Input operation variables for the current case study [18].
\r\n\t
",isbn:"978-1-80355-607-9",printIsbn:"978-1-80355-606-2",pdfIsbn:"978-1-80355-608-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"6cf0b844f6881c758c61cca10dc8b134",bookSignature:"Associate Prof. Gülşen Akın Evingür and Dr. Önder Pekcan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11830.jpg",keywords:"Elasticity, Toughness, Modulus, Compression, Extension, Optical Properties, Swelling, Drying, Diffusion, Release, Transmission Loss, Sound Absorption Coefficient",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 5th 2022",dateEndSecondStepPublish:"June 15th 2022",dateEndThirdStepPublish:"August 14th 2022",dateEndFourthStepPublish:"November 2nd 2022",dateEndFifthStepPublish:"January 1st 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"21 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Evingür is a researcher in polymer composites and a lecturer at a maritime university. She has edited 2 books and has had 5 chapters published in international books, and 3 international and 5 national projects, respectively.",coeditorOneBiosketch:"Prof. Pekcan received their Ph.D. from the University of Wyoming, United States of America, in 1974. He has more than 362 SCI articles, 26 chapters, and 10 projects and is a member Science Academy in Turkey.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"180256",title:"Associate Prof.",name:"Gülşen",middleName:null,surname:"Akın Evingür",slug:"gulsen-akin-evingur",fullName:"Gülşen Akın Evingür",profilePictureURL:"https://mts.intechopen.com/storage/users/180256/images/system/180256.jpeg",biography:"Gülşen Akın Evingür graduated from Physics Department at the Yıldız Technical University (YTU, İstanbul, Turkey) in 1996. She completed her Master of Science degree in 2002 at the same department. The titled of her thesis was 'Electrical Properties of Polystyrene”. She received her PhD from Physics Engineering at İstanbul Technical University in 2011. The title of the thesis was 'Phase Transitions in Composite Gels”. She worked as an Assistant Professor between 2011 and 2018, and she is currently working as an Assosciate Professor at Pîrî Reis University, Istanbul, Turkey. She has been engaged in various academic studies in the fields of composites and their mechanical, optical, electrical, and acoustic properties. She has authored more than 60 SCI articles, 92 proceedings in national and international journals, respectively. She has edited \n 2 book, and has had 5 chapters published in international books, 3 international and 5 national projects, respectively.",institutionString:"Piri Reis University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Piri Reis University",institutionURL:null,country:{name:"Turkey"}}}],coeditorOne:{id:"27949",title:"Dr.",name:"Önder",middleName:null,surname:"Pekcan",slug:"onder-pekcan",fullName:"Önder Pekcan",profilePictureURL:"https://mts.intechopen.com/storage/users/27949/images/system/27949.jpeg",biography:"Prof. Pekcan received his MS Degree in Physics at the University of Chicago in June 1971, and then in May 1974 his PhD thesis on solid state physics was accepted at the University of Wyoming. \n\nHe started his career at Hacettepe University, Ankara, Turkey as Assistant Professor in 1974. Habilitation thesis on solid state physics was accepted in 1979. He became Associate Professor at Hacettepe University in 1979. \nHe visited ICTP Trieste, Italy as Visiting Scientist between June and August 1980. Between 1980 and 1981 he was a Visiting Scientist at the Technical University of Gdansk, Poland. \nHe worked as Visiting Professor at the Department of Chemistry, University of Toronto, Canada between 1981 and 1988. \nHe was appointed as full Professor at the Department of Physics, Istanbul Technical University, Turkey and worked there between 1988 and 2005. \nHe became an Elected Member of the Turkish Academy of Sciences (TÜBA) in January 1995. \nHe became the Dean of School of Arts and Sciences at the Istanbul Technical University in 1997. \nHe received the Science Award from the Scientific and Technological Research Council of Turkey (TÜBİTAK) in 1998. Prof. Pekcan was elected as Member of the Council of TÜBA in 2001 and Scientific Board of TÜBİTAK in 2003, respectively. \nHe was Head of the Department of Physics, and then became Dean of School of Arts and Sciences at the Işık University between 2005 and 2008.\nHe worked as Dean at the School of Art and Sciences, Kadir Has University (2008—2012). \nNow he is Professor at the Department of Bioinformatics and Genetics, Kadir Has University. Since 2012 he is a member of Science Academy. In the last few years Prof. Pekcan’s work covers mostly the area of biopolymers and nanocomposites.",institutionString:"Kadir Has University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Kadir Has University",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"14",title:"Materials Science",slug:"materials-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Churchill, Maja Dutour Sikirić, Božana Čolović and Helga Füredi Milhofer",coverURL:"https://cdn.intechopen.com/books/images_new/8812.jpg",editedByType:"Edited by",editors:[{id:"219335",title:"Dr.",name:"David",surname:"Churchill",slug:"david-churchill",fullName:"David Churchill"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6851",title:"New Uses of Micro and Nanomaterials",subtitle:null,isOpenForSubmission:!1,hash:"49e0ab8961c52c159da40dd3ec039be0",slug:"new-uses-of-micro-and-nanomaterials",bookSignature:"Marcelo Rubén Pagnola, Jairo Useche Vivero and Andres Guillermo Marrugo",coverURL:"https://cdn.intechopen.com/books/images_new/6851.jpg",editedByType:"Edited by",editors:[{id:"112233",title:"Dr.Ing.",name:"Marcelo Rubén",surname:"Pagnola",slug:"marcelo-ruben-pagnola",fullName:"Marcelo Rubén Pagnola"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"48166",title:"New Frontiers in Alzheimer's Disease Diagnosis",doi:"10.5772/59652",slug:"new-frontiers-in-alzheimer-s-disease-diagnosis",body:'Alzheimer´s disease (AD) is the most prevalent form of dementia accounting for 60-70% of all cases worldwide. As the world\'s population ages the incidence of AD is expected to increase rapidly turning into a global epidemic disease with incalculable sociological and economic consequences. In 2006, the prevalence of AD worldwide was calculated in 26.6 million and it is estimated that by 2050 current prevalence will be triplicated or quadruplicated, affecting 1 out of 85 persons worldwide [1, 2]. An accurate diagnosis and a timely detection are critical for improving the physical, clinical, emotional and financial impacts of the disease. However, this aim is far to be achieved and several studies indicate that less than 35 percentage of people living with AD or related dementias are correctly diagnosed [3, 4]. As a consequence, between 18% and 67% of the dementia patients are treated with a potentially inappropriate medication [5].
In this dramatic scenario, new technical, methodological and notional approaches are explored in order to overcome the inherent limitations in AD clinical diagnosis. Indeed, the identification of reliable diagnostic tools in AD remains impeded by the clinical, neuropathological and molecular overlap existing between AD and other types of dementia such as Mild Cognitive Impairment (MCI), or mixed forms of dementia, such as Vascular Dementia (VaD), Frontotemporal Lobar Degeneration (FTLD) or Lewy Body Dementia (LBD), and by the high AD heterogeneity according to disease onset, progression and duration [6-8].
Since the complexity of this scenario impairs the use of current diagnostic tools for a correct data interpretation, in the recent years, new strategies such as the integrated and combined use of neuropsychological profiles, imaging and biological fluids biomarkers have been developed, improving current diagnosis classification [9-11] and predicting the conversion from MCI to AD [12, 13].
Despite recent solid advances in the topic, up to date, no single diagnostic tool or combination of diagnostic tools can unequivocally confirm AD diagnosis. Indeed absolute confirmation and definite AD diagnostic still requires histopathologic analysis on the post-mortem brain certifying the presence of the pathologic disease hallmarks such as β-amyloid plaques and neurofibrillary tangles.
Since AD is a progressive disease and no treatment is available to recover neuronal integrity, the inaccuracy of AD early diagnosis and prognosis makes early therapeutic intervention difficult and impedes the prevention of neurodegeneration and cognitive dysfunction.
Identification of disease specific clinical, imaging and biochemical-based tools at early stages will help to greater extent to an early treatment which may restrain the disease progression. Additionally, a thorough understanding of the role of biomarkers in AD disease and their modulated levels in AD patients will facilitate the comprehension of their role in AD etiopathology and would help to establish a link between diagnostic and therapeutic fields. Therefore, the ultimate goal is to develop early and reliable diagnosis methods to establish an appropriate and prompt treatment. Indeed this aspect is imperative to maximize the efficiency of potential therapies and decrease symptomatology before pathological changes spread throughout the brain and massive death of neurons has already occurred. Finally, it should also be taken into consideration that the development of successful epidemiological risk assessment and diagnosis programs, including a routinely monitoring of disease progression, will need to be established through the development of new methodologies and protocols at low cost and with non-invasive approaches.
The present chapter summarizes the most recent findings in the field of AD, including neuropsychological profiles and brain and biological fluids biomarkers, which are currently paving the way for new focused approaches in AD diagnosis and prognosis.
According to the International Classification of Diseases (ICD-10) and the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association (DSM IV) dementia is defined as a a worsening of cognitive function from a preexisting individual level. The major symptom is decline in memory and should be followed by at least one dysfunction in another major cognitive core skill, severe enough to impair a person\'s ability to perform everyday activities. The cognitive impairments should be irreversible and not be attributable to e.g. a delirium or another psychiatric disorder and must be present for at least 6 month.
Moreover, the German Society of Psychiatry, Psychotherapy and Neurology (DGPPN) as well as the German Neurological Society (DGN) refer to a subtle change in personality and behavior in the process of dementia [14]. The criteria of the American National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer’s disease and Related Disorders Association (NINCDS/ADRDA) are more often referred to in the literature, which differentiate the degree of diagnostic accuracy in “possible” or “probable dementia” [15, 16].
Based on the latter, commonly accepted dementia criteria, a “probable Alzheimer’s dementia” (AD dementia) is diagnosed by signs of dementia on clinical examination and neuropsychological tests whereby the memory impairment should be followed by another deficit in an additional cognitive skill. In alternative there is impairment in two cognitive skills with a recognized progression and without evidence of a reduced consciousness.
The age at onset should range between 40 and 90 years and other reasons for the cognitive decline, e.g. treatable causes, should have been carefully ruled out in the diagnostic work up.
The clinical criteria of a “possible Alzheimer’s dementia” comprise a dementia syndrome of untypical clinical presentation or duration in absence of other recognizable factors causative of dementia, or if there is a progressive cognitive deficit without a recognized underlying cause.
Exclusion criteria are referred to as sudden onset, focal neurological signs (hemiparesis, hemianopsia) at onset as well as early appearance of gait disorders or epileptic fits.
This categorization is kept according to different revisions of the NINCDS/ADRDA-criteria [16, 17]. Additionally, next to deficits in episodic memory, detection of specific biomarkers in the cerebrospinal fluid (CSF) and imaging (Magnetic Resonance Imaging (MRI) and/or Emission Computed Tomography (PET) is suggested which can increase sensitivity of AD diagnosis.
Further supporting results are, e.g., a progressive worsening of specific cognitive function, disabling in all-day activities, and occurrence of behavioral changes, a positive family history of AD (especially if neuropathologically confirmed), a normal CSF result (basic analysis) and unspecific electroencephalogram (EEG) changes.
A diagnosis of AD is compatible with plateaus during disease course, side symptoms as depression, aggressive behavior, paranoia etc., neurological symptoms in progressed disease state (myoclonus, gait problems, epileptic fits) and a normal Computerized Tomography (CT)-scan [14].
While both terminologies: “probable AD”, “possible AD” are proposed for the clinical setting, a third category of “probable and possible AD” was suggested for research purposes. Recent research criteria for clinical AD diagnosis include next to mnestic deficits an occurrence of deficits in non-mnestic function, e.g., language, visual-spatial orientation, executive function. Furthermore, an early diagnosis of AD is proposed already during prodromal stages of dementia, which refer to the clinical picture of a mild cognitive impairment (MCI) [18].
A MCI is a recognized risk factor for AD. Yet, there are presently no commonly agreed criteria [14]. According to international consensus criteria, MCI is considered a condition between normal and demented, a worsening of cognitive function (on self-observation or observation by others) that can be demonstrated on neuropsychological tests, a worsening of cognitive function during an observational time period during disease as well as conserved or only minimally impaired dysfunction in complex all-day activities [19]. The difference between MCI and dementia is based mostly on well-functioning in all-day activities. Standard measurements for cognitive function comprise 1-1.5 standard deviation below the age- and education-matched age group and a mini-mental status test of 24 or above points [18, 20].
The prevalence and conversion rates are variable according to the distinct examination setting. In the clinical setting the annual conversion rate from MCI to AD has been calculated at around 10 percent [14, 21].
At present 4 different MCI subtypes are characterized: amnestic single domain, amnestic multiple domains, non-amnestic single domain and non-amnestic multiple domains [20], whereas the probability to develop AD is highest in MCI with memory deficits [14, 21].
AD is generally characterized by a slowly progressive preclinical (pre-symptomatical) state over several years, an approximately 1-2 years lasting pre-dementia phase until development of dementia, which can be categorized into 3 states (mild, medium, severe) [22, 23].
The progressive cognitive deficits hereby parallel neuropathological changes in the brain, whereby cognitive deficits vary individually. The degree of disease severeness refers to cognition and life skills, whereby transition of states can merge. A mild dementia is considered when complex tasks cannot be performed anymore, but an independent life organisation is still possible. A medium-state dementia is referred to if an independent life organisation is impaired but possible with help and observation of family and care-givers. In severe dementia constant guidance and help is required, an independent life organisation is not possible anymore.
At early stages of AD deficits are predominantly characterized by impairment of declarative memory, visual-spatial orientation and lexical-semantic language. Emotionally, in social contacts as well as in personality, patients with AD appear normal for a long period of time (“facade”). They tend to trivialize their deficits. When they recognize cognitive dysfunction, AD patients often describe themselves to be more forgetful without further specification.
In further disease progression according to a time-associated gradient (first in- last out) also long term memories (semantic and biographical memory) are impaired, with affection of identity and personality in medium and severe AD stages [23].
Deficits of the
Deficits in visual-spatial orientation with massive impairment in complex visual awareness are the main characteristic and leading symptom of the
Affection of
Material that they read is less often understood, the understanding of complex facts or contents in the figurative sense (collocations) is declining. Verbal fluency is reduced, whereas the semantic is more affected than the phonematic [28]. During disease, language becomes progressively poor of content, stays however relatively fluent with difficulties in word finding as well as with imprecise, diffuse and less informative comments, drifting from topic, talking cross purposes and setting phrases. This results in abrupt sentences, mistakes of syntax, phonematic paraphrases and in problems of speech comprehension for simple comments. In the final stages a total loss of speech occurs [23].
Next to the 3 main symptoms, disturbances in
During disease progression, also alertness is impaired which presence of a more rapid exhaustion.
During medium stages all components of attention are majorly affected [22]. Last but not least, apraxia (affected brain region: parietal lobe) and agnosia (affected brain region: occipital lobe and both basal temporal neocortex) can occur already during early and middle stages of dementia. Simple movements are not possible any longer, inaccurate moves cannot be corrected, this can present as, e.g., body-part-as object-mistakes (ideomotoric apraxia), impairment of planning and conducting of sequential tasks (ideatoric apraxia), recognition of line drawing is inhibited.
Cognitive-related
The onset of cognitive deterioration is more distinct in patients with depression, whereas in patients with AD this occurs more gradually. The deficits can affect the whole spectrum of cognition, whereby executive dysfunction (predominantly flexibility) and problems with attention dominate. However, also memory deficits are described [30]. In detailed observation of single tasks aspects, e.g. in recalls of wordlists, primacy more than recency effects are shown, and recall is generally better.
While demented patients guess more often and describe things, depressed patients react with omissions and hesitant answers. Orientation is widely intact and confabulation, aphasic and apractic elements don‘t occur [22, 31].
Major tasks and aims of a neuropsychological examination comprise 1) determination and quantification of impaired cognitive function and resources as well as their consequences for maintaining all-day life, 2) assessment on changes of cognitive dysfunctions in progressive or reversible disease conditions, 3) differential diagnosis and securing of diagnosis as well as 4) evaluation of therapeutic benefits.
An important detail of the examination is a thorough interview with exploration of the clinical history, self-observation and observation of others, orientation, current mood situation (psychiatric side symptoms), as well as observation of behaviour during both interview and test situation. A final judgement is built from the test results with reference to emotional and motivational processes, a qualitative mistake analysis, and observation of behaviour during tests and interview, the resulting information derived from the interview and an evaluation of all-day competences during course of disease.
Neuropsychological testing represents an essential diagnostic tool in dementia diagnosis. It should be thoroughly performed and comprise the essential key competences. An “overtesting” should be avoided. In general, the choice of tests should orientate according to the individual differential diagnosis that is being questioned, the capacity of the individual patient and the time that is available.
Consecutively, a choice of test procedures is presented, that have been established in dementia diagnosis. As some of them cannot be administered solely for securing the diagnosis, a combination of several test procedures should be used.
For assessment of cognitive deficits in AD both screening methods as well as standardized psychometric tests are applied. Presumably, the most practical screening test in the clinical setting is the MMSE (Mini-Mental-State-Examination) according to Folstein et al. [32]. It comprises the examination of orientation in time and space, retentiveness and memory, attention and working memory, language (reading, writing, naming, speech comprehension, reading and meaning comprehension) as well as visual-spatial competences. The test takes usually approximately 10-12 minutes, the analysis results from a simple summation of points. At maximum 30 points can be achieved. The specificity ranges at 87 percent and the sensitivity at 82 percent [33]. However screening tests- as the mentioned MMSE- are only suitable, using cut off levels, for overviewing and determining severity of the dementia and for follow-up during disease course.
The MMSE is not acceptably sensitive in early onset dementia and does not allow, amongst other due to missing age and education correction, a satisfactory differentiation between “healthy” and “ill”. For quantification of disease severity standard values of interpretation are provided, that can vary easily. Alternatively also CDR (Clinical Dementia Rating) or GDS (Global Deterioration Scale) can be applied. A general drop in points of around 3 MMSE points per year substantiates the suspected diagnosis of AD [34].
The
After introduction in 1986 in the USA from the
To trace better on subcortical dysfunction, since 2005 additional tasks were included that aim to quantify on cognitive processing speed and flexibility (Trail making test A and B) as well as phonematic word fluidity tasks (words with initial letter “s”) (CERAD-Plus). The whole test duration ranges between 30-45 minutes. The raw score are age- and education-matched (school and professional education) and also gender-matched. They are designated as z-levels as a measure of deviation to normal. The CERAD-Plus test battery allows a qualitative assessment on cognitive ability, on evaluation of disease severity and a follow up on repeated testing.
However, at present a parallel test version is not available, thus it is recommended to use an alternative test for memorising word lists when test intervals are on short-term. In suspicion of other underlying dementia causes further psychometric tests can be applied.
As an additional screening instrument for calculation of disease severity and for follow-up, the
In mind of the low specificity of the clock drawing test, Schmidtke et al. suggest an additional
In suspicion of an
In order to examine
Psychiatric side symptoms, e.g
Computerized Tomography (CT) is helpful in the detection of atrophy as well as other focal processes in brain and spinal cord, however it is not sufficient to substantiate AD diagnosis. Based on the low tissue contrast in comparison to magnetic resonance imgaging (MRI), CT serves well in the diagnostic classification of dementia syndromes. Advantages compared to MRI include a shorter time of investigation, low costs and a broad distribution [44]. In addition, CT allows an uncomplicated monitoring of critically ill patients.
With progressing age, brain volume decreases due to dying neurons and decline in water content. The annual atrophy rate ranges at around 0.24 % of total brain volume and is visible by the expansion of the ventricular system [45].
In AD, patients show a progressive brain atrophy in advancing disease which lies above the age matched population. This is demonstrated by enlargement of sulci and a dilatation of the ventricular system. Hereby, the dilatation of the ventricular system points to a subcortical tissue loss whereas the enlargement of the outer CSF interspaces points to cortical tissue loss [46]. The senso-motoric and the primary-visual cortex stay unaffected.
MRI allows a high-contrast presentation of neuro-anatomical structures, pathological processes as well as of functional changes in brain activity. With progressive age a higher exchange rate of fluids exists between the ventricular system and the brain parenchyma. This is visible in T2- and Fluid-attenuated inversion recovery (FLAIR)-sequences by signal alterations in the ependyma of the anterior horns [47]. Intermittent, subcortical and central signal increases in the white matter (white-matter-lesions) increase with progressive age. Additionally brain iron accumulation can be detected in basal ganglia by increasing signal changes in T2-sequences.
Already in early AD stages MRI can display brain atrophy patterns. These can predominamtly be located in the medial temporal lobe, in the hippocampus and the gyrus parahippocampalis. Also, the entorhinal cortex, the amygdala, basal ganglia as well as thalamus and the parietal cortex can be involved [44]. An important role in the early detection of AD plays the Nucleus basalis Meynert. The voxel-based morphometry (VBM) reduces the weaknesses of predominantly investigator-dependent manual volumetry [48]. Modern computer techniques allow the spatial recognition of specific brain regions or the whole brain [49]. Hereby the volume of the typically affected brain region is exactly displayed and is comparable to that of other control groups. The majority of published studies show that patients with a MCI present with a smaller hippocampal volume than healthy controls and patients with AD have a smaller hippocampal volume in comparison to patients with MCI [50]. Patients with MCI hold an elevated risk for the development of AD [51]. Typical AD changes can also occur after brain trauma and long-lasting epilepsy.
Functional MRI (fMRI) has the potential to demonstrate cerebral blood flow as well as oxygen use of certain brain areas in response to specific stimuli or while processing certain cognitive tasks.
Due to the inherent magnetic properties of blood, represented by hemoglobin and deoxyhemoglobin, different patterns of activiation are visible [44].
Despite of the high spatial resolution, this method presents with a high sensitivity for minor head movements. Studies of AD patients show a decrease of activitiy in the hippocampus, the parahippocampal areas as well as in the parietal and pre-frontal cortex in comparison to healthy control groups. Furthermore, fMRI is useful in monitoring of medical treatment in AD patients.
Imaging via single photon emission computed tomography (SPECT) and positron emission computed tomography (PET) allows the detection of local hemodynamic and metabolic dysfunction. After intravenous injection of a radioactive tracer and uptake in brain, the tracer localizes at the region of regional acitivity and images are taken. As the tracers often have short radioactive half life, the radioactive decay (emission of positrons) can be measured.
SPECT imaging shows the regional cerebral blood flow (rCBF) at rest by the regional uptake of glucose as an expression of neuronal activity. Hereby functional abnormalities can already be detected before symptom onset. The tracers 99mTc-HMPAO and 99mTc-ECD are mostly used in clinical practise. Due to their lipophilic character the tracers reach the cells in the first minutes after injection proportionately to rCBF [52]. The typical SPECT image in AD is characterized by a reduced rCBF in the medial and superior temporal lobes as well as in the posterior cingulum and precuneus without a reduced striatal DAT-binding [53]. Due to a very low spatial resolution of SPECT the diagnostic accuracy is lower than PET [54]. However application can be meaningful in clinical practise in order to differentiate other dementia causes.
PET imaging illustatrates a regional dysfunction of glucose metabolism via application of 18F-FDG. Patients with AD demonstrate here, according to SPECT, a typical nuclide-distribution pattern of neuronal loss. Over 85 % of PET diagnosed AD patients could be neuropathologically verified [53]. At early AD disease stage and before symptom onset, a temporoparietal metabolic dysfunction is visible by voxel-based (volumetric pixel) analysis. Also patients with a genetic risk for development of AD show early decreases in signal activity [55]. As PET is the most efficient method for diagnostic verification of an AD, it has meanwhile established to a standard tool in dementia research [56].
For further diagnostic approaches the tracer 11C-PIB was developed, which allows detection and distribution of Aß-plaques
Biomarkers are used as indicators of normal and pathogenic processes in a broad range of tissues, especially in peripheral tissues, which facilitates the accessibility of testing samples with minimal invasive methods. Despite substantial progress has been made in the area of biomarker development to confirm the diagnosis at early-clinical AD stages, less is known about the potential role of biomarkers in peripheral tissues in the prediction of AD [58]. Since it has been demonstrated for decades the existence of biochemical changes in the brain preceding the clinical AD onset (up to 20 years in advance) [59, 60] it is suggested that these changes may be also indirectly reflected in biological fluids. However, no tests are currently available to confirm an early AD diagnosis prior to clinical or symptomatic manifestations. The ongoing standardization efforts and quality control programs in biomarkers analysis, the development of tests in fully automated instruments, the combined detection of the well-established core biomarkers, the discovery of new regulated molecules improving current sensibility and sensitivity and the analysis of novel promising biomarkers in large independent cohorts will boost biomarker´s performance and facilitate the introduction of new AD diagnosis and prognosis tests in biological fluids in clinical routine.
CSF is the prime target among biological fluids in the search of specific biomarkers related to neurological disorders. The easy accessibility to this biofluid and its singular biophysic-chemical characteristics make CSF ideal for biomarkers investigation. On one hand, CSF is not a very complex fluid, being composed of a restricted amount of metabolites [61], which facilitates technical screening for regulated molecules. On the other hand, the direct contact between CSF and the extracellular space of the brain puts CSF in a valuable position to be considered as a potential indicator of the pathological processes occurring in the brain during different disease stages. This last aspect has not been analysed in depth since real comparisons and correlations are cumbersome and can only be formally made when using CSF and brain tissues from the same patients and the same disease stages.
The performance of CSF biomarkers as a diagnostic tool has greatly improved in parallel with the improvement of detection methodologies such as new generation proteomic technologies and high-throughput transcriptomic methodologies (deep-sequencing, microarrays and quantitative PCR panels), which eased and expanded the possibilities to measure full expression signature in a single assay enabling the inference of networks and biological functions associated to deregulated datasets. Indeed, current data indicate the existence of deregulated levels of proteins, peptides, small RNAs, mitochondrial DNA and a broad range of metabolites in the CSF of AD samples. In addition new outcomes are expected from worldwide undergoing large longitudinal studies in very-well defined cohorts [62].
In recent years, a number of reports have exploited proteomic techniques to study the levels of selected proteins and peptides in the CSF of healthy and diseased individuals. Current data indicate that proteins and peptides such as β-amyloid (Aβ1-42/Aβ42 and Aβ1-40/Aβ40), total tau and phosphorylated tau (p-tau) meet the criteria to discriminate AD from individuals suffering from other types of dementias, as well as from healthy individuals and are considered as the core AD biomarkers [63]. According to different studies these biomarkers meet the consensus recommendations on AD biomarkers that should have >80% sensitivity and >80% specificity [64]. Importantly, core AD biomarkers molecules correlate with neuropathological hallmarks of AD, such as the presence of extracellular amyloid plaques (Aβ peptides), axonal degeneration (tau protein) and neuronal tangles (p-tau).
Three main observations unveil the clinical relevance of these molecules. Firstly, their appropriate sensibility and sensitivity have been successfully validated by independent large-scale multicentre studies [65-69], although these studies also point out that biomarkers measurements present significant inter-laboratory variations [70]. Secondly, Aβ42, tau and p-tau have been validated as predictors of AD in patients with MCI [71-74]. Lastly, longitudinal studies indicate that, at least, Tau and Aβ42 in CSF reflect the underlying disease state in early clinical and late stages of AD.
Aβ42 along with Aβ40 is secreted into the extracellular space and biological fluids, including CSF, as consequence of the proteolytic activity of proteinases on the Amyloid precursor protein (APP). Both peptides are found in senile plaques but their intracellular production, aggregation rates and proposed pathogenic functions are significantly distinct [75-77].
A consistent decrease in Aβ42 levels has been observed in the CSF of patients suffering from AD in several studies [78-80] but also in Subcortical White-matter Dementia (SWD) [81] and in Down Syndrome (DS) [82]. Reduced Aβ42 levels in AD are suggested to reflect either sequestration of Aβ42 in senile plaques, since Aβ42 CSF levels inversely correlate with the presence of senile plaques [83], or due to non-detectable Aβ42 oligomers in the assay, although alternative explanations may be plausible. In FTD, Aβ42 levels are significantly lower than in control samples, but higher than in AD cases [81, 84]. Aβ42 sensitivity and specificity in AD samples ranges from 78 to 100% (mean 85,6%) and from 67 to 100% (mean 88,5%), respectively [78]. A recent meta-analysis of 50 analytical studies indicates that CSF Aβ42 concentrations are significantly lower in AD when compared to MCI, FTD, PD and VaD but only moderately lower when compared to LBD [85].
Contrary to what is observed with Aβ42, Aβ40 and Aβ38 levels are not altered in the CSF of AD patients [79, 86, 87], but a significant decrease in Aβ40 levels is observed in FTLD when compared to AD and control cases [88]. In addition, Aβ40 levels, and more markedly Aβ38 levels, are decreased in FTD when compared to control samples [89].
A growing body of evidence suggests the superior performance of Aβ42/Aβ40 ratio when compared to Aβ42 alone using different analytic assays [79, 90, 91]. Importantly Aβ42/Aβ40 ratio is able to predict the conversion from MCI patients to AD when compared to cognitively stable MCI patients and MCI patients who developed other forms of dementia [79]. Aβ42/Aβ40 ratio is also able to discriminate better AD from VaD, LBD and non-AD dementia than Aβ42 alone and equally AD from FTD and non-AD dementia than the combination of Aβ42, p-tau and total tau [92]. Multiple studies also show an increased sensitivity and specificity in the use of Aβ ratio when compared to Aβ42/tau ratio, although the performance of combined biomarker analysis in AD diagnosis and prognosis is still a matter under discussion [93-96].
In addition to the regulated levels of monomeric Aβ species in the CSF of AD patients, encouraging observations have been reported in the potential diagnostic and prognostic role of BACE1, one of the main enzymes involved in the pathological cleave of the APP. Several independent observations indicate the presence of higher BACE1 levels and activity in the CSF of MCI and AD samples when compared to controls [97-100]. BACE1 activity is also increased in CJD samples [101] suggesting common pathological mechanisms among both diseases. Importantly, BACE1 correlate with classical AD biomarker’s profile, brain atrophy in AD cases [102] and ApoE4 genotype [99], the latter being associated with an increased Aβ peptide
Recent studies demonstrated the presence of increased levels of Aβ oligomeric species in the CSF of AD patients when compared to controls using a broad range of methodological approaches [105-110]. Indeed, the analysis of individual Aβ oligomeric species is gaining experimental momentum due to their potential specific role in AD pathogenesis. Aβ40 oligomers levels are found to be increased in the CSF of AD patients at different disease severity stages, and a combined analysis of Aβ40 oligomers and monomeric Aβ42 greatly improved sensitivity and specificity to 95% and 90%, respectively [108]. Although the pathogenic role of Aβ40 in AD is still under discussion Aβ40 deposits have been reported both in control and AD brains [111, 112]. Aβ40-positive senile plaques with amyloid core are frequently associated with microglia in contrast to Aβ42-positive plaques [111], suggesting a role of microglia in the generation and aggregation of Aβ40 species in diseased brain. However, the different ability of Aβ fibrils and oligomers to react with microglia suggests a more complex scenario [113].
Aβ42 oligomers are increased in the CSF of AD patients [114] and the ratio of Aβ oligomers to Aβ42 is significantly elevated in AD patients [115]. Interestingly, the increased levels of Aβ42 oligomers in the CSF of MCI and AD samples may explain decreased levels of monomeric Aβ42. The recent development of the protein misfolding cyclic amplification assay (PMCA), based on the seeding activity of Aβ oligomers catalysing the polymerization of the monomeric Aβ, permits the discrimination of AD samples from other neurodegenerative non-degenerative neurological diseases with a sensitivity of 90% and specificity of 92% [109]. The use of Aβ-PMCA as a prognostic tool for detection of MCI still needs to be established. Importantly, detection of Aβ oligomers in the CSF is highly dependent on the native or disaggregated state of these oligomers [114, 116].
The finding that regulated levels of Aβ oligomer species are present in the CSF of AD patients’ biofluids has a tremendous translational interest, since growing evidences indicate that soluble Aβ oligomers rather than aggregated Aβ plaques are more likely to be the main pathogenic agents of disease [117-119]. Consequently, preliminary data indicate that the analysis of Aβ oligomers, combined with levels of soluble Aβ peptides, may be relevant disease predictors and valuable tools for the analysis of AD progression.
The levels of total tau in the CSF, contrary to Aβ42 levels, increase with age [120]. Increments in tau levels have also been described in the CSF of AD and MCI patients in a broad range of several studies [121, 122] ranging from moderate to severe depending on the methodology and cohort used [78]. It is believed that deregulated tau may be reflecting the neuronal and axonal damage present in brain tissue and, as a consequence, the presence of increased tau levels is not a specific event for AD. Accordingly, transient tau increments have been also reported in acute stroke [123], and the most increased tau levels are observed in prion diseases such as in CJD, where massive neuronal cell death is present [124, 125]. Higher CSF tau is also associated with smaller brain volume in individuals with AD [126]. On the other hand, neurological diseases with minor neuronal loss and other dementias such as VaD, LBD and alcoholic dementia reflect minor or no significant changes in the levels of tau protein in the CSF, and tauopathies such as FTD also present inconsistent data [121, 127, 128].
A meta-analysis from different studies comparing tau levels in different dementia samples found that, although tau levels in AD are significantly increased when compared to controls, tau concentrations are moderately elevated in LBD, FTLD and VaD impeding a clear stratification between disease groups. Nevertheless, tau levels are useful to differentiate VaD from stroke [129] and, as expected, only CJD is characterized by extremely increased tau values, resulting in a sensitivity and specificity over 90% [130].
The improved performance of tau when analysed together with other AD biomarkers has been widely demonstrated [131, 132]. The combined use of Aβ42 and tau discriminates better between controls and AD and is very useful to predict MCI progression [69, 133]. A recent study also showed that decreased Aβ42 and increased tau levels are able to discriminate LBD from PD patients in spite of both being synucleopathies [134]. In the same line of evidences, combination of α-synuclein levels and Aβ42/tau ratios improves the diagnostic accuracy of PD [135].
A broad range of studies also demonstrated the helpfulness of the combined analysis of tau with non-AD core biomarkers. Assessment of tau and neuronal thread protein raises specificity and sensitivity for AD when compared to the individual analysis of both proteins [136]. Similarly, integrated analysis of tau and the regional cerebral blood flow in the posterior cingulate cortex discriminates MCI progressing to AD from non-progressive MCI [137]. The combined analysis of tau is also valuable for discriminating other diseases besides AD. As an example, the merged analysis of tau and midbrain-to-pons atrophy is reported to be useful for early identification of progressive supranuclear palsy (PSP), discriminating PSP cases from controls and patients suffering from corticobasal syndrome (CBS) and FTD [138].
Similarly to total tau, p-tau levels are increased in AD samples, although higher variability on its specificity and sensibility is reported when compared to the non-phosphorylated tau form [78, 127]. Several considerations should be done in this regard.
On one hand, the number of studies analysing p-tau levels is not as large as those performed for its non-phosphorylated form. In addition, sensitivity and sensibility may depend on the analysed phosphorylation site, although sensitivity for AD seems equal for at least the three main epitopes used in clinical diagnosis [139]. Importantly, results from a meta-analysis study indicate that tau phosphorylated at the Threonine 181 levels are able to discriminate AD from other dementias and MCI [140].
On the other hand, the utility of p-tau in the differential AD diagnosis against other neurodegenerative diseases is advantageous over total tau since p-tau levels reflect AD pathogenesis [141]. Indeed, p-tau levels in the CSF may reflect the levels of tau phosphorylation in AD brains. Tau is more increased in the CSF of sCJD patients than in AD, while p-tau is only modestly increased in sCJD [142]. In addition, tau levels are increased in neurological diseases such as in acute ischemic stroke, while p-tau levels remains unaltered [123]. Indeed, tau phosphorylation is physiologically regulated during several biological processes such as neuronal development, while tau levels usually remain more stable. Therefore, a direct correlation between total tau and p-tau levels cannot be established, and several lines of evidence indicate that p-tau levels are differently regulated, not only in AD, but also in other neurodegenerative diseases. In this regard, the main tau kinase, Glycogen synthase kinase 3 (GSK3) is assumed to be hyperactivated in AD brain, inducing pathogenic tau hyperphosphorylation, aggregation and formation of the intracellular NFTs. Although a direct correlation between GSK3 activity and tangle formation in AD is still under discussion [143], GSK3 levels and activity are markedly reduced in sCJD brain [144]. Thus, the distinct regulation of tau phosphorylation in the brain of AD and CJD, may explain the different p-tau/tau ratios observed in both diseases, which permits a differential diagnosis [145].
Recently it has been observed that patients suffering from rpAD present highly increased p-tau levels in the CSF [146] when compared to controls and classical AD patients. Since it is estimated that rpAD may be accounting for 10-30% of all AD cases, it is urgently needed to establish if lack of disease stratification may lead to misinterpretation of p-tau analysis between rapidly progressive and classical AD forms. In this regard, a combination of high CSF tau without proportionally elevated p-tau-181 is associated with a faster rate of cognitive decline [147]. In this regard, longitudinal studies indicates that a combination of low Aβ42 and high tau and p-tau levels is highly predictive of MCI progression and cognitive decline rate [74, 148].
A common feature in the Central Nervous System of neurodegenerative diseases is the presence of chronic neuroinflammation associated with an exacerbated gliosis [149]. The role of a chronic and sustained inflammation in neurodegeneration is still a matter of debate as neuroinflammation has been suggested to play both detrimental and protective functions depending on disease stage, brain region, activation of anti-inflammatory mechanisms and cellular milieu among others [150]. Besides these considerations point out a critical role of neuroinflammation in the molecular mechanisms linked to AD pathology [151] and a broad range of inflammatory cytokines and immune response mediators are increased in the CSF of AD patients. A correlation between inflammatory markers and biomarkers of neurodegeneration has been described [152], and consequently, neurodegenerative disorders with high inflammatory chronic profiles such as prion diseases [153] present higher inflammatory-related deregulations in the CSF [154, 155]. However, the specific inflammatory profile observed in different types of dementia and at different disease stages indicates that inflammatory biomarkers could be used as surrogate markers for AD diagnosis and prognosis.
The anti-inflammatory cytokine TGFβ–1 is consistently upregulated in AD cases [156, 157]. Interestingly, during the progression from MCI to AD, a pro-inflammatory state is proposed since MCI patients who progressed to AD showed higher TNFα and lower TGFβ–1 and Aβ42 levels than control individuals or those non progressing to AD [158]. These data are in agreement with increased levels of the acute-phase C-reactive protein (CRP) and IL-6 in the CSF of MCI patients when compared to AD patients, indicating that inflammatory mechanisms are already progressing even before changes in core AD biomarkers such as Aβ42 and tau can be detected in the CSF [159].
In relation to this, a comparative analysis between Amnestic Mild Cognitive Impairment (aMCI) and MS patients indicated that pro-inflammatory cytokines and CD45+ lymphocytes are present in the same levels in both diseases. Taking into account that MS can be considered the most representative neuroinflammatory disease, these observations indicate that inflammatory mechanisms may be crucial for AD etiopathology.
In this regard, the pro-inflammatory cytokine osteopontin (OPN), also known as the secreted phosphoprotein 1 (SPP1) and involved in macrophage recruitment to inflammatory sites and cytokine production [160], is also elevated in the CSF of AD patients and in MCI patients developing AD. OPN levels correlate with cognitive decline and with increased levels in early disease phases [161, 162]. OPN has also been found elevated in the CSF during attacks of MS [163].
In addition, the major acute-phase protein SAP (Serum amyloid P component) has lower levels in MCI patients who progressed to AD than in those who did not progress to AD [164], suggesting that low SAP levels are linked to an increased risk of progression to AD.
Alternative promising inflammatory-biomarkers have been proposed. On one hand, lipocalin 2, whose levels are decreased in the CSF of MCI and AD patients and increased in brain regions with associated AD pathology [165]. On the other hand, the astrocytic marker YKL-40, has been reported to be increased in AD at early stages of the disease [166-169] and in FTD and aMCI patients [166]. In addition YKL-40 levels correlate positively with the classical core biomarkers tau and p-tau [166].
microRNAs (miRNA) are endogenous small non-coding RNAs (20-22 nucleotides) that are involved in post-transcriptional gene regulation by targeting mRNAs for cleavage or translational repression [170]. miRNAs have emerged as key regulators of various aspects of neuronal development and dysfunction. Deregulated small RNA signatures, especially miRNAs, have been observed in the brain of a broad range of neurodegenerative diseases such as AD, PD, HD or ALS [171, 172] and experimental evidences ascribe a functional role to miRNAs in the pathogenic molecular mechanisms leading to neurodegeneration [173-175]. With the advent of high-throughput technologies, full transcriptomic signatures can be provided not only from tissues, but also from samples with small amounts of starting material such as biological fluids and associated exosomes [176-178]. In this regard, more than 100 circulating miRNAs are deregulated in pathological conditions [179] and some of them have been proposed as potential biomarkers for disease diagnosis and prognosis, mainly in cancer and neurodegenerative diseases. Regarding the levels of circulating miRNAs in AD, several studies already reported changes when compared to control samples. A recent pilot study in two different cohorts showed that hsa-miR-27a-3p expression is reduced in the CSF of AD patients [180]. Decreased levels of this miRNA correlate with high tau and low Aβ amyloid levels. A second study analysed a selected group of miRNA candidates and observed that miRNAs 34a, 125b and 146a levels were significantly lower in the CSF of AD patients when compared to control cases, while the levels of the miRNAs 29a and 29b were significantly higher [181]. In an independent study low levels of miRNA-146a were also detected in the CSF of AD patients [182]. In this regard the expression of miRNA-146a is increased in AD [183] and CJD brains [184], in AD mice models [185] and in scrapie mice [184]. miRNA-146a expression in AD mice models also correlates with senile plaque density and synaptic pathology [185]. This miRNA is induced by the interleukin IL-1β, modulating the expression of IL-6 and the cyclooxygenase COX-2 and acting as a negative regulator of the astrocyte-mediated inflammatory response [186, 187]. In addition miRNA-146a negatively regulates TLR signalling to prevent exacerbated inflammation, thus, it seems to play a key role in the modulation of the neuropathology associated to chronic inflammation in neurodegenerative diseases. Whether the regulation of miRNAs in CSF is a consequence of neuronal cell damage or a modulated pathogenic response is still a matter of discussion.
In summary, all preliminary studies argue for the presence of deregulated levels of miRNAs in the CSF of AD patients with potential translational value. Exclusion of blood contamination effects, standardization of the assays, together with cross-disease and technical validation in larger cohorts need to be carried out to assess the potential role of miRNAs signatures as specific diagnostic and prognosis biomarker tool in AD and to define new diagnostic therapeutic opportunities related to the miRNA field.
A pioneering study demonstrated that asymptomatic patients at risk of AD and symptomatic AD patients exhibit a significant decrease in the levels of circulating cell-free mtDNA in the CSF [188]. Data were generated by qPCR and digital droplet PCR and validated in an independent cohort of patients. Interestingly, this decrease is disease-specific, as mtDNA levels in the CSF of FTLD patients remain unchanged. Since decreased levels of mtDNA precede the appearance of the classical AD biomarkers such as Aβ42, mtDNA is an excellent potential pre-clinical AD biomarker. Further studies in larger cohorts including rpAD and CJD samples will determinate the clinical use of mtDNA analysis as a prognosis AD biomarker.
The use of analytic technologies such as Nuclear magnetic resonance and Liquid chromatography–mass spectrometry to analyse the metabolic signatures of biological fluids deserves special attention [189]. The metabolic profile in human CSF samples of AD patients and age-matched healthy controls unveils the presence of a significant presence of deregulated metabolites in AD cases [190]. Among them, higher corticols levels are found in AD cases, which correlate with AD progression and severity. In addition, the same study proved that combined analysis of different metabolites may increase sensitivity and specificity above 80%.
A second metabolic profile study identified the deregulated metabolic pathways in the CSF of MCI and AD patients [191]. The number of altered pathways increased with disease severity. Among them, Krebs cycle was significantly affected in MCI and cholesterol and sphingolipids transport was altered in AD. A high percentage of altered pathways in the CSF were also deregulated in plasma from the same individuals (30% in MCI and 60% in AD, respectively). Deregulated pathways performing the best disease discrimination were biosynthesis and metabolism of cortisone and prostaglandin 2.
Finally, a third study using metabolomics in the CSF of MCI and AD patients demonstrated the presence of elevated methionine (MET), 5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid, xanthosine and glutathione levels in AD patients and elevated 5-HIAA, MET, hypoxanthine and other metabolites in MCI patients when compared to healthy controls. Metabolite ratios revealed changes within tryptophan, MET and purine pathways [192], showing a partial overlap between MCI and AD.
Metabolomics is a promising tool for AD diagnosis indicating a slightly lower or similar performance when compared to classical AD biomarkers such as tau and Aβ42 depending on the study. Further analysis in large independent cohorts, technical updates as well as a combination of metabolic profiling with classical or alternative biomarkers will define the potential use of high throughput metabolic analysis in the AD diagnostic field. Besides, metabolite signatures may help to unveil the progression mechanisms and pathways leading to different dementia stages.
Despite the description of altered levels of several molecules in the blood levels of several molecules in the blood of AD patients as AD clinical biomarkers. Direct analysis in blood or blood-derived serum or plasma samples presents a broad range of advantages over CSF analysis. Blood extraction is minimally invasive and sample is easily collected, processed and stored over time. However, variations in the levels of blood metabolites may be reflecting a broad spectrum of changes not directly related to the neurodegenerative process. In addition, the dynamic range of the changes are lower than in CSF obtaining, most of the times, inconsistent data. Additionally, contrary to CSF, blood is a very complex fluid composed of different types of metabolites and cell types that present significant oscillations in response to external factors not related to pathogenic events. The analysis of specific blood cells could be an alternative approach to link potential biomarkers levels with AD pathology, being a field under intense study.
The core CSF AD biomarkers present minimal alterations in plasma. Aβ40 levels are higher in AD than in controls, although a high overlap is observed between groups. No changes have been observed for Aβ42, and Aβ40 and both Aβ40 and Aβ42 levels showed no association with cognitive decline [86]. Albeit some partial overlap between groups, tau levels in plasma are increased in AD when compared to control and MCI patients. Interestingly, tau levels cannot differentiate non-progressive from AD progressive MCI patients and there is a lack of correlation between CSF and plasma tau levels [193].
High-throughputs proteomic studies have tried to report the complex deregulated signatures between control and AD samples. A 2D-Mass spectrometry-based study detected a deregulated set of proteins in AD plasma complement factor H precursor and α-2-macroglobulin, which were validated and correlated with disease severity [194]. Independent multi-analyte profiling studies also demonstrated the presence of deregulated levels of proteins in MCI and AD samples when compared to controls both in serum and plasma. Among them some hits are related to AD pathogenesis such as the apoE [195, 196] as well as a broad range of inflammatory mediators [196, 197]. In an array-based ELISA study, 18 signalling proteins were able to distinguish AD from control samples with high accuracy (90%) and to predict MCI to AD progression [198], although the validation of this dataset has been ambiguous [199, 200]. The observation of a high variability between independent analyses indicates that further validations by independent methodologies in different cohorts need to be performed before resolving the clinical relevance of high-throughput blood-based analysis.
Alternative plasma biomarkers include the brain-reactive autoantibodies, present in sera irrespective of the presence of any pathology. This finding led to the analysis of the potential AD-specific autoantibody signature, which has been suggested to possess diagnostic value due to its ability to distinguish AD cases from controls, PD and breast cancer samples [201].
miRNA signature from CSF is only slightly more stable when compared to serum, suggesting that both biofluids are appropriate for the screening analysis of small RNAs [202]. Therefore, several studies addressed the potential deregulated miRNA signature in blood-derived AD samples. Using a microarray and qPCR validation approach the miR-125b, miR-23a and miR-26b were downregulated in the serum of AD cases when compared to non-inflammatory and inflammatory neurological controls and to FTD cases [203]. miR-125b presented the best accuracy discriminating AD from other groups. The same study observed that miR-125b and miR-26b levels were also diminished in the CSF of AD patients. An independent validation study was able to replicate downregulation of miR-125b in AD serum [204].
In a different approach, using RNA-sequencing and qPCR validation, downregulated levels of the miR-98-5p, miR-885-5p, miR-483-3p, miR-342-3p, miR-191-5p and let-7d-5p in the serum of AD cases were reported. The miR-342-3p showed the best sensitivity and specificity and correlated with cognitive decline [205]. However, downregulated levels of miR-342-3p in biological fluids are also a common hallmark in cancer [206]. Using a similar approach a 12 blood-based miRNA signatures was suggested to discriminate AD patients from controls and samples from patients suffering from different neurodegenerative diseases with high diagnostic accuracies [207]. Nonetheless, the different sample origin impedes a formal comparison between disease group’s studies. The analysis of peripheral blood mononuclear cells identified upregulated levels of miR-34a, miR-181b in AD cells [208].
Despite the promising future of miRNA as biomarkers tools of clinical relevance, several considerations needs to be done. Lack of validation among current available studies, even when using similar platform, indicates that sample collection and methodology needs further standardization. In addition, high-throughput data need to be cross-validated in longitudinal studies using different cohorts and selected miRNAs validated in multicentre studies. Under these conditions miRNA in blood-related samples may serve as prognostic and diagnostic through the analysis of miRNA signatures alone or combined with the analysis of classical AD biomarkers.
The use of combined analysis of current AD diagnostic tools is gaining experimental momentum due to its demonstrated value as a better prognostic and diagnostic tool when compared to individual assessments. As most promising candidates, CSF markers as well as methods of in vivo neuroimaging have been identified. Among them, we can find structural MRI, 18F-FDG-PET and novel in vivo amyloid-PET imaging [209, 210]. In longitudinal studies it was shown that with the help of these biomarkers AD could be diagnosed already in mild symptomatic states with high accuray allowing a predictability of its development [210]. Investigations of patients with genetic AD have demonstrated already 15 years prior to the onset of dementia significant pathological alterations in distinct biomarkers [211, 212].
Although these results are only assignable in a limited way to sporadic AD, the latter study provides impressive evidence on the long preclinical course of AD.
Current diagnostic concepts should therefore apply not at first when AD dementia has developed, but support explicitly the application of biomarkers at distinct stages of AD as it was shown that biomarkers become positive already at early and presymptomatic stages [213, 214].
In conclusion, differential diagnosis of a dementia syndrom requires esides clinical history and neuropsychological testing, analysis of metabolites in biological fluids as well as imaging methods. All these diagnostic approaches will not only allow an explanation towards the underlying cause of dementia but will also be useful in monitoring disease treatment and progression. The detection of AD at an early stage is hereby essential, as a further disease progression can be influenced positively by early initiation of treatment.
Integration of data generated during the last decades should be used to build up a worldwide rational algorithm based in the use of standardized, economically affordable methodologies and easily accessible samples.
AD: Alzheimer’s disease, rpAD: rapidly progressive Alzheimer’s disease, CJD: Creutzfeldt-Jakob disease, aMCI: Amnestic Mild Cognitive Impairment, MCI: Mild cognitive impairment, FTLD: Frontotemporal Lobe Degeneration, FTD: Frontotemporal Dementia, CSF: Cerebrospinal Fluid, PD: Parkinson Disease, HD: Huntington Disease, ELISA: Enzyme-Linked ImmunoSorbent Assay, MS: Multiple Sclerosis, SWD: Subcortical White-matter Dementia, MMSE: Mini–mental state examination, APP: Amyloid precursor protein, DS: Down Syndrome, CRP: C-reactive protein, PSP: progressive supranuclear palsy, CBS: corticobasal syndrome; NINCDS/ADRDA: American National Institute of Neurological and Communicative Disorders and Stroke /Alzheimer’s disease and Related Disorders Association; DGPPN: German Society of Psychiatry, Psychotherapy and Neurology; DGN: German Neurological Society; MRI: Magnetic Resonance Imaging, PET: Emission Computed Tomography; SPECT: single photon emission computed tomography; EEG: Electroencephalogram; CT; Computerized Tomography; CDR: Clinical Dementia Rating; GDS: Global Deterioration Scale; ADL-/IADL: Activities of Daily Living /Instrumental Activities of Daily Living; VBM: voxel-based morphometry; FLAIR; Fluid-attenuated inversion recovery; rCBF: regional cerebral blood flow.
The study was supported by the EU grants JPND-DEMTEST (Biomarker based diagnosis of rapid progressive dementias-optimization of diagnostic protocols, 01ED1201A) and PRIORITY (Protecting the food chain from prions, FP7-KBBE-2007-2A) and by funds from the Federal Ministry of Health (grant no. 1369-341] and from the German Center for Neurodegenerative Diseases (DZNE).
Heat exchangers are an essential component of thermal systems and increase system efficiency by recovering heat from the waste streams [1]. Heat exchangers play a vital role in several applications i.e., waste heat recovery, thermal desalination units, power plants, air conditioning, refrigeration, manufacturing industry, food, chemical, and process industries, etc. The water purification industry that fulfills ∼40% of water demand worldwide is based on thermal-based desalination systems [2]. These systems include mechanical/thermal vapor compression (TVC/MVC) systems, adsorption systems, multi-effect desalination (MED), and multistage flash (MSF) [3]. These systems are mostly used due to their high operational reliability, ability to use low-grade energy, low pre-and-post treatment requirement, and capability to treat harsh feeds [4]. Thermal-based desalination systems operate at high brine temperature, and several pieces of research have been carried to improve their thermal and economic performance [5]. One of the major improvements in this regard is energy recovery by using a preheater. The additional component recovers heat from the waste stream i.e., brine, and preheat the intake stream which reduces thermal losses, decreases the evaporator loads, area, and investment [6].
Plate heat exchangers (PHXs) are widely used for heat recovery in thermal-based desalination units as a preheater. The plate heat exchanger offers many benefits including narrow temperature control (ΔT ≤ 5°C), easy maintenance and cleaning, margin to accommodate different loads, and high operational reliability [7]. Furthermore, it is significant to indicate that PHXs as preheaters have rarely been examined in thermal-based desalination units from an optimized cost design and analysis viewpoint [8]. Rather, the conducted studies either are restricted to preliminary sizing [9] or heat exchanger design is missing [10]. In conventional studies, the heat transfer area is calculated by the temperature-based heat transfer coefficient correlation offered by Dessouky et al. [11]. However, this method gives a fast estimation of heat transfer area, but the accuracy and reliability of the method are doubtful. This is because, in the heat exchangers, the heat transfer coefficient is the function of different parameters such as pressure, temperature, thermophysical properties, flow characteristics, and geometric parameter [12].
For example, in many previous studies, the plate chevron angle (β) is reported as the most influential geometrical variable of PHXs from the thermal–hydraulic performance viewpoint [13]. Likewise, the heat duty, thermophysical properties, and flow rates also have a remarkable impact on PHXs performance [14]. Some recent optimization studies highlighted the importance of various other process and geometric variables that significantly affect the PHXs performance [15]. For instance, the most critical and influential parameters that have been reported are dimensions of chevron corrugation, number of passes, number of plates, type of plate, and channel flow type (parallel, counter, mixed, etc.) [16].
As it appears from the above literature review that there is a requirement for a laborious optimum cost design and detailed investigation of the preheaters for the thermal-based water treatment systems. In this regard, Jamil et al. [17] moderately addressed the issues and conducted a detailed thermal–hydraulic analysis but have deficiencies in the economic analysis viewpoint. This book chapter is focused on the combinatory effect of thermal, hydraulic, and economic analysis. Furthermore, normalized sensitivity analysis and exergoeconomic analysis are also conducted. This chapter will discuss the sections as follow (a) exergoeconomic analysis methodology, (b) normalized sensitivity analysis methodology, (c) experimentally validation of the numerical model, (d) normalized sensitivity analysis in term of NSC and RC, and (e) exergoeconomic analysis. The normalized sensitivity and exergoeconomic analysis are conducted for a preheater (PHX) of a single evaporator based MVC desalination system as a case study.
Figure 1 represents the schematic diagram of the current considered system. The system includes PHXs and two centrifugal pumps to maintain the desire flow rates and overcome the pressure losses. The PHXs are used as a preheater in single evaporator based MVC water treatment system [18] to preheat the intake seawater using hot brine water. The operational variables i.e., mass flow rates, salinity, the temperature of hot and cold streams are extracted from our recent studies, as mentioned in Table 1 [18].
Plate heat exchanger configuration for current case study.
Parameter | Value | |
---|---|---|
Mass flow rate | Seawater, | 13 |
Brine, | 13 | |
Temperature of seawater | Inlet, | 21 |
Outlet, | 57 | |
Temperature of Brine | Inlet, | 63 |
Outlet, | 23 | |
Salinity | Sea water, | 40 |
Brine, | 80 |
Input operation variables for the current case study [18].
The thermo hydraulic design of the PHXs presented previous study [17] is used for the calculation of different parameters such as flow rates, temperature, area, pressure drop, heat duty, local and global heat transfer coefficient, etc. In the thermal investigation, Nusselt number (Nu) is one of the most important parameters and can be calculated using a correlation (Eq. (1)) which is primarily dependent on the Reynold number (Re) and Prandtl number (Pr) [19].
Where the value of Ch and n with different Reynold number and Chevron angle is given in [19]. The governing equations for the calculation of a detailed thermal model are summarized in Table 2. While the implementation and selection of correlation are discussed and summarized in [17].
Variables | Units | Formula |
---|---|---|
Reynold number | — | Re = ν |
Mass velocity per channel | kg/m2s | ν |
Number of channels per pass | — | |
Single-channel flow area | m2 | |
Mean channel flow gap | — | |
Plate pitch | m | |
Hydraulic diameter | m | |
Projected plate area | m2 | |
Enlargement factor | — | |
Effective area | m2 | |
Effective number of plates | — | |
Local heat transfer coefficient | kW/m2K | |
Overall clean heat transfer coefficient | kW/m2K | |
Overall heat transfer coefficient | kW/m2K | |
Factor of Cleanliness | — | |
Over surface design | % | |
Heat duty | kW |
Thermal design equations of PHXs [19].
The hydraulic analysis includes the investigation of pumping power and total pressure drop, which is dependent on various pressure losses i.e., ports losses, manifolds losses, and channels losses as shown below [13, 19].
The pumping power can be calculated as.
The governing equation of the remaining hydraulic model is summarized in Table 3.
For the heat exchanger analysis, exergy analysis is a significant and reliable technique because it includes the exergy destruction calculation [20]. The exergy analysis measures overall performance and concurrently responsible for the changes in temperature and pressure. The exergy destruction calculations estimate the performance index of the analysis [21]. For the analysis, the flow exergy is determined at boundaries (inlet and outlet) of pumps and heat exchangers based on their operational parameters such as mass flow rates, temperature, pressure, and salinity, as given in Eq. (4). After that, Eq. (6) is solved for all the components to get the exergy destruction. In the present study, the seawater database is used for the calculation of specific flow exergy
For the heat exchanger, the economic investigation is depending on the capital/purchasing investment (CI) and operational/running cost (OC) [23]. However, for the large component of the system, such as power plants and desalination units, the product cost is more important than purely capital investment and operational cost [24] because, in these systems, the performance of HX is primarily dependent upon the plant process variables. Therefore, the HX is analyzed and designed to meet the plant requirement [6, 18] instead of optimum HX performance.
The total cost of the heat exchanger is the sum of the capital investment (CI) and operational cost (OC) as given below [25].
The capital investment (CI) is the initial amount required to purchase equipment based on time and location of analysis. The finest method to calculate the capital investment to use the experimental correlations purposed by researchers and vendors after extensive study and survey. In the current study, the capital investment of the pump and heat exchanger is calculated using the most common and reliable correlations presented in [26, 27].
The capital investment correlations used for the heat exchanger are generally dependent upon the heat transfer area as [28].
After that, an installation factor (IF) range from 1.5 to 2.0 is used to predict accurately the monetary of the equipment at the utility. In contrast, the capital investment of the pump is calculated as [27].
A detailed discussion regarding the capital investment correlation is given in the reference study [29]. Furthermore, the constant in the correlation is varying with material selection and the applicability range. The empirical correlations are developed a long time ago based on the fiscal policy of that era. Therefore, all the above correlations need a slight correction to accurately estimate the capital investment in the current time. In this aspect, the cost index factor (Cindex) is commonly used. The Cindex is calculated by using Eq. (10) in which the chemical engineering plant cost index (CEPCI) is used for the original year and the present year as given as [30, 31].
In the present analysis, the Cindex 1.7 is used based on their CEPCI 390 [32] and CEPCI 650 for the year of 1990 and 2020 [33] respectively. However, the importance of the Cost index is analyzed from different ranges in the result and discussion section. Likewise, the operation cost (OC) is calculated using Eq. (12). The OC is primarily dependent on the pumping power, PPower (kW), yearly current cost, Cy ($/y), the unit cost of electricity, Cele ($/kWh), inflation rate, i (%), operating hours,
Whereas, the values operating hours
The output cost of the hot stream can be calculated by implementing the general cost approach [18]. For this purpose, the pre-calculated capital investment is converted into the yearly capital investment rate
After that, the annual rate is transferred into the fixed cost rate
After determining the cost flow rate, the cost balance takes the form mentioned below.
Whereas the
The cost of the inlet stream is varying from case to case. For the current case study, the inlet cost of the seawater is chosen from the study. It is important to mention that the equipment with various outputs such as RO trains, HXs, flashing stages, evaporation effects, etc.,) need an additional equation for the result. For instance, for the component with “k” outputs, a “k-1” number of additional equations are required. The cost balance of the plate heat exchanger (PHXs) can be solved by using the supplementary equation (Eq. (21)). The equivalency of the average inlet cot and outlet cost of streams depends on these additional Equations [29].
The sensitivity analysis is an important tool to examine the behavior of output performance parameters against the different input variables [34]. Sensitivity analysis is a significant tool to identify the influential and critical performance parameters and highlights the design improvements for future research. For this purpose, calculus-based (partial derivative-based) sensitivity analysis is one of the most trustworthy and widely used methods. In this approach, all the independent parameters sum up their nominal values and uncertainty as given below [35].
where
The total uncertainty for the multi-variable function is given as.
The partial derivative parameter in the total uncertainty equation denotes the sensitivity coefficient (SC) of the selected output parameter. These SC are converted into modified forms knowns as the Normalized Sensitivity Coefficient (NSC) by regulating the uncertainty in the outlet variable Y and input variable X by their corresponding nominal value (
Where NU denotes the normalized uncertainty, and NSC denotes the normalized sensitivity coefficient. Thus, the Eq. (25) can be written for the selected output performance parameters in term of NSC as follow.
Where in the above equations the parameters correspond to the following:
The relative contribution (RC) is an important parameter in a normalized sensitivity analysis that is used to identify the variable with dominant uncertainty contribution through combining the sensitivity coefficient (SC) with the actual uncertainty. It can calculate as [35].
The working of normalized sensitivity analysis is quite simple. Figure 2 represents the working methodology of normalized sensitivity analysis. At the start, all the input variables and output performance variables are selected. After that, the uncertainty/perturbation is selected that is generally 1% of the nominal value. In the next step, the partial derivative is taken for each output variable against the various input parameters. After the partial derivate of each variable, the sensitivity coefficient is calculated by using Eq. (23) for all the output variables. In the next step, the total uncertainty and normalized sensitivity of the output variable are calculated by using Eqs. (24) and (25). In the end, derived all the most significant, critical, and dominant input variables in terms of NSC and RC by using Eqs. (26)–(30).
Working flow chart of normalized sensitivity analysis.
The normalized sensitivity and exergoeconomic techniques are applied on a preheater (plate heat exchanger) of SEE-MVC based-thermal desalination system for which the input data is already summarized in Table 1.
For the analysis purpose, a numerical model is developed on Engineering Equation Solver (EES) based using the governing equation mentioned above for which the solution flow chart is presented in Figure 3. After that, the developed numerical code is validated with the laboratory/experimental readings from a small-scale PHX as illustrated in Figure 4. The specifications of the laboratory scale PHX are mentioned in our previous study [17]. Then, the experiment is carried out for two different operating conditions. For each scenario, the experimental setup is operated for 35 minutes, and readings are saved through a data acquisition system (edibon SCADA) when the system becomes stable. After that, the experimental data are compared with numerical data, as shown in Figure 5. The numerical and experimental readings have very close values, which shows the accuracy of the numerical data.
Solution flow chart for numerical analysis.
Experimental test setup.
Model validation with experimental data [
The analysis is carried to identify the most critical and crucial input variable that affects the selected output performance parameters. The desired output performance parameters are local cold side heat transfer coefficient, cold side pressure drop, operational cost, and product cost of the cold stream. Figure 6 presents the sensitivity analysis results from Normalized Sensitivity Coefficient (NSC) and Relative Contribution (RC). From Figure 6a, it can be concluded that for the local heat transfer coefficient, the most crucial variables in terms of NSC are in the following order: cold side mass flow rate
Normalized Sensitivity analysis results for performance parameters of (a) heat transfer coefficient (hc) (b) pressure drop (ΔPc) (c) operational cost (OC) (d) product Cost (
Overall, it was observed that the exergoeconomic analysis of PHX is affected by both fascial and process variables. Therefore, fascial parameters must consider equally while designing/analyzing PHX.
The thermal–hydraulic performance of PHXs is significantly affected by plate chevron angle (β) and mass flow rate [17]. The heat transfer coefficient and pressure drop of the cold stream are increased by varying the Reynold number (Re). However, the rise in heat transfer coefficient is desirable, but the rise in pressure drop is not favorable from a monetary viewpoint. Therefore, the comprehensive parameters (h/ΔP) are calculated to provide a reasonable estimate of heat transfer per unit pressure drop.
From Figure 7, the comprehensive performance parameters are declined with the increasing Reynold number. This is because with increasing Reynold number, the pressure drop increased at a higher-order rise compared to the heat transfer coefficient. Furthermore, the analysis is carried out for different chevron angles (β). It can be observed, the h/ΔP is highest for β = 60° followed by β = 50° > 45° > 30°. This is because the pressure drop faces less resistance at a high chevron angle. Meanwhile, from the economic viewpoint, the operation cost (OC) increased as the Reynold number increased. This is because, at the high6Reynold number, the pressure drop is increased which increased the energy consumption and ultimately the pumping power. The operational cost is highest for the chevron angle β = 30° and lowest for chevron angle β = 60° due to low-pressure loss.
Effect of Reynold number on heat transfer per unit pressure drop (h/ΔP) and operational cost (OC).
Similarly, the product cost of the cold stream
Effect of Reynold number rate on the outlet cold stream cost (
The traditional analysis is majorly focused on evaluating the consequence of both process and geometric variables. However, in recent studies, the combined analysis of fiscal and process variables gained remarkable importance on the exergoeconomic performance [7, 24]. The primary reason is that the system operating with different economic variables i.e., interest rate, electricity cost, and intake chemical cost would have different operation cost (OC) with like thermal and hydraulic performance [6, 18].
Therefore, an economic analysis is conducted for various economic policies over time as the importance of fiscal parameters is observed on performance parameters by sensitivity analysis as well in the above section. The cold stream product cost
Effect of monetary (a) cold water product cost (
An exergoeconomic flow diagram is a noteworthy pictorial demonstration of the thermo-economics output at every significant position of the system. It presents the economics and exergy of all streams at important points, i.e., inlet and outlets of each section of the large system. The visual representation is very substantial for the system with the multiple components to recognize how efficiently the induvial components are working from an economic and exergetic point of view. For the current case study, Figure 10 demonstrates the exergoeconomic flow diagram.
Exergy-cost flow diagram for current PHX arrangement.
A corrugated plate heat exchanger (PHX) is examined as a preheater in SEE-MVC based-thermal desalination system to preheat the intake feedwater using the hot waste brine stream. The system is examined from the thermal, hydraulics, and economics point of view. For the case study, the EES-based numerical code is developed using governing equations. After that, the experimental data is used to validate the developed numerical model. Furthermore, sensitivity analysis is conducted in form of NSC and RC to classify the influential input variables. After that, the one-factor-at-a-time (OFAT) technique is used for the detailed parametric analysis to recognize the effect of influential variables. In the end, the exergoeconomic flow diagram is demonstrated to compute the exergies and product cost of the stream at each component of the system. The output of the current case study is as follows.
The sensitivity analysis highlights that the utmost critical input variables in form of NSC are cold water mass flow rate followed by cold water inlet temperature, and salinity for the local cold water heat transfer coefficient. Similarly, the most critical parameters for the cold side pressure drop are the cold-water mass flow rate followed by the cold-water inlet temperature. Furthermore, the operation cost (OC), the most critical input variable are mass flow of cold water > mass flow of hot water > electricity cost > interest rate > and efficiency of the pump while the cold water outlet cost, the critical variables are cost index > inflation rate > inlet temperature of hot > efficiency of the pump > mass flow rate of water > mass flow rate of hot water >unit cost of electricity.
The parametric analysis reflects that the comprehensive parameter (h/ΔP) is decreased with an increase of Reynold number due to higher-order increment in pressure drop. Likewise, the operational cost (OC) and cold stream of outlet cost are increased because at high Reynold number, the pressure losses are increased which consume more energy and ultimately increase the pumping power to maintain the desired pressure and overcome the losses. The OC and cold fluid outlet cost is highest for the β = 30° and lowermost for β = 60° because at a high chevron angle, the pressure loss is low.
The cold stream outlet cost increased by ∼17.7% and ∼3.80% by increased the inflation rate and unit cost of electricity respectively for the β = 30°.
The authors would like to thank KAUST Saudi Arabia and Northumbria University UK under reference # RDF20/EE/MCE/SHAHZAD for funding this research.
The authors declare no known conflict of interest.
heat transfer area, m2
effective area, m2
projected plate area, m2
single plate area, m2
mean channel width, m
constant parameter for calculation of Nusselt number in Eq. (1)
Outlet/product cost, ($/h)
total equipment cost, $
yearly current cost, $/y
Electricity cost, $/kWh
cost index factor
diameter of port, m
hydraulic diameter, m
specific exergy, k.J/kg
friction factor for pressure drop calculation
mass velocity per channel, kg/m2s
heat transfer coefficient (local), W/m2K
enthalpy, kJ
inflation/interest rate, %
thermal conductivity, W/mK
constant variable for friction factor calculation in Table 3
compressed plate length, m
length of horizontal port, m
vertical port distance from between port ends, m
vertical port distance between port centers, m
effective channel width, m
mass flow rate, kg/s
Nusselt number
equipment life, year
effective number of plates
number of flow passes
number of HX plates
number of flow channels per pass
plate pitch, m
pumping power, W
pressure drop, Pa
Prandtl number
capital recovery factor
Reynolds number
total fouling resistance, m2 K/W
salinity, g/kg
entropy, J/K
temperature, °C
thickness of plate, m
global/overall heat transfer coefficient, W/m2K
velocity of fluid, m/s
specific volume, m3/kg
work of pump, kW
exergy flow rate, kW
total exergy destruction, kW
yearly capital investment rate, $/y
rate of fixed cost, $/s
chevron angle, deg.
variation in magnitude
partial
density, kg/m3
viscosity, kg/ms
plant availability/operating hours, hour/year
Pump efficiency
dead state
brine
cold water
clean
cold inlet
cold outlet
per channel
fouled
hot
hot inlet
hot outlet
in
manifold
out
pump
port
Seawater
total
wall
constant parameter for calculation of friction factor in Table 3
constant parameter for calculation of Nusselt number in Eq. (1)
wall
CI | capital investment |
CEPCI | chemical engineering plant cost index |
FOC | factor of cleanliness |
EF | enlargement factor |
HX | heat exchange |
IF | installation factor |
LMTD | log mean temperature difference |
MED | multi-effect desalination |
MSF | multistage flash |
MVC | mechanical vapor compression |
NSC | normalized sensitivity coefficients |
PHXs | plate heat exchangers |
OSD | over surface design |
OFAT | one-factor-at-a-time |
OC | running/operational cost |
RC | relative contribution |
TVC | thermal vapor compression |
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\n\nA Conflict of Interest is a situation in which a person's professional judgment may be influenced by a range of factors, including financial gain, material interest, or some other personal or professional interest. For IntechOpen as a publisher, it is essential that all possible Conflicts of Interest are avoided. Each contributor, whether an Author, Editor, or Reviewer, who suspects they may have a Conflict of Interest, is obliged to declare that concern in order to make the publisher and the readership aware of any potential influence on the work being undertaken.
\n\nA Conflict of Interest can be identified at different phases of the publishing process.
\n\nIntechOpen requires:
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\n\nAll Authors are obliged to declare every existing or potential Conflict of Interest, including financial or personal factors, as well as any relationship which could influence their scientific work. Authors must declare Conflicts of Interest at the time of manuscript submission, although they may exceptionally do so at any point during manuscript review. For jointly prepared manuscripts, the corresponding Author is obliged to declare potential Conflicts of Interest of any other Authors who have contributed to the manuscript.
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\n\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
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\n\nAcademic Editors should declare if the Author of a submitted manuscript is affiliated with the same department, faculty, institute, or company as they are.
\n\nPolicy last updated: 2016-06-09
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Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"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. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",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:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",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:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. 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"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"17",type:"subseries",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,series:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983"},editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",slug:"anca-pantea-stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",slug:"attilio-rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",slug:"yanfei-(jacob)-qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},onlineFirstChapters:{paginationCount:8,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:12,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81471",title:"Semantic Map: Bringing Together Groups and Discourses",doi:"10.5772/intechopen.103818",signatures:"Theodore Chadjipadelis and Georgia Panagiotidou",slug:"semantic-map-bringing-together-groups-and-discourses",totalDownloads:12,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79491",title:"Fuzzy Perceptron Learning for Non-Linearly Separable Patterns",doi:"10.5772/intechopen.101312",signatures:"Raja Kishor Duggirala",slug:"fuzzy-perceptron-learning-for-non-linearly-separable-patterns",totalDownloads:14,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Raja Kishor",surname:"Duggirala"}],book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81331",title:"Machine Learning Algorithm-Based Contraceptive Practice among Ever-Married Women in Bangladesh: A Hierarchical Machine Learning Classification Approach",doi:"10.5772/intechopen.103187",signatures:"Iqramul Haq, Md. 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