Inherited syndromes associated with increased risk of pancreatic cancer.
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",isbn:"978-1-80356-357-2",printIsbn:"978-1-80356-356-5",pdfIsbn:"978-1-80356-358-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"3aba1eb3600a8c9ff880c628f70b3298",bookSignature:"Ph.D. Delfín Ortega-Sánchez",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11481.jpg",keywords:"Integrated Curriculum, Transdisciplinarity, Integrated Active Learning, Educational Programs, Contemporary Social Problems, Critical Thinking, Creative Thinking, Social Thinking, Agenda 2030, Sustainable Development Goals, Educational Paradigm, Social Reality",numberOfDownloads:10,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 18th 2022",dateEndSecondStepPublish:"March 18th 2022",dateEndThirdStepPublish:"May 17th 2022",dateEndFourthStepPublish:"August 5th 2022",dateEndFifthStepPublish:"October 4th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Internationally recognized researcher in the field of historical and social science education. Author of more than 100 publications, awarded three Doctorate degrees and the National End of Degree Award, granted by the Ministry of Education to the best academic records of Bachelor's degrees in Spain. Dr. Ortega-Sánchez has been Vice-Rector for Social Responsibility, Culture, and Sports at the University of Burgos since 2021.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"302925",title:"Ph.D.",name:"Delfín",middleName:null,surname:"Ortega-Sánchez",slug:"delfin-ortega-sanchez",fullName:"Delfín Ortega-Sánchez",profilePictureURL:"https://mts.intechopen.com/storage/users/302925/images/system/302925.jpg",biography:"I hold a PhD in Didactics of Social Sciences from the Autonomous University of Barcelona, a PhD in Educational Sciences from the University of Burgos, and a PhD in History from the University of Extremadura. 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In 2016 in the USA, it is estimated that pancreatic cancer will become the third leading cause of cancer-related deaths with over 53,000 individuals diagnosed and over 41,000 deaths [1]. Only 9% of newly diagnosed pancreatic cancer is localized and the 5-year overall survival is 7%, which lags behind other solid tumor malignancies [1]. It is estimated that by the year 2030, pancreatic cancer will be the second leading cause of cancer death in the USA [2]. Thus, due to most pancreatic cancers presenting at a later stage with poor overall survival, early detection methods must be implemented to improve treatment outcomes. Pancreatic cancer has been shown through several studies to have a hereditary disposition with estimates ranging from 3 to 16% of newly diagnosed cases [3, 4]. There are several germline mutations that have shown to be at risk for the development of pancreatic cancer, including
Currently, there are no guidelines for pancreatic cancer screening from the American Cancer Society, National Comprehensive Cancer Network, and the US Preventative Task Force. The International Cancer of the Pancreas Screening (CAPS) Consortium convened in 2011 to examine the best available evidence regarding pancreatic cancer screening to provide guidelines [7]. This book chapter focuses on what constitutes hereditary pancreatic cancer and what risk factors are associated with pancreatic cancer development. We also examine recent literature discussing further methods of early detection of pancreatic cancer.
\nEstimates vary in the literature, but it is recognized that approximately 3–16% of cases of pancreatic cancer have a hereditary component [3, 4, 8, 9]. What constitutes the definition of hereditary pancreatic cancer also varies between known germline mutations with associations with pancreatic cancer to a family history of pancreatic cancer. In individuals with a family history of pancreatic cancer, the risk increases several fold with each first-degree relative (sibling, child, or parent) who is affected. In families with pancreatic cancer, one first-degree relative with pancreatic cancer increased risk by twofold. This risk was increased to nearly threefold if the individual was diagnosed before the age of 60 years [10]. A National Familial Pancreas Tumour Registry (NFPTR) study indicated that the risk of pancreatic cancer increased to 57-fold with three or more affected family members with pancreas cancer [8]. Typically, individuals with hereditary pancreatic cancer also are diagnosed at younger ages (<50 year) compared with those with sporadic pancreatic cancer, which occurs at 61 years of age and older [11]. Also of note is that tobacco use can lead to an increased risk of pancreatic cancer in those individuals at risk due to hereditary pancreatic cancer [11].
\nThere are several genetic syndromes that are related to an increased risk of pancreatic cancer and are summarized in Table 1. Hereditary breast and ovarian cancer (HBOC) is associated by germline mutations of
Syndrome | \nGene (s) involved | \nClinical features | \n
---|---|---|
Hereditary breast and ovarian cancer (HBOC) | \n\n | \nBreast cancer, ovarian cancer, male breast cancer, uveal melanoma, stomach cancer, biliary cancer, endometrial cancer. | \n
Ataxia-telangiectasia | \n\n | \nCerebellar ataxia, telangiectasia of the conjunctiva and skin, immunodeficiency, leukemia. | \n
Lynch | \n\n | \nColorectal cancer, endometrial cancer, ovarian cancer, stomach cancer, small bowel cancer, transitional cell cancer, glioma, and sebaceous neoplasms. | \n
Peut-Jeghers (PJS) | \n\n | \nMucocutaneous pigmentation, hamartomatous polyps; colorectal, stomach, small bowel, testicular, breast, cervical, and ovarian cancer | \n
Familial adenomatous polyposis (FAP) | \n\n | \nColorectal cancer, desmoid tumors, medulloblastomas, osteomas, fibromas, supernumerary teeth, gastric polyps | \n
Familial atypical mole and multiple melanoma (FAMMM) | \n\n | \nMelanoma, CNS malignancies, Acute Lymphoblastic leukemia. | \n
Familial pancreatitis | \n\n | \nPancreatitis, cystic fibrosis, diabetes | \n
Li-Fraumeni | \n\n | \nSarcomas, breast cancer, Gliomas, Choroid plexus carcinomas, and adrenocortical carcinomas | \n
Fanconi’s anemia | \n\n | \nAplastic anemia, bone marrow failure, acute myeloid leukemia, myelodysplatic syndrome | \n
Inherited syndromes associated with increased risk of pancreatic cancer.
Other DNA repair mechanism-based genes implicated in familial pancreatic cancer include the gene
Peutz-Jegher syndrome (PJS) is another autosomal dominant syndrome that leads to risks in several malignancies. It is characterized clinically by multiple hamartomatous polyps in the gastrointestinal tract and mucocutaneous pigmentation. Individuals with PJS are affected by mutations in
Familial atypical mole and multiple melanoma (FAMMM) syndrome is yet another autosomal dominant disorder that is characterized by family members with multiple nevi along with cutaneous and ocular melanomas. This disorder involves mutations in the
Chronic or recurrent acute pancreatitis also has a hereditary component that has become associated with pancreatic cancer development [29]. In families with hereditary pancreatitis, the risk for pancreatic cancer increases substantially past the age of 50, with the risk being 10% by the age of 50, increasing to 19% by the age of 60, and then to 54% by the age of 75 years in one study [30]. In individuals who smoke with hereditary pancreatitis, the risk of pancreatic cancer increase by twofold if they are current or former smokers and pancreatic cancer in these individuals occurs 20 years earlier [31]. The genetics of hereditary pancreatitis is complex. Germline mutations in the serine protease 1 gene (
There are other syndromes that do not have nearly as strong evidence as the aforementioned syndromes for pancreatic cancer risk, but studies have indicated there are associations. Li-Fraumeni syndrome is an autosomal dominant disorder that is a result of germline mutations in the tumor protein p53 gene (
As genetic sequencing become more cost effective for the greater population there will likely be more genes discovered in relation to the risk of pancreatic cancer. Traditional sequencing of gene discovery may uncover additional germline mutations along with single nucleotide poylmorphisms (SNPs). Epigenetic sequencing analysis may also unveil methylated promoters that place an individual at risk for the development of pancreatic cancer. An example of whole genomic sequencing identifying new risks in the human genome came from a study examining over 1890 individuals with pancreatic cancer and compared with 2654 controls that showed an association between a locus on 9q34 and pancreatic cancer through the SNP rs505922 [47]. This SNP maps to the first intron of the ABO blood group gene which leads credence to prior epidemiologic evidence suggesting that individuals with blood group O may have a lower risk of pancreatic cancer than those with groups A or B [47].
\nOther risk factors exist for the development of pancreatic cancer and may be associated with changes in the human genome based upon enhanced environmental susceptibility with specific germline alterations. What is currently known is that obesity plays a risk for the development of pancreatic cancer. Two longitudinal US cohort studies, the Health Professionals Follow-up study and the Nurses’ Health Study, had decades of follow-up through questionnaires which examined the risk of Body Mass Index (BMI), height, and level of physical activity. Individuals with a BMI of at least 30 kg/m2 had an elevated risk of pancreatic cancer compared with those with a BMI less than 23 kg/m2 with a relative risk of 1.72. Height was also associated with an increased pancreatic cancer risk of 1.81 RR when comparing the tallest category (equal to or greater than 185.4 cm in men, greater than 167.6 cm in women) to the shortest category (less than or equal to 172.7 cm in men, less than 157.5 cm in women). In individuals with moderate physical activity defined as equal to or greater than 11.0 MET hours per week in men and equal to or greater than 10.8 MET hours per week in women, the risk of pancreatic cancer was reduced by 59% in men and 48% in women [48]. The “Western” dietary pattern of high intake of saturated fats and smoked or processed meats has also been shown to influence the risk of pancreatic cancer development. In a 7-year prospective study, those that had the highest quintile of intake of processed meat had a 68% increased risk compared with those in the lowest quintile. Higher intake pork and red meat compared with the lowest quintiles were also associated with a 50% risk of pancreatic cancer [49]. A meta-analysis of 11 prospective studies published in 2012 confirmed that processed meat consumption was associated with an increased risk of pancreatic cancer in men but not in women. The relative risk in men was 1.29 [50]. Other social risk factors that are modifiable include tobacco use and alcohol use, particularly heavy alcohol use since it contributes to the development of pancreatitis. Heavy alcohol use is defined as having at least nine drinks per day [51]. Tobacco use leads to a 2.2-fold increased risk of developing pancreatic cancer and cessation must take place for 20 years prior to returning to the risk of nonsmokers [52]. Of note is that exposure of second-hand smoke confers an odds ratio of 1.21 relative to those individuals with no exposure [53]. The risk increases in active smokers by cigarette pack-years.
\nDiabetes has been recognized more and more as a tremendous risk factor for pancreatic cancer development. Due to one of the functions of the pancreas in regulating glucose metabolism, diabetes is a common presenting finding for pancreatic cancer. In one dose-response meta-analysis of prospective observational studies, it was shown that every 0.56 mmol/L increase in fasting blood glucose is associated with a 14% increase in the rate of pancreatic cancer [54]. This roughly translates to around a 0.35% change in the Hemoglobin A1C (HgbA1C). In another meta-analysis examining 44 studies of diabetes and pancreatic cancer risk, the duration of diabetes was associated with increased risk. For individuals with longer duration of diabetes, the relative risk slowly decreased, with RR being 1.64 for having diabetes for at least 2 years, then RR of 1.58 for diabetes at least five years, and 1.50 RR for having diabetes for at least 10 years [55]. In a nested case-control study within the Health Improvement Network in the UK, new onset of type 2 diabetes was associated with an estimate odds ratio (OR) of 2.16 of pancreatic cancer [56].
\nOther environmental risk factors include Hepatitis B virus infection and
The microbiome is a burgeoning field involving the study of the microbial flora in humans and its effect on health. Human flora in the mucocutaneous surfaces are composed of a variety of aerobic and anaerobic bacteria. The human microbiome is altered by several environmental factors along with host hygiene such as alcohol use, periodontitis, and the use of antibiotics. The microbiome may play a greater role in inflammation and the eventual development of several malignancies, including pancreatic cancer [62]. A study examining the microbiome by oral wash samples from 361 individuals who developed pancreatic cancer and comparing it to 371 matched controls showed the presence of two species of bacteria,
Because pancreatic cancer carries a poor prognosis, identifying those individuals at risk for developing it is vital due to a phenomenon called anticipation. Anticipation in genetics is defined as when a genetic disorder is passed on the next generation, the symptoms of the genetic disorder become apparent at an earlier age. This is true in several familial forms of cancer such as breast or colorectal and influences when an individual is recommend screening modalities such as mammogram or colonoscopy. In a large study examining 1223 individuals from 106 families with familial pancreatic cancer, anticipation was noted. For individuals affected with pancreatic cancer, the median age of death from pancreatic cancer was 70 years old in generation one, 64 years old in generation two, and 49 years old in generation three [64].
\nIn 2010, the International Cancer of the Pancreas Screening (CAPS) consortium was formed to help organize global pancreatic screening [7]. The consortium was composed of 50 experts from 10 countries in the fields of epidemiology, genetics, gastroenterology, radiology, oncology, surgery, and pathology. This group was composed of physicians, research scientists, and genetic counselors from a wide variety of practice settings including community-based practices and academic centers. The consensus on who should be offered screening was based upon best available evidence. For individuals with three or more blood relatives with pancreatic cancer, with at least one affected first-degree relative (FDR) should be considered for screening. FDR denotes either a parent, brother, sister, or child. Those with at least two affected FDRs should be considered for screening. And finally those with two affected blood relatives with pancreatic cancer with at least one FDR should be considered. For those individuals with only a young onset pancreatic cancer relative (not an FDR), there was no consensus reached on offering screening. In regards to mutation carriers, those with Peutz-Jeghers regardless of family history should be considered for screening. For
There are a handful of published studies on screening for pancreatic cancer. One study published in 2010 from Columbia University Medical Center/NewYork Presbyterian Hospital utilized a screening program similar to the recommendations put forth from the CAPS consortium. In this study, individuals with a family history of pancreatic cancer were allowed to enroll in a prospective screening study and were placed into three risk groups: average, moderate, or high risk. The individuals in average risk were those with one family member with pancreas cancer diagnosed over the age of 55 years old. Individuals with moderate risk were defined as those with two first-, second-, or third-degree relatives with pancreatic cancer or those with one first-degree relative with pancreatic cancer less than 55 years of age. A second-degree relative denotes an aunt, uncle, grandparent, grandchild, niece, nephew, or half-brother or half-sister. A third-degree relative denotes a great grandparent, great grandchildren, and first cousins. Individuals were defined as high risk for pancreatic cancer if there were three or more first-, second-, or third-degree relatives with pancreatic cancer; two or more first-degree relatives with pancreatic cancer; one first-degree relative and one second-degree relative if one was diagnosed at 55 years or younger, and any genetic syndrome associated with pancreatic cancer such as BRCA, Peutz-Jeghers Syndrome, Lynch, Familial Melanoma, or hereditary pancreatitis. Depending upon the individual’s risk, they were offered basic blood testing (average risk) or blood testing with MRI (moderate risk) or blood testing, EUS, and MRI (high risk). In these asymptomatic individuals, pancreatic cancer was detected in two of them- one resectable and one stage IV pancreatic cancer. Four patients has intraductal mucinous neoplasms (IPMN) lesions, two individuals were diagnosed with ovarian cancer, one individual with retroperitoneal carcinoid, and one with papillary carcinoma of the thyroid [4]. All told, 18% of the 51 asymptomatic individuals in the program were found to have a preneoplastic or neoplastic lesion. In a follow-up report on their website, 7 of the 29 individuals that have continued screening have also been found to have abnormalities of the pancreas [66]. In another study enrolling 411 asymptomatic individuals performed through a multi-institutional collaboration in Europe, surveillance was offered through individuals at risk for pancreatic cancer [67]. Individuals with a confirmed
Biomarkers for pancreatic cancer continue to be developed. As mentioned previously, the occurrence of diabetes typically precedes pancreatic cancer and bears monitoring in those individuals at risk for pancreatic cancer development. Carbohydrate antigen aka cancer antigen 19–9 (CA 19–9) is the most common pancreatic cancer serum biomarker. However, this biomarker’s sensitivity and specificity are not high as several benign conditions involving the gastro-biliary system can cause its elevation [68]. Furthermore, in individuals with negative Lewis phenotype, the CA 19–9 is not elevated, even in the setting of widely metastatic pancreatic cancer. Therefore, the development of newer biomarkers is of interest for pancreatic cancer and those at risk for developing it. A study originating from MD Anderson and Dr. Raghu Kalluri’s lab looked at exosomes, which are lipid-bilayer-enclosed extracellular vesicles that contain protein and nucleic acids. The identification of cell surface proteoglycan, glypican-1 (GPC1) is specifically enriched in cancer cells, particularly pancreatic cancer. The assay itself simply uses a small amount of peripheral blood. In comparing individuals without cancer and those with pancreatic cancer, the expression of GPC1 was always elevated in individuals with pancreatic cancer. In a study examining the GPC1 levels in response to surgical resection of pancreatic cancer, individuals who had a greater decrease of detectable GPC1 levels than those that did not correlated to an improved overall survival [69]. Thus, GPC1 may be a biomarker of interest for individuals at risk for pancreatic cancer. Another study identified elevated plasma levels of branched-chain amino acids (BCAAs) are associated with an increased risk of future pancreatic cancer diagnosis. The study examined cases of pancreatic cancer with matched controls from four prospective cohort studies with blood collected at least 2 years before cancer diagnosis with the median time between blood collection and pancreatic cancer diagnosis being 8.7 years. Several metabolites were analysed, and the BCAAs isoleucine, leucine, and valine stood out as an increased risk of greater than twofold for pancreatic cancer development [70]. Circulating BCAAs are elevated in obese individuals and those with insulin resistance [71]. These findings also suggest that muscle protein loss happens much earlier than anticipated in pancreatic cancer, thus contributing further to the debilitating nature of this cancer. Other noninvasive serum approaches include the use of circulating tumor DNA (ctDNA). In a study of patients who underwent resection of their primary pancreatic tumor, the detection of ctDNA preceded the presence of measurable recurrent pancreatic cancer on CT by 9.9 months [72]. Other clinical studies have examined the use of urinary KRAS in advanced cancers requiring very small copy numbers of the KRAS ctDNA [73]. The implications of this and other work suggests that noninvasive testing is improving for detecting pancreatic cancer and may be a consideration for clinical trial design in cohorts of those individuals at risk of the developing pancreatic cancer.
\nPancreatic cancer remains a difficult cancer to diagnose and treat. The identification of individuals who are at risk for pancreatic cancer is important for several reasons. The first significant benefit is for the individual to be aware of their risk and to undergo a surveillance program for the early detection of pancreatic cancer. This program should be compromised of multi-disciplinary panel composed of oncologists, gastroenterologists, radiologists, pathologists, and genetic counsellors. The benefit of early detection for an individual at high risk is for improved survival for pancreatic cancer. Through remarkable work from Johns Hopkins and Dr. Christine Iacobuzio-Donahue’s lab from autopsy series, pancreatic cancer appears to take about 20 years from the first mutated cell to clinical presentation and metastasis [74]. This provides a window of opportunity in regards to early detection of the disease and a chance to change the natural course of pancreatic cancer in an individual. The other benefits lie in identifying individuals at risk for pancreatic cancer development and offering these individuals opportunities to participate in clinical trials examining biomarkers—through either serum, urine, or imaging or a combination. The improvement in the overall 5-year survival in breast cancer was not born out of improved treatment for metastatic breast cancer, but through the use of mammography, increased awareness of the cancer, and optimal management of localized disease through a multi-modality approach. In an editorial by Hait and Levine, they argued that the main focus by oncologists has been in treating tumors that are genetically complex [75]. Their argument focuses on examining premalignant lesions and the likelihood of dealing with less genetically complex lesions with treatment. One example of this approach is the use of imatinib, a Bcr-abl inhibitor that is incredibly effective against chronic myelogenous leukemia (CML). The effectiveness of this drug is measured by complete haematological response (CHR). During the chronic phase of CML where the cancer genome is relatively simple, the CHR is 95% for imatinib. As the tumor becomes more genetically complex through disease progression of accelerated phase and then myeloid blast crisis, the CHR falls to 38% and 7%, respectively [75]. This is what oncologists deal with pancreatic cancer, a cancer that has likely metastasized by the time of clinical presentation, hence the low response rates of current therapies.
\nThe oppurtunity to detect pancreatic cancer early through better assays may also allow for intervention. For instance, an individual without confirmed histological confirmation by way of EUS with an elevated biomarker that is highly sensitive and specific to pancreatic cancer may consider watchful waiting or treatment. The treatment may include agents such as metformin, a biguanide which reduces weight and decreases fasting plasma insulin concentrations by enhancing insulin sensitivity via activation of 5ʹ-AMP-activated protein kinase (AMPK) [76]. Metformin has also shown benefit in preclinical models in pancreatic cancer [77]. In a study of individuals who underwent resection for pancreatic cancer, metformin use was associated with an improved survival [78]. The use of immunotherapeutic agents that have so far been met with disappointment in treating advanced pancreatic cancer, may have significantly improved outcomes in premalignant or early pancreatic cancer. Another example of early intervention may be through the use of RANK ligand inhibitors that are available commercially such as Denosumab (Xgeva, Prolia). In a three-dimensional organoid breast cancer model from women that have
Costs of screening protocols in individuals at risk for pancreatic cancer development have been examined and appears to be cost-effective [80]. Utilizing early detection programs through several institutions may define better the risk factors for pancreatic cancer along with initiating clinical trials for earlier intervention. The potential benefits of identifying and improving survival in individuals with pancreatic cancer is tremendous. By examining those at high risk for developing the disease, the possibility of developing a more widespread early detection tool for pancreatic cancer in the vein as mammograms may save countless lives in the future.
\nThe COVID-19 pandemic began in China in late 2019 and is perhaps one of the biggest health threats the world has faced this century. This highly infectious disease spread quickly across the globe, mutating into a number of variants that have made containment extremely difficult. It is clear that this global pandemic will leave in its wake extensive social, economic and health impacts for many years to come and we are only just beginning to recognise the extent of its legacy.
During the outbreak, citizens around the world experienced significant restrictions in terms of their social and economic activities in the form of quarantining at home for prolonged periods of time so that social interaction (and thus, the ability of the virus to transmit between people) is limited. Behavioural guidelines to help prevent infection and slow the spread of disease have mandated the wearing of face coverings in confined spaces and recommended the adoption of a wide range of hygienic practices (for example frequent hand washing, cleansing surfaces more regularly and using hand sanitizer when hand washing was not possible). These measures have signified large-scale changes in behaviour that are psychologically burdensome for individuals to successfully achieve [1]. However, human behaviour plays a decisive role in in shaping the progression and spread of COVID-19 [2] and therefore it is a matter of urgency that behavioural scientists understand the psychological drivers that underpin such behaviour to help swiftly implement interventions to promote behavioural changes on a population level that are necessary to stem the spread of the virus and protect vulnerable groups from contagion [1, 3].
The Capability, Opportunity, Motivation-Behaviour (COM-B) model of behaviour change (Figure 1) [4] is widely used in behavioural science research to explore influences on behaviour. This model proposes that a person must have sufficient psychological and physical capability (strength, knowledge, skills, etc.), physical and social opportunity (time, social cues, etc.) as well as reflective and automatic motivation (intentions, planning, emotion regulation, etc.) to enact a given behaviour. Michie, West and Harvey [5] argue that each of these factors could contribute to lower levels of adherence than are needed to enact behaviours that prevent the spread of the COVID-19 virus. The COM-B model is at the centre of the Behaviour Change Wheel (BCW), which is a tool kit for designing tailored behaviour change interventions (BCIs) [6]. Thus, once a behavioural ‘diagnosis’ has been conducted utilising the components of the COM-B model, suitable targets for intervention can then be identified [1]. These targets will be the components of the COM-B that are most likely to influence a particular behaviour and can be developed into BCIs to improve adherence to protective health behaviours.
The COM-B model.
In this chapter, we apply the COM-B model to two key sets of COVID-19 transmission-related protective behaviours: ‘hygienic practices’ (including frequent hand washing and wearing a face covering) and ‘social distancing practices’ (involving staying at home where possible, keeping a 2-metre distance from others in public and not gathering in large groups). These behaviours are key in reducing transmission of the virus and it is likely that such measures will remain in place for some time in most countries, to some extent [7, 8]. Indeed, despite the inception of widespread vaccination programmes across the globe, maintaining protective behaviours will ensure the continued reduction in the spread of infection to mitigate low vaccination uptake rates, difficulties in vaccine supply and variants immune to the vaccine. It is vital therefore that behavioural scientists understand the psychological factors influencing such behaviours in the context of the COVID-19 pandemic within a theoretical framework to feed into efforts to promote continued adherence to essential protective behaviours.
To inform BCIs, an understanding of the drivers that underpin protective behaviours are required, along with a deeper exploration that addresses the nuances in how people might understand, accept and adhere to such a set of behaviours. As yet, there is a dearth of evidence relating to how protective behavioural practices could be adopted on a population-wide level [4] and so it is important to assess behavior under the current adverse circumstances. Protective behaviours are largely under the volitional control of individuals, in that one can choose whether or not to follow the suggested practices. Further, whilst wearing a face covering and washing or sanitising hands in specified situations represents a fairly clear set of actions, the actions required to achieve ‘social distancing’ successfully are arguably more complex and nuanced. Some social distancing behaviours rely on the individual themselves committing to and enacting the behaviour (e.g., staying at home) and others require the reciprocal observance of others (e.g., gathering in groups, close contact greetings). We also know that social isolation could have a negative impact on health and well-being, which impacts upon decisions about adherence to behaviours [9].
Whilst there is a wide and good-quality literature on the enactment of hygiene behaviour, especially handwashing [10], we know little about these behaviours in the current context where the drivers of behaviour and nature of the threat may be entirely different from usual circumstances.
The term ‘social distancing’ has been coined during the pandemic and is complex and nuanced. Although large-scale population surveys have shown that social distancing practices have been sustained as the pandemic unfolded and citizens generally support these measures (e.g., [11, 12, 13]), there is evidence that motivation to comply over time may be threatened by other psychological factors. For example, as psychological resources are cumulatively depleted over time with lengthy and repeated lockdowns [11]; as competing drivers of behaviour begin to take priority (e.g., the inherent drive for social connection) [14]; as confidence in the government reduces [15]; and ‘moral’ judgements impact upon decision making [16] adherence to social distancing practices may diminish.
Indeed, evidence suggests that the extent to which different groups of individuals have been willing and able to comply with these important protective behaviours is mixed. Population surveys have found that 1 in 4 individuals struggle to follow social distancing guidelines, due to difficulties in meeting up with family or friends outside because of bad weather or feeling worn out by the pandemic [11, 17]. For other groups in society, it is likely that enacting social distancing behaviours is difficult for other, more practical, reasons. For example, individuals who do not have access to a garden, those who share private spaces with other families, or those who are required to work outside the home may not have the opportunity to comply and are inevitably at increased risk of exposure and infection [18]. These ‘structural’ factors are likely to be more impactful on the ability to comply with social distancing in groups who are already disadvantaged and who are faring worse due to the pandemic – reflecting the ‘slow burn of inequality’ exposed by epidemics, described by Marmot [19].
Exploring protective behaviours in relation to the COM-B is useful for understanding the conditions that must be in place for these behaviours to be successfully enacted and therefore developing BCIs that promote adherence. We conducted this investigation using data from a large-scale survey of UK citizens.
The COVID-19 Psychological Research Consortium (C19PRC) Study (www.sheffield.ac.uk/psychology-consortium-covid19) is a longitudinal study mapping changes in behaviour and mental health over time from the very early days of the COVID-19 outbreak. The C19PRC study has collected data from 2025 participants in five waves over 12 months (March 2020–March 2021) from the four UK Nations, with comparable data sets from Ireland, Italy, Spain, and Saudi Arabia. A multitude of detailed demographic, health, behavioural and psychosocial measures have been collected, including socio-demographic characteristics, health status, depression, anxiety, traumatic stress, somatic symptoms, loneliness, resilience as well as health behaviours and lifestyle habits (see McBride et al. for full methodology [20, 21]). We modelled the complex relationships between the social, physical and mental health of our sample and conducted extended behavioural analyses on protective behaviours and the COM-B model [17, 22, 23, 24, 25, 26].
Participants self-reported motivation, capability and opportunity to enact protective behaviours in the C19PRC survey. Items were adapted from a preliminary version of the COM-B self-evaluation questionnaire and other guidelines (COM-B-Qv1) [4, 6] and respondents indicated the extent to which seventeen statements were true for them during the COVID-19 pandemic on a 5–point scale (labelled: strongly agree, agree, neither agree nor disagree, disagree, strongly disagree). Three items measured psychological capability: e.g., “I knew about why it was important and had a clear idea about how the virus was transmitted”. Two items measured physical opportunity: e.g., “It was easy for me to do it” and four items measured social opportunity: e.g., “I had support from others”. Five items measured reflective motivation: e.g., “I intended to do it” and three items measured automatic motivation: e.g., “I would feel bad if I didn’t do it”.
Analysis of the C19PRC data revealed three main themes in relation to protective behaviours. First, we identified specific components of the COM-B model that drive different types of protective behaviours. Second, we identified specific demographic groups that have particular difficulties with such behaviours. And third, there are significant emotional drivers that influence adherence to protective behaviours.
The first set of behaviours explored in Wave 1 during the first lockdown in the UK (March 2020) were five self-reported hygienic practices: Touching eyes or mouth, washing hands with soap and water more often, using hand sanitising gel if soap and water were not available, using disinfectants to wash surfaces in the home more frequently and covering nose and mouth with a tissue or sleeve when coughing or sneezing. Response scales were ‘No’, ‘Occasionally’ and ‘Whenever possible’.
After controlling for demographic variables (age, gender, ethnicity, income, etc.), psychological capability, social opportunity and reflective motivation predicted hygienic practices most and reflective motivation had the largest influence [20]. This means that adults who knew why hygienic practices were effective in reducing the transmission of the virus, who had social support, and had made plans to carry out hygienic practices were more likely to successfully carry out these protective health behaviours. Notably, we observed that older age and higher levels of household income were associated with more engagement with hygienic practices. Hygienic practices were practiced less by males (compared to females) and those living in suburban areas (compared to those living in more rural areas).
For social distancing behaviours, participants in Wave 2 (April 2020) self-reported which behaviours in the past week they had engaged in, out of seven social distancing practices; e.g., “Stayed at least 2 metres (6ft) away from other people when in”, “Met up with friends or extended family (outside of your home)”; “Engaged in close contact greetings with people outside of your family (e.g., shaking hands, hugging)”; “Gathered in a group of more than two people in a park and other public space”. These behaviours represented clear violations of or adherence to social distancing guidelines in the first UK lockdown (responses were: Not at all, 1–2 days a week, 3–4 days a week, Most days, Every day).
Here, a different picture emerged. Of the COM-B components, only Psychological Capability exhibited a direct and positive association with adherence to social distancing [21]. Older adults and city dwellers were more likely to report higher levels of psychological capability and women were more likely to report increased motivation for social distancing. As with hygienic practices, those with higher levels of education and income were more likely to practice social distancing.
We explored adherence to social distancing further using a list experiment, embedded in Wave 4 of the C-19PRC survey (December 2020). This method allows researchers to measure responses to sensitive items that may normally invoke untrue or inaccurate answers due to social desirability concerns. The C19PRC survey list experiment used four control states and included a fifth sensitive item, as follows:
“We would now like to ask you how willing you are to break rules or conventions. Please look at the following list of common rules and indicate how many of these you have done in the last 6 months:
I have driven a car at more than 100 miles an hour.
I have travelled illegally to North Korea.
I have sometimes not paid my bills on time.
I have borrowed something from a friend and forgotten to return it.
I have socialised in another household during lockdown (
One-quarter of our sample revealed that they had violated government guidelines by socialising in another household during lockdown. An examination of whether any particular social or psychological factors were associated with agreement to the sensitive item, we found that the only statistically significant predictor was anxiety related to COVID-19. This anxiety was in response to the question ‘How anxious are you about the coronavirus COVID-19 pandemic?’; participants were provided with a ‘slider’ (electronic visual analogue scale) to indicate their degree of anxiety with ‘0’ and ‘100’ at the left- and right-hand extremes, respectively, and 10-point increments. This produced continuous scores ranging from 0 to 100 with higher scores reflecting higher levels of COVID-19-related anxiety. This factor was negatively correlated with agreement to the sensitive item - indicating that experience of COVID-related anxiety was strongly associated with a tendency to follow the lockdown rules.
Previous research has found that emotions are an important influencing factor in the behavioural responses to pandemics; in particular, worry has been found to motivate action to control danger [27]. Liao et al. [28] conducted a multi-wave longitudinal survey study in Hong Kong during the influenza A(H7N9) pandemic and reported that worry about infection from the virus was positively associated with the enactment of protective behaviours (e.g., avoiding crowds, rescheduling travel plans). The authors reported that, as worry about the virus changed over time, so did protective behaviours, implying a causal link between worry and engaging in protective behaviours. Other evidence from the Swine Flu pandemic also illustrates how emotional status mediates behavioural responses; Jones and Salathe [29] reported that self-reported anxiety over the epidemic mediated the likelihood that US citizens engaged in protective behaviours such as social distancing. Exploring emotional factors that might mediate protective behavioural responses during the current pandemic, may help enormously with the design of BCIs to promote the enactment of essential protective behaviours such as social distancing.
The findings of the C19PRC Study in relation to the COM-B have clear implications for the design of BCIs to promote protective behaviours at a population level. For hygienic practices, interventions should focus on increasing and maintaining motivation to act and should contain behaviour change techniques (BCTs) that focus on self-regulatory processes involving planning and goal setting. We have suggested utilising implementation intentions, a specific planning technique found to help successfully bridge the ‘intention-behaviour’ gap [30, 31]. Further, to make it feasible that individuals are able to enact such techniques independently (e.g., during the lockdown), we suggest utilising the compendium of self-enactment BCTs [32] in intervention design (self-regulatory techniques #5 - #18 are especially relevant for hygienic practices). Our data show that groups in particular need of targeting for interventions to increase hygienic practices are males and those living in cities and suburbs.
For social distancing, interventions should focus on increasing psychological capability and include BCTs that bolster knowledge around social distancing and why it is important, to enable citizens to develop psychological skills in enacting and maintaining these behaviours. For increasing psychological capability, it is important that it is clear why social distancing is important and how social contact transmits the virus; as well as specifying the situations in which social distancing should be enacted and exactly how to do that. BCIs would help people to overcome physical or psychological barriers to action (or inaction) and should be specifically tailored to those sociodemographic groups who display particular difficulties in enacting social distancing, namely, younger people and those living in cities. For those with lower incomes and lower levels of education, who may struggle with social distancing for more practical reasons, wider functions of intervention from the BCW would need to be employed, whereby economic and social policy would assist in overcoming practical or structural barriers to enable these groups to follow guidelines (e.g., if working from home is not possible, ensuring COVID-safe workspaces where social distancing is achievable and implementing paid time off for isolation). It is important that individuals who feel anxious about COVID-19 are supported in managing their anxiety levels.
This chapter has explored psychological and demographic influences on citizens’ ability to enact protective behaviours during the COVID-19 pandemic. We have discussed how enacting social distancing and hygienic practices are influenced by different components of the COM-B model and made recommendations for intervention. Behavioural scientists face the challenge of urgently developing interventions that help citizens to maintain adherence to protective behaviours to control the spread of the COVID-19 virus.
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. 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:null},{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:"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:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{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:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{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:null},{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:"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:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/52221",hash:"",query:{},params:{id:"52221"},fullPath:"/chapters/52221",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()