*Review* **Advantages and Pitfalls in Fluid Biomarkers for Diagnosis of Alzheimer's Disease**

**Syed Haris Omar 1,2,\* and John Preddy 1,2**

Received: 8 June 2020; Accepted: 6 July 2020; Published: 17 July 2020

**Abstract:** Alzheimer's disease (AD) is a commonly occurring neurodegenerative disease in the advanced-age population, with a doubling of prevalence for each 5 years of age above 60 years. In the past two decades, there has been a sustained effort to find suitable biomarkers that may not only aide with the diagnosis of AD early in the disease process but also predict the onset of the disease in asymptomatic individuals. Current diagnostic evidence is supportive of some biomarker candidates isolated from cerebrospinal fluid (CSF), including amyloid beta peptide (Aβ), total tau (*t*-tau), and phosphorylated tau (p-tau) as being involved in the pathophysiology of AD. However, there are a few biomarkers that have been shown to be helpful, such as proteomic, inflammatory, oral, ocular and olfactory in the early detection of AD, especially in the individuals with mild cognitive impairment (MCI). To date, biomarkers are collected through invasive techniques, especially CSF from lumbar puncture; however, non-invasive (radio imaging) methods are used in practice to diagnose AD. In order to reduce invasive testing on the patients, present literature has highlighted the potential importance of biomarkers in blood to assist with diagnosing AD.

**Keywords:** Alzheimer's disease; cerebrospinal fluid; amyloid beta peptide; total tau; phosphorylated tau; diagnosis

#### **1. Introduction**

Alzheimer's disease (AD) is one of the most common neurodegenerative disease in an ageing population. AD is the most common cause of dementia and is characterized by cognitive impairment and the impedance of daily activities, including communication, decision making and behavioral changes [1]. It has been shown that the frequency of AD doubles for each 5 years of life above the age of sixty years. It is predicted that by 2050, 130 million globally will be symptomatic from AD [2]. In terms of risk factors, advanced age is the most important risk for the sporadic or late onset of AD as well as the presence of APOE e4 alleles. Inherited mutations in chromosome 11 amyloid precursor protein (APP), Presenilin-1 (PSEN1), and Presenilin-2 (PSEN2) are prevalent in the less common familial form of AD. In addition, women are more prone to AD compared with men, early menopause is also risk factor for AD. Cardiovascular disease and diabetes mellitus type-2 are associated with an increased risk of AD. The exact pathophysiology of AD is still under investigation; however, the deposition of senile plaques, neurofibrillary tangles (NFTs), and astrogliosis are cardinal features [3]. Moreover, studies have shown that pathological involvement of oxidative stress, neuron degeneration induced synaptic alteration, inflammation and microgliosis are important in the pathogenesis of AD [4]. Despite almost 3 decades of research into the exact molecular mechanism causing AD, unfortunately, none of the hypothesis completely answers the question. The still amyloid cascade hypothesis suggests a core pathological role of amyloid beta in AD [5]. The presence of Aβ peptides in cerebral and peripheral tissues mainly consists of amino acids and their sequences ranging from 1 to 43. Aβ42 is very prone

to aggregate and proceed to form the senile plaques found in hippocampus, neocortex and in the cerebrovasculature region [6]. Another highly aggregated peptide called tau (which undergoes extensive hyperphosphorylation) is responsible for the formation of neurofibrillary tangles inside neurons and ultimately results in extensive brain and nerve damage [7]. Currently, approved drugs only provided symptomatic relief for patients with AD without modifying the disease or slowing disease progression. However, for the treatment of mild cognitive impairment (MCI), there is no FDA-approved drugs available and suggested to consider off-label treatment, such as an acetylcholinesterase (AChE) inhibitor, which has provided a modest impact but is also associated with the risk of side effects. In order to reduce the side effects, research has been undertaken to modify the chemical moiety of drugs with compatible substitutes and also focused on natural products with the potential to act as disease modifying agents [8,9]. Several natural products including curcumin, ginkgolides, resveratrol, oleuropein etc. have been shown to be effective against AD pathology in vitro or in vivo models but have not shown success in randomized trails [10–13]. Lifestyle modification, including exercise and dietary modification, especially the Mediterranean diet (MedDi) and Mediterranean-Dietary Approaches to Stop Hypertension (DASH) diet Intervention for Neurological Delay (MIND) diet, have been associated with improved cognition among elderly subjects [14].

It has been shown that pathological changes of AD occur long before the appearance of clinical symptoms. Therefore, it is important to establish a diagnosis as early as possible especially for people above the age 60 years. Biomarkers offer essential tools for AD diagnosis, monitoring, early detection, therapeutic intervention, as well as prevention of inaccurate diagnoses. Body fluid biomarkers in cerebrospinal fluid (CSF) and blood have shown potential for AD diagnosis, individual prognosis and patient stratification. Despite the availability of numerous theoretical and clinical diagnostic tools, AD is still poor diagnosed, especially in the early stage of the disease. AD has a prolonged pre-symptomatic prodromal phase; however, the lack of specific biomarker, procedural and methodological inconsistencies, inconsistent cut-off values as well as a lack of assay standardization, have thwarted attempts to establish a diagnosis and treat AD during this early phase.

#### **2. Search Methods**

Potential studies were identified in electronic database PubMed, Embase, ScienceDirect, Cochrane Library, SpringerLink, Scopus and Google Scholar using combination of following keywords "Alzheimer's Disease", "biomarkers" and "Alzheimer's disease", "cerebrospinal fluid", "CSF", "invasive biomarkers", "non-invasive biomarkers", "plasma biomarkers", "blood biomarkers", "plasma amyloid", "plasma tau", "inflammatory biomarkers", "imaging biomarkers", "proteomic biomarkers", "salivary biomarkers", "olfactory biomarkers" and "ocular biomarkers". Selected studies published between 1990 and May 2020 were included to ensure that all randomized trial, pilot studies, and critical reviews or systematic reviews published evidence on potential Alzheimer's disease biomarkers for three decades were encompassed. The preclinical studies, in vitro studies, published media, as well as duplicate articles were excluded due to being outside the scope of the clinical study aim.

#### **3. Biomarkers in Alzheimer's Disease**

A biomarker is usually characterized by substances (synthetic molecules, specified cells, proteins, enzymes, hormones or genetic material) or imaging finding, which is used as a metric characteristic to indicate the presence of a specific physiological state and may assist with establishing a diagnosis well before a clinical diagnosis can be made. Furthermore, the use of biomarkers is increasingly for assisting with the prognosis and diagnosis of AD, reflected by a tremendous increase in research from 1980 to current time (Figure 1).

**Figure 1.** Publications statistics for "Biomarkers in Alzheimer's disease", source PubMed.

On the basis of AD pathogenesis and clinical condition, a set of diagnostic criteria were established in 1984, which was updated by the National Institute on Aging and Alzheimer's Association (NIA-AA) [15]. The updated NIA-AA guideline was mainly based upon the pathophysiological advancement in clinical, imaging, and research technologies in AD. Similarly, based upon clinical *probable*, *possible*, or *definite* symptoms, National Institute on Neurological and Communicative Disorder and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) have also published a diagnostic criteria for AD [16]. The clinical conditions of AD are considered to fall into three stages; however, some studies have expanded this to 1–5 or 1–7 stages. Of all the stages of AD, the prodromal period has the longest duration. This has resulted in a revision of NIA-AA diagnostic criteria, which are mainly based upon the identification of biomarkers, including CSF and imaging as valid diagnostic tools [17]. Based on modern diagnosis criteria, three sets of biomarkers are used as diagnostic tools, including Aβ peptide (A), tau peptide (T) and neurodegeneration (N), which are classified as A/T/N framework (Table 1) of AD diagnosis [17].

**Table 1.** Biomarkers based upon National Institute on Aging and Alzheimer's Association (NIA-AA) classification [17].

NIA-AA: National Institute on Aging and Alzheimer's Association; ↑: increase; ↓: decrease; CSF: cerebrospinal fluid; Aβ: β-amyloid; PET: positron emission tomography; FDG: fluorodeoxyglucose; MRI: magnetic resonance imaging.

#### **4. Biomarkers Based upon Alzheimer's Disease Stages**

*Stage 1:* assigned to the individuals who do not have functional impairment but might have cognitive impairment, which can only be detected through neuropsychological sensitive instruments. There is increasing evidence that certain biomarkers can predict the pathological changes at an early preclinical phase, namely the presence of amyloid imaging and a reduced CSF Aβ42 concentration. Early diagnosis based on biomarkers may assist with the approval of AD treatment, which could provide clinical benefits and improve outcomes [18]. Further trials are required to evaluate the reliability of clinical measurement and access the potential improvement with the drug-placebo conclusion.

*Stage 2:* Presence of biomarkers that predict pathophysiological changes of AD, a subtle cognitive effect, but no functional deficits in the patients, which can be detected with the use of sensitive instruments; however, they do not fulfil the criteria for dementia. According to the FDA guidance, sensitive neuropsychological testing should be considered alongside biomarker changes to diagnose AD stage 2 [18].

*Stage 3:* Pathophysiological biomarkers are present, and patients have started to have difficulty in doing some daily tasks which are measurable. This stage of the disease corresponds with mild cognitive impairment, whereas the first two stages are preclinical.

*Stage 4, Stage 5 and Stage 6:* Pathophysiological biomarkers are present with the consecutive stages of mild, moderate, and severe AD dementia with worsening cognitive impairment.

#### *Assessment of Stages in AD*

From the FDA classification of stages in Alzheimer's disease, stage 1 and 2 should be considered critical and monitored seriously. However, from stage 3 onwards, AD patients have similar pathophysiological biomarkers and ongoing cognitive decline. There are two basic questions that stem from the stages of AD. Which biomarkers may predict the presence of stage 1 and stage 2 AD in individuals? Secondly, from the treatment perspective, how can we establish the clinical effect of current FDA-approved drugs for patients with stage-1 AD, which is preclinical (based on the presence of amyloid and reduced CSF Aβ42 concentration) without evidence of cognitive decline? There is a need to evaluate predictive biomarkers and establish whether changes in biomarkers is a predictor of treatment success.

#### **5. Biomarkers through Invasive Diagnostic Methods**

#### *5.1. Cerebrospinal Fluid Biomarkers*

Cerebrospinal fluid (CSF) is a clear liquid that is present in the subarachnoid space and ventricular system of the brain and spinal cord. The volume of CSF in the body varies between 125 and 150 mL. The composition of CSF can demonstrate minor biochemical change in the brain. Currently, CSF is considered an excellent biologic fluid that may contain potential biomarkers for AD, which may be able to identify without going through autopsy or biopsy. Furthermore, the presence and concentration of biomarkers may change in parallel to AD progression. The three most suggestive biomarkers in AD are Aβ, total tau (t-tau), and phosphorylated tau (p-tau) (Table 2). It has been suggested that CSF biomarkers did not vary with severity with stable levels noted in the follow-up patients with clinical AD [19].

**Table 2.** Established diagnostic biomarkers in the cerebrospinal fluid CSF of Alzheimer's disease (AD) a [20] and showed 85% sensitivity cutoff values for AD dementia diagnosis [21].

a Data obtained using innogenetics single 96-well ELISA kits. b This is not relevant for sporadic AD, because it is only for patients >60 years of age. \* *p* < 0.001.

#### 5.1.1. CSF Aβ Biomarker

The amyloid isoforms Aβ40 and Aβ42 concentration in CSF are considered to be the most dependable biomarkers for the diagnosis of the AD disease. The production of both amyloid isoforms Aβ40 and Aβ42 was 24% higher for mutation carriers than noncarriers in the autosomal dominant AD patients. However, it was suggested that the fractional turnover rate of Aβ42 was noted 65% higher in mutation carriers [22]. Interestingly, it was also reported that there is no change in CSF Aβ40, while it is present in a 10-fold higher concentration than Aβ42 in the CSF of AD patients. It was suggested that Aβ42 be used as a proxy of total Aβ concentration. The amyloidogenic protein is found throughout the human body, and studies showed that Aβ42 concentrations in CSF often correlate with Aβ levels in the patient's brain [23]. It was found that the Aβ42 concentration was significantly reduced in CSF, which is a consequence of its presence in fibrils and plaques in the brains of patients with AD [24–26]. There are variations in quantification; however, it was found that Aβ42 concentration declined by 50% in CSF of patients with AD as a result of its deposition in the brain parenchyma [27].

The underlying mechanisms of the reduction CSF Aβ42 is not clear; however, few studies have suggested that it is due to the excessive hydrophobic aggregation of Aβ42 sequestration in plaques, a reduction in its diffusion from interstitial fluid to CSF and/or decreased Aβ clearance as a consequence of an impaired blood–brain barrier [28,29]. It was reported that the other isoform of amyloid peptides, Aβ38, was also found to have an increased concentration in CSF. Furthermore, the ratio of Aβ42/Aβ38 closely corresponds with imaging findings in patients with AD and thus results in a robust biomarkers for AD pathogenesis, which is more useful than the concentration of Aβ42 alone in CSF [30,31]. In contrast, several studies have reported that the Aβ40 concentration was unchanged in CSF from patients with AD and does not correlate with amyloid deposits in the brain [32]. In spite of the discrepancy in the diagnosis of CSF Aβ40 levels, several studies supported a decrease CSF Aβ42/40 ratio in the diagnosis of MCI patients compared to controls [33]. Because of the observed Aβ isoforms ratio and their positive relationship with AD pathogenesis, the NIA-AA has accepted Aβ42 concentration as well as a comparative ratio of Aβ42/Aβ40 as important biomarkers in the diagnostic guideline for AD [17].

#### 5.1.2. Assessment of CSF Aβ42 Biomarker

CSF Aβ42 biomarkers support a diagnosis of AD in its preclinical stage and are predictors of disease progression in cognitively unimpaired individuals and in those with MCI. One of the main limitations of CSF sampling is its invasive collection technique i.e., through lumbar puncture compared with blood sampling. Post lumbar puncturing, headache as most common adverse effect.

Most of the studies showed a significant decline in Aβ42 levels as diagnostic biomarker and agreed that upto 40% reduction was observed in AD patients when compared with those of healthy individuals [34]. In contrast to the reduction of Aβ42 concentration in CSF, some of the past studies have shown an unchanged Aβ42 concentration [35] and an elevated level [36] in CSF Aβ42 concentration compared to AD patients and healthy controls. CSF Aβ42 was found to reach the plateau state early in the disease progression and produce a conflicting outcome, which demonstrates a process of preceding aggregation of Aβ mainly detected with amyloid PET analysis. There is a lack of standard protocol and universal agreement because of the varying biomarker concentrations and contradictory outcomes, which required further investigation in the age- and stage-matched individuals considering prodromal stage individuals with different ethnic groups. It has been demonstrated that CSF Aβ42/40 ratio may predict abnormal cortical amyloid deposition (visualized with PET) compared with CSF Aβ42. However, this diagnosis could result as false positive (low CSF Aβ42) or false negative (high CSF Aβ42) in fewer patients [25]. Further studies have been reported the presence of oligomers formation prior to the formation of Aβ fibrils in the pathogenesis of AD and suggested oligomers as potential early target in the prodromal stages, which required further confirmation in randomized trial as early diagnostic biomarker.

#### 5.1.3. CSF Tau Biomarker

The Aβ and tau peptides have been suggested to interact mutually and prompt both aggregation and toxicity followed by proposed mechanisms including Aβ encourage tau pathology or tau induces Aβ toxicity, or synergistic toxicity exists between Aβ and tau [37,38]. Basically, tau is a microtubule associated protein have a pivotal action in intracellular transportation. Tau proteins classified as p-tau representing hyperphosphorylation, and t-tau representing several isomers of the tau protein. It was suggested that the hyperphosphorylation occur at threonine-231 (p-tau231), threonine-181 (p-tau181), and at serine-199 (p-tau199). The involvement of p-tau in the assembly of neurofibrillary tangles represented as 'T' marker, and their presence in the CSF proving a sign of neuronal death as 'N' marker [39,40]. It was reported that CSF p-tau231 was involved in the neurofibrillary neocortical pathology [40], and showed a significant increase in concentration correlated with a decline in cognitive performance and conversion to AD [41]. The concentration of t-tau in CSF was found to be highly age dependent and observed <300 pg/mL in 21–50 years, <450 pg/mL in 51–70 years, and <500 ng/L in 71–93 years age group of normal cognitive healthy individuals [42]. Several lines of studies have supported the results of significant rise in CSF tau peptide concentration in AD patients [39,43]. In particular, p-tau and t-tau were found to be increased by 200% 300% concentration in AD compared with nondemented elderly subjects [27,44]. Tau pathology cause an elevated level of CSF t-tau and p-tau and strongly associated with cognitive decline compared to the amyloid pathology.

Furthermore, the degree of neurodegeneration and neuronal/axonal damage in AD patients' brains marked by the presence of considerable CSF t-tau concentrations and constituted in the A,T,N Framework as a marker of 'N' [17,45,46]. A systematic review included 15 studies showed the presence and accuracy of CSF t-tau in seven studies, while six studies have showed the presence and accuracy of the CSF p-tau in mild cognitive impaired patients [47].

The intermediate filaments known as neurofilament light (NfL) were found to be present in the axons cytoplasm and may interfere with cytoplasmic function of axonal homeostasis as well as synaptic transmission. Studies have shown the presence of elevated NfL in AD patients and suggested end results of neuronal and axon damages [48]. More pronouncedly, the increase level of NfL could serve as a risk factor for MCI. However, elevated NfL levels were also recognized as a biomarker in other neurodegenerative diseases having marked axonal degeneration, white matter injury, or both, such as frontotemporal dementia, amyotrophic lateral sclerosis, Creutzfeldt–Jakob disease, multiple sclerosis and traumatic brain injury. A few post-mortem studies have reported significant elevated NfL levels in patients with amyotrophic lateral sclerosis and frontotemporal dementia more than the AD patients and suggested that NfL could be used for differentiation of two types of dementia [49]. Due to non-specificity in disease diagnosis, NfL is less popular as confirmatory biomarkers compared to Aβ and tau in AD.

#### 5.1.4. Assessment of CSF Tau Biomarker

The increase in both CSF t-tau and p-tau concentrations are well settled in AD compared with controls, specifically the intensity of neuronal injury and neurodegeneration are indicated by CSF t-tau in AD. The concentration of CSF t-tau was reported two to three folds higher in patients with AD compared to the normal controls [34]. Still, it is not confirmed about the agreement in distinct tau phosphorylation sites for AD. Most studies have showed the significant rise in tau concentration with aging as well as in patients with AD; however, few studies have reported contradictory information and showed no significant change in the CSF tau level in normal healthy aged individuals [50]. It was postulated that the incidence of tau phosphorylation and the building of neurofibrillary tangles inside neuron is a results of cellular protective mechanism against oxidative stress and suggesting normal physiological pathway rather than a toxic pathway [51]. It is still unknown about the inconsistent results for tau analysis and required to develop a new technique for consistent outcome. It has been suggested that, due to the presence of heterogeneity in trial subjects, the consistency of the result varies and not being reproducible. Thus, there is a requirement to agree on one productive model for the diagnosis of CSF tau biomarkers and run the trial in a large cohort that can be reproducible or verify in a repetitive/confirmatory trial.

#### *5.2. Blood Biomarkers in AD*

Blood is a most commonly accessible biological sample than other body fluids such as CSF and offer inexpensive clinical diagnosis or screening methods and even convenient for getting reproducible results in clinical trials. In cardiovascular disease and cancer diagnosis and research, biofluid blood has been established as biomarkers; therefore, it may perform as a critical measure in the early diagnosis of AD [52]. Due to the presence of Aβ in the prodromal stage of AD and their ability to pass through the blood–brain barrier, for diagnostic purposes, Aβ received a considerable amount of attention as a potential blood biomarker.

#### 5.2.1. Plasma Aβ Biomarker

In order to evaluate and understand amyloid clearance, studies reported a significant decline in amyloid clearance using the stable isotope labelling kinetic method in late-onset AD patient [53]. Both Aβ40 and Aβ42 production were found to be increased by 24% in individuals with mutation carriers than noncarriers autosomal dominant form of AD (ADAD), and, furthermore, the Aβ42 fractional turnover rate was 65% faster deposition in mutation carriers individuals [22]. Several studies have reported the existence of Aβ in blood plasma and showed the rise of both Aβ40 [54] and Aβ42 [55] levels. In contrast, studies have also reported a fall in both Aβ40 [56,57] and Aβ42 [58] concentrations in individuals susceptible to AD. More recently, a comprehensive meta-analysis study showed the inconsistency in plasma for both Aβ42 and Aβ40 in AD [59]. In order to achieve consistency and accuracy in the biomarker analysis, trials based on immunoprecipitation-mass-spectrometry-based assays for evaluation showed a significant decline in both Aβ40 and Aβ42 concentration in plasma Aβ42/Aβ40 ratio (in the line with CSF test) with approximately 90% of diagnostic accuracy [60,61].

#### 5.2.2. Assessment of Plasma Aβ Biomarkers in AD

Blood considered as highly complex fluid connective tissue containing cellular components and several compounds, such as proteinases nature compounds, genetic materials, and metabolites, appear in plasma. The primary barrier for inconsistency in the biomarkers analysis results was suggested due to the presence of low blood Aβ concentration as well as victims of matrix effects. In addition, a lack in assay sensitivity, specificity and methods selectivity are responsible for inconsistency in Aβ finding as biomarkers in blood. In general, biomarkers localized in the brain are not easily available in blood because of the restriction of movement through the blood–brain barrier, poor expression of AD pathology biomarkers in blood and the interference of blood containing endogenous antibodies with the assay reagents that finally resulted in a false rise and fall of measurement. There is a need to develop the analytical sensitive plasma-based assay, which can minimize the event of reaction of biomarkers such as Aβ with the reagent, and careful validation work. A few attempts have been made to analyze Aβ in blood through a new diagnostic technique; however, such attempts were unable to resolve the cerebral expression of Aβ including plasma protein and blood platelets [62] but still represent an important step forward.

#### 5.2.3. Plasma Tau Biomarker

Studies have been reported that the elevated plasma tau levels but with overlapping ranges of results across diagnostic groups of AD patients compared with the normal control [63]. It has been suggested that the plasma tau is a late-stage marker of AD and did not show any change in the plasma of the MCI-stage individual followed by missing the interrelationship between tau levels in plasma and CSF due to the differential regulation of tau in both fluids [63]. Further study showed a positive associations between increased plasma tau level and AD hallmarks [64]. The elevated level of plasma total tau and pTau181 were investigated in patients with dementia compared to the cognitively unimpaired individuals [65]. In general, total tau protein concentration was found to be approximately seven times higher than p-tau181 in human plasma. Based on immunomagnetic reduction technique, a study evaluated the concentration ratios of p-tau181 to t-tau in plasma are 14.4% for healthy controls, 13.6% for patients with MCI due to AD, and 19.5% for very mild AD, respectively, and suggested that p-tau181 in plasma can be used to differentiate memory disorder/cognitive decline in early-stage AD patients [66]. Further study based on the evaluation of plasma p-tau181 as a biomarker using ultrasensitive immunoassay methods showed a significantly higher plasma p-tau181 level in the AD group compared with the control group [67]. A recent study measured plasma-phosphorylated tau concentration and found a significantly higher concentration in the AD group compared with age-matched cognitively normal controls [68]. Tau hyperphosphorylation-induced neuronal damage was also investigated and suggested the presence of neurofilament as biomarker for neurodegeneration in AD [69]. Neurofilament was measured by using an ultrasensitive immunoassay method and showed increased serum neurofilament light (NfL) concentration in familial AD prior to symptomatic disease [70]. In the line of the previous report, a recent study outcome showed an early rise of serum neurofilament light in the presymptomatic phase of familial AD and continue to increase in neurofilament light level, respectively [71]. Increased plasma neurofilament light was found in MCI and AD dementia patients compared with controls and correlated with poor cognition [72].

NfL in plasma well determined, reported their elevated levels in the serum of familial AD patients usually appear a decade ahead to the onset of symptom and well correlated with whole-brain atrophy intensity through MRI and an assessment of cognition [73]. In addition, a high level of NfL was determined in plasma of MCI compared to AD patients and healthy controls, which can be used as a determinant assay to easily distinguish individual between MCI and AD [72].

#### 5.2.4. Assessment of Plasma Tau Biomarkers in AD

In the past two decades, it is still in debate that tau pathology starts early in normal cognitive individuals upon ageing (>60 years) than Aβ pathology. However, research based on tau pathophysiology have showed high consistency in the increase level of tau protein (neurofilament light, p-tau181, and total tau as biomarkers) and easily detected in serum. In contrast, Aβ (Aβ40, Aβ42 and Aβ42/Aβ40 as biomarkers) protein appearance in blood do not show those consistent results in early stage of AD progression regardless with the determination techniques. It was suggested that ultrasensitive immunoassays granted the accurate quantification of tau in blood. Further research is required to justify and validate tau protein as an early diagnostic biomarker in AD.

#### **6. Biomarkers through Non-Invasive Diagnostic Methods**

#### *6.1. Cognitive Biomarkers*

Currently, non-invasive diagnostic criteria for AD based on a group of assessments, including individual clinical history, cognitive and neuropsychological state and clinical rating score resulted from Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and the Wechsler Memory Scale (WMS) Logical Memory (LM) test [74]. According to the MMSE score (0–30), if an individual has received a score between 20 and 24, then suggested mild dementia, followed by a score between 13 and 20 suggest moderate dementia and have a score less than 12 designated severe dementia [75]. A study evaluated the accuracy of the MMSE for diagnosing dementia subtypes in people aged 65 years who do not examine earlier for dementia and supported the diagnostic use of MMSE as part of the process for deciding whether or not someone has dementia [76]. The LM test consist of LM-I (immediate recall), LM-II (delayed recall), and LM Recognition (delayed recognition), used to investigate and measure verbal episodic memory in individuals [77]. The LM-I directed individual to immediately recall details of two short passages, while LM-II phase based on recall the passages after a 20 to 30-min delay. The LM recognition phase test provided questionnaire-based evaluation on an earlier provided passage in the form of yes or no.

#### *6.2. Assessment of Cognitive Biomarkers*

Several studies have indicated the psychometric limitations of MMSE analysis, including large ceiling and floor effects, and sensitivity to practice effects, limiting the clinical efficacy of MMSE in MCI and AD dementia investigation. Moreover, the scoring system in MMSE was found to lack accuracy in the investigation of individuals with MCI or mild AD dementia. The LM subtest is not only useful for distinguishing certain types of dementia such as AD dementia but is also known as a tool that can detect subtle memory changes in the individuals with MCI [78].

#### *6.3. Imaging Biomarkers*

The imaging of the brain in the diagnosis of AD has been used as a second line of diagnostic criteria, including magnetic resonance imaging (MRI), functional MRI (fMRI) and positron emission tomography (PET). In a clinical setting, current guidelines follow the structural imaging, i.e., magnetic resonance imaging (MRI) or computerized tomography (CT), mainly required for the evaluation of patients presenting with a cognitive/dementia syndrome [79]. In order to investigate the visualization of AD-linked cortical atrophy and changes in brain connectivity, MRI is used to provide a structural and functional imaging technique [80]. In addition, fMRI investigation provided the functional connectivity of the brain such as abnormality in the hippocampus [81]. Positron emission tomography (PET) is an advanced imaging technique using compounds labelled with short-lived positron-emitting radionuclides to detect Aβ associated metabolic activity and plaques deposition in AD [82]. Commonly used PET tracers, including Pittsburgh Compound-B (PiB) and Fluoro-2-deoxy-D-glucose (FDG), have a high sensitivity and specificity of diagnosis, particularly in the early stages, and are utilized in imaging biomarkers of amyloid plaque progression in individuals [83].

#### *6.4. Assessment of Imaging Biomarkers*

In a clinical setting for AD diagnosis, imaging techniques are often used as a second or third line of investigation due to the unavailability of imaging facility at every center, especially in rural areas. Imaging require rigorous measurement, expertise to interpret the findings and burden of high cost, preventing their frequent use in the routine clinical assessment of individual during their first visit. 18F-FDG-PET analysis may differentiate dementia from normal aging and is used as an indicator biomarker for neurodegeneration; however, it is unable to track down pathology at an early stage of AD. In addition, the current practicing guidelines and expert opinions suggested that amyloid PET analysis was unable to anticipate the trajectory of AD disease progression for an individual patient [84,85]. Moreover, it is also uncertain how effective the PET analysis is at characterizing differences across the pathophysiological phase of AD.

#### **7. Promising Biomarkers in AD**

#### *7.1. Proteomic or Enzymatic Biomarkers in AD*

In order to investigate novel protein and their capacity to predict AD, an early study analyzed 120 plasma proteins and discovered 18 signaling proteins, which showed 90% of accuracy in diagnosis for AD patients and 91% for MCI patients [86]. Further studies have been reported a total of 1590 AD-related proteins, including 296 proteins encoded with 115 up-regulated and 181 down-regulated genes, and supposed to be blood-secretory proteins involved in the pathogenesis of AD [87]. It was suggested that around 35 AD-related proteins are consistent, including four key proteins (APP, apolipoprotein E, PSEN-1, and PSEN-2) involved in AD pathology [87]. Synaptic proteins such as synaptosomal-associated protein 25 (SNAP-25) and synaptotagmin-1 (SYT1) were found to be significantly increased in the CSF of AD dementia and prodromal AD patients; however, SNAP-25 and SYT1 were specified to decline in cortical areas [88,89]. To facilitate early diagnosis in AD, a protein profiling of blood samples in mild AD patients showed a downregulation of apolipoprotein A1, α-2-HS-glycoprotein, and afamin, while, apolipoprotein A4 and the fibrinogen gamma chain were identified upregulated [90].

The CSF BACE1 enzymatic activity and protein concentration has been elevated in AD patients [91], and represented as an indicator biomarker of MCI [92]. In support of elevated BACE1 activity, a study reported a significant rise in plasma BACE1 activity by 53.2% in subjects with MCI and by 68.9% in patients with AD and suggesting plasma BACE1 activity as a diagnostic biomarker [93]. The study focused on plasma-based biomarkers showed a significantly elevated level of BACE1, and soluble forms of APP were observed in AD patient [94]. Further study showed the presence of increased BACE1 enzymatic activity along with the increase in phospholipase-A2 activity in platelets and brains of patient with AD [95]. One of the main interests for determining BACE1 biomarker is the development of specific BACE1 inhibitors, which may help in the reduction of amyloid production. It is unfortunate that the present plasma BACE1 investigation as biomarkers have not demonstrated a consistent result in terms of significant rise in BACE1 in AD patients compared to normal individuals. In search of other proteomic or enzymatic biomarker, the protein kinases such as glycogen synthase kinase 3β (GSK-3β) has been observed in tau protein hyperphosphorylation, and reported significantly elevated in plasma of MCI and AD patients compared with aged-matched controls [96] as well as their elevated level was also observed in white blood cells of AD and MCI patients compared with healthy individuals [97].

#### *7.2. Assessment of Proteomic or Enzymatic Biomarkers*

Several studies have shown the presence of proteins in plasma or serum, including albumin, lipoproteins, Aβ autoantibodies, fibrinogen, immunoglobulin, apolipoprotein-J, apolipoprotein-E, transthyretin, α-2-macroglobulin, serum amyloid p-component, plasminogen and amylin were found to be strongly interferes with the estimation of specific protein biomarkers. It was found that studies that have been conducted on proteomic or enzymatic biomarkers were only in cohorts with unmatched age patients and a target single protein or enzymatic marker; therefore, such studies were required to be focused on proteomics and enzymatic research in individuals with MCI and AD compared to aged-matched controls. Proteomic or enzymatic biomarkers could be an excellent and potential candidate for blood biomarkers as diagnostic tools, but due to the presence of low concentration and the incidence of protein-protein interactions in plasma result the inconsistency in outcomes.

Therefore, it is difficult to replicate due to the lack of specificity and accuracy involved in the current methods of investigation. Thus, the development of highly sensitive and reproducible novel methods that might have the capability to detect the plasma biomarkers in low concentration with accuracy is required.

#### *7.3. Inflammatory Biomarkers in AD*

Inflammation is one of the major cellular events considered at the initial pathogenic factor causing neurodegeneration in AD mainly through the activation of microglia. Out of the several investigated inflammatory biomarkers, the diminished C-reactive protein (CRP) level and increased level of triggering receptor expressed on myeloid cells-2 (TREM-2) were observed in the CSF of the patient with AD compared to the normal elderly controls [98]. Moreover, the reduced levels of plasma CRP was also observed in AD patients compared to MCI or normal cognition individuals [99]. The estimation of TREM-2 in CSF and blood is a marker of microglia response. Several studies have investigated the concentration of TREM-2 and reported their increased levels in CSF of AD patient, which was mainly associated with tau pathology compared to the controls [100,101].

Further studies have shown polymorphism in about 23 cytokines, and their 13 types were found to be observed in AD pathogenesis, including interleukins, TNF-α, TGF-β and IFN-γ [102]. Pro-inflammatory cytokines such as TNF-α level were significantly increased followed by decrease in the anti-inflammatory cytokine TGF-β level in CSF, which showed a positive correlation with a higher risk of disease progression from MCI to AD [103]. The concentration of cytokine I-309 level was found to be increased in CSF and suggested as a possible predictor of progression from MCI to AD patients [104]. It has been suggested

that the presence of cytokines elevated steadily or reached the highest level upon progression from MCI to AD individuals, including IL-1β, IL-6, TNF-α, IL-18, monocyte chemotactic protein (MCP)-1 and IL-10 can be used as a predicting biomarker for early diagnosis [102].

Microglial activation involved in the stimulation of astrocytic expression of YKL-40 (also known as chitinase-3-like protein-1 (CHI3L1)), which was observed in significantly higher levels in the CSF of AD patients compared to the cognitively normal individuals [105]. Most of the studies have investigated the elevated effect of YKL-40 in CSF; however, less studies have been able to detect YKL-40 in blood, showing a similar finding of increased YKL-40 level in plasma [106].

#### *7.4. Assessment of Inflammatory Biomarkers in AD*

Despite the elevation of the inflammatory biomarker TREM-2 in AD, few studies have reported conflicting results with no significant change in CSF TREM-2 levels in MCI and AD patients compared to the controls [107]. In addition, to recognize TREM-2 as a potential inflammatory biomarker in AD diagnosis, their physiological function has been questioned whether it has a constructive or a harmful effect in human body. Due to non-specific diagnosis of TREM-2 in other neuroinflammatory diseases, the biomarkers have faced challenges and required further studies to answer these questions on the molecular basis. The other biomarker YKL-40 investigation in AD have shown a limited diagnostic value due to their non-specific findings in neurological disease other than AD. However, it has been suggested that YKL-40 has a potential role in astroglial activation and the assessment of neuroinflammation treatment, which required further investigation with a specified sensitive method and an inflammatory biomarker in AD.

The majority of studies investigated inflammatory cytokines or chemokines as biomarkers insufficiently sensitive in plasma and could not be reproduced. In addition, there was a lack in the conduction of study in a large cohort using imaging or CSF samples of individuals with MCI and AD for the detection of inflammatory biomarkers. Therefore, it is warranted to investigate and confirm inflammatory biomarkers targeting individuals for early intervention in future studies.

#### *7.5. Oral, Ocular and Olfactory Fluid Biomarkers in AD*

Body fluid other than CSF and blood, including oral, ocular and olfactory have received attention for detecting the biomarkers of AD because of their easily available, noninvasive nature and inexpensive sample collection methods. A decade ago, one pilot study reported a significant rise in saliva Aβ42 levels in patients with mild AD compared to the control, while saliva Aβ40 level was detected unchanged [108]. Further study reported a two-folds increase in salivary Aβ42 concentration in AD patients compared to the controls, which have identical levels of salivary Aβ42 regardless of sex or age [109]. Further pilot study using the 1H NMR metabolomics technique for detecting salivary Aβ42 found a significant rise in salivary Aβ42 concentration in MCI and AD patients compared to controls [110]. In the line of previous results showing the elevated level of salivary Aβ42, a study based on enzyme-linked immunosorbent-type assays methods reported an increased Aβ42 level in patients with AD compared to controls [111].

In order to attempt early diagnosis and the development of a non-invasive method for AD, studies have reported the presence of significant Aβ plaque concentration deposited in the retina [112]. A quantitative and histological study showed a two-fold increase in retinal Aβ deposition in the form of protofibrils and fibrils among AD patients versus controls [113]. A similar study has detected retinal Aβ plaque deposition two months prior to their presence in the hippocampus and cortices of AD murine models and suggested the appearance of Aβ in the retina is an early event in the pathogenesis of AD [113]. The emerging single visit, label-free, cost-effective eye scan along with the emergence of mobile imaging modalities for the early detection of Aβ plaques in retina through the polarization imaging of retinal Aβ and retinal fluorescence lifetime imaging ophthalmoscopy have been suggested as early diagnostic tools [114].

The olfactory dysfunction has been associated with tau pathology from early to advanced AD and is well recognized. A study based on the Braak stage of AD showed the presence of tau pathology 90% and Aβ 9% in stage 4 followed by 44.6% of tau pathology and Aβ 9% in stage 3 [115]. The olfactory tau pathology in stage 2 represented by 36.4% without Aβ deposition; however, Braak stages 0 and 1 do not show any positive association of tau pathology [115]. Further study showed significant elevation in t-tau protein and p-tau tau protein levels in the patients with AD suffering from loss of smell, compared to the healthy controls [116]. The results of a recent meta-analysis suggested that the identification of olfactory dysfunction was more profound in AD patients compared to MCI patients [117].

#### *7.6. Assessment of Oral, Ocular and Olfactory Fluid Biomarkers in AD*

The oral, ocular and olfactory biofluid as potential biomarkers in AD diagnosis are still in the preliminary stage of research, requiring further investigation to overcome the methodological heterogeneity and discrepancy in accuracy. Thus, there is a need to develop a reliable quantitative method among consecutive or random samples to determine these biomarkers as an effective tool for diagnosing AD.

#### **8. Conclusions**

AD is a multifactorial disease without a confirmatory biomarker; however, based on the NIA-AA guideline, the currently available biomarkers may provide a positive direction to identify individuals who are on risk of AD, and need of routine screening for early diagnosis. The most studied and practiced biomarkers for AD diagnosis are CSF Aβ42, CSF Aβ42/Aβ40, CSF p-tau, amyloid PET, tau PET, structural MRI and fMRI. The accumulated evidences have been established that Aβ42 and tau levels were found quite lower (~30 to 100 times) in plasma compared to CSF. Furthermore, because plasma and serum already enriched with immense level of several proteins (~50–70 g/L) which interferes in detection and face challenges in poor outcome of NIA-AA suggested biomarkers in blood compared to CSF. Therefore, there is no doubt that the investigation of biomarkers in blood face inconsistency in findings due to their low appearance (concentration) in blood (plasma or serum) than in the CSF. To date, there are weak evidences supporting the dominance of biomarker over the other (CSF/imaging) for diagnostic tools in AD. Thus, in terms of availability or diagnosis, CSF have less challenges in the A/T/N framework-based biomarkers compared to blood-based biomarkers (Figure 2). Despite of challenges in blood-based biomarkers, the early detection of biomarkers (plasma NfL) could be a promising diagnostic tool in body fluid blood which can be routinely investigate in the individual reaching the age of 60 years. In a nut-shell, blood-based biomarkers should pay more attention in order to support the patient's comfort, highly sensitive, least invasive and cost-effective inexpensive technologies for biomarker detection are required for detection and analysis in the range of 10−15 to 10−12 M in individuals before entering MCI stage.

**Figure 2.** Overview of investigation and development CSF and blood-based biomarkers in Alzheimer's disease diagnosis.

**Author Contributions:** S.H.O. has created idea, designed and wrote the manuscript; J.P. has assisted in drafting the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** Rural Clinical School, Faculty of Medicine, University of New South Wales, Wagga Wagga, NSW, Australia & *Murrumbidgee Local Health District*, NSW Health, Wagga Wagga, NSW 2650.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

#### *J. Pers. Med.* **2020**, *10*, 63

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**5. Impact of fabrication-induced defects**

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172 Advances in Micro/Nano Electromechanical Systems and Fabrication Technologies

is still a considerable gap between experiments and theory.

varies between 2 to 5 nm and the thickness of the silicon cantilever.

ˆ

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3 1 8 6 12

*Ox*

where *EOx* and *tOx* are the native oxide Young's modulus and layers' thickness at the top and bottom of the cantilever. The thickness of the native oxide layers have been reported to vary between 2 to 5 nm [11] and its Young's modulus reported between 50 to 75 GPa [11, 29]. These variations, especially the thickness (since it has a higher influence on the effective elasticity) have been used to calculate the *Ê* of silicon cantilevers with different thicknesses. The result is shown in Fig. 13. The dotted and dashed lines in Fig. 14 show a comparison between the resultant *Ê* as a function of cantilever thickness considering the various native oxide scenarios' influence (ranges of thicknesses and Young's modulus reported in the literature) and the measured values. It is clear from the figure that taking the native oxide layer scenarios into account reduces the difference and partially explains the distinctive reduction in *Ê*, yet there

**Figure 13.** The effective Young's modulus of silicon cantilever as a function of native oxide layers thickness, which

3

There are still some remaining issues that require resolution in order to close the gap between experiments and theory [25]. Among them, accounting for the fact that experimentally fabricated nanosystems are not defect-free, has been recognized as the most important effect, which has not yet been investigated and quantified [25].

As described in Section *2*, reducing the cantilever thickness was done by alternating thermal oxidation and etching in HF solution. It was observed that the density of fabrication-induced defects increased when reducing the thickness of the silicon device layer of SOI wafers. During the thermal oxidation process, the lattices of silicon and silicon oxide mismatch, thus intro‐ ducing stress in both layers [53]. The precipitated oxygen and local strains in Si-SiO2 interface are likely sources of defects [29]. Further reduction of the thickness of the silicon layer would result in the initiation of defects and cracks, which can extend through the whole layer thickness similar to a pinhole, reaching the underlying BOX layer [53, 29]. These are called HF defects, since the existence of these defects is tested and observed by using the HF etching [38]. The HF solution that reaches the underlying BOX layer etches isotropically the SiO2 under‐ neath, resulting in a suspended membrane-like layer with a tiny defect in the middle. An optical microscope image of a HF defect at a 14 nm thick silicon device layer is shown in Fig. 15.a. The figure clearly shows a buckled membrane with a HF defect in the middle. The buckling is due to the residual stress between silicon and silicon oxide.

**Figure 15.** (a) Optical microscope image of a HF defect. (b) SEM of a HF defects. The BOX layer below the HF defects is etched by the HF solution, resulting in the formation of a membrane with a defect in the middle.

The existence of HF defects was also confirmed by SEM, which is observed as a circle (mem‐ brane) with the defect at the center (Fig. 15.b). Actually, the HF modifies the defect size and shape, and makes it visible enough to locate it. Using AFM the defect can be observed without HF modifications. The AFM image is shown in Fig. 16.a.

**Figure 16.** (a) AFM image of a defect without HF modification (shape is caused by a AFM probe). (b) Extending the HF etching of the defects for a longer time causes suspension of relatively large membranes, which finally collapse to the layer below and create a Swiss cheese-like structures.

Continuing the HF etching of the defects results in formation of membranes with a much larger diameter, which are no longer stable and collapse to the layer underneath. This has been shown in Fig. 16.b. What is ultimately remaining, is a structure with big holes, similar to a "Swiss cheese" shape.

thickness similar to a pinhole, reaching the underlying BOX layer [53, 29]. These are called HF defects, since the existence of these defects is tested and observed by using the HF etching [38]. The HF solution that reaches the underlying BOX layer etches isotropically the SiO2 under‐ neath, resulting in a suspended membrane-like layer with a tiny defect in the middle. An optical microscope image of a HF defect at a 14 nm thick silicon device layer is shown in Fig. 15.a. The figure clearly shows a buckled membrane with a HF defect in the middle. The

**Figure 15.** (a) Optical microscope image of a HF defect. (b) SEM of a HF defects. The BOX layer below the HF defects is

The existence of HF defects was also confirmed by SEM, which is observed as a circle (mem‐ brane) with the defect at the center (Fig. 15.b). Actually, the HF modifies the defect size and shape, and makes it visible enough to locate it. Using AFM the defect can be observed without

**Figure 16.** (a) AFM image of a defect without HF modification (shape is caused by a AFM probe). (b) Extending the HF etching of the defects for a longer time causes suspension of relatively large membranes, which finally collapse to the

etched by the HF solution, resulting in the formation of a membrane with a defect in the middle.

HF modifications. The AFM image is shown in Fig. 16.a.

layer below and create a Swiss cheese-like structures.

buckling is due to the residual stress between silicon and silicon oxide.

174 Advances in Micro/Nano Electromechanical Systems and Fabrication Technologies

The appearance of defects on the surface of the cantilevers can be observed with white light interferometry. As an example, Fig. 17.a shows a white light interferometry image of 40 nm thick silicon cantilevers with defects. The depth profile of one of the defects on the cantilever is shown in Fig. 17.b.

**Figure 17.** (a) Reconstructed 3D measurements of 40 nm thick silicon cantilevers with defects, using a white light in‐ terferometer. (b) The profile measurement of a defect taken along the line 1 in (a), showing the depth and geometry of the defect.

It can be speculated that HF defects contribute to a decrease in *Ê* and thus, play a role in the gap between experiments and theory of scale-dependence. We utilized MD to examine the extent that the defects contribute to the scale-dependent behavior. Similarly to Section 4, silicon nanoplates were created along the [100] and [110] directions. Defects with cylindrical shapes of different sizes were created by removing the atoms inside the cylinder. Fig. 18 shows the relaxed [100] and [110] nanoplates with defects. After reaching equilibrium configurations, the nanoplates underwent a quasi-static tensile loading. The virial stresses [54] were calculated for each level of strain and *Ê* was calculated from the slope of the resulting stress-strain curve. Details of the calculations were published in [38]. The MD calculations were repeated for different nanoplates (small, medium and large size) with different dimensions of the defects. The ratio of effective elastic modulus with and without defects, *Ed/ E0*, versus the ratio of the defects' surface area and total surface area, *Ad/A0* are plotted in Fig. 19. The inset shows the results as a function of the ratio of the number of removed atoms and total number of atoms, *Nd/NT*. It is noteworthy that creating the defect did not change the average total energy per atom significantly, except for the atoms near the surface of the defects, which have higher excess energy. An example is shown in Fig. 18.e. The coloring is according to the energy. Apart from the two layers closest to the defect, the energy of the other atoms did not substantially change.

**Figure 18.** Snapshots of (a) a [100] silicon nanoplate with one defect at the centre, (b) and (c) patterned defects in a [100] silicon nanoplate and (d) [110] nanoplate with one defect at the centre. (e) a mid-plane of a [100] nanoplate with a defect in the centre, indicating the atoms with different energy due to the presence of a defect. Coloring is according to energy.

The reason that the first two layers have a different energy is because removing atoms creates free surfaces inside the defects, causing atoms inside the defect to reconstruct resulting in changes in energy. In order to determine the contribution of each atom to the reduction of *Ê*, the changes in the virial stress of each individual atom as a function of strain, which is the stiffness contribution of each atom, was calculated. The results are shown in Fig. 20, indicating substantial changes when the size of the defects is increased.

**Figure 19.** Reduction of the effective Young's modulus versus the size of a defect for different dimensions and direc‐ tions of nanoplates. The inset shows the re-plotting with respect to the number of removed atoms.

Besides MD, an analytical solution based on continuum theory was used to approximate the effects of defects on *Ê*. It is based on the approximation described in [55] and is as follows:

**Figure 18.** Snapshots of (a) a [100] silicon nanoplate with one defect at the centre, (b) and (c) patterned defects in a [100] silicon nanoplate and (d) [110] nanoplate with one defect at the centre. (e) a mid-plane of a [100] nanoplate with a defect in the centre, indicating the atoms with different energy due to the presence of a defect. Coloring is

The reason that the first two layers have a different energy is because removing atoms creates free surfaces inside the defects, causing atoms inside the defect to reconstruct resulting in changes in energy. In order to determine the contribution of each atom to the reduction of *Ê*, the changes in the virial stress of each individual atom as a function of strain, which is the stiffness contribution of each atom, was calculated. The results are shown in Fig. 20, indicating

substantial changes when the size of the defects is increased.

176 Advances in Micro/Nano Electromechanical Systems and Fabrication Technologies

according to energy.

$$E\_d = \frac{E\_0 \left(1 - \frac{A\_d}{A\_0}\right)}{\left(1 + 2\frac{A\_d}{A\_0}\right)}\tag{6}$$

The result is plotted in Fig. 19 as a dashed line. A remarkably good agreement between MD and analytical solutions can be seen. The results show that *Ê* decreases monotonically with increasing defect density (defect volume fraction). The decrease is significant and therefore, it has to be considered as an additional factor contributing to the scale-dependence.

**Figure 20.** Snapshots of middle layers of silicon (a) [110] nanoplate without defect, (b) [110] nanoplate with a defect in the centre, (c) and (d) [100] nanoplates with patterned defects. Each individual atom is coloured by its stiffness con‐ tribution. Blue colours denote atoms of higher stiffness.

The experimental observations, which were described earlier in this section, indicate that the defect density (*Ad/A0*) is between 0.005% to 0.12%, depending on the thickness of the silicon device layer [38]. Taking into account the range of experimentally observed defect density and the results presented in Fig. 19, one can include the effect of defects in the scale-dependent *Ê*. The results including the effects of surface elasticity, native oxide layers and fabricationinduced defects were calculated for the thicknesses that were experimentally measured. The results are presented in Fig. 21. Due to variations in the defects' density, an upper and lower limit of the influence of defects is introduced in the figure. The shaded area in the figure demonstrates the extent that defects influence *Ê*. Further quantification of the defects' density versus the thickness of nanocantilevers has to be done. It can be seen, that taking the defects into account, could explain the observed gap between experimental measurements and theoretical calculations.

**Figure 21.** Scale-dependent *Ê* in bending mode, taking into account the surface elasticity, native oxide layers and the fabrication-induced defects. In order to take into account the variations in the defect density, an upper and a lower limit are introduced. Thus, the scale-dependence observed in silicon nanostructures can be explained by a contribu‐ tion of surface effects, native oxide layers and fabrication-induced defects.


**2. Lab‐on‐a‐chip based on CMOS technology**

#### **2.1. Polymerase chain reaction (PCR)**

In the field of cell biology, the use of micro‐ and nano‐fabricated lab‐on‐a‐chip using active substrates and integrated sensors [1] is still at experimental stage. In order to replace traditional optical inspection techniques, these techniques have been tested for the identification of objects

Many research groups believe that lab‐on‐a‐chip technology will be the major contributor to novel diagnostic tools. The target of these research groups is to have lab‐on‐a‐chip systems that will allow healthcare suppliers and workers in poorly equipped clinics to do different tests which need more sophisticated equipment in a simpler way, for example, nucleic acid assays and immunoassays tests [3]. Thus, lab‐on‐a‐chip innovation is a vital part of the endeavors to upgrade and enhance worldwide healthcare system, especially through the advancement of

One dynamic area of lab‐on‐a‐chip research includes approaches to analyze and oversee HIV infections. In view of UNAIDS 2014 report [5], more than 40 million individuals are tainted with HIV around the world; 13 million of these individuals get hostile to retroviral treatment.

Measuring the quantity of CD4+ T lymphocytes in a man's blood is an exact and right approach to figure out whether a man has HIV and to track the advancement of a HIV contamination. Right now, flow cytometry is the brilliant standard for acquiring CD4 checks; yet, flow cytometry is a confounded method that is not accessible in most developing regions since it

The vicinity of the complementary metal oxide semiconductor (CMOS) technology allows the integration of sensors, amplifiers, filters and other circuitry on a single chip. Also, CMOS technology leads to a totally electronic integrated lab‐on‐a‐chip utilizing a homogenous technology [8, 9]. CMOS is an innovation used in building integrated circuits. It is used in microcontrollers, static RAM, registers, microchips and other digital circuits. CMOS technol‐ ogy is utilized also for a wide assortment of analog circuits, for example, image sensors, amplifiers, analog to digital converters, and transceivers for communication modules. Low static power consumption and high noise immunity are two advantages of CMOS devices [10].

Numerous specialists and research teams have begun to utilize the CMOS technology in lab‐ on‐a‐chip applications [11, 12]. These applications incorporate, yet not restricted to, identifi‐ cation of neurons' activities, microorganism discovery and portrayal, electric field imaging,

This chapter covers and presents the state of the art in CMOS circuits and systems for lab‐on‐ a‐chip applications. It summarizes and reviews different circuits and systems of CMOS‐based lab‐on‐a‐chip technology at the cell level. These circuits and systems include: polymerase chain reaction (PCR), microorganism detection and characterization, neuronal activity detection,

flow cytometry and polymerase chain reaction (PCR) applications [13–17].

flow cytometry and electric field imaging applications.

Around 55% of individuals with HIV have never been tested for the infection [5].

requires prepared professionals and costly instruments [6, 7].

at microscopic level to envision several applications [2]**.**

18 Lab-on-a-Chip Fabrication and Application

clinics and laboratories' testing techniques [4].

CMOS‐based lab‐on‐a‐chip has numerous applications in the science and biomedical fields. It is likewise utilized as a part of numerous different fields, for example, environmental appli‐ cations. In medicine, lab‐on‐a‐chip can be utilized as a part of real‐time polymerase chain reaction (PCR) applications to identify bacteria, viruses and cancers [18, 19]. PCR is a bio‐ chemical technology in molecular biology to duplicate one or few DNA pieces, creating thousands to a huge number of duplicates of a specific DNA sequence [20].

The incredible preference about PCR is in the snappy and specific enhancements of target qualities through a cyclic and enzyme‐catalyzed [21]. PCR procedures have been connected to mutation analysis and DNA [18, 19, 21]. As of late, Norian et al. [22] displayed an ongoing PCR lab‐on‐a‐chip that was implemented using 0.35 μm CMOS technology. It performs electro‐ wetting droplet‐based transport, reagent heating, temperature sensing and integrated fluorescence measurements.

**Figure 1** demonstrates the 4 × 4mm chip. It has a 7 × 8 array of 200 × 200 μm electrodes [22]. Using electrowetting‐on‐dielectric transport technique, each droplet can be dislodged [23]. The three temperature zones on the top of the chip are managed by a heater and temperature sensors. Based on this PCR lab‐on‐a‐chip, a droplet can be allocated at a thermal cycled area on the chip. Also, a thermal equilibrium can be obtained in a 500 ms at most, if a droplet's volume is under 1nL. Likewise, fluorescent measurement using integrated Geiger‐mode single‐photon avalanche photodiodes (SPADs) allows for sensitive fluorescent detection. **Figures 2** and **3** show the on‐chip thermal regulation and PCR thermal cycling on chip, respectively.

**Figure 1.** PCR Chip [22].

**Figure 2.** On‐chip thermal regulation. (a) Layout of metal‐resistor temperature sensors; (b) layout of polysilicon heat‐ ers; (c) cross‐sectional COMSOL simulation; (d) IR image displays distinct temperature region generated by a single polysilicon heater [22].

**Figure 3.** PCR thermal cycling on chip. (a) Fluorescence measurements for *Staphylococcus aureus* target; (b) on‐chip tem‐ perature profile [22].

#### **2.2. Dielectrophoresis (DEP)**

**Figure 2.** On‐chip thermal regulation. (a) Layout of metal‐resistor temperature sensors; (b) layout of polysilicon heat‐ ers; (c) cross‐sectional COMSOL simulation; (d) IR image displays distinct temperature region generated by a single

**Figure 3.** PCR thermal cycling on chip. (a) Fluorescence measurements for *Staphylococcus aureus* target; (b) on‐chip tem‐

polysilicon heater [22].

20 Lab-on-a-Chip Fabrication and Application

perature profile [22].

Lab‐on‐a‐chip based on CMOS technology is utilized as a part of biochemical tests, where it is utilized to quantify the presence of a target element which can be a biochemical substance or a medication or a cell in a sample [24]. Diverse range of uses is the dielectrophoresis applica‐ tions where CMOS‐based lab‐on a‐chip is utilized to recognize microbes, bacteria and cancer cells [14].

**Figure 4.** The lab‐on‐a‐chip architecture for cells manipulation and sensing [25].

Manaresi et al. [25, 26] proposed a CMOS lab‐on‐a‐chip for cell control and identification. The proposed lab‐on‐a‐chip is fabricated on a standard 0.35 μm CMOS technology. This CMOS lab‐on‐a‐chip comprises three principle parts, they are: actuation, sensing and microfluidic package. The lab‐on‐a‐chip framework, see **Figure 4**, has two‐dimensional array of microsites; these microsites provide the following functions: (1) produce the electric field which is important to make dielectrophoretic cages and (2) optically sense the existence of a single particle or cluster on the top of the microsite. Each microsite comprises of an actuation electrode, executed with a top metal plate, and underneath embedded circuitry for program‐ ming and identification. Row and column decoders can address any microsite in a random access mode, for both actuation and sensing. Particles are detected by photodiodes, embedded in the substrate that measure signal variations from uniform light impinging on the chip surface. Recently, Miled et al. presented a CMOS‐based lab‐on‐chip system for dielectropho‐ resis‐based cell manipulation and detection [27]. The lab‐on‐a‐chip based on CMOS technology is fabricated utilizing a CMOS full‐custom chip and a microfluidic chamber. The CMOS chip is used to pass on all parameters required to control the dielectrophoresis (DEP) parameters, for instance, electric field intensity, speed of the field and the profile of the electric field applied on the lab‐on‐a‐chip.

#### **2.3. Flow cytometry**

Flow cytometry applications are implemented using CMOS‐based lab‐on‐a‐chip. In flow cytometry, fluorescence markers are used to tag either biological cells or single particle that is immersed in an aqueous media. These cells are hydrodynamically focused and made to traverse a small region of space across which a focused laser beam is used [28].

The input laser light will be scattered and/or fluorescence produced light will be released when a cell passes on a light source. These optical signs provided by the particles will be aggregated as spectral components of predominantly visible light. Utilizing optical multipliers and other light instrumentation, the obtained signals can be utilized to recognize and evaluate the biochemical or biophysical attributes of the cell sample.

Hartley et al. introduced a CMOS‐based platform for microfluidic cytometry, where optical image detecting is incorporated on the microfluidic substrate. The stage is using flip‐chip bundling innovation. **Figure 5** shows a schematic diagram of the general flip‐chip digital cytometry [29].

**Figure 5.** Photograph of the linear active pixel CMOS sensor chip with interface pads, digital logic block and analog signal path indicated [29].

As of late, Dupont et al. displayed a lab‐on‐a‐chip that was manufactured based on silicon involving a framework of 84 diodes, i.e., single photon avalanche diodes (SPADs), to distinguish and separate fluorescently labeled single cells and fluorescent beads in a polydimethylsiloxane (PDMS) cartridge that is situated on the CMOS chip [30]. The identification depends on the diverse photon tally when either a fluorescent or a non‐ fluorescent bead or cell is available over a SPAD, because of the extra photons radiated from a fluorescent item. This strategy permits magnifying instrument‐less fluorescence recognition and allows simple trade of the expendable microfluidic cartridge [30, 31].

#### **2.4. Neuronal activity monitoring**

is used to pass on all parameters required to control the dielectrophoresis (DEP) parameters, for instance, electric field intensity, speed of the field and the profile of the electric field applied

Flow cytometry applications are implemented using CMOS‐based lab‐on‐a‐chip. In flow cytometry, fluorescence markers are used to tag either biological cells or single particle that is immersed in an aqueous media. These cells are hydrodynamically focused and made to

The input laser light will be scattered and/or fluorescence produced light will be released when a cell passes on a light source. These optical signs provided by the particles will be aggregated as spectral components of predominantly visible light. Utilizing optical multipliers and other light instrumentation, the obtained signals can be utilized to recognize and evaluate the

Hartley et al. introduced a CMOS‐based platform for microfluidic cytometry, where optical image detecting is incorporated on the microfluidic substrate. The stage is using flip‐chip bundling innovation. **Figure 5** shows a schematic diagram of the general flip‐chip digital

**Figure 5.** Photograph of the linear active pixel CMOS sensor chip with interface pads, digital logic block and analog

As of late, Dupont et al. displayed a lab‐on‐a‐chip that was manufactured based on silicon involving a framework of 84 diodes, i.e., single photon avalanche diodes (SPADs), to distinguish and separate fluorescently labeled single cells and fluorescent beads in a polydimethylsiloxane (PDMS) cartridge that is situated on the CMOS chip [30]. The identification depends on the diverse photon tally when either a fluorescent or a non‐ fluorescent bead or cell is available over a SPAD, because of the extra photons radiated from

traverse a small region of space across which a focused laser beam is used [28].

biochemical or biophysical attributes of the cell sample.

on the lab‐on‐a‐chip.

22 Lab-on-a-Chip Fabrication and Application

**2.3. Flow cytometry**

cytometry [29].

signal path indicated [29].

Neurons are biological cells specialized in transmission and processing of information [32]. The basic neural signals are action potentials, which are transient changes of the voltage drop across the cell membrane with a typical and defined shape.

The action potential is typically 70 mV peak‐to‐peak. Today's standard devices utilized to measure the action potential are patch pipettes [32] or microelectrodes [33]. These apparatuses have prompted awesome change in the field of neural cell observing. Though, these strategies face downsides, for example, they require an elaborate mechanical setup, which permits observing not many cells in parallel as it were. Along these lines, they are for the most part not suitable to satisfy high‐throughput prerequisites. Additionally, long‐time recording is not feasible, because of the intrusive sort of contact, decreasing the life time of the cell.

**Figure 6.** Architecture of sensor array with complete system [34].

Noninvasive (extracellular) recording techniques open a way to overcome these drawbacks [34–36].

In extracellular recording strategy, cells are refined straightforwardly on top of a transducing component, which is for the most part either a metallic cathode or a floating gate transistor [34, 35]. At the point when an action potential happens in a cell, the nearby stream of particles all through the cell causes the membrane to be polarized in a non‐uniform way. The ionic current streams over the cleft resistance and sets up an electric field which prompts electrical charge in the fundamental transducer, which is the recorded signal.

Eversmann et al. proposed a CMOS lab‐on‐a‐chip for non‐invasive recording and detection of neuronal activity [34, 35]. **Figure 6** shows the system setup and chip architecture. Heer et al. [37] presented a CMOS lab‐on‐a‐chip for bidirectional communication (stimulation and recording). **Figure 7** shows a picture of the CMOS lab‐on‐a‐chip. **Figure 8** shows a schematic of the overall system.

**Figure 7.** Micrograph of the chip [37].

**Figure 8.** Block schematic of the overall system architecture [37].

Recently, Rothberg et al. [38] presented a CMOS sensor chip which can be used to harbor neurons and glial cells in vitro. The CMOS chip incorporates a vast cluster of sensor compo‐ nents, each with a single floating gate connected to an underlying ion‐sensitive field‐effect transistor (ISFET), to gauge the pH [39].

#### **2.5. Electric field imager**

through the cell causes the membrane to be polarized in a non‐uniform way. The ionic current streams over the cleft resistance and sets up an electric field which prompts electrical charge

Eversmann et al. proposed a CMOS lab‐on‐a‐chip for non‐invasive recording and detection of neuronal activity [34, 35]. **Figure 6** shows the system setup and chip architecture. Heer et al. [37] presented a CMOS lab‐on‐a‐chip for bidirectional communication (stimulation and recording). **Figure 7** shows a picture of the CMOS lab‐on‐a‐chip. **Figure 8** shows a schematic

in the fundamental transducer, which is the recorded signal.

of the overall system.

24 Lab-on-a-Chip Fabrication and Application

**Figure 7.** Micrograph of the chip [37].

Ghallab et al. [12, 40] presented a CMOS‐based lab‐on‐a‐chip. The used technology is CMOS 180 nm TSMC technology. This lab‐on‐a‐chip is utilized to get electric field images for biocells. These images depend on the electrical characteristics of the cells which can be utilized to distinguish between various sorts of cells [12].  The CMOS‐based lab‐on‐a‐chip includes two main parts. They are: (1) the actuation part, that generates the planned non‐uniform electric field pattern; the actuation part has four electrodes that generate a DEP force to levitate the cell that should be identified; (2) the sensing part, which is an array of the differential electric field sensitive field effect transistor (DeFET) sensor [12, 40, 41].

#### *2.5.1. The actuation part*

The actuation electrodes are the quadrupole setup, see **Figure 9**. Utilizing this arrangement, the profile of the non‐uniform electric field can be controlled by associating the entire four electrodes or few of them. Likewise, the quadrupole levitator contains an azimuthally symmetric electrode arrangement fit for supporting passive stable molecule levitation [42].

**Figure 9.** The Die picture shows the quadrupole electrodes and the DeFET sensors [42].

#### *2.5.2. The sensing part*

The sensing part is made out of an array of DeFETs. This array is situated around the center of the chip, where the cell is levitated (suspended). In DEP levitation, the controlling electric field is a non‐uniform electric field. Consequently, we can recognize the electric field by utilizing the electric field sensitive field effect transistor [eFET] as an electric field sensor.

**Figure 10.** Physical structure of an eFET [12, 42].

**Figure 10** demonstrates the physical structure of the eFET. It comprises of two neighboring drains, two nearby floating gates with separation space "d" between the two floating gates, and one source.

For the eFET, it is proportionate to two indistinguishable enhancement MOSFET transistors. Affected by the non‐uniform electric field, different current passes through each drain. Because of the drain current reliance on the gate voltage, the eFET can sense the difference between the two gate voltages, which mirrors the magnitude of the used non‐uniform electric field. To increase the measurement range of the eFET, CMOS technology is used to implement the differential electric field sensitive field effect transistor (DeFET) sensor, and this sensor will be the basic sensor in the sensing part of the proposed electric field imager [40–42].

#### *2.5.3. DeFET sensor*

**Figure 9.** The Die picture shows the quadrupole electrodes and the DeFET sensors [42].

The sensing part is made out of an array of DeFETs. This array is situated around the center of the chip, where the cell is levitated (suspended). In DEP levitation, the controlling electric field is a non‐uniform electric field. Consequently, we can recognize the electric field by utilizing the electric field sensitive field effect transistor [eFET] as an electric field sensor.

**Figure 10** demonstrates the physical structure of the eFET. It comprises of two neighboring drains, two nearby floating gates with separation space "d" between the two floating gates,

For the eFET, it is proportionate to two indistinguishable enhancement MOSFET transistors. Affected by the non‐uniform electric field, different current passes through each drain. Because of the drain current reliance on the gate voltage, the eFET can sense the difference between the two gate voltages, which mirrors the magnitude of the used non‐uniform electric field. To

*2.5.2. The sensing part*

26 Lab-on-a-Chip Fabrication and Application

**Figure 10.** Physical structure of an eFET [12, 42].

and one source.

The DeFET consists of two complementary eFETs, one is a P eFET and the second one is an N eFET [12, 42]. The equivalent circuit of the DeFET is shown in **Figure 11**.

**Figure 11.** An equivalent circuit of a DeFET [12], the two gates of P eFET and N eFET are connected with each other, and there is a cross coupling between the two drains of the P eFET and the N eFET. The output current *I*Out is equal to the difference between the two drain currents *I*p − *I*n (i.e., *I*Out = *I*p − *I*n,). *I*p and *I*n are functions of the two applied gate voltages *V*in1 and *V*in2, respectively.

The DeFET is intended to accomplish a voltage *V*Out, specifically identified with the difference between the two gate voltages (*V*in1 − *V*in2), and *V*in1 − *V*in2 is equivalent to the generated electric field over the two gates (*E*) (*E*) multiplied by the distance between them (*V*in1 − *V*in2/*Y* = *E*), where *Y* is the distance between the two split gates, which is constant. Thus, *V*Out is related straightforwardly to the non‐uniform electric field's intensity. In this way by measuring *V*Out, the non‐uniform electric field intensity can be obtained.

To extract an expression that relates *V*Out and *E*, and from **Figure 11**, the output current (*I*out) is:

$$I\_{\rm out} = I\_{\rm p} - I\_{\rm n} \tag{1}$$

The sensitivity is given by

$$\mathbf{S} = \frac{\mathbf{d}I\_{\text{out}}}{\mathbf{d}E} = \mathbf{g}\_{m} Y$$

where *g*m is the transconductances of the transistors M2 and M4, which have equal *g*m and *Y* is the distance between the two gates.

From (2)

$$I\_{\rm out} = SE + \text{Constant} \tag{3}$$

The output voltage (*V*out) can be expressed in terms of the sensitivity and the electric field:

$$V\_{\rm out} = I\_{\rm out} R\_{\rm L} = SE \, R\_{\rm L} + V\_{\rm Unit} \tag{4}$$

where *V*uni is the output voltage due to a uniform electric field (i.e., *V*in1 = *V*in2), and *R*L is the load resistance. From (2) into (4)

$$W\_o = \mathbf{g}\_{\,\,n} \, Y \, R\_{\,\,n} \, E + V\_{\,\,\text{Uni}} \tag{5}$$

From equation (5), it can be observed that *V*o is related linearly to the intensity of the non‐ uniform electric field generated on the top of the DeFET sensor. Thus, in an array form of DeFET sensors, an image related to the electric field intensity at different locations can be extracted.

#### *2.5.4. Experimental results*

The CMOS electric field imager is experimentally examined with micro‐beads having a relative permittivity of 2.5 and radius of 4.5 and 10.45 μm, respectively. The experiment was done in two steps. First, the chip is tested on air. Second, it is tested with micro‐beads suspended on medium. This CMOS electric field imager allows sensing the impact of the cells on the electric field intensity profile.

**Figures 12** and **13** show sample of the results. The configuration used is as follows: (1) a 3V sinusoidal signal with 3 MHz frequency is connected to Electrode 2 and Electrode 4, see **Figure 9**; (2) an out of phase (−3V) sinusoidal signal with 3 MHz is connected to Electrode 1 and Electrode 3. The cells were suspended in de‐ionized water with a measured conductivity between 1.3 and 1.9 μs/cm. A microscope with digital camera is used to observe the particles from the top.

The sensitivity is given by

28 Lab-on-a-Chip Fabrication and Application

From (2)

extracted.

*2.5.4. Experimental results*

field intensity profile.

from the top.

is the distance between the two gates.

load resistance. From (2) into (4)

out d d *m*

where *g*m is the transconductances of the transistors M2 and M4, which have equal *g*m and *Y*

The output voltage (*V*out) can be expressed in terms of the sensitivity and the electric field:

where *V*uni is the output voltage due to a uniform electric field (i.e., *V*in1 = *V*in2), and *R*L is the

From equation (5), it can be observed that *V*o is related linearly to the intensity of the non‐ uniform electric field generated on the top of the DeFET sensor. Thus, in an array form of DeFET sensors, an image related to the electric field intensity at different locations can be

The CMOS electric field imager is experimentally examined with micro‐beads having a relative permittivity of 2.5 and radius of 4.5 and 10.45 μm, respectively. The experiment was done in two steps. First, the chip is tested on air. Second, it is tested with micro‐beads suspended on medium. This CMOS electric field imager allows sensing the impact of the cells on the electric

**Figures 12** and **13** show sample of the results. The configuration used is as follows: (1) a 3V sinusoidal signal with 3 MHz frequency is connected to Electrode 2 and Electrode 4, see **Figure 9**; (2) an out of phase (−3V) sinusoidal signal with 3 MHz is connected to Electrode 1 and Electrode 3. The cells were suspended in de‐ionized water with a measured conductivity between 1.3 and 1.9 μs/cm. A microscope with digital camera is used to observe the particles

*I S gY E* = = (2)

*I SE* out = + Constant (3)

*V I R SE R V* out out L = =+ L Uni (4)

m L *V g YR E V* o = + Uni (5)

**Figure 12.** Levitated polystyrene cell with diameter 8.9 μm [42].

**Figure 13.** Levitated polystyrene cell with diameter 20.9 μm [42].

**Figures 12** and **13** demonstrate a trapped cell not long after the electric field is turned on, simply over the center of the imager. The levitated/trapped cell shows up out of focus of the micro‐ scope's focal length [42]. **Figure 14** shows the measured output voltage from the DeFET sensor numbers 1, 4, 7 and 10, in air and after adding the suspending fluid, which contains 4.5 and 10.5 μm radius polystyrene microspheres. From this figure, it can be noticed that DeFETs 4 and 7 show lower readings compared with DeFETs 1 and 10. Also, note from **Figure 14** that for the cells with 20.9 μm diameters, DeFETs 1, 4 and 10 provide high readings, while DeFET 7 does not. Leading to the surmise that there are cells above 1, 4, and 10, but no cells above DeFET 7 [42].

**Figure 14.** The DeFET sensors' response in air and in fluid contains different cell size [42].


**4. Discussion**

The biodegradation of contaminants and the production of renewable energies are among the most important challenges of modern society. Bioethanol is a sustainable fuel that can be obtained by the yeast fermentation of sugars [42]. When using sugars from lignocellulosic by-products instead of sugars from food crops, the product is called second-generation bioethanol. However, the hydrolysis of these polymers generates, in addition to fermentable sugars, compounds that act as inhibitors of the yeast catalyzing further fermentation. The formation of furfural from pentoses was described many years ago [43]. Furfurals, and specially HMF and its derivatives, are feedstock for the synthesis of numerous valuable products. Therefore, their production in a sustainable manner is receiving an increasing interest [44]. On the other hand, from the bioethanol-production point of view, it is a challenge eliminating these inhibitors leaving sugars intact, with the purpose of increasing efficiency in second-generation ethanol production. Furfural and 5-hydroxymethyl furfural (HMF) are not the only inhibitory compounds present in the hydrolysates, but many others are not so well characterized, such as methanol or acetate [39]. The selection of yeast resistant to aldehydes may partially circumvent the problem. In fact, yeasts resistant up to 40 mM furfural and 80 mM HMF have been described recently [45,46]. Nevertheless, and taking into account the diversity of hydrolysates, the direct elimination of inhibitory compounds has to be taken into account as an alternative (bio)technology.

In this manuscript, we describe the selection of an adaptive mutant of *Pseudomonas pseudoalcaligenes* CECT 5344 able to assimilate furfural. The mutation was found to be located in a regulatory gene of the AraC/XylS family of activators [47]. In *E. coli, araC* is necessary for the assimilation of L-arabinose, a five carbon sugar [37]. Although speculative, perhaps it is not casual that the homologous gene in *P. pseudoalcaligenes* CECT 5344 could recognize as an activator the five-carbon dehydrated and oxidized pentose FA. This hypothesis has to be tested experimentally in the future. AraC homologous are proteins approximately 300 aa long, whose C-terminal segment is the HTH domain interacting with DNA. The mutation in the adapted strain was detected just in this domain. The substitution of a

hydrophobic amino acid by a basic one (L262R) introduces a positive charge in the protein that seems to be essential for interacting with the negatively-charged DNA molecule. The less-conserved N-terminal domain of the AraC family is presumed to contain binding sites for specific activator molecules that confer specificity to each member [47].

Adaptive mutations frequently target regulatory genes [34], although there are interesting examples in which the genetic adaptation lies in catabolic genes. For example, in *P. putida* KT2440, a single point mutation was detected causing the suppression of a frameshift mutation in the transporter (*galT*), thus allowing the evolved strain to grow in gallic acid [48]. The *hmf* locus in *P. pseudoalcaligenes* contains two di fferent modules, a catabolic one homologous to *C. baisilensis* HMF14 (plus *benE*) and another one homologous to *P. putida* Fu1 containing the regulatory gene *araC* (Figure 1B). As far as we know, no gene homologous to *benE* has been described in the context of FA assimilation. *benE* is a member of the MSF-family of transporters, homologous to the benzoate transporters. Since both compounds, benzoate and furoate, are aromatic monocarboxylic acids, it can be speculated that BenE is a furoate transporter, although this hypothesis needs further experimental evidence. The *hmf* locus (Figure 1) is flanked by mobile genetic elements, thus suggesting that it has been horizontally transferred. Furthermore, this hypothesis is in agreement with the sequence composition of the locus. The average GC content of the genes flanking the *hmf* locus in *P. pseudoalcaligenes* (green genes in the Figure 4B) is around 62%, which is also the %GC content of the genome of *P. mendocina* ymp (Figure 4A, 62.8%) and *P. pseudoalcaligenes* CECT 5344 itself (62.34%). By contrast, the average composition (%GC) of the *hmf* operon (BN5\_2298 to BN5\_2305) is 66.5%, close to the composition of *Cupriavidus basislensis* (65.3%) and other betaproteobacteria. The other two genes present in the island are BN5\_2306 (*psfD*) and BN5\_2307 (*araC*), whose %GC are 67.69% and 57.16%, respectively. It is also remarkable that all genes present in the *hmf* locus (Figure 1B) are singletons in relation to most Pseudomonaceae, except *psfD* and *benE*. In the evolution of prokaryotic metabolic networks and their regulation, the number of transcriptional regulators grows faster than the metabolic genes [49]. The horizontally transferred *hmf* pathway homologous to that described in *C. basilensis* HMF14 does not include its dedicated transcriptional regulator, but it seems that it has been acquired in a separate module from a second donor strain. Curiously, the regulator was originally in an inactive form, but evolved to the active form under selective pressure (Figure 4).

Even though *P. pseudoalcalignes* CEC T 5344 R1D assimilates furfural very e fficiently, it could not be useful for the pre-treatment of lignocellulosic residues because it simultaneously assimilates glucose. By contrast, the *edd*− mutant assimilated furfural leaving the glucose intact (Figure 6). This is an important feature in comparison to the equivalent mutant in *P. putida* KT2440, that accumulates 6-phosphoogluconate from glucose [40]. On the other hand, *P. pseudoalcaligens* could not assimilate HMF (Figure 3), the other main inhibitory component of hydrolysates. In fact, the genome analyses anticipated this result due to the absence of homologous genes to *hmfH* and *hmfFG* genes. These genes in *C. basilensis* code for the enzymes catalyzing the oxidation of HMFA to FDCA and the decarboxylation of the latter to generate furoic acid, respectively. FA is the metabolite in which converges the assimilation of HMF and furfural in *C. basilensis* HMF14 [24]. To circumvent the problem, it would be convenient to have bacteria capable of eliminating HMF. We have isolated, by selective enrichment from ashes, some bacterial strains belonging to the genus *Pseudomonas*, which are able to assimilate HMF in addition to furfural, furoic acid or furfuryl alcohol [50]. These abilities could be used in mixed cultures, provided that they do not assimilate sugars, or by constructing the required traits using the available genetic modules. Even though *P. pseudomonas* CECT 5344 R1D fully transformed HMF into two new compounds, 5-Hydroxymethylfuranoic acid (HMFA) and 2,5-furandicarboxylic acid (FDCA), both of which are versatile chemical intermediates of high industrial potential [51,52]. Therefore, the *edd*− mutant of the evolved R1D strain of *P. pseudoalcaligenes* described in this manuscript may increase the productivity of second generation bioethanol both by eliminating yeasts' inhibitory chemicals and by producing value-added chemicals from biomass.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4425/10/7/499/s1, Figure S1: Schematic construction of PGEM-TE *edd aacC1* plasmid. Figure S2. Schematic construction of PGEM-TE *araC aacC1* plasmid.

**Author Contributions:** Conceptualization, R.B. and M.I.I.; methodology, R.B. and M.I.I.; investigation, D.M. and M.I.I., writing, R.B.; visualization, R.B., D.M. and M.I.I.; supervision, R.B. and M.I.I.; project administration, R.B.; funding acquisition, R.B.

**Funding:** This research was funded by IB16062, Junta de Extremadura (Consejería de Economía e Infraestructuras), GR18031, Fondo Europeo de Desarrollo Regional (FEDER), European Union. The work of Daniel Macias was supported by a fellowship from Universidad de Extremadura (Accion III-41, UEX 2010-2014).

**Acknowledgments:** The technical support of Gloria Gutiérrez and Gracia Becerra are gratefully acknowledged.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


**2.3. Material characterization by SEM, FTIR, TGA**

PAQ composite samples prepared in the last section were coated with gold and then were imaged by the scanning electron microscopy (SEM, FEI Nova NanoSEM450, USA) operating at 5 kV. Both surface and cross section of samples were examined by SEM. Examination of Fourier transform infrared spectroscopy (FTIR, Bruker Vertex 70, USA) spectra for all compo‐ site samples was performed under conditions that FTIR data were taken from 500 to 4000 cm‐1. OMNIC software (Thermo Electron Corporation) was used to correct and normalize the baseline of FTIR spectra. Thermogravimetric analyses (TGA) of all composite samples were performed by the TGA Q600 (TA Instrument, USA). Thermograms of samples were recorded between 36 and 600°C at a heating rate of 10°C/min and a nitrogen flow of 100 mL/min. TA Universal Analysis 2000 (TA Instrument, USA) was used to calculate the percentage of weight loss, the first derivatives of the thermograms (DTG), and the decomposition temperatures.


**5. Conclusions**

Four weeks of CIT+GSH supplementation improves brachial artery FMD in apparently healthy postmenopausal women with slightly elevated FBG. CIT+GSH reduced brachial PWV and attenuates BP responses to sympathetic activation. These beneficial effects on the endothelial function and BP reactivity may be attributed to an increased ARG/ADMA ratio. Therefore, CIT+GSH supplementation may reduce the risk of cardiovascular events during physiological stress in postmenopausal women. Although CIT supplementation alone caused greater increases in the serum ARG levels and ARG/ADMA ratio, it did not improve FMD or BP responses to CPT in healthy postmenopausal women.

**Author Contributions:** A.F., M.M. and D.N. were responsible for the study design. A.F. was responsible for the conduction of the study. A.M., S.M.F., M.A.M., Y.K. and K.N.D. performed the experiment and data collection. A.F., A.M., S.M.F., M.A.M., Y.K. and K.N.D. participated in the data analysis and interpretation. A.F., M.A.M., Y.K. and K.N.D. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by Kirin Holdings Co., Ltd., grant A20-0164-001.

**Institutional Review Board Statement:** The experimental protocol and informed consent was approved by Texas Tech University Institutional Review Board.

**Informed Consent Statement:** Written informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.

**Acknowledgments:** The authors recognize the cooperation and enthusiasm of our participants.

**Conflicts of Interest:** M.M. and D.N. are employees of Kirin Holdings Co., Ltd. M.M., D.N. and Kirin had no influence on participant handling, data collection, analysis or interpretation, and manuscript writing. The other authors declare no conflict of interest.


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http://dx.doi.org/10.5772/56736