Levels of PBDEs reported in the environment and biota from different locales worldwide.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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She has been actively involved in the clinical research with particular focus on mesothelioma, lung cancer, pancreatic and breast cancer. She collaborated with Italian Mesothelioma Group (Gi.Me) and had been an advisor to Buzzi Foundation in drafting research projects and clinical trials on malignant mesothelioma. She also wrote a chapter titled “Mesotelioma Pleurico (Pleural Mesothelioma)” in the book \\Medicina Interna Sistematica\\ of Claudio Rugarli (Elsevier/Masson, 6th Ed.), a standard text-book for the students of Internal Medicine in Italy. Recently, she is more focused on pancreatic cancer and is particularly involved in studying the role of diabetic condition and hyperinsulinism in progression of pancreatic tumors. 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He has double Master’s degree in Physics (IIT Kanpur, India) and in Computer Science (University of Nice, France), respectively, and has extensive experience in interdisciplinary and translational research. \nHis main research interest is in integration and analysis of different kinds of biological data (genomics, proteomics, NGS) to understand the molecular basis of genetic diseases. He is particularly interested in biomarker studies and has worked on biomarkers for lung cancer, fabry disease and pancreatic \ncancer. Currently he is involved in several clinical and basic research projects in type-I diabetes (T1D). These include finding ways to increase the efficacy of bone marrow islet transplant (in T1D pts.) and (pre-clinical) stem cell therapy; and dissecting the roles of neutrophil, Teff and Treg cells in T1D. In the past, he worked as bioinformatician at Telethon Institute of Genetics and Medicine (Naples, Italy) and managed and analyzed all the data of EU integrated project EURExpress.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"San Raffaele Hospital",institutionURL:null,country:{name:"Italy"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1093",title:"Thoracic Oncology",slug:"thoracic-oncology"}],chapters:[{id:"41540",title:"Mineralogy and Malignant Mesothelioma: The South African Experience",slug:"mineralogy-and-malignant-mesothelioma-the-south-african-experience",totalDownloads:3267,totalCrossrefCites:1,authors:[{id:"104977",title:"Prof.",name:"James",surname:"Phillips",slug:"james-phillips",fullName:"James Phillips"},{id:"107333",title:"Prof.",name:"Jill",surname:"Murray",slug:"jill-murray",fullName:"Jill Murray"},{id:"107334",title:"Prof.",name:"David",surname:"Rees",slug:"david-rees",fullName:"David Rees"},{id:"107335",title:"Prof.",name:"John",surname:"Davies",slug:"john-davies",fullName:"John Davies"}]},{id:"41536",title:"Effect of Asbestos on Anti-Tumor Immunity and Immunological Alteration in Patients with Malignant Mesothelioma",slug:"effect-of-asbestos-on-anti-tumor-immunity-and-immunological-alteration-in-patients-with-malignant-me",totalDownloads:1194,totalCrossrefCites:3,authors:[{id:"34101",title:"Prof.",name:"Takemi",surname:"Otsuki",slug:"takemi-otsuki",fullName:"Takemi Otsuki"},{id:"48627",title:"Dr.",name:"Naoko",surname:"Kumagai-Takei",slug:"naoko-kumagai-takei",fullName:"Naoko Kumagai-Takei"},{id:"48630",title:"Dr.",name:"Yoshie",surname:"Miura",slug:"yoshie-miura",fullName:"Yoshie Miura"},{id:"48631",title:"Dr.",name:"Yasumitsu",surname:"Nishimura",slug:"yasumitsu-nishimura",fullName:"Yasumitsu Nishimura"},{id:"104888",title:"Dr.",name:"Megumi",surname:"Maeda",slug:"megumi-maeda",fullName:"Megumi Maeda"},{id:"104893",title:"Dr.",name:"Suni",surname:"Lee",slug:"suni-lee",fullName:"Suni Lee"},{id:"104894",title:"Dr.",name:"Hidenori",surname:"Matsuzaki",slug:"hidenori-matsuzaki",fullName:"Hidenori Matsuzaki"},{id:"104897",title:"Associate Prof.",name:"Kazuya",surname:"Fukuoka",slug:"kazuya-fukuoka",fullName:"Kazuya Fukuoka"},{id:"104898",title:"Prof.",name:"Takashi",surname:"Nakano",slug:"takashi-nakano",fullName:"Takashi Nakano"},{id:"104902",title:"Dr.",name:"Takumi",surname:"Kishimoto",slug:"takumi-kishimoto",fullName:"Takumi Kishimoto"}]},{id:"41537",title:"Role of Inflammation and Angiogenic Growth Factors in Malignant Mesothelioma",slug:"role-of-inflammation-and-angiogenic-growth-factors-in-malignant-mesothelioma",totalDownloads:1248,totalCrossrefCites:0,authors:[{id:"99156",title:"Prof.",name:"Loredana",surname:"Albonici",slug:"loredana-albonici",fullName:"Loredana Albonici"},{id:"99170",title:"Dr.",name:"Camilla",surname:"Palumbo",slug:"camilla-palumbo",fullName:"Camilla Palumbo"},{id:"137852",title:"Prof.",name:"Vittorio",surname:"Manzari",slug:"vittorio-manzari",fullName:"Vittorio Manzari"}]},{id:"41538",title:"The Role of Cyclooxygenase-2, Epidermal Growth Factor Receptor and Aromatase in Malignant Mesothelioma",slug:"the-role-of-cyclooxygenase-2-epidermal-growth-factor-receptor-and-aromatase-in-malignant-mesotheliom",totalDownloads:1564,totalCrossrefCites:0,authors:[{id:"101876",title:"Dr",name:"Rossella",surname:"Galati",slug:"rossella-galati",fullName:"Rossella Galati"}]},{id:"41539",title:"Neoadjuvant Chemotherapy in Malignant Pleural Mesothelioma",slug:"neoadjuvant-chemotherapy-in-malignant-pleural-mesothelioma",totalDownloads:1205,totalCrossrefCites:0,authors:[{id:"102446",title:"Dr.",name:"Adolfo",surname:"Favaretto",slug:"adolfo-favaretto",fullName:"Adolfo Favaretto"},{id:"102452",title:"Dr.",name:"Giulia",surname:"Pasello",slug:"giulia-pasello",fullName:"Giulia Pasello"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"24375",firstName:"Sandra",lastName:"Bakic",middleName:null,title:"Ms.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"sandra.b@intechopen.com",biography:"As a Commissioning Editor at InTech, I work closely with our collaborators in the selection of book topics for the yearly publishing plan and in preparing new book catalogues for each season. This requires extensive analysis of developing trends in scientific research in order to offer our readers relevant content. Creating the book catalogue is also based on keeping track of the most read, downloaded and highly cited chapters and books and relaunching similar topics. I am also responsible for consulting with our Editorial Advisory Board members on which book topics to add to our catalogue and sending possible book proposal topics to them for evaluation. Once the catalogue is complete, I contact leading researchers in their respective fields and ask them to become possible book editors for each book project. Once a book editor is appointed, I prepare all necessary information required for them to begin their work, as well as guide them through the editorship process. I also assist editors in inviting suitable authors to contribute to a specific book project and each year, I identify and invite exceptional editors to join InTech\\'s Editorial Advisory Board. 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by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"50734",title:"Genetic Association Studies on Prostate Cancer",doi:"10.5772/63280",slug:"genetic-association-studies-on-prostate-cancer",body:'\nAlarming statistics on prostate cancer (PCa) incidence and mortality, as well as the results of epidemiological studies, have led to focusing research efforts on discovering molecular mechanisms underlying its onset and progression [1]. Still, molecular basis of PCa pathogenesis remains largely unknown, while the results of studies in this area of research suggest that PCa is one of the most genetically and molecularly heterogeneous malignant tumors [2]. Among PCa cases, most are sporadic, while a significantly smaller percent represents familial type, including hereditary cases. High-penetrability PCa-related loci are not common in populations and are found to be associated with hereditary PCa. Since PCa represents a multifactorial disease with polygenic basis, and sporadic cases are much more frequently diagnosed, most of the research in the area of PCa molecular genetics has focused on genetic variants with low penetrability [3].
\nThe modern research on molecular basis of PCa development includes studies aiming to identify potential genetic markers which could be used in diagnostics and/or monitoring of PCa [1]. This is of utmost importance, since one of the major issues in clinical practice related to PCa is a large percent of latent PCa among newly diagnosed [4]. The overdiagnosis of PCa in early diagnosed cases, due to indolent forms, leads to unnecessary morbidity because of application of invasive therapeutic procedures [5]. This led to focusing the research efforts on discovering genetic markers that could be used for assessing the biological potential of early diagnosed PCa. Therefore, the use of these genetic markers, together with standard prognostic parameters of PCa progression, which include initial serum PSA level, Gleason score, and clinical stage, could greatly improve the current clinical protocols by being implemented in algorithms for evaluating the patient’s risk of PCa and/or PCa aggressiveness [1].
\nStudies aiming to identify potential PCa-related loci are designed as case-control or case-only studies, which evaluate the differences in genotype distributions between cases and controls, as well as between different groups of patients, classified according to clinical characteristics. The most validated loci associated with PCa risk were identified through Genome-wide association studies (GWAS) [6]. Nevertheless, numerous PCa-related genetic variants were found in studies based on selected candidate genes [7].
\nLinkage analyses have led to identification of the first high-penetrability PCa susceptibility loci [1]. These studies were based on analyses in hereditary PCa, which is a less frequent type of PCa, and yielded high or moderate-penetrability loci, such as HPC1 (eng. Hereditary Prostate Cancer 1, HPC1) mapped in chromosomal region 1q24-25 [8], PCAP (eng.
The Human genome project was critical for making high-throughput genome-wide analyses possible. Not only that this project yielded DNA sequence information but also provided basis for development of methodology, including high-throughput genotyping assays, as well as software tools for analyzing large amount of genetic data [13]. Therefore, sequencing of human DNA provided basis for GWASs, including those on PCa [14].
\nTo date (February 2016), GWASs have identified over 75 variants associated with prostate cancer risk, predominantly in populations of European ancestry (Figure 1) [15, 16]. The first GWASs were conducted in 2007, for which a large collection of samples were obtained from PCa patients and healthy controls, as well as databases that included clinical data of patients were constructed [17–19]. The necessity of a large number of subjects for this type of study was obvious even in this early period of conducting GWASs.
\nIdeograms of human chromosomes with marked PCa susceptibility loci identified through GWASs. Ideograms were obtained from NCBI Map Viewer, while GWAS hits were found in NHGRI-EBI GWAS catalog.
As in other complex diseases, PCa GWASs are usually designed in a multistage manner, with the whole set of tag-single nucleotide polymorphisms (tag-SNPs) being evaluated in the first phase, and only subsets of the most significant SNP being replicated in much larger groups of patients and controls in next phases [20, 21]. Thus, repeating the tests yields the most significant results [20].
\nThe results of initial GWASs showed that most of the PCa-associated genetic variants are located in so-called “gene-deserts”. The lack of protein-coding genes in these regions was explained by the supposed presence of regulatory sequences of major proto-oncogenes and tumor-suppressive genes [22, 23]. Today, another explanation is also the presence of genes encoding regulatory RNA molecules within PCa-risk regions [24].
\nOne of the major PCa-related regions was found to be 8q24. Within approximately 1 million base pairs segment of 8q24 reside multiple variants associated with PCa [25]. This region was first identified as associated with PCa susceptibility in a genome-wide linkage study conducted in Icelandic population [26]. Later on, the association of genetic variants within this region with PCa risk was shown in initial GWASs from 2007. Gudmunsson et al., Haiman et al. and Yeager et al. have shown the association between previously reported rs1447295 and PCa risk [17–19]. Also, these first GWASs identified other PCa susceptibility variants within 8q24, rs6983267, and rs16901979. Afterward, GWASs have provided evidence for association of other single-nucleotide genetic variants (SNVs) from 8q24 with PCa risk, such as rs4242382, rs7017300, and rs7837688 [27, 28]. In the recent years, by implementing clinical data and by using case-only design, both GWASs and validation studies have provided evidence for an association of several loci within 8q24 with PCa aggressiveness or survival [29–32].
\nPCa-susceptibility region within 8q24 was defined as gene desert, since no known protein-coding genes were located within it. Nevertheless, the possible biological explanation for the effect of genetic variants located in 8q24 on PCa risk was their influence on the regulation of the expression of nearby genes, mainly C-MYC. It was suggested that regulatory sequences controlling the transcription rate of C-MYC gene were located in 8q24, and that functional genetic variants which are in strong linkage disequilibrium (LD) with PCa susceptibility locus or several loci effect the sequence and therefore the function of regulatory elements [23]. Previous studies on molecular mechanisms of PCa pathogenesis have shown the functional significance of C-MYC, both by analyzing mutational signatures of malignant prostate tissue and by conducting functional analyses in cell cultures, which included stimulation or silencing of C-MYC expression [33]. Other than prostate cancer, several other malignancies were associated with 8q24, including breast and colorectal cancer. Some of the subregions of 8q24 associated with these cancers are found to overlap with those related to PCa, while others differ (Figure 2) [34].
\nPCa risk-associated regions within 8q24. Lower part of the figure represents Haploview output for a segment of 8q24 (ch8:127500000..129000000) with marked subregions associated with PCa in GWASs. The upper part of the figure is a representation of genes located in the region of interest obtained from Ensembl genome browser (GRCh37).
17q12 is another PCa susceptibility region identified through initial GWAS. Two of the genetic variants located in 17q12, rs7501939 and rs3760511, were found to be associated with the risk of developing PCa in the study by Gudmundsson et al. conducted in 2007 [35]. In this GWAS, minor alleles of these two single nucleotide genetic variants were found to confer the increased risk of PCa in cohorts of participants from Iceland, Netherlands, and the USA, while in the group of Hispanics this genetic association was not shown [35]. The results of this GWAS were further validated in multiple populations, mostly of European origin [36–43]. Validation studies were even conducted in Africans in which genetic association studies on PCa are scarce [37, 44–47]. The most recent meta-analysis of both GWASs and validation studies has also shown the association of these genetic variants with PCa risk [43].
\nSNVs rs7501939 and rs3760511 are located in the first intron of the hepatocyte nuclear factor 1 β(HNF1β ) or transcription factor 2 (TCF2) which is a transcription factor showing tissue-specific expression pattern. Therefore, the association of genetic variants located in 17q12 with PCa risk could be explained by the effect of functional genetic variants on HNF1β function or expression [41].
\nAnother PCa-susceptibility region on chromosome 17 is 17q24. Genetic variants located within this region which were found to be associated with PCa are intergenic variants. Similar to 8q24 genetic variants, those located in 17q24 are found in a gene desert, probably harboring multiple regulatory sequences controlling the expression of surrounding genes [48]. One of the most proximal genes is SOX9, which is an important proto-oncogene in prostatic tissue. Recent findings have shown the location of PCa-associated genetic variants in an enhancer looping to SOX9 gene [48]. Among these genetic variants is a tag-SNP previously identified through GWAS, as well as potentially functional genetic variants found by deep sequencing of PCa-susceptibility region [35, 48].
\nTwo out of the three GWASs, which were published in 2008 in the same issue of Nature Genetics, have identified PCa-associated genetic variants in the region 10q11 [27, 36]. Afterward, other studies have provided additional evidence to support the association between 10q11 and PCa susceptibility, including both GWASs and validation studies [49–55]. One of these PCa risk-associated genetic variants was located in the close proximity of the transcription start site of the gene Microseminoprotein B (MSMB ) which encodes a tumor-suppressor, and was, therefore, even considered as potentially functional. For the risk allele of this genetic variant, it was further shown to affect the expression of MSMB gene in a negative manner [56, 57]. The other gene in proximity to this genetic variant is Nuclear receptor coactivator 4 (NCOA4). NCOA4 protein interacts with androgen receptor (AR) and acts as corepressor of androgen-responding genes. Therefore, functional genetic variants in LD with GWAS hits could potentially contribute to PCa risk by affecting the expression of these two genes, or others in proximity [58].
\nRegion 19q13 harboring kallikrein genes KLK2 and KLK3 was found to be associated with PCa susceptibility through GWASs [59]. Several genetic variants associated with PCa risk were located in KLK3 gene, such as missense SNV rs17632542 identified by fine-mapping of PCa-associated subregion 19q13.33. These genes encode serine-proteases, one of which is PSA, used for PCa diagnosis and disease monitoring. Therefore, the association of PCa-risk genetic variants with serum PSA level was evaluated, yielding statistically significant results for potentially functional SNV rs17632542 [15, 59].
\nAnother subregion associated with PCa risk is 19q13.4 in which a GWASs hit is in strong LD in Chinese population with germline deletion affecting LILRA3 gene, involved in inflammatory pathways [60].
\nEven before GWASs, the necessity of conducting association studies in order to identify low and moderate penetrability genetic variants that contribute to PCa risk was obvious. Therefore, numerous candidate genes were analyzed for genetic variants associated with PCa, with questionable success due to false discoveries and the lack of replication [61]. Candidates were selected based on their potential functional significance in normal prostatic cell growth, malignant transformation, or in the development of metastases. Therefore, among these candidate genes are those encoding proteins involved in androgen signaling, cell-cycle control mechanisms, major tumor-suppressors, or proto-oncogenes, as well as those involved in cellular adhesion or communication with surrounding cellular or matrix components of prostate epithelium [62, 63]. This implies the need for previous knowledge when designing case-control studies using candidate gene approach [64].
\nEven though these studies were common before GWASs, they are still conducted in numerous populations, aiming to confirm previously found associations, or to identify new ones by analyzing other candidates, selected by using modern research results, such as those involved in regulatory functions of non-coding RNAs [65].
\nSince androgen signaling is essential for growth and survival of prostate epithelial cells, genes involved in androgen biosynthesis, signal reception and transduction, as well as in androgen metabolism have emerged as candidates for case–control studies [63]. Most of these studies involved Androgen receptor (AR), as the major component of androgen signaling and regulation of expression of androgen-responding genes. Among these studies, major percentage relied on analyzing the potential association of the length of CAG repeat string with exon 1 which encodes a poly-glutamine tract of AR with PCa risk [66]. This homopolymeric tract is located in N-terminal domain of AR, which possesses transactivational properties and its length is inversely correlated with transactivation function [67]. Even though initial results were promising, the supposed association was not confirmed in a large percentage of later studies, and the effect sizes were not large enough to support the substantial biological role. Therefore, the association of this genetic variant with PCa risk remains controversial [68–70].
\nAnother three-nucleotide (GGN) repeat string, encoding polyglycine tract in AR, was analyzed for potential association between its length and PCa risk. This repeat string is also located in exon 1, but less studied than the CAG repeat tract, possibly due to technical problems in amplifying GC-rich DNA regions [71]. The effect of the length of GGN repeat string on transactivational properties of AR is still unclear, and the other proposed mechanism of potential functional significance is the effect on AR translation [72]. Studies on the potential association of this microsatellite on PCa risk and progression yielded contrasting results [73–79].
\nMixed results were also found for SRD5A2 (type II steroid 5α-reductase), which is the major enzyme converting testosterone to dihydrotestosterone. Similarly, studies analyzing genetic variants within CYP17, CYP19A, HSD17B, and HSD3B have shown initial promising results, lacking consistent validation [63, 80].
\nAmong genes involved in cell detoxification, those encoding glutathione-S-transferases have been mostly analyzed. Nevertheless, most of these studies yielded insignificant results on association with PCa risk [81]. Other frequently analyzed genes involved in metabolism of carcinogens are PON1, CYP1A1, CYP1B1, and CYP3A4 [80, 82–85].
\nTwo genetic variants within PON1 have been analyzes in multiple populations, L55M and Q129R. The results to date are inconclusive, but the meta-analysis conducted in 2012 suggested the association of L55M missense variant with PCa risk [82]. Also, a recent meta-analysis on only three PCa studies and Q129R showed statistically significant association for several genetic models of association [83].
\nThe most commonly analyzed SNVs in CYP1A1 are missense variants rs1048943 (p.Ile462Val) and rs4646903, which are also called MspI polymorphisms, since they alter the recognition site for MspI restriction enzyme. Numerous studies and also the recent meta-analyses showed the association between these SNVs and PCa risk [84–86].
\nThe results obtained for genetic variants in CYP1B1 and CYP3A4 are controversial, with the recent meta-analyses suggesting the association of L432V, N453S, and A119S polymorphisms of CYP1B1 and A392G in CYP3A4 with PCa susceptibility [87, 88].
\nDysfunctions of DNA repair pathway, apoptosis regulation, and cell cycle control mechanisms alter the cells response to DNA damage and lead to uncontrolled proliferation, progression and metastasis of malignant diseases. Also, genetic variants in genes involved in these processes could potentially attribute to cancer susceptibility and/or progression risk [62].
\nAmong the genes analyzed for association between genetic variants and prostate cancer risk or aggressiveness are XRCC1 and XRCC3 (X-ray repair cross-complementing proteins 1 and 3), ERCC1 and ERCC2 (Excision repair cross-complementing rodent repair deficiency, complementation group 1 and 2), LIG4 (Ligase IV), ATM (Ataxia telangiectasia mutated), XPD (Xeroderma pigmentosum group D), MDM2 (Human mouse double-minute 2 protein), CDKN1A, and CDKN1B (Cyclin-Dependent Kinase Inhibitors 1A, and 1B), CCND1 (Cyclin D1) as well as BCL2 (B-cell lymphoma 2) and TP53 (tumor protein p53) [89–102]. Genetic variants within most of these genes were found to be associated with PCa aggressiveness or response to therapy. Nevertheless, these results were seldom replicated in multiple populations.
\nThe most common SNVs in XRCC1 studied in case–control studies on cancer risk are rs1799782 (p.Arg194Trp), rs25489 (p.Arg280His), and rs25487 (p.Arg399Gln) [89, 103]. These genetic variants were also analyzed for their potential association with PCa risk in numerous studies, but the obtained results were inconsistent [89, 90]. For rs25489, association with radiation-induced late toxicity in PCa patients was also shown [104]. Similarly, rs861539 (p.Thr241Met) in XRCC3 was found to be associated with early adverse effects induced by radiotherapy, based on quantitative data synthesis of 6 studies [105].
\nA recent study conducted in Spain showed the association of rs11615 in ERCC1 and rs17503908 in ATM with PCa aggressiveness [93]. Genetic variants in the same chromosomal region as ERCC1 were previously analyzed in a large study that provided opposing results. Nevertheless, this previous study was designed as to include subjects from multiple populations, and its results could therefore be influenced by genetic backgrounds of study participants [93, 106].
\nAmong genetic variants located in MDM2, missense variant SNP309 in the promoter region was most frequently analyzed. This SNV was found to be associated with both PCa risk and aggressiveness in multiple studies [107, 108]. The first study on this subject yielded no evidence of the supposed association [109]. Nevertheless, results obtained in several later studies suggested the association of SNP309 with the risk of PCa progression to the more advanced stage, or the statistical trend of significance was reached [108].
\nNumerous studies conducted on a potential association between CCND1 genetic variant rs603965 (p.Ala870Gly) and PCa risk, yielding inconsistent results [99]. This SNV was found to affect alternative splicing and thus alter the C-terminal domain. Other genetic variants within this gene were shown to be associated with the risk of PCa biochemical reoccurrence after radical prostatectomy [110].
\nThe most extensively analyzed SNV located in TP53 gene is rs1042522 (p.Arg72Pro). This genetic variant was found to be associated with PCa risk, especially among Caucasians [102]. When it comes to BCL2, encoding the founding member of apoptosis regulatory proteins, promoter SNV c.-938C > A was associated with PCa risk, although lacking replication, as well as with disease-free survival and biochemical recurrence of PCa after radical prostatectomy [100, 101, 111].
\nVitamin D signaling in PCa has stimulatory effect on apoptosis, as well as inhibitory effect on the progression of cell cycle. Therefore, multiple genetic variants within the gene encoding the receptor for vitamin D (VDR) were analyzed for their potential association with PCa risk and/or progression. Most of them are loci named FokI, BsmI, ApaI, and T\nI, according to restriction enzyme used for genotyping, Cdx2 in promoter region and polyA microsatellite, which were most frequently tested [112–114].
\nEven though the initial results on these loci were promising, in multiple populations, they were not replicated [113, 115]. The association of these genetic variants with PCa progression parameters and the disease outcome also remains inconclusive [113, 116].
\nNumerous genes involved in chronic inflammation have been studies for association of genetic variants that reside within them with PCa risk and/or progression [117]. Also, the importance of vascular support to cancer growth stimulated the association studies on PCa analyzing genetic variants located in angiogenesis-related genes [62]. Since these processes are codependent, numerous genes primarily found to be involved in chronic inflammation are also discussed as angiogenesis-related genes, and vice versa.
\nAmong the most important factors of chronic inflammation are TGF-β, COX2, TNF-α, and IL-1-β, as well as PPAR-γ. To date, several SNVs in TGF-β1 have been identified as PCa-susceptibility variants, some of them also associated with PCa aggressiveness [118–123]. The studies on the most of the chronic inflammation-related genes provided conflicting results [117].
\nThere have been various PCa case–control studies involving Vascular endothelial growth factor (VEGF) gene, encoding the important proangiogenic growth factor, as well as genes encoding Interleukin 8 (IL-8 ) and Interleukin 10 (IL-10) [96, 124–128] for genetic variant rs1570360 [c.-1154G > A] located in the promoter region of VEGF, statistically significant association with PCa risk was shown in several studies [126]. Most other VEGF genetic variants analyzed for potential association with PCa risk and/or progression are also located in the promoter region [126, 129–131]. These SNVs could be associated with transcription rate of VEGF [132], which is positively correlated with tumor stage, Gleason score, as well as with shorter period of disease-free survival [133].
\nCandidates for this type of studies were also genes encoding transcription factors which regulate the expression of VEGF, such as Hypoxia inducible factor 1 (HIF1A ), Epidermal growth factor (EGF ), and Lymphotoxin α (LTA). Nevertheless, except for HIF1A, association of genetic variants within these genes with PCa risk was not shown, or was mostly found in small sample studies and poorly replicated [62, 125, 126, 134].
\nSome of the key regulators of angiogenesis are also fibroblast growth factors (FGFs). Therefore, receptor FGFR4 gene has been analyzed for genetic variants associated with PCa risk and/or progression. The most commonly tested SNV is a missense variant rs351855 (p.Gly388Arg), found to be associated with PCa risk and aggressiveness in a relatively small number of studies [135].
\nAmong the most extensively analyzed candidate genes in PCa-related case-control studies are NOS3 and NOS2A, encoding nitric oxide synthases [136]. Both endothelial and inducible nitric oxide synthases, encoded by these genes, are enzymes that catalyze the production of NO from L-arginine and L-citrulline amino acids [137]. Being the major producer of NO in endothelial cells, eNOS, encoded by NOS3, is involved in the control of vascular tone and angiogenesis, which is essential for tumor growth and the development of metastases. Yet, the synthesis of NO is associated with apoptosis, which has the opposing effect on carcinogenesis [138]. Numerous genetic variants within these genes, especially NOS3, have been analyzed for potential association with PCa risk and/or progression [136]. Most commonly analyzed SNVs are -786 T > C (rs2070744) and 894G > T (rs1799983) [139–147], while several studies included insertion-deletion polymorphism 4a4b located in intron 4 of NOS3 [140, 146, 148, 149]. For rs1799983, which is a missense genetic variant, it was hypothesized to affect NOS3 stability [150]. The other common SNV, rs2070744, affects promoter activity by allele C creating a binding site with validation protein 1A (RPA1) [151].
\nAngiogenesis process and tumor invasion also require degradation of extracellular matrix and basal membranes, which are catalyzed by matrix metalloproteinases. Among the genes encoding this class of enzymes, MMP2 and MMP9 are analyzed for genetic variants associated with PCa risk, and also for disease aggressiveness, due to their functional significance in tumor invasiveness [139, 152–156]. Commonly analyzed genetic variant in MMP2 promoter is rs243865. For minor allele of this SNV it was shown to be associated with reduced transcription rate of MMP2 [157].
\nAmong genes involved in cellular adhesion, CDH1 encoding E-cadherin was the candidate gene for the most case-control studies on PCa. Since aberrant expression of this gene is correlated with the increased metastatic potential of PCa, genetic variants in its promoter region were analyzed for potential association with PCa risk and progression [158, 159]. Most extensively studied SNV−160C > A was found to affect CDH1 expression and was identified as PCa susceptibility genetic variant in multiple populations [158, 160].
\nOnly few studies also included genetic variants in genes encoding intercellular adhesion molecules (ICAMs), proteins involved in cellular adhesion and signaling. The analyzed genetic variants are those located in ICAM-1, ICAM-4, and ICAM-5 genes and need a further evaluation for potential association with PCa risk and/or progression [161, 162].
\nThe potential involvement of long noncoding RNAs (lncRNAs) in prostate carcinogenesis was suggested not only by the results of expression analyses that showed several known oncogenic and/or tumor-suppressive lcnRNAs to be aberrantly expressed in malignant prostatic tissue or plasma samples from patients with PCa but also by the identification of several PCa-specific lncRNAs [163, 164].
\nSeveral SNVs in lncRNA genes were identified as PCa susceptibility variants in case–control studies on PCa. In their study published in 2011, Jin et al. have stated that eight SNVs identified to that time through GWAS are located in lncRNA intervals [165]. They also identified a SNV in a putative lncRNA which was not later experimentally confirmed as a PCa-susceptibility variants [165]. In a study published in 2013, Xue et al. have shown the association between two tag-SNPs in Prostate cancer gene expression marker 1 (PCGEM1 ) and PCa risk in Chinese population [166]. Genetic variant in another PCa-specific gene, prostate cancer associated 3 (PCA3), was analyzed for the length of a TAAA repeat string in the promoter region. This genetic variant was also found to be associated with PCa risk [167]. In a GWAS published in 2014, Cook et al. have identified rs7918885 in RP11-543 F8.2 gene as a PCa-susceptibility SNV in West African men, although GWAS statistical significance threshold was not reached [168]. Also, by using fine-mapping and resequencing of PCa-susceptibility subregion of 8q24, lncRNA gene prostate cancer noncoding RNA 1 (PRNCR1) was found to be located between the most significantly associated genetic variant [169].
\nDysregulation of diverse regulatory mechanisms based on microRNA activity has been implicated in prostate carcinogenesis. Therefore, possibly functional genetic variants located in microRNA genes emerged as potential PCa-associated loci. Among these genetic variants are those that potentially influence microRNA biogenesis, stability of mature microRNAs, efficiency of target gene regulation, as well as target specificity. By affecting these features of microRNA regulatory mechanisms, microRNA SNVs could be associated with aberrant expression of various important PCa-related oncogenes or tumor-suppressive genes [170–172].
\nMicroRNA genetic variants have been analyzed for their potential association with PCa in only a few studies conducted in Asian populations and in a single population of European origin. These studies have provided discordant results on the effects of genetic variants in rs2910164 in hsa-miR-146a [173–176], hsa-miR-196a2 [174, 176, 177], and rs3746444 in hsa-miR-499 gene on PCa risk [174, 177]. In a recent study, rs4705342 located in hsa-miR-143 gene promoter was found to be associated with the risk of developing PCa [178]. Since the number of conducted studies is small, additional findings from multiple populations are needed in order to make further conclusions.
\nAnother SNV, rs895819 located in a gene encoding miR-27a, which is androgen-regulated and stimulates the androgen signalization in a positive feedback loop, was found to be associated with PCa risk, as well as with the development of distant metastases. Nevertheless, these results are derived from a single study on PCa risk and rs895819 conducted relatively recent and needs further validation [177].
\nDifferences in genetic backgrounds are an important issue in genetic association studies. Therefore, interpretation of data requires discussing the potential differences between populations. Therefore, in order to analyze such differences, multiple validation analyses are conducted in various population and ethnicities. These studies are designed so that they resemble as much as possible to the original study that yielded genetic associations, or the lack of it. The ratio for conducting such studies is the possible lack of association between identified PCa-susceptibility variants with PCa risk in certain populations, or the differences in effect sizes [179]. Replication studies, conducted in confirmation group of participants from the same population in which the initial results were found, is a method of checking reproducibility and evaluating possible false positives and effect overestimation [179, 180].
\nCurrently, replications and validations are conducted for both GWASs results, as well as for results from candidate gene-based studies. Of utmost importance is conducting replication and validation analyses of hits from studies with relatively small sample sizes, as well as with poorly clinically characterized cases with the lack of data on possible confounders, or questionable recruitment of controls [180]. Also, an important issue in case–control studies on PCa is the type of control group, which is in some cases healthy controls, while in others group of patients with benign prostatic hyperplasia (BPH). Furthermore, classification systems for patients with PCa which are used for evaluating potential genetic associations with PCa progression differ between studies, which together with small sizes of patient groups, calls for replication of acquired statistically significant data.
\nAll of these issues are a potential reason for the opposing results on the association of the most of genetic variants analyzed in multiple studies with PCa risk and progression. Therefore, in order to elucidate the effect of these genetic variants, meta-analyses of eligible studies are frequently conducted. Combining the results from smaller studies through data synthesis in meta-analysis could result in increased statistical power [181]. Therefore, meta-analyses could provide more precise estimations, as well as the insight in the potential effect of confounders [182], such as ethnicity, participant recruitment strategy, or study size.
\nThe main aim of genetic association studies on PCa is the identification of potential PCa genetic markers which could be used for constructing reliable algorithms for evaluating the risk for PCa development and/or PCa progression [1]. Therefore, it is important not only to identify these PCa-related genetic variants, but also to precisely characterize their effect sizes. In order to do that, ethnic differences need to be taken into account [179]. Other important issues in interpreting results of association studies are gene–gene and gene-environment interactions. Therefore, future research and designing such algorithms require integration of knowledge on genetic associations, cellular pathways, and statistical epistasis in which real biological interaction could be reflected.
\nSince the major problem in clinical practice related to PCa is the overdiagnosis and monitoring of patients [4], additional studies on PCa aggressiveness with clinically well characterized groups of PCa patients are needed to identify genetic variants associated with PCa progression risk. The later implementation of algorithms based on these genetic variants could greatly improve clinical protocols in monitoring and treating PCa.
\nThe efforts for improving clinical protocols in PCa diagnostics, monitoring and treatment resulted in conducting genetic association studies on PCa. These studies aim to identify potential PCa genetic markers and characterize their association with PCa risk and/or progression through measuring effect sizes. The identified and validated genetic markers could then be used for constructing reliable algorithms for evaluating the risk for PCa development and, more importantly, for PCa progression. Implementing such algorithms in clinical practice is expected to improve the distinction between early diagnosed PCa cases that require aggressive treatment and latent PCa cases which remain indolent during patient’s lifetime.
\nThe preservation and conservation of the environment are of great significance for healthy living. However, efforts to conserve the environment have been futile due to escalated pollution from biogenic and anthropogenic sources, which constantly release pollutants to the environment [1]. In the recent past, increased industrial and agricultural activities have immensely contributed to the pollution of aquatic environments such as rivers and streams, which pose major detrimental environmental problems to humans [2]. It is evident that industrial development has generated a myriad of new chemicals produced and applied in daily activity, which is becoming a major concern for citizens, the research community, and authorities [3]. Among the pollutant chemicals that have been introduced into the environment are polybrominated diphenyl ethers (PBDEs). PBDEs are toxic, lipophilic, hydrophobic, and persistent artificial chemicals characterized by high physical and chemical stability [4]. They are commonly applied as flame retardants in polymer products such as electronics, plastics, textiles, and building materials [5, 6]. PBDEs have become a growing concern over the last two decades due to their ubiquity, persistence and accumulation capacity in the environment, as well as their potential risks to human health and wildlife [7, 8]. PBDEs are normally additive compounds, meaning they are not covalently bound to the polymeric products [9]. Therefore, they may leach out into the surrounding environment during their production, usage, disposal, or recycling process [10]. PBDEs can be transported away from their sources for long-ranges through aqueous and/or terrestrial environmental compartments [11, 12]. In this context, monitoring and assessment of environmental pollution by these compounds are very important.
Their determination involves a series of steps from sample pre-treatment to quantification of analytes using various detection systems. Different sample preparation strategies that range from conventional to advanced strategies have been applied for the determination of PBDEs in environmental samples. Some of the conventional sample enrichment methods include Soxhlet extraction [13, 14] and liquid-liquid extraction (LLE) [15]. More recently, ultrasound-assisted extraction (UAE) [16, 17], pressurized liquid extraction (PLE) [18, 19], microwave-assisted extraction (MAE), solid-phase extraction (SPE), and solid-phase microextraction (SPME) have exhibited successful extraction of PBDEs from environmental samples [20, 21]. The application of SPE and SPME has advanced from conventional adsorbent formats to the most improved formats which allow easy transfer of analytes from their complex matrices. This has been achieved by using novel adsorbent materials to replace conventional silica-based adsorbents which exhibit low selectivity towards targeted analytes [22]. Similarly, analytical techniques for the qualitative and quantitative determination of PBDEs have advanced from well-known gas chromatography-electron capture detection (GC-ECD) to sensor-based techniques that are more advantageous in terms of excellent selectivity, with opportunities for in-situ application. The following sections provide detailed information on PBDEs, advances in sample pre-treatment methods and detection techniques with a view of providing the current state-of-the-art as far as their monitoring is concerned.
PBDEs comprise of two halogenated aromatic rings bonded by an ester bond and are classified in relation to the number and position of bromine atoms in a particular molecule [23]. They have a general molecular formula of C12H(10 - x) BrxO, where x is the number of bromine atoms in a molecule with numerical values [x = 1, 2, 3, …, 10 = m + n] (Figure 1). Substitution of bromine atoms can take place at 10 possible positions on the two benzene rings resulting in 209 possible congeners [24].
General structural formula of PBDEs.
Different congeners are easily identified by their corresponding IUPAC numbers ranging from 1 to 209. In this case, 2,2′,4,4′-tetrabromodiphenyl ether is BDE-47, with bromine atoms in ortho and para positions on the first and second benzene rings, respectively (Figure 2).
Chemical structure of 2,2′,4,4′-tetrabromodiphenyl ether (BDE 47).
Molecules with one to four bromine atoms are classified as low molecular mass PBDEs, whereas the ones with five to ten bromine atoms are categorized as high molecular mass PBDEs. Less brominated PBDEs are more persistent and toxic than highly brominated diphenyl ethers [25]. The substitution pattern also affects the physicochemical properties of PBDEs, whereby the solubility of PBDEs decreases significantly with an increase in bromine substitution. The aqueous solubility (SW) of low molecular mass PBDEs at room temperature ranges from 6.57 × 10−7 to 7.82 × 10−11 mol L−1 while those of high molecular mass have aqueous solubility values lower than 7.82 × 10−11 mol L−1 [26]. A wide range of PBDE congeners exhibit high lipophilic capacity and high resistance to degradation; a property that makes them bioaccumulate and magnify in biota [7]. PBDEs are also associated with high octanol-air partition coefficients (KOA) with values ranging between 9.3 and 12.0 from BDE-17 to -126, which is approximately 1 to 2 orders of magnitude greater than PCBs [27]. Therefore, PBDEs are easily transported through air from one point to another, increasing their chances of exposure to humans. Dissolved organic matter has shown a high tendency to interact with hydrophobic compounds such as PBDEs, which hinders their mobility and degradation in the environment [28]. Reported binding coefficients of PBDEs (log KDOC) towards organic matter range from 5.1 to 7.14, which implicates the high capability of PBDEs to adsorb and partition on organic matter [29].
PBDEs were commercially produced in three technical mixtures, typically known as pentaBDE, octaBDE, and decaBDE, basing on the number of bromine atoms [10]. By early 2000, the global production of commercial PBDE formulations was approximately 67,000 tons in the ratio 1:1.98:14.8 for octa-BDE, penta-BDE, and deca-BDE respectively, of which the United States production was approximately 50% of the global production [30]. Several governmental regulations and international environmental agencies have restricted and completely banned the use and production of some PBDE congeners [31]. In 2004, the European Union phased out the use and production of penta-BDE and octa-BDE. Consequently, in December 2004, Great Lakes Chemical Corporation, a sole manufacturer of penta-BDE and octa-BDE in North America, voluntarily phased out the production of these BDE formulations [32]. These efforts were boosted by the Stockholm Convention in 2012 when it listed commercial octa-BDE and penta-BDE among persistent organic pollutants that need to be eliminated. Despite the ban in the production of most PBDEs, they are still reported in air, soil and aquatic environments, which is attributed to their stability and subsequent release from techno-ecosystems, and production of deca-BDE, which still continues to be produced in some countries [33, 34].
There are diverse pathways by which PBDEs enter the environment. Major environmental sources of PBDE pollution comprise of leakage from consumer products and industrial facilities that synthesize PBDEs or PBDE-containing products [5]. Besides, PBDEs may enter the aquatic environment from illegal disposal of obsolete electrical appliances and electronic devices flame-retarded with PBDEs or other PBDEs-containing products [7]. They can also enter the aquatic environment through raw sewage and into the surrounding air through volatilization from products containing PBDEs and toxic fumes from e-waste recycling plants [35]. Since the first discovery of PBDEs in the aquatic environment on the West coast of Sweden in 1981, several studies have reported the presence of PBDEs in the environment [36]. This is despite the strict regulatory measures imposed by some governments and international environmental agencies to phase out some PBDE congeners and subsequent reduction in the production of particular PBDEs. BDE-47, 99, 100, and 153 are the ones that are frequently investigated because they are primary components of commercial mixtures, therefore, their ratios in the environment are expected to be significantly high. Moreover, less substituted BDE congeners such as BDE-28 and 47 are more toxic and non-biodegradable, hence their investigation in the environment and biota is of great significance in the monitoring of these pollutants [37]. Soil and sediment harbour higher concentrations of PBDEs, which is attributed to the organic carbon content, which makes them a sink for most organic pollutants [38]. Elevated levels of PBDEs have since been reported in agricultural soils after the application of sewage sludge at a concentration of 21 to 690 ng g−1 dry weight (dw) [39]. From statistics, human beings spend more than 70% of their lifetime indoors, in occupational offices, homes, learning institutions, and transport vehicles, and are therefore exposed to an array of contaminants from indoor dust [40]. The highest levels of PBDEs in dust samples have been reported in major industrialized cities in China and Europe at a concentration of 397–40,236 ng g−1 and 950–54,000 ng g−1, respectively [41, 42], with comparably lower levels of 1710 ng g−1 in African regions [43]. Table 1 presents a summary of reported PBDE levels in selected environmental matrices.
Country | Sample matrix | Concentration | Reference |
---|---|---|---|
South Africa | River water | 2.60–4.83 ng L−1 | [44] |
North America | River water | 0.00013–0.01 ng L−1 | [45] |
Great Britain | Indoor dust | 950–54,000 ng g−1 | [46] |
South Africa | Home dust | 1710 ng g−1 | [43] |
Office dust | 1520 ng g−1 | ||
Nigeria | Indoor dust | 3700–19,000 ng g−1 | [47] |
China | Indoor dust | 397–40,236 ng g−1 | [41] |
Uganda | Air | 0.00340–0.00984 ng m−3 | [48] |
Kenya | Soil | 0.19–35.64 ng g−1 | [49] |
China | Soil | 4.8–533 ng g−1 | [50] |
China | Sediment | 0.03- 5.22 ng g−1 | [51] |
China | Sediment | 0.13–1.98 ng g−1 | [52] |
Sweden | Sewage sludge | nd-450 ng g−1 | [53] |
Spain | Sewage sludge | 197–1185 ng g−1 | [39] |
Kuwait | Sewage sludge | 52.5–377* ng g−1 | [54] |
USA | Serum | 5.0–27.9 | [55] |
South Africa | Tigerfish | 5.8 | [56] |
Uganda | Breast milk | 0.59–8.11 | [57] |
Levels of PBDEs reported in the environment and biota from different locales worldwide.
Mean concentration.
nd, not detected.
The principal route for PBDE exposure to humans was thought to be through food consumption [58]. However, inhalation of contaminated indoor and outdoor dust is also a significant pathway via which human beings may be exposed to PBDEs [46, 59]. Dermal absorption is another potential route for PBDE exposure [60]. Numerous studies have reported levels up to 160.3 ng g−1 of PBDEs in human samples, such as serum and milk. Increased application of PBDEs in electronics has significantly aroused more research work on the concentration of these pollutants in the blood of workers in e-waste processing plants and other exposed populations [61]. BDE 47, 153, and 209 are the most predominant congeners reported in human serum and milk [55, 62]. The toxicity of PBDEs is backed up by numerous epidemiological studies. Scientific research has linked PBDE exposure to an array of adverse health effects [63]. To mention a few, penta- and octa-BDEs at a concentration of 10,000 ng g−1 have been associated with disruption of thyroid hormone homeostasis [7]. Moreover, penta- and tetra-BDEs, within the range of 8000–18,000 ng g−1, have been reported to affect the neurodevelopment of mice [64]. Exposure to high levels of deca-BDEs is likely to cause breast cancer [7]. PBDEs have been linked to developmental neurotoxicity and hence leading to severe effects on cognitive ability, behaviour, and health of both animals and humans [65, 66]. Several studies have also linked PBDEs with adverse effects on the human reproductive system. In particular, BDE-47, BDE-153, and BDE-154 in the range of 0.2–1.6 ng g−1 have been confirmed to have negative impacts on testosterone, luteinizing hormone, and estradiol [67]. Therefore, there is a need to have robust, accurate and reproducible methods to quantify PBDEs in different environmental matrices. The sections that follow will discuss these aspects with a particular focus on aquatic media.
Sample pre-treatment steps such as pre-concentration and clean-up are paramount before instrumental analysis [2, 68]. These steps ensure that analytes are enriched and converted into the right form/state to achieve their detection and any matrix that may interfere with the determination of the analytes is removed [69]. The choice of sample pre-treatment step is dependent on the physicochemical properties of the targeted analytes, their concentration in the environment, and the complexity of matrix interference [70, 71]. Soxhlet extraction, a traditional liquid-solid extraction method, has been used for decades in the extraction of analytes from their complex solid matrices. With the combination of polar and non-polar solvents, the Soxhlet extraction strategy has been proved to be efficacious, achieving extraction efficiencies greater than 70% [72, 73]. However, this method is hindered by several factors such as long extraction duration, excessive solvent consumption, and the need for subsequent clean-up steps [74]. With increasing demand for economical and fast sample extraction strategies with high enrichment factors, coupled with SPE clean-up procedures, techniques such as UAE, PLE, MAE, and supercritical fluid extraction (SFE) have been adopted in enrichment of analytes from solid matrices.
UAE encompasses the introduction of a finely divided sample contained in a sample holder in an ultrasonic bath with solvent and subjected to ultrasonic radiation. UAE is a vital technique in achieving sustainable green chemistry and is primarily employed in the extraction of analytes from solid sample matrices [75, 76]. This technique can achieve complete extraction with high reproducibility within a short duration. Moreover, small quantities of extraction solvents are used as compared to conventional Soxhlet extraction [77]. Methanol, acetonitrile, ethanol, and acetone are typical extractants used in this method in minimal volume. UAE based on ultrasound assisted-dispersive solid phase extraction (UAE-DSPE) coupled to GC-MS has been reported to achieve exemplary limits of detection and extraction efficiencies for 7 BDE congeners from dust samples collected from air conditioning filters in the range of 1.4–8.4 ng g−1 and 90–102%, respectively [78]. Some of the benefits of UAE include faster kinetics and an increase in extraction yield. Ultrasound can also reduce the operating temperature allowing the extraction of thermally labile compounds [79].
Unlike traditional Soxhlet extraction that consumes a large volume of solvent, PLE, also referred to as pressurized solvent extraction, has been of great interest due to its extraction effectiveness. Extraction of analytes from their environmental matrices is achieved via a synergistic mechanism that proceeds through liquid solvents at elevated temperature and pressure, which altogether enhance extraction throughput as compared with other techniques performed at ordinary atmospheric conditions [80]. PLE is viewed as another \'green\' option for traditional sample extraction methods. High temperature accomplishes a higher dispersion rate, while high pressure keeps the extraction solvent below its boiling point. During the determination of brominated flame retardants in e-waste samples, PLE and UAE were evaluated in regard to extraction efficiencies. PLE demonstrated high extraction efficiencies of 95–100% as compared to 10–50% for UAE [81]. When contrasted with the conventional methods, PLE shows a decrease in extraction time and a significant decrease in the overall consumption of organic solvents [82].
Another type of extraction technique that enables a three-fold reduction in extraction time and solvent is MAE. This is a sample extraction method that employs microwave energy to extract analytes from solid sample matrices in contact with extraction solvents. Microwave energy directly generates heat which initiates molecular motion of the analytes in the solid-solvent complex mixture, hence facilitating the mass transfer of the target analyte from the solid matrix to the extracting solvent [83, 84]. MAE has been reported to achieve good recoveries of 80–106%, 72.4–108.4%, and 80–110% in the extraction/pre-concentration of PBDEs from airborne particulate matter [85], e-waste materials [86], and sewage sludge samples [87], respectively. Compared with Soxhlet extraction, MAE achieves better recoveries and uses small amounts of solvents (30 mL versus 200 mL for Soxhlet extraction), at the same time allowing control of extraction parameters, such as extraction time and temperature [88]. However, MAE has some shortcomings, whereby the extracted sample usually contains some matrix interferences, such as lipids and lipophilic compounds, therefore, filtration and clean-up steps are required, which subsequently consume extra organic solvents.
Supercritical fluid extraction (SFE) is another method employed to extract PBDEs from solid matrices. Supercritical CO2 is often used as an extracting solvent, which has the capability of attaining recoveries above 97%. Moreover, the extraction efficiency of SFE can be further improved by the use of modifiers such as acetonitrile, toluene, and tetrahydrofuran [89]. A successful application of SFE in the extraction of PBDEs from polymeric materials was reported by Peng et al. [90]. The authors used supercritical CO2 as a solvent and SFE operating parameters such as temperature and pressure were optimized at 65°C and 20 MPa, respectively, achieving 97.6% extraction efficiency. This technique is a greener alternative to other techniques that use a large volume of solvents.
Numerous methodologies have been adopted in the determination of PBDE pollutants in liquid matrices. SPE and conventional LLE have been embraced as routine extraction techniques for PBDEs in liquid samples. The extractive capability of LLE is based on the transfer of analytes from an aqueous polar phase to a non-polar organic phase [91]. LLE coupled with GC-MS has been applied in the determination of 13 PBDEs and their metabolites in water, with recoveries of 77%-102% [92]. LLE has also been a desirable extraction method in the preparation of biota samples for the determination of PBDEs. Recently, a study aimed at assessing in utero exposure of 24 tri- to deca- BDE congeners on primiparous mothers in Kampala, Uganda reported a successful application of LLE, with appreciable recoveries of 81–91% [93]. However, LLE has some shortcomings; it suffers from low recovery, poor selectivity, high matrix interference in chromatographic analysis and increased sample loads [94]. In addition, the extraction of PBDEs from water samples requires extremely large volumes of solvents due to their hydrophobic character and low concentration in water, thus limiting its applications [95]. To overcome these challenges, different configurations of SPE have been adopted in sample enrichment strategies. SPE is a modern sample pre-treatment technique employed to concentrate analytes from liquid samples and to remove matrix interferents during the clean-up step, achieving exemplary recoveries and reproducible results over LLE [96, 97]. SPE protocols are usually performed by the use of a small column or separation cartridge packed with an appropriate sorbent material [98, 99]. Target analytes are adsorbed by the sorbent materials and later eluted with a solvent that has a greater affinity for the analytes. The chemistry behind this separation is based on intermolecular forces between the analytes, active sites of the adsorbent, and the liquid phase of the matrix [100]. SPE can be performed through an on-line or off-line approach. The on-line SPE configuration, which may enable automation, is directly coupled with specific analytical systems such as gas chromatography (GC) or high-performance liquid chromatography (HPLC). Whereas in the off-line protocol, a pre-concentration step is done separately using cartridges and further eluting the adsorbed analyte with an appropriate solvent for eventual chromatographic analysis [101]. Because of its robustness and flexibility, SPE has been widely employed in different analytical procedures in pre-concentration and clean-up steps in the determination of PBDEs [96, 102].
While SPE continues to be used because of its affordability and ease of use, other formats that offer high enrichment factors and shorter extraction times, such as SPME, stir-bar sorptive extraction (SBSE) and dispersive solid-phase extraction (DSPE), have been introduced [103]. SPME is an innovation and improvement of conventional SPE. Its stationary phase comprises of fused-silica fibers coated with a polydimethylsiloxane (PDMS) layer which are reusable. With this new formulation, the application of SPE has become versatile such that it can accommodate small volumes of samples. Furthermore, SPME has been considered an almost solvent-free extraction technique and can be easily automated as compared to conventional SPE [104, 105]. A miniaturized SPME has been applied in the extraction of PBDEs in environmental water samples followed by GC-MS quantitation, with low limits of detection and appreciable recoveries of 76.5–125.4% [106]. SBSE is a similar technique to SPME that has been adopted in the enrichment of PBDEs in liquid samples due to its improved extraction efficiency. The stir bars are coated with a thinner PDMS layer, as opposed to a thicker layer in SPME, a factor that allows improved enrichment efficiency [107, 108]. DSPE is another format of SPE based on the dispersion of solid sorbent materials in liquid samples to facilitate the isolation and extraction of target analytes from the complex sample matrix. In this process, matrix interferences remain embedded in the supernatant, which is later discarded while the target analyte is bound to the sorbent material and which is eventually eluted with a viable solvent [109]. DSPE has been employed in the enrichment and determination of PBDEs with recoveries within the range of 60–140% [110].
Complexity and matrix interferences encountered during sample preparation steps have attracted the invention of more selective sorbents to replace conventional silica sorbents that are associated with a number of drawbacks, such as instability at extreme pHs and low extraction efficiencies [111]. The new sorbents that include, nanocomposite materials, metal-organic frameworks, and molecularly imprinted polymers, among others, are characterized with high sensitivity and selectivity towards various environmental organic pollutants. They achieve fast dispersion and efficient recycling when applied in complex sample matrices [112, 113]. Reported nanocomposite sorbents in SPE for PBDE-containing samples include carbon nanotubes, graphene oxide (GO) [114, 115], and magnetic nanocomposite materials [113]. However, nanocomposite sorbents in classical SPE schemes have been associated with various drawbacks. A few of these challenges have been described in flow as well as batch systems, which originate from a slow flow rate of the sample through the packed SPE column and difficulty in separating the sorbent from the large volume of aqueous sample [113].
Other sorbent materials with fascinating properties are metal-organic frameworks (MOFs). These are hybrids of organic and inorganic materials characterized by a porous structure, large surface area, uniform nanoscale cavities, high adsorption capacity, and high thermal and chemical stability. Due to these advantageous properties, this class of materials has recently attracted enormous attention in the field of sample preparation [116]. The development of MOF adsorbents is still at its infancy stages, therefore, a limited number of studies have reported their application particularly in enrichment and determination of environmental PBDEs. A zirconium-based metal-organic framework material (UiO-66-OH) is a good example of a MOF. It has been synthesized and successfully applied as an adsorbent in SPME for enrichment and detection of 5 BDE congeners in milk samples using GC-MS, with low limits of detection in the range of 0.15–0.35 ng L−1 and excellent recoveries of 74.7%–118.0% [117]. A contrast study using silica-based sorbents in SPE for determination of 12 PBDEs in human serum, achieved mean recoveries of 64–95% and limits of detection in the range of 0.1–4.0 ng g−1 by using GC-MS [102], an evidence that MOF sorbents offer promising analytical results as compared with conventional sorbents.
With growing interest in sorbents that offer extraordinary extractive capability in SPE, molecularly imprinted polymers (MIPs) have been extensively explored as attractive options due to their robustness and selectivity towards particular target analytes providing exemplary substitute sorbents in sample clean-up and pre-concentration steps, especially in SPE and SPME [118]. MIPs are synthesized through molecular imprinting technology that involves polymerizing functional and cross-linking monomers in the presence of a target analyte, followed by the removal of the analyte to leave behind analyte-specific cavities. Their selectivity enables substantive removal of matrix interferents during the sample pre-treatment step [119]. MIP-based sorbents are readily available substitutes to silica-based adsorbents, which are reported to suffer from matrix interference, low selectivity, and sensitivity towards organic pollutants and may involve multiple steps that are labour-intensive for complete removal of interferences [120]. For example, commercial molecularly imprinted solid-phase extraction (MISPE) cartridges alongside alkaline extraction have been applied in aqueous enrichment and quantitation of PBDEs using GC-MS [121]. The extraction of PBDEs using MISPE gave recoveries above 60% compared to alkaline extraction which was below 60%. This confirms the selectivity capability of MIPs towards PBDEs from a complex environmental matrix. A more recent study has also reported recoveries of 60–87% in clean-up of soil and sediment samples using dummy molecularly imprinted polymers as SPE sorbent materials during determination of BDE-47 and BDE-99 [122].
However, a wide range of limitations still exist in MIPs, especially their poor water compatibility. Consequently, since MIPs and target analytes mainly interact through hydrogen-bonding, their recognition capability would be easily disturbed by polar solvents such as water. Therefore, the adsorption process is normally performed in non-polar or low-polar solvents such as dichloromethane and n-hexane rather than polar solvents. Additionally, polar solvents have a tendency to occupy binding sites, which affects the recognition capacity for the target analytes. In this context, it is necessary to continually invent new synthesis strategies for water-compatible MIPs [123, 124]. A summary of some of the sample pre-concentation strategies and their extraction efficiencies is presented in Table 2.
Sample preparation technique | PBDE congeners | Sample analyzed | Analytical technique | % Recoveries | Reference |
---|---|---|---|---|---|
SPE | BDE-28, 47, 49, 66, 85, 99, 100, 138, 153, 154, 183 & 209 | Human serum | GC-ECD | 64–95 | [102] |
BDE-47 and 99 | Soil and bottom sediment | GC-MS | 60–87 | [125] | |
PLE | BDE-28, 47, 99, 100, 153, 154 & 183 | Soil | GC-MS | 95 ± 9 | [68] |
BDE-28, 47, 99, 100, 154, 155 & 183 | Soil and sediment | GC-MS | 84–103 | [92] | |
LLE | BDE-17, 28, 47, 66, 71, 85, 99, 100, 138, 153, 154, 183 & 190 | Soil and sediment | GC-MS | 85–103 | [92] |
Soxhlet extraction | 42 mono- to deca-BDEs | Indoor dust sample | GC-MS | ≥ 70 | [72] |
UAE | BDE-1, 3, 7, 8, 28 & 47 | Industrial effluent | HPLC | 98.7 | [126] |
SPME | BDE-49, 99, 100, 153 & 154 | Milk and water | GC-ECD | 90–119 | [127] |
MAE | BDE-47, 99, 100, 138, 153, 154, 184 & 209 | Sewage sludge | GC-MS | 80–110 | [87] |
Examples of sample preparation strategies.
Sample pre-treatment steps are followed by quantification of the analytes using various detection systems. The choice of detection system depends on the physicochemical properties of the target analyte and the required detection levels. Detection techniques for quantification of PBDEs have evolved from liquid chromatography to gas chromatography and recently, miniaturized systems that involve the use of sensors. For chromatographic techniques, it’s important to optimize the operational parameters to actualize reliable instrumental results. It is highly recommended to use a sample injector with programmed temperature vaporization (PTV) to avoid degradation of thermally labile BDE congeners. Additionally, the temperature of injection should be accurately defined, especially when using a split/splitless injector, which minimizes chances of thermal degradation of higher BDEs congers as well as discrimination of lower brominated congeners [95, 128]. The choice of a column is another important aspect in the analysis of PBDEs where lower brominated congeners are well separated on longer columns, whereas higher brominated congeners are well separated on shorter columns. In the case of a mixture comprising of a wide range of BDE congeners, a short column is highly recommended, which well separates nona- and deca-BDEs [129]. HPLC coupled with mass spectrometry (MS), is one of the chromatographic techniques which has rarely been applied in the quantification of some PBDE congeners. The HPLC separation is hindered by several factors such as poor solubility of highly brominated diphenyl ethers in the polar solvents of the mobile phase, especially in reversed-phase, and, thus, requiring the sample to be enriched with an organic modifier. Normal phase HPLC has offered better separation of some PBDEs though it still results in incomplete separation, especially when an electrospray ionization detector is incorporated [130]. One group used an automated on-line sample preconcentration device coupled with HPLC-MS to determine decabrodiphenyl ether in human serum samples. This method achieved detection limits of 26.0 ng L−1 [130]. Otherwise, better detection limits of 0.2-25 ng L−1 were tenable when similar samples were analyzed for 12 PBDEs including decabromodiphenyl ether using gas chromatography-electron capture detection (GC-ECD) [102]. However, GC-ECD exhibits low selectivity and suffers from matrix interferences originating especially from halogenated species, as compared to GC-MS, which overcomes these challenges [131]. Fontana et al. [16] employed a coupled system, ultrasound-assisted emulsification microextraction-GC-MS (UAEMA-GC-MS) to determine PBDEs in water samples, with appreciably low detection limits of 1–2 ng L−1. Moreover, lower limits of detection are achievable when tandem-mass spectrometry (MS2) is utilized. For example, GC-MS2 has been reported to achieve detection limits within the range of 0.002–0.0136 ng g−1 lipid weight (lw) in the determination of PBDEs in breast milk and serum samples [132].
With the recent technological revolution, a more sensitive mass spectrometer, a high-resolution mass spectrometer (HRMS), has been found to be a promising alternative to a conventional mass spectrometer as it identifies the analyte without mass fragmentation and at the lowest mass unit [133]. With this new format of detection, very low detection limits of 0.000262–0.046 ng g−1 for 23 PBDEs in dust samples were achieved [134]. However, GC-HRMS is more expensive than conventional GC-MS, compelling researchers to often rely on GC-MS since it is less expensive and readily available. Besides, the demand for techniques that provide rapid results at minimal cost has resulted in the introduction of sensor technology in the determination of PBDEs. In this context, various detection systems have been fabricated and shown a discerning capability in the detection of PBDEs. For instance, an immunoassay detection system based on graphene oxide-polydimethylsiloxane has demonstrated desirable limits of detection of 0.018 ng g−1 for PBDEs in a standard solution and environmental water samples [135]. Similarly, a novel electrochemical immunoassay sensor used for the detection of BDE-28, 47, 99, 100, 153, and 154 in food samples, achieved a detection limit of 0.00018 ng L−1 [136]. These limits are comparable with those obtained by HPLC, GC-MS, or GC-HRMS. A surface-enhanced Raman scattering-based sensor is another detection system that has been successfully applied for rapid detection of BDE 47 in aqueous media, with detection limits of 0.0364 ng L−1 [137]. The use of sensory techniques is cheaper and a low concentration of contaminants can be detected. Moreover, the analysis duration is reduced from 10 minutes to 3 minutes. Thus, these sensor methods offer scope for further evaluation.
This chapter has discussed PBDEs as emerging environmental pollutants, their sources, and toxicological implications on humans and their determination in the environment. Sample pre-concentration methods for PBDE-containing samples that include UAE, PLE, UAME, PLE, SFE, SPE, SPME, SBSE, and DSPE have been critically reviewed as preferred alternatives to LLE and Soxhlet extraction due to their enhanced extraction efficiency. Novel SPE and SPME sorbents that provide the desired selectivity in the determination of PBDEs have also been discussed. Though these sorbents are promising, their application in MISPE in the determination of PBDEs has been scantly employed and its dynamics are still at its infancy stages. Therefore, there is room for continuous introduction of highly selective materials for the quantification of PBDEs in the environment. Alongside the evolution of sample pre-treatment techniques for the detection of PBDEs, rapid sensor-based techniques that achieve the desired figures of merit similar to traditional instrumentation techniques have demonstrated great potential.
ENN is grateful to the FLAIR fellowship programme, which is a partnership between the African Academy of Sciences and the Royal Society, funded by the UK Government’s Global Challenges Research Fund (GCRF), for financial support. BSM and VON thank the National Research Foundation (NRF) of South Africa, the University of KwaZulu-Natal (UKZN) and the UKZN Nanotechnology Platform for research support. BSM is also grateful for support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 734522, and for funding from NAS, USAID and DST (South Africa), under the PEER program cooperative agreement number: No. AID-OAA-A-11-00012.
The authors declare no conflict of interest.
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