \n7 | \n72.6 | \n97.5 | \n2.07 | \n1.68 | \n|
18.75 | \n18.75 | \n0.72 | \n2.70 | \n1.19 | \n2.73 | \n45 | \n21 | \n72.1 | \n103.5 | \n1.14 | \n3.92 | \n|
Saccharin sodium | \n1 | \n0.4 | \n0.12 | \n1.44 | \n1.13 | \n1.43 | \n0.7 | \n1.1 | \n67.5 | \n93.4 | \n2.75 | \n2.36 | \n
2.4 | \n5 | \n0.77 | \n0.58 | \n2.04 | \n3.28 | \n5 | \n5.5 | \n70.1 | \n93.9 | \n0.33 | \n2.84 | \n|
15 | \n15 | \n0.42 | \n1.93 | \n0.77 | \n2.11 | \n22.5 | \n16.5 | \n78.4 | \n91.2 | \n0.80 | \n1.78 | \n|
Benzoic acid | \n1 | \n0.5 | \n1.03 | \n1.99 | \n1.18 | \n3.29 | \n0.7 | \n1.5 | \n65.5 | \n90.6 | \n3.42 | \n3.09 | \n
2.4 | \n6.25 | \n0.11 | \n1.88 | \n0.46 | \n4.96 | \n4 | \n7.5 | \n71.0 | \n95.2 | \n0.42 | \n0.34 | \n|
15 | \n18.75 | \n0.13 | \n2.53 | \n0.40 | \n1.87 | \n20 | \n22.5 | \n79.1 | \n91.1 | \n0.36 | \n0.34 | \n|
Sorbic acid | \n1 | \n0.4 | \n0.06 | \n2.25 | \n1.54 | \n5.44 | \n1.25 | \n1.25 | \n71.2 | \n100.6 | \n0.08 | \n1.09 | \n
2.4 | \n5 | \n0.19 | \n0.90 | \n0.25 | \n3.74 | \n5 | \n6.25 | \n72.2 | \n102.8 | \n0.16 | \n0.79 | \n|
15 | \n15 | \n0.21 | \n2.22 | \n0.20 | \n4.82 | \n22.5 | \n18.75 | \n75.7 | \n94.5 | \n0.18 | \n2.65 | \n|
Ethyl maltol | \n2.5 | \n2 | \n3.72 | \n0.90 | \n3.48 | \n0.73 | \n9 | \n5 | \n71.6 | \n105.4 | \n4.00 | \n0.58 | \n
6 | \n25 | \n0.76 | \n0.98 | \n4.80 | \n2.90 | \n22.5 | \n25 | \n70.1 | \n98.2 | \n0.85 | \n2.26 | \n|
37.5 | \n75 | \n0.16 | \n1.28 | \n1.89 | \n2.98 | \n45 | \n75 | \n80.2 | \n96.5 | \n0.87 | \n1.25 | \n|
Ethyl vanillin | \n1 | \n0.4 | \n0.41 | \n0.38 | \n2.65 | \n1.52 | \n2 | \n0.51 | \n82.8 | \n92.1 | \n0.05 | \n3.36 | \n
2.4 | \n5 | \n0.12 | \n0.83 | \n1.05 | \n4.31 | \n4 | \n2.55 | \n84.5 | \n92.6 | \n0.17 | \n3.92 | \n|
15 | \n15 | \n0.04 | \n1.00 | \n0.29 | \n1.84 | \n20 | \n7.65 | \n79.3 | \n98.0 | \n0.09 | \n0.55 | \n|
Vanillin | \n1 | \n0.4 | \n1.81 | \n2.24 | \n1.82 | \n1.68 | \n2.5 | \n0.49 | \n81.7 | \n92.6 | \n1.36 | \n1.14 | \n
2.4 | \n5 | \n0.70 | \n0.33 | \n1.21 | \n0.84 | \n7 | \n2.45 | \n86.5 | \n93.7 | \n0.69 | \n2.66 | \n|
15 | \n15 | \n0.69 | \n0.11 | \n1.29 | \n0.47 | \n22.5 | \n7.35 | \n85.5 | \n98.4 | \n1.46 | \n3.00 | \n|
α-CN | \n200 | \n/ | \n4.55 | \n/ | \n4.28 | \n/ | \n506 | \n/ | \n86.6 | \n/ | \n3.07 | \n/ | \n
1200 | \n/ | \n0.46 | \n/ | \n4.14 | \n/ | \n1210 | \n/ | \n99.2 | \n/ | \n1.12 | \n/ | \n|
5000 | \n/ | \n0.15 | \n/ | \n6.45 | \n/ | \n4050 | \n/ | \n94.7 | \n/ | \n3.73 | \n/ | \n|
α-Lac | \n20 | \n/ | \n1.99 | \n/ | \n2.48 | \n/ | \n49 | \n/ | \n87.7 | \n/ | \n3.07 | \n/ | \n
120 | \n/ | \n0.51 | \n/ | \n3.39 | \n/ | \n120 | \n/ | \n86.4 | \n/ | \n1.49 | \n/ | \n|
500 | \n/ | \n0.33 | \n/ | \n4.11 | \n/ | \n395 | \n/ | \n88.3 | \n/ | \n2.75 | \n/ | \n|
β-CN | \n30 | \n/ | \n3.70 | \n/ | \n3.71 | \n/ | \n78 | \n/ | \n105.2 | \n/ | \n1.66 | \n/ | \n
180 | \n/ | \n0.42 | \n/ | \n2.68 | \n/ | \n188 | \n/ | \n94.0 | \n/ | \n2.28 | \n/ | \n|
780 | \n/ | \n0.41 | \n/ | \n1.12 | \n/ | \n392 | \n/ | \n101.7 | \n/ | \n1.93 | \n/ | \n|
β-LgB | \n30 | \n/ | \n2.57 | \n/ | \n4.60 | \n/ | \n73.5 | \n/ | \n101.1 | \n/ | \n2.22 | \n/ | \n
180 | \n/ | \n1.55 | \n/ | \n3.24 | \n/ | \n176.4 | \n/ | \n99.2 | \n/ | \n3.24 | \n/ | \n|
750 | \n/ | \n0.67 | \n/ | \n3.08 | \n/ | \n588 | \n/ | \n83.5 | \n/ | \n2.78 | \n/ | \n|
β-LgA | \n30 | \n/ | \n2.93 | \n/ | \n3.10 | \n/ | \n52 | \n/ | \n99.4 | \n/ | \n3.39 | \n/ | \n
180 | \n/ | \n1.06 | \n/ | \n1.14 | \n/ | \n125 | \n/ | \n98.2 | \n/ | \n1.99 | \n/ | \n|
750 | \n/ | \n0.03 | \n/ | \n0.86 | \n/ | \n600 | \n\n | 91.7 | \n/ | \n4.84 | \n/ | \n
Accuracy of the two methods (n = 6).
Four different commercial milk and milk powder samples purchased from local supermarkets were analyzed using the developed 2DLC method. The chromatograms are shown in Figure 9. Figure 9A and B were infant formula milk powder (IFMP), Figure 9C was skimmed milk powder, and Figure 9D was fresh bovine milk. Benzoic acid and ethyl vanillin were detected only in the IFMP 1 sample. α-CN, β-CN, and α-Lac were detected in the four milk products. β-LgB and β-LgA were detected in the IFMP 2 and SMP samples.
\nChromatograms of four brands of commercial milk and milk powders. (A) Infant formula milk powder 1, (B) infant formula milk powder 2, (C) skimmed milk powder, and (D) bovine milk. (1) α-Casein (α-CN), (2) α-lactalbumin (α-Lac), (3) β-casein (β-CN), (4) β-lactoglobulin B (β-LgB), and (5) β-lactoglobulin A (β-LgA).
Table 6 showed the contents of the five major proteins and the seven additives. The contents of α-CN and β-CN were much higher than that of α-Lac, β-LgB, and β-LgA in all the milk products. The contents of α-Lac, β-LgB, and β-LgA were lower in the infant formula milk powder than that in the skimmed milk powder. The results are consistent with those from the literature [17], which is probably due to the denaturation of the thermosensitive whey proteins [21] or intentional removal of β-LgB to prevent allergic reactions [22].
\nSample (μg g−1) | \nIFPMa1 | \nIFPM2 | \nSMPa | \nBMa | \n
---|---|---|---|---|
α-CN | \n43.11 ± 0.45c | \n67.59 ± 0.60 | \n225.41 ± 3.00 | \n22.65 ± 0.30 | \n
α-Lac | \n0.87 ± 0.02 | \n2.48 ± 0.05 | \n4.47 ± 0.14 | \n0.28 ± 0.01 | \n
β-CN | \n56.38 ± 0.52 | \n27.91 ± 0.32 | \n98.59 ± 1.89 | \n9.00 ± 0.11 | \n
β-LgB | \nNDb | \n4.97 ± 0.28 | \n5.52 ± 0.19 | \nND | \n
β-LgA | \nND | \n3.75 ± 0.12 | \n6.19 ± 0.16 | \nND | \n
MAL | \nND | \nND | \nND | \nND | \n
Saccharin sodium | \nND | \nND | \nND | \nND | \n
Benzoic acid | \n1553.00 ± 0.04 | \nND | \nND | \nND | \n
Sorbic acid | \nND | \nND | \nND | \nND | \n
EMA | \nND | \nND | \nND | \nND | \n
EVA | \nND | \nND | \nND | \nND | \n
VAN | \n20.51 ± 0.24 | \nND | \nND | \nND | \n
Contents of food additives determined in milk powder samples by 2DLC (n = 3).
IFPM, infant formula powder milk; SMP, skimmed milk powder; BM, bovine milk.
ND, not detected.
The values of the concentration are means ± SD (n = 3).
In order to evaluate the accuracy of protein determination using the proposed method in this work, four brands of commercial milk products were analyzed using both the 2DLC and Kjeldahl methods. Table 7 shows the total major protein contents in the various milk matrices determined by these methods, 2DLC, the Kjeldahl method, and TPC, as given by the manufacturers. The RSD of the three groups were less than 3%, which means that the milk protein contents were similar for our method and the Kjeldahl method as well as that given by the manufacturers.
\n\n | TMPCa with 2DLC | \nKjeldahl method | \nTPCb indicated by manufacturers | \nRSDd | \n
---|---|---|---|---|
IFMP 1 | \n10.04 ± 0.10ce | \n9.77 ± 0.09 | \n10.4 | \n0.03 | \n
IFMP 2 | \n10.67 ± 0.14 | \n10.83 ± 0.07 | \n11.4 | \n0.03 | \n
SMP | \n32.85 ± 0.54 | \n34.19 ± 0.35 | \n33.0 | \n0.02 | \n
Milk | \n3.19 ± 0.04 | \n2.96 ± 0.08 | \n3.1 | \n0.03 | \n
Comparison between the total major protein concentrations (TMPC) in the various milks determined with 2DLC method and the total protein concentration (TPC) determined with Kjeldahl method and TPC given by the manufacturers.
TMPC, the total major protein concentrations.
TPC, the total protein concentration.
Powder milks in g/100 g and liquid milks in g/100 ml.
RSD among the data determined by the two methods and indicated by manufacturers.
The values of the concentration are means ± SD (n = 3).
In this chapter, two kinds of analysis methods for common additives are introduced. One is HPLC, and the other is 2DLC. Poly-Sery HLB cartridge was confirmed as the most appropriate material for HPLC because of the higher recovery. As to 2DLC, the sample preparation method is much easier, time-saving, and efficient, and this method possesses much higher recovery of food additives by avoiding the sample loss; and the analysis process was performed on an automated instrument within 30 mins. Therefore, it is simpler, faster, and more accurate than current standard methods.
\nThis work was supported by the Independent Innovation Ability Promotion Program of Xi‘an Jiaotong University (PY3A012), the Fundamental Research Funds for the Central University (xjj2013119), and the National Natural Science Foundation of Shaanxi Province (2017JQ8024).
\nThe figures in this chapter were taken from my previously published papers [6, 23], and I have got the permission to reuse it.
\nSicen Wang on behalf of other authors declares that all authors of this article have no conflict of interest. This article does not contain any studies with human or animal subjects.
HPLC | high-performance liquid chromatography |
RPLC | reversed-performance liquid chromatography |
2DLC | two-dimensional liquid chromatography |
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Prior to his academic appointment, Dr. Lai worked as a Senior Scientist at the Ministry of Science, Technology and Innovation, Malaysia. His current research areas include antimicrobial resistance and plant-pathogen interaction. His particular interest lies in the study of the antimicrobial mechanism via membrane disruption of essential oils against multi-drug resistance bacteria through various biochemical, molecular and proteomic approaches. 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Over the past few decades, no major new types of antibiotics have been produced and almost all known antibiotics are increasingly losing their activity against pathogenic microorganisms. The levels of multi-drug resistant bacteria have also increased. It is known that worldwide, more than 60% of all antibiotics that are produced find their use in animal production for both therapeutic and non-therapeutic purposes. The use of antimicrobial agents in animal husbandry has been linked to the development and spread of resistant bacteria. Poultry products are among the highest consumed products worldwide but a lot of essential antibiotics are employed during poultry production in several countries; threatening the safety of such products (through antimicrobial residues) and the increased possibility of development and spread of microbial resistance in poultry settings. This chapter documents some of the studies on antibiotic usage in poultry farming; with specific focus on some selected bacterial species, their economic importance to poultry farming and reports of resistances of isolated species from poultry settings (farms and poultry products) to essential antibiotics.",book:{id:"6978",slug:"antimicrobial-resistance-a-global-threat",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A Global Threat"},signatures:"Christian Agyare, Vivian Etsiapa Boamah, Crystal Ngofi Zumbi and\nFrank Boateng Osei",authors:[{id:"182058",title:"Dr.",name:"Christian",middleName:null,surname:"Agyare",slug:"christian-agyare",fullName:"Christian Agyare"},{id:"261271",title:"MSc.",name:"Crystal Ngofi",middleName:null,surname:"Zumbi",slug:"crystal-ngofi-zumbi",fullName:"Crystal Ngofi Zumbi"},{id:"261272",title:"MSc.",name:"Frank Boateng",middleName:null,surname:"Osei",slug:"frank-boateng-osei",fullName:"Frank Boateng Osei"},{id:"261273",title:"Dr.",name:"Vivian Etsiapa",middleName:null,surname:"Boamah",slug:"vivian-etsiapa-boamah",fullName:"Vivian Etsiapa Boamah"}]},{id:"49246",doi:"10.5772/61300",title:"Chitosan as a Biomaterial — Structure, Properties, and Electrospun Nanofibers",slug:"chitosan-as-a-biomaterial-structure-properties-and-electrospun-nanofibers",totalDownloads:4735,totalCrossrefCites:27,totalDimensionsCites:63,abstract:"Chitosan is a polysaccharide derived from chitin; chitin is the second most abundant polysaccharide in the world, after cellulose. Chitosan is biocompatible, biodegradable and non-toxic, so that it can be usedin medicalapplications such as antimicrobial and wound healing biomaterials. It also used as chelating agent due to its ability to bind with cholesterol, fats, proteins and metal ions.",book:{id:"4648",slug:"concepts-compounds-and-the-alternatives-of-antibacterials",title:"Concepts, Compounds and the Alternatives of Antibacterials",fullTitle:"Concepts, Compounds and the Alternatives of Antibacterials"},signatures:"H. M. Ibrahim and E.M.R. El- Zairy",authors:[{id:"90645",title:"Dr.",name:"Hassan",middleName:null,surname:"Ibrahim",slug:"hassan-ibrahim",fullName:"Hassan Ibrahim"},{id:"175694",title:"Dr.",name:"Enas",middleName:null,surname:"El- Zairy",slug:"enas-el-zairy",fullName:"Enas El- Zairy"}]},{id:"70919",doi:"10.5772/intechopen.90891",title:"Antimicrobial Effect of Titanium Dioxide Nanoparticles",slug:"antimicrobial-effect-of-titanium-dioxide-nanoparticles",totalDownloads:1826,totalCrossrefCites:21,totalDimensionsCites:47,abstract:"The widespread use of antibiotics has led to the emergence of multidrug-resistant bacterial strains, and therefore a current concern for food safety and human health. The interest for new antimicrobial substances has been focused toward metal oxide nanoparticles. Specifically, titanium dioxide (TiO2) has been considered as an attractive antimicrobial compound due to its photocatalytic nature and because it is a chemically stable, non-toxic, inexpensive, and Generally Recognized as Safe (GRAS) substance. Several studies have revealed this metal oxide demonstrates excellent antifungal and antibacterial properties against a broad range of both Gram-positive and Gram-negative bacteria. These properties were significantly improved by titanium dioxide nanoparticles (TiO2 NPs) synthesis. In this chapter, latest developments on routes of synthesis of TiO2 NPs and antimicrobial activity of these nanostructures are presented. Furthermore, TiO2 NPs favor the inactivation of microorganisms due to their strong oxidizing power by free radical generation, such as hydroxyl and superoxide anion radicals, showing reductions growth against several microorganisms, such as Escherichia coli and Staphylococcus aureus. Understanding the main mechanisms of antimicrobial action of these nanoparticles was the second main purpose of this chapter.",book:{id:"9521",slug:"antimicrobial-resistance-a-one-health-perspective",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A One Health Perspective"},signatures:"Carol López de Dicastillo, Matias Guerrero Correa, Fernanda B. Martínez, Camilo Streitt and Maria José Galotto",authors:[{id:"244902",title:"Dr.",name:"Carol",middleName:null,surname:"Lopez De Dicastillo",slug:"carol-lopez-de-dicastillo",fullName:"Carol Lopez De Dicastillo"},{id:"315494",title:"Mr.",name:"Matias",middleName:null,surname:"Guerrero Correa",slug:"matias-guerrero-correa",fullName:"Matias Guerrero Correa"},{id:"315495",title:"Ms.",name:"Fernanda",middleName:null,surname:"B. Martínez",slug:"fernanda-b.-martinez",fullName:"Fernanda B. Martínez"},{id:"315496",title:"Mr.",name:"Camilo",middleName:null,surname:"Zuñiga",slug:"camilo-zuniga",fullName:"Camilo Zuñiga"},{id:"315497",title:"Dr.",name:"Maria José",middleName:null,surname:"Galotto",slug:"maria-jose-galotto",fullName:"Maria José Galotto"}]},{id:"65613",doi:"10.5772/intechopen.84411",title:"The Methods for Detection of Biofilm and Screening Antibiofilm Activity of Agents",slug:"the-methods-for-detection-of-biofilm-and-screening-antibiofilm-activity-of-agents",totalDownloads:9301,totalCrossrefCites:16,totalDimensionsCites:27,abstract:"Biofilm producer microorganisms cause nosocomial and recurrent infections. Biofilm that is a sticky exopolysaccharide is the main virulence factor causing biofilm-related infections. Biofilm formation begins with attachment of bacteria to biotic surface such as host cell or abiotic surface such as prosthetic devices. After attachment, aggregation of bacteria is started by cell-cell adhesion. Aggregation continues with the maturation of biofilm. Dispersion is started by certain conditions such as phenol-soluble modulins (PSMs). By this way, sessile bacteria turn back into planktonic form. Bacteria embedded in biofilm (sessile form) are more resistant to antimicrobials than planktonic bacteria. So it is hard to treat biofilm-embedded bacteria than planktonic forms. For this reason, it is important to detect biofilm. There are a few biofilm detection and biofilm production methods on prosthetics, methods for screening antibacterial effect of agents against biofilm-embedded microorganism and antibiofilm effect of agents against biofilm production and mature biofilm. The aim of this chapter is to overview direct and indirect methods such as microscopy, fluorescent in situ hybridization, and Congo red agar, tube method, microtiter plate assay, checkerboard assay, plate counting, polymerase chain reaction, mass spectrometry, MALDI-TOF, and biological assays used by antibiofilm researches.",book:{id:"8427",slug:"antimicrobials-antibiotic-resistance-antibiofilm-strategies-and-activity-methods",title:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods",fullTitle:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods"},signatures:"Sahra Kırmusaoğlu",authors:[{id:"179460",title:"Associate Prof.",name:"Sahra",middleName:null,surname:"Kırmusaoğlu",slug:"sahra-kirmusaoglu",fullName:"Sahra Kırmusaoğlu"}]},{id:"63397",doi:"10.5772/intechopen.80624",title:"Antibiotic Resistance in Lactic Acid Bacteria",slug:"antibiotic-resistance-in-lactic-acid-bacteria",totalDownloads:2500,totalCrossrefCites:13,totalDimensionsCites:22,abstract:"Most starter cultures belong to the lactic acid bacteria group (LAB) and recognized as safe by the US Food and Drug Administration (FDA) and the European Food Safety Authority (EFSA). However, LAB may act as intrinsic or extrinsic reservoirs for antibiotic resistance (AR) genes. This fact may not constitute a safety concern itself, as the resistance gene transfer is vertical. Nevertheless, external genetic elements may induce changes that favor the horizontal transfer transmission of resistance from pathogens as well as from the human intestinal microbiota, which represents a severe safety issue. Some genus of AR LAB includes Enterococcus, Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, and Streptococcus isolated from fermented meat and milk products. Currently, the WHO recommends that LAB used in the food industry should be free of resistance. Therefore, the objective of this chapter is to present an overview of the LAB antibiotic resistance and some methods to determine the same.",book:{id:"6978",slug:"antimicrobial-resistance-a-global-threat",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A Global Threat"},signatures:"Yenizey M. 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After attachment, aggregation of bacteria is started by cell-cell adhesion. Aggregation continues with the maturation of biofilm. Dispersion is started by certain conditions such as phenol-soluble modulins (PSMs). By this way, sessile bacteria turn back into planktonic form. Bacteria embedded in biofilm (sessile form) are more resistant to antimicrobials than planktonic bacteria. So it is hard to treat biofilm-embedded bacteria than planktonic forms. For this reason, it is important to detect biofilm. There are a few biofilm detection and biofilm production methods on prosthetics, methods for screening antibacterial effect of agents against biofilm-embedded microorganism and antibiofilm effect of agents against biofilm production and mature biofilm. The aim of this chapter is to overview direct and indirect methods such as microscopy, fluorescent in situ hybridization, and Congo red agar, tube method, microtiter plate assay, checkerboard assay, plate counting, polymerase chain reaction, mass spectrometry, MALDI-TOF, and biological assays used by antibiofilm researches.",book:{id:"8427",slug:"antimicrobials-antibiotic-resistance-antibiofilm-strategies-and-activity-methods",title:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods",fullTitle:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods"},signatures:"Sahra Kırmusaoğlu",authors:[{id:"179460",title:"Associate Prof.",name:"Sahra",middleName:null,surname:"Kırmusaoğlu",slug:"sahra-kirmusaoglu",fullName:"Sahra Kırmusaoğlu"}]},{id:"62553",title:"Antibiotic Use in Poultry Production and Its Effects on Bacterial Resistance",slug:"antibiotic-use-in-poultry-production-and-its-effects-on-bacterial-resistance",totalDownloads:7339,totalCrossrefCites:43,totalDimensionsCites:92,abstract:"A surge in the development and spread of antibiotic resistance has become a major cause for concern. Over the past few decades, no major new types of antibiotics have been produced and almost all known antibiotics are increasingly losing their activity against pathogenic microorganisms. The levels of multi-drug resistant bacteria have also increased. It is known that worldwide, more than 60% of all antibiotics that are produced find their use in animal production for both therapeutic and non-therapeutic purposes. The use of antimicrobial agents in animal husbandry has been linked to the development and spread of resistant bacteria. Poultry products are among the highest consumed products worldwide but a lot of essential antibiotics are employed during poultry production in several countries; threatening the safety of such products (through antimicrobial residues) and the increased possibility of development and spread of microbial resistance in poultry settings. 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Kocazeybek",authors:[{id:"179460",title:"Associate Prof.",name:"Sahra",middleName:null,surname:"Kırmusaoğlu",slug:"sahra-kirmusaoglu",fullName:"Sahra Kırmusaoğlu"},{id:"248288",title:"Prof.",name:"Bekir",middleName:null,surname:"Kocazeybek",slug:"bekir-kocazeybek",fullName:"Bekir Kocazeybek"},{id:"406463",title:"Dr.",name:"Nesrin",middleName:null,surname:"Gareayaghi",slug:"nesrin-gareayaghi",fullName:"Nesrin Gareayaghi"}]},{id:"63397",title:"Antibiotic Resistance in Lactic Acid Bacteria",slug:"antibiotic-resistance-in-lactic-acid-bacteria",totalDownloads:2497,totalCrossrefCites:13,totalDimensionsCites:21,abstract:"Most starter cultures belong to the lactic acid bacteria group (LAB) and recognized as safe by the US Food and Drug Administration (FDA) and the European Food Safety Authority (EFSA). However, LAB may act as intrinsic or extrinsic reservoirs for antibiotic resistance (AR) genes. This fact may not constitute a safety concern itself, as the resistance gene transfer is vertical. Nevertheless, external genetic elements may induce changes that favor the horizontal transfer transmission of resistance from pathogens as well as from the human intestinal microbiota, which represents a severe safety issue. Some genus of AR LAB includes Enterococcus, Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, and Streptococcus isolated from fermented meat and milk products. Currently, the WHO recommends that LAB used in the food industry should be free of resistance. Therefore, the objective of this chapter is to present an overview of the LAB antibiotic resistance and some methods to determine the same.",book:{id:"6978",slug:"antimicrobial-resistance-a-global-threat",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A Global Threat"},signatures:"Yenizey M. 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It also used as chelating agent due to its ability to bind with cholesterol, fats, proteins and metal ions.",book:{id:"4648",slug:"concepts-compounds-and-the-alternatives-of-antibacterials",title:"Concepts, Compounds and the Alternatives of Antibacterials",fullTitle:"Concepts, Compounds and the Alternatives of Antibacterials"},signatures:"H. M. Ibrahim and E.M.R. El- Zairy",authors:[{id:"90645",title:"Dr.",name:"Hassan",middleName:null,surname:"Ibrahim",slug:"hassan-ibrahim",fullName:"Hassan Ibrahim"},{id:"175694",title:"Dr.",name:"Enas",middleName:null,surname:"El- Zairy",slug:"enas-el-zairy",fullName:"Enas El- Zairy"}]}],onlineFirstChaptersFilter:{topicId:"897",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81704",title:"Quorum Sensing Inhibition Based Drugs to Conquer Antimicrobial Resistance",slug:"quorum-sensing-inhibition-based-drugs-to-conquer-antimicrobial-resistance",totalDownloads:22,totalDimensionsCites:0,doi:"10.5772/intechopen.104125",abstract:"Quorum sensing is the cell to cell communication mechanism in microorganism through signalling molecules. Regulation of virulence factor, sporulation, proteolytic enzymes production, biofilm formation, auto-inducers, cell population density are key physiological process mediated through quorum-sensing (QS) signalling. Elevation of innate immune system and antibiotic tolerance of pathogens is highly increased with perspective of quorum-sensing (QS) activity. Development of novel drugs is highly attractive scenario against cell-cell communication of microbes. Design of synthetic drugs and natural compounds against QS signal molecules is vital combat system to attenuate microbial pathogenicity. Quorum sensing inhibitors (QSIs), quorum quenchers (QQs), efflux pump inhibitors (EPIs) act against multi-drug resistance strains (MDR) and other pathogenic microbes through regulation of auto-inducers and signal molecule with perceptive to growth arrest both in-vitro and in-vivo. QQs, QSIs and EPIs compounds has been validated with various animal models for high selection pressure on therapeutics arsenal against microbe’s growth inhibition. Promising QSI are phytochemicals and secondary metabolites includes polyacetylenes, alkaloids, polyphenols, terpenoids, quinones.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Kothandapani Sundar, Ramachandira Prabu and Gopal Jayalakshmi"},{id:"82372",title:"Unlocking the Potential of Ghost Probiotics in Combating Antimicrobial Resistance",slug:"unlocking-the-potential-of-ghost-probiotics-in-combating-antimicrobial-resistance",totalDownloads:24,totalDimensionsCites:0,doi:"10.5772/intechopen.104126",abstract:"Antimicrobial resistance is a global concern that requires immediate attention. Major causes of development of antimicrobial resistance in microbial cells are overuse of antimicrobials along the food chain especially in livestock, in preventing infections as well as misuse of antimicrobials by patients. Probiotics could be a viable alternative to antibiotics in the fight against antimicrobial resistance. Probiotic strains can act as a complement to antimicrobial therapy, improving antimicrobial function and enhancing immunity. However, there are safety concerns regarding the extensive use of live microbial cells especially in immunocompromised individuals; these include microbial translocation, inhibition of other beneficial microorganisms and development of antimicrobial resistance, among other concerns. Inevitably, ghost probiotics have become the favored alternative as they eliminate the safety and shelf-life problems associated with use of probiotics. Ghost probiotics are non-viable microbial cells (intact or broken) or metabolic products from microorganisms, which when administered in adequate amounts have biologic activity in the host and confer health benefits. Ghost probiotics exert biological effects similar to probiotics. However, the major drawback of using ghost probiotics is that the mechanism of action of these is currently unknown, hence more research is required and regulatory instruments are needed to assure the safety of consumers.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Abigarl Ndudzo, Sakhile Ndlovu, Nesisa Nyathi and Angela Sibanda Makuvise"},{id:"82178",title:"Managing Antimicrobial Resistance beyond the Hospital Antimicrobial Stewardship: The Role of One Health",slug:"managing-antimicrobial-resistance-beyond-the-hospital-antimicrobial-stewardship-the-role-of-one-heal",totalDownloads:16,totalDimensionsCites:0,doi:"10.5772/intechopen.104170",abstract:"Infections caused by micro-organisms affect the health of people and animals, causing morbidity and mortality, with Asia and Africa as the epicenters. Some of the infectious diseases are emerging and re-emerging in nature. Examples include viral hepatitis, Lassa fever, Ebola, yellow fever, tuberculosis, covid-19, measles, and malaria, among others. Antimicrobials have been playing an important role in the treatment of infections by these microbes. However, there has been a development of resistance to these antimicrobials as a result of many drivers. This write-up used secondary data to explore the management of antimicrobial resistance (AMR) beyond the hospital antimicrobial resistance steward using the one health concept. The findings showed AMR to be a transboundary, multifaceted ecosystem problem affecting both the developed and developing countries. It is also one of the top ten global public health threats facing mankind. Globally, AMR will cost over US$100 trillion in output loss by 2050, about 700,000 deaths a year, and 4,150,000 deaths in Africa by 2050. About 2.4 million people could die in high-income countries between 2015 and 2050 without a sustained effort to contain AMR. The drivers of AMR are beyond the hospital and hospital AMR stewardship. Therefore, the need for one health concept to manage it.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Istifanus Anekoson Joshua, Mathew Bobai and Clement Sokfa Woje"},{id:"81918",title:"Machine Learning for Antimicrobial Resistance Research and Drug Development",slug:"machine-learning-for-antimicrobial-resistance-research-and-drug-development",totalDownloads:57,totalDimensionsCites:0,doi:"10.5772/intechopen.104841",abstract:"Machine learning is a subfield of artificial intelligence which combines sophisticated algorithms and data to develop predictive models with minimal human interference. This chapter focuses on research that trains machine learning models to study antimicrobial resistance and to discover antimicrobial drugs. An emphasis is placed on applying machine learning models to detect drug resistance among bacterial and fungal pathogens. The role of machine learning in antibacterial and antifungal drug discovery and design is explored. Finally, the challenges and prospects of applying machine learning to advance basic research on and treatment of antimicrobial resistance are discussed. Overall, machine learning promises to advance antimicrobial resistance research and to facilitate the development of antibacterial and antifungal drugs.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Shamanth A. Shankarnarayan, Joshua D. Guthrie and Daniel A. Charlebois"},{id:"81891",title:"Alternatives to Antibiotics in Semen Extenders Used in Artificial Insemination",slug:"alternatives-to-antibiotics-in-semen-extenders-used-in-artificial-insemination",totalDownloads:29,totalDimensionsCites:0,doi:"10.5772/intechopen.104226",abstract:"Antimicrobial resistance is a serious global threat requiring a widespread response. Both veterinarians and medical doctors should restrict antibiotic usage to therapeutic use only, after determining the sensitivity of the causal organism. However, the addition of antibiotics to semen extenders for animal artificial insemination represents a hidden, non-therapeutic use of antimicrobial substances. Artificial insemination for livestock breeding is a huge global enterprise with hundreds of million sperm doses prepared annually. However, reporting of antimicrobial resistance in semen is increasing. This review discusses the consequences of bacteria in semen samples, as well as the effect of antimicrobial substances in semen extenders on bacteria in the environment and even on personnel. Alternatives to antibiotics have been reported in the scientific literature and are reviewed here. The most promising of these, removal of the majority of bacteria by colloid centrifugation, is considered in detail, especially results from an artificial insemination study in pigs. In conclusion, colloid centrifugation is a practical method of physically removing bacteria from semen, which does not induce antibiotic resistance. Sperm quality in stored semen samples may be improved at the same time.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Jane M. Morrell, Pongpreecha Malaluang, Aleksandar Cojkic and Ingrid Hansson"},{id:"81699",title:"Efflux Pumps among Urinary E. coli and K. pneumoniae Local Isolates in Hilla City, Iraq",slug:"efflux-pumps-among-urinary-e-coli-and-k-pneumoniae-local-isolates-in-hilla-city-iraq",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.104408",abstract:"Urinary tract infections (UTI) are the most common bacterial infections affecting humans. Escherichia coli and Klebsiella pneumoniae were common enterobacteria engaged with community-acquired UTIs. Efflux pumps were vital resistance mechanisms for antibiotics, especially among enterobacteria. Overexpression of an efflux system, which results in a decrease in antibiotic accumulation, is an effective mechanism for drug resistance. The ATP-binding cassette (ABC) transporters, small multidrug resistance (SMR), and multidrug and toxic compound extrusion (MATE) families, the major facilitator superfamily (MFS), and the resistance-nodulation- cell division (RND) family are the five superfamilies of efflux systems linked to drug resistance. This chapter highlights the results of studying the prevalence of efflux pump genes among local isolates of E. coli and K. pneumoniae in Hilla City, Iraq. class RND AcrAB-TolC, AcrAD-TolC, and AcrFE-TolC genes detected by conventional PCR of E. coli and K. pneumoniae respectively. The result revealed approximately all studied efflux transporter were found in both E. coli and K. pneumoniae in different percentages. Biofilm formation were observed in 50(100%) of K. pneumoniae and 49(98%) of E. coli isolates were biofilm former and follow: 30(60%), 20(40%) were weak, 12(24%), 22(44%) were moderate and 7(14%) and 8(16%) were Strong biofilm former for E. coli and K. pneumoniae, respectively.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Hussein Al-Dahmoshi, Sahar A. Ali and Noor Al-Khafaji"}],onlineFirstChaptersTotal:13},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:11,numberOfPublishedChapters:91,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:333,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:11,numberOfPublishedChapters:144,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:126,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:113,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:23,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:13,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{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"}}}}]},series:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"August 14th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",slug:"robert-koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. 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"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",isOpenForSubmission:!0,editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo, Ph.D., is a professor in the Department of Engineering, University of Naples “Parthenope,” Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino and Southern Lazio, Italy. Her research interests include multi-criteria decision analysis, industrial plants, logistics, manufacturing, and safety. She serves as an associate editor for the International Journal of the Analytic Hierarchy Process and is an editorial board member for several other journals. She is also a member of the Analytic Hierarchy Process (AHP) Academy.",institutionString:"Parthenope University of Naples, Italy",institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null},{id:"92",title:"Health and Wellbeing",coverUrl:"https://cdn.intechopen.com/series_topics/covers/92.jpg",isOpenForSubmission:!0,editor:{id:"348225",title:"Prof.",name:"Ann",middleName:null,surname:"Hemingway",slug:"ann-hemingway",fullName:"Ann Hemingway",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035LZFoQAO/Profile_Picture_2022-04-11T14:55:40.jpg",biography:"Professor Hemingway is a public health researcher, Bournemouth University, undertaking international and UK research focused on reducing inequalities in health outcomes for marginalised and excluded populations and more recently focused on equine assisted interventions.",institutionString:null,institution:{name:"Bournemouth University",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},{id:"93",title:"Inclusivity and Social Equity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/93.jpg",isOpenForSubmission:!0,editor:{id:"210060",title:"Prof. Dr.",name:"Ebba",middleName:null,surname:"Ossiannilsson",slug:"ebba-ossiannilsson",fullName:"Ebba Ossiannilsson",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6LkBQAU/Profile_Picture_2022-02-28T13:31:48.png",biography:"Professor Dr. Ebba Ossiannilsson is an independent researcher, expert, consultant, quality auditor and influencer in the fields of open, flexible online and distance learning (OFDL) and the 'new normal'. Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.",institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null},{id:"94",title:"Climate Change and Environmental Sustainability",coverUrl:"https://cdn.intechopen.com/series_topics/covers/94.jpg",isOpenForSubmission:!0,editor:{id:"61855",title:"Dr.",name:"Yixin",middleName:null,surname:"Zhang",slug:"yixin-zhang",fullName:"Yixin Zhang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYWJgQAO/Profile_Picture_2022-06-09T11:36:35.jpg",biography:"Professor Yixin Zhang is an aquatic ecologist with over 30 years of research and teaching experience in three continents (Asia, Europe, and North America) in Stream Ecology, Riparian Ecology, Urban Ecology, and Ecosystem Restoration and Aquatic Conservation, Human-Nature Interactions and Sustainability, Urbanization Impact on Aquatic Ecosystems. He got his Ph.D. in Animal Ecology at Umeå University in Sweden in 1998. He conducted postdoc research in stream ecology at the University of California at Santa Barbara in the USA. After that, he was a postdoc research fellow at the University of British Columbia in Canada to do research on large-scale stream experimental manipulation and watershed ecological survey in temperate rainforests of BC. He was a faculty member at the University of Hong Kong to run ecological research projects on aquatic insects, fishes, and newts in Tropical Asian streams. He also conducted research in streams, rivers, and caves in Texas, USA, to study the ecology of macroinvertebrates, big-claw river shrimp, fish, turtles, and bats. Current research interests include trophic flows across ecosystems; watershed impacts of land-use change on biodiversity and ecosystem functioning; ecological civilization and water resource management; urban ecology and urban/rural sustainable development.",institutionString:null,institution:{name:"Soochow University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",isOpenForSubmission:!0,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:{name:"Swiss Federal Institute of Aquatic Science and Technology",institutionURL:null,country:{name:"Switzerland"}}},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:9,paginationItems:[{id:"82936",title:"Soil Degradation Processes Linked to Long-Term Forest-Type Damage",doi:"10.5772/intechopen.106390",signatures:"Pavel Samec, Aleš Kučera and Gabriela Tomášová",slug:"soil-degradation-processes-linked-to-long-term-forest-type-damage",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Forest Degradation Under Global Change",coverURL:"https://cdn.intechopen.com/books/images_new/11457.jpg",subseries:{id:"94",title:"Climate Change and Environmental Sustainability"}}},{id:"82777",title:"Sustainability and Social Investment: Community Microhydropower Systems in the Dominican Republic",doi:"10.5772/intechopen.105995",signatures:"Michela Izzo, Alberto Sánchez and Rafael Fonseca",slug:"sustainability-and-social-investment-community-microhydropower-systems-in-the-dominican-republic",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}},{id:"82387",title:"Kept Promises? The Evolution of the EU Financial Contribution to Climate Change",doi:"10.5772/intechopen.105541",signatures:"Cecilia Camporeale, Roberto Del Ciello and Mario Jorizzo",slug:"kept-promises-the-evolution-of-the-eu-financial-contribution-to-climate-change",totalDownloads:11,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Mario",surname:"Jorizzo"},{name:"Cecilia",surname:"Camporeale"},{name:"ROBERTO",surname:"DEL CIELLO"}],book:{title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}},{id:"82524",title:"Italy’s Small Exporting Companies: Globalization and Sustainability Issues",doi:"10.5772/intechopen.105542",signatures:"Roberta Pace and Francesca Mandanici",slug:"italy-s-small-exporting-companies-globalization-and-sustainability-issues",totalDownloads:15,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg",subseries:{id:"91",title:"Sustainable Economy and Fair Society"}}}]},overviewPagePublishedBooks:{paginationCount:1,paginationItems:[{type:"book",id:"10897",title:"Food Systems Resilience",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/10897.jpg",slug:"food-systems-resilience",publishedDate:"July 13th 2022",editedByType:"Edited by",bookSignature:"Ana I. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:null,institution:null},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"417317",title:"Mrs.",name:"Chiedza",middleName:null,surname:"Elvina Mashiri",slug:"chiedza-elvina-mashiri",fullName:"Chiedza Elvina Mashiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"352140",title:"Dr.",name:"Edina",middleName:null,surname:"Chandiwana",slug:"edina-chandiwana",fullName:"Edina Chandiwana",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"342259",title:"B.Sc.",name:"Leonard",middleName:null,surname:"Mushunje",slug:"leonard-mushunje",fullName:"Leonard Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"347042",title:"Mr.",name:"Maxwell",middleName:null,surname:"Mashasha",slug:"maxwell-mashasha",fullName:"Maxwell Mashasha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"2941",title:"Dr.",name:"Alberto J.",middleName:"Jorge",surname:"Rosales-Silva",slug:"alberto-j.-rosales-silva",fullName:"Alberto J. Rosales-Silva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"437913",title:"Dr.",name:"Guillermo",middleName:null,surname:"Urriolagoitia-Sosa",slug:"guillermo-urriolagoitia-sosa",fullName:"Guillermo Urriolagoitia-Sosa",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"435126",title:"Prof.",name:"Joaquim",middleName:null,surname:"José de Castro Ferreira",slug:"joaquim-jose-de-castro-ferreira",fullName:"Joaquim José de Castro Ferreira",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"437899",title:"MSc.",name:"Miguel Angel",middleName:null,surname:"Ángel Castillo-Martínez",slug:"miguel-angel-angel-castillo-martinez",fullName:"Miguel Angel Ángel Castillo-Martínez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"289955",title:"Dr.",name:"Raja",middleName:null,surname:"Kishor Duggirala",slug:"raja-kishor-duggirala",fullName:"Raja Kishor Duggirala",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jawaharlal Nehru Technological University, Hyderabad",country:{name:"India"}}}]}},subseries:{item:{id:"12",type:"subseries",title:"Human Physiology",keywords:"Anatomy, Cells, Organs, Systems, Homeostasis, Functions",scope:"Human physiology is the scientific exploration of the various functions (physical, biochemical, and mechanical properties) of humans, their organs, and their constituent cells. The endocrine and nervous systems play important roles in maintaining homeostasis in the human body. Integration, which is the biological basis of physiology, is achieved through communication between the many overlapping functions of the human body's systems, which takes place through electrical and chemical means. Much of the basis of our knowledge of human physiology has been provided by animal experiments. Because of the close relationship between structure and function, studies in human physiology and anatomy seek to understand the mechanisms that help the human body function. The series on human physiology deals with the various mechanisms of interaction between the various organs, nerves, and cells in the human body.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/12.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11408,editor:{id:"195829",title:"Prof.",name:"Kunihiro",middleName:null,surname:"Sakuma",slug:"kunihiro-sakuma",fullName:"Kunihiro Sakuma",profilePictureURL:"https://mts.intechopen.com/storage/users/195829/images/system/195829.jpg",biography:"Professor Kunihiro Sakuma, Ph.D., currently works in the Institute for Liberal Arts at the Tokyo Institute of Technology. He is a physiologist working in the field of skeletal muscle. He was awarded his sports science diploma in 1995 by the University of Tsukuba and began his scientific work at the Department of Physiology, Aichi Human Service Center, focusing on the molecular mechanism of congenital muscular dystrophy and normal muscle regeneration. 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