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",isbn:"978-1-83968-681-8",printIsbn:"978-1-83968-680-1",pdfIsbn:"978-1-83968-682-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"ddc0dea7e5b98335c187688d9c0c5b42",bookSignature:"Dr. Urvashi Sharma",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10298.jpg",keywords:"Internet of Things, Smart Biosensor and Hardware, Reliability, Patients Data, Context-Specific and Aware, Integrated and Connected, Funding Structures, Policy and Its Implications, Electronic Medical Records, Electronic Health Records, Design, Implementation and Evaluation",numberOfDownloads:60,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 3rd 2020",dateEndSecondStepPublish:"October 1st 2020",dateEndThirdStepPublish:"November 30th 2020",dateEndFourthStepPublish:"February 18th 2021",dateEndFifthStepPublish:"April 19th 2021",remainingDaysToSecondStep:"5 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Sharma obtained her Ph.D. from Brunel University London, U.K. Her work has contributed to understanding the role of a user and the context in relation to the successful application of e-health modalities in primary care settings in the U.K.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"105398",title:"Dr.",name:"Urvashi",middleName:null,surname:"Sharma",slug:"urvashi-sharma",fullName:"Urvashi Sharma",profilePictureURL:"https://mts.intechopen.com/storage/users/105398/images/system/105398.jpg",biography:"Dr Urvashi Sharma started her research career as a biomedical engineer exploring barriers and facilitators to remote patient monitoring and use of electronic health records. Her work has contributed to understanding the role of a user and the context in relation to successful application of e-health modalities in primary care settings in the U.K. Through her work, she also explored whether employing randomised controlled trials to ascertain the effectiveness of technological interventions is viable. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"56499",title:"Control of Aflatoxin Production Using Herbal Plant Extract",doi:"10.5772/intechopen.69867",slug:"control-of-aflatoxin-production-using-herbal-plant-extract",body:'\nAflatoxins are poisonous, carcinogenic, mutagenic, immunosuppressive, and teratogenic secondary metabolites formed by Aspergillus flavus, A. parasiticus [1], and A. nomius [2]. These fungi are ubiquitous species and generally contaminate agricultural products such as rice, wheat, maize, barley, sorghum, black pepper, chili, ginger, coriander, turmeric, pistachios, almonds, walnuts, Brazil nuts, peanuts, oilseeds (cotton, sunflower, sesame, and soybean), milk, cheese, and animal feed [3–9]. The Food and Agriculture Organization (FAO) estimated that around 25% of the world’s cereals are contaminated by mycotoxins, including aflatoxins [10]. Aflatoxins were first identified as causative agent of “Turkey X disease” in 1961. Due to this disease, about more than 100,000 young turkeys, ducks, and poultry birds died in England by eating contaminated Brazilian groundnut meal [11–15].
\nThe aflatoxin was a combination of three words: first letter “A” from genus Aspergillus, next three letters “FLA” from species flavus, and the noun “TOXIN” [16]. Aflatoxins are quite stable and found resistant to degradation. Among the 18 different groups, aflatoxins B1, B2, G1, G2, M1, and M2 are the major classes and derivative of bifuranocoumarins. The aflatoxins B1 and B2 give blue color, while G1 and G2 give a yellowish green color under UV light. Aflatoxins M are hydroxylated derivatives of aflatoxins B and first isolated from milk. A. flavus produces only AFB1 and AFB2, but it is also able to synthesize cyclopiazonic acid. However, A. parasiticus produces AFBI, AFB2, AFG1, and AFG2 [17, 18].
\nThe International Agency for Research on Cancer (IARC) classified AFB1 as class I human carcinogens [19] and have a positive association between dietary aflatoxins and liver cell cancer (LCC). This was the third leading reason of cancer death around the world [20]. The cytochrome p450 metabolized AFB1 in their epoxide form. Depurination occurs, when epoxide reacts with DNA or RNA. That will obstruct DNA and protein synthesis in active tissues of bone marrow, intestine, and liver. The order of toxicity of aflatoxins is AFB1 > AFB2 > AFG1 > AFG2 [21], and the critical point, which determined the biological activity of this group of mycotoxins, is terminal furan moiety of aflatoxin [22]. In cereal and their derivatives, maximum residual limits (MRLs) of aflatoxins are 2 μg kg1 for AFB1 and 4 μg kg⁻1 for the sum of four aflatoxins. In processed cereal‐based foods and baby foods for infants and young children, the level of AFB1 is 0.1 μg kg‐1. These values were recommended by the European Union Commission Regulation (EC) [21]. According to the Food and Drug Administration (FDA), the safe limit of aflatoxins is 20 ppb (Figure 1) [23].
\nMajor classes of aflatoxins.
In developing countries, about 4.5 billion people are chronically exposed to uncontrolled amounts of aflatoxins [24]. Consumption of contaminated products causes aflatoxicosis in humans and animals. Aflatoxicosis may be acute and chronic. Acute condition caused death, while chronic condition results in immune suppression and cancer. In human, it is characterized by vomiting, abdominal pain, pulmonary edema, convulsions, coma, and death with cerebral edema and fatty involvement of the liver, kidneys, and heart [25]. Due to aflatoxicosis, in Kenya about 215 people died in 2004 [26–28]. In animal, aflatoxicosis is characterized by gastrointestinal dysfunction, reduced feed utilization, anemia, jaundice, liver damage, decreased milk and egg production, and immunity suppression [29]. In plants, AFs retarded seed germination, seedling growth, and root elongation. It also inhibits chlorophyll, carotenoid, and some enzymes synthesis [30].
\nAlthough A. parasiticus and A. flavus are related fungi, they are different from each other on the basis of their color and length of conidiophore. Sterigmata were the main characteristics, which differentiate the two Aspergillus species. The sterigmata of A. flavus were biseriate as compared to A. parasiticus, which has uniseriate sterigmata [31]. In 2006, Cary and Ehrlich reported that about 12 A. flavus groups are 96% similar to A. parasiticus. Another character that distinguishes the two fungi species is their adapted environment. The A. flavus acclimated to aerial and foliar environment, mostly prominent in tree nuts, corn, and cottonseed, while the A. parasiticus adapted to soil environment and dominated in peanuts [32]. A. flavus exists in two forms: one is the S type, while the other is the L type on the basis of morphological, physiological, and genetic characteristics [33]. On average, S‐strain isolates produce much more aflatoxins than L‐strain isolates [34]. S strain synthesized frequently small sclerotia that are less than 400 μm and processes lesser conidia as compared to L‐strain isolates whose sclerotia sizes are greater than 400 μm [34, 35].The members of genus Aspergillus mostly contaminate agriculture commodities in tropic and sub‐tropic region. Contamination may occur at different stages such as in pre‐harvesting stage, harvesting stage, post‐harvesting stage, or in storage and transportation stage. In pre‐harvesting, the field fungi attack on growing crop because of different reasons. It may be the environmental stress (hot and dry condition and soil moisture), mechanical damage (by arthropods, birds, rodents, and nematodes), or delayed harvesting. While in post‐harvesting, contamination occurred due to improper drying, storage in polythene bags, damage during shelling, or storage in poorly ventilated warm environment.
\nContamination rate of aflatoxin depends upon humidity, temperature, storage, and soil conditions [36]. Optimum condition for fungal growth in cereal is moisture content about 18% (equal to 85% relative humidity) and temperature about 12–42°C with an optimum at 27–30°C in tropical and sub‐tropical areas [37]. An important point to be considered was the time of incubation that effects the production of toxin by Aspergillus species [38]. Optimum duration for the production of aflatoxins was 14 days of incubation at 30°C. When the length of incubation time increased, there will be reduction in aflatoxin level because of re‐adsorption or degradation by fungus [39]. The fungal growth is effected by 20% CO2 and 10% O2 level [40]. The metals such as manganese and zinc are crucial for aflatoxin production. But the mixture of cadmium and iron mixture reduces the mold growth and aflatoxin synthesis [41].
\nThe infectious cycle of Aspergillus species is mostly dependent upon host species. Overwinter fungus developed either mycelium or sclerotia (resistant structure) that have the ability to grow on soil surface [42, 43]. Under favorable condition (high temperature and moisture level) in summer, it either produced hyphae or conidia (asexual spores). Through air or insects, conidia spread in soil and on silk and kernels and contaminate agriculture commodities (Figure 2) [44].
\nLife cycle of A. flavus in field.
Aflatoxin contamination is inescapable due to health hazards in human and animal, crops deterioration, and economical loses. In the past, many strategies (physical, chemical, and biological) are used to avoid aflatoxin contamination. Physical strategies usually used are rodent‐proof room, cold storage of feeds with less than 100‐g/kg moisture level, use rapid drying and gamma radiation, and so on. In chemical strategies, propionic acid, acetic acid, benzoic acid, citric acid, hydrogen peroxide, copper sulfate, and ammonium hydroxide are used to inhibit the growth of fungi and aflatoxin production. But the formation of toxic residue by chemical treatment was the main concern that causes potent health problems. As compared to chemical, physical practice is a healthier option but it is slow processes. Other strategies used were the biological control in which different microorganisms such as bacteria, yeast, and non‐toxic stain of A. flavus and A. parasiticus were used to detoxify aflatoxins by microbial binding and biotransformation [45–48]. This is a laborious and costly process. Therefore, to avoid potential risk, the use of safe, renewable, and biodegradable natural plant extracts to remove aflatoxin contamination [49] is required.
\nModern research found that phytophenols as plant secondary metabolite existed above 8000 structures. These structures resemble with tannin and phenolic acid [50]. Phytophenols showed antiallergenic, antioxidant, anti‐inflammatory, antimicrobial, antiorthrogenic, and antithrombotic activity [51]. These plant compounds exhibited key biological activity in the degradation of many microorganisms [52]. Plants, herbs, essential oils, and spices in powder or extracts form are used to detoxify microbes due to the presence of flavonoids, betalain, phenolics, phytoalexins, and thiosulfonates. But mostly antimicrobial and antioxidant activities of plant extracts were due to their phenolic alignments [53].
\nA recent study exposed the antifungal and antiaflatoxigenic nature of phenolic components of plant extracts [54–56]. The syringaldehyde, sinapic acid, and acetosyringone were the plant phenolic compounds that inhibited the production of aflatoxin B1 [57]. However, salicylic acid, thymol, vanillyl acetone, cinnamic acid, and vanillin were phenolic compounds that ceased A. flavus growth by targeting oxidative mitochondrial stress as defense system [58].
\nMedicinal plants have been used from centuries for the treatment of various diseases. There are about 53,000 medicinal plants around the world [59]. In developing countries, according to World Health Organization, about 70–95% people used medicinal plants as primary health care for the treatment of diseases [20]. In current scenario, 70% of synthetic medicines are derived from plants [60]. Medicinal plants have antifungal, antimicrobial, anthelmintic, antibiotic, antiviral, anti‐inflammatory, antiarthritic, antirheumatic, and antihemorrhoidal properties.
\nThe various medicinal plants native to Southeast Asia including bitter cucumber (Momordica charantia), Asiatic pennywort (Centella asiatica), betel nut (Areca catechu), betel vine (Piper betle), Chaa Phluu (Piper sarmentosum), false coriander (Eryngium foetidum), Chinese radish (Raphanus sativus), clove (Syzygium aromaticum), Eucalyptus (Eucalyptus globules), Indian mulberry (Morinda citrifolia), Madagascar periwinkle (Catharanthus roseus),bmangosteen (Garcinia mangostana), mandarin (Citrus reticulate), onion (Allium cepa), pepper (P. nigrum), pomegranate (Punica granatum), tomato (Lycopersicon esculentum), hedge flower (Lantanacamara), roselle (Hibiscus sabdariffa), Non Taai Yaak (Stemona tuberosa), Raang Chuet (Thunbergia laurifolia), Saab Sue (Chromolaena odorata), turmeric (Curcuma longa), water primrose (Jussiaeda repens), and wishing tree (Cassia bakeriana) were tested for their ability to control aflatoxins producing fungus [61]. The above study found that ethanolic extracts of some medicinal plant showed the inhibition of aflatoxins producing fungus.
\nThe highest activity was showed by betel vine, a traditional Thai medicine, followed by false coriander, Indian mulberry, Chaa Phluu, Chinese radish, and clove. The leaf of betel vine is used topically for urticaria, contains eugenol and chavicol, and mostly chewed by mouth as antiflatulent, antimicrobial, and antipruritic [62].
\nCrude ethanolic extract of olive callus in different ratios was used to inhibit the aflatoxins synthesis [63] by the addition of appropriate amounts of extracts onto potato dextrose agar (PDA) to obtain the final concentration of 0.5 and 1%, and Aspergillus was then point‐inoculated into PDA. The results showed that ethanolic extract of olive callus had no inhibitory effect on fungal growth but it reduced 90% of aflatoxin synthesis. The main compounds in olive callus are reported as caffeic acid, coumarin; o‐, p‐, or m‐coumaric acid and catechin which facilitate the reduction of aflatoxin. Only o‐coumaric acid and caffeic acid showed antifungal and antibacterial activity.
\nVarious concentrations (0, 2, 4, 6, 8, and 10% (w/v)) of clove, garlic, and carrot’s crude aqueous extracts were tested for their possible inhibitory effect on Aspergillus growth and aflatoxin production in 50 g of rice. The results showed that garlic and clove at 10% (w/v) and carrot at 2% inhibited the Aspergillus growth and also reduced the level of aflatoxin production in rice [64]. Crude extracts of garlic, eugenol, and onion were used to reduce A. flavus growth as well as aflatoxin synthesis in maize and SKMY liquid medium [65]. The study showed that garlic extract inhibited 61.94% fungus growth. However, onion extract ceased about 60.44% aflatoxin synthesis. While on maize grain, eugenol extract reduced 60.35% aflatoxins synthesis. Hussain and Ali [48] compared the antifungal activity of some herbal spices, chemicals, and plants to inhibit the growth of aflatoxins producing fungus like A. flavus and A. parasiticus. They found that benzoic and propionic acid showed complete inhibition of A. flavus at (0.1–0.5%) and A. parasiticus at (0.2–0.5%), while clove (0.5%), garlic (0.5%), and onion (0.5%) showed complete inhibition of both Aspergillus.
\nThe aqueous and phenolic extracts of several other natural and medicinal plants have been tested against Aspergillus [66]. Aqueous extracts of Lupinus albus (Leguminosae), Ammi visnaga (Umbelliferae), and Xanthium pungens (Compositae) were found to cease the growth of A. flavus and also the production of aflatoxin [67]. It was also found that the inhibitory effect was proportional to the applied concentration.
\nThe search for naturally occurring compounds or metabolites having bioactivity against aflatoxins producing fungi has been the target of interest in the search for ecologically friendly products [68]. There are many essential oils produced by medicinal plants that have been tested for their inhibiting ability of aflatoxin production [69, 70].
\nEssential oils were extracted from 16 aromatic plants, that is, safflower (Carthamus tinctorius), marigold (Tagetes erecta), coriander (Coriandrum sativum), pomelo (C. maxima), mangosteen (G. mangostana), Kaempferia parviflora, ginger (Zingiber officinale), pepper (P. nigrum), Boraphet (Tinospora crispa), aloe (Aloe vera), lavender (Lavendula officinalis), rosemary (Rosemarinus officinalis), cinnamon (Cinnamomum cassia), eucalyptus (E. globules), thyme (Thymus vulgaris), and white wood (Melaleuca cajuputi), and their ability to inhibit the Aspergillus on PDA by agar diffusion test [71].
\nDifferent ratios (50, 25, 12.5, 6.25%) of each essential oil were placed onto a cylinder cup (6 mm dia) on agar plate streaked with A. flavus. It was observed that the essential oil extracted from white wood showed the highest inhibition followed by the essential oils of cinnamon and lavender, respectively. Sindhu et al. [72] used Curcuma longa leaves essential oil of 0.01, 0.05, 0.1, 0.5, 0.75, 1, and 1.5% concentration in YES broth that was inoculated with A. flavus spores. C. longa oil of 1 and 1.5% concentration reduced 95.3 and 100% aflatoxin (AFB1, AFG1) synthesis, respectively. They analyzed α‐phellandrene, terpinolene, and p‐cymene as an active compound in turmeric leave oil extract by gas chromatography‐mass spectrometry (GC‐MS). Mahmoud [73] also used 0.01% of five essential oils namely geraniol, nerol and citronellol (aliphatic oils), cinnamaldehyde (aromatic aldehyde), and thymol (phenolic ketone) to suppress the Aspergillus growth. The result showed the complete inhibition of A. flavus growth.
\nDespite all efforts, it has been very difficult to control the exposure of man and animals to aflatoxins, because of their natural occurrence in the environment. Although the prevention of aflatoxin contamination by inhibiting the fungal growth in food and feeds is the best practice, other measures are also necessary. The advantage of using active compound based on natural plant is that they are safer, ecologically friendly than any chemical compounds, and synthetically produced antimicrobial agents. Other procedures such as the removal or decomposition of aflatoxins are also necessary as the prevention of contamination alone may not always be successful.
\nAs novel COVID-19 testing develops, saliva has become of increasing interest as an alternate biological sample for rapid testing [1]. The appeal in saliva-based testing lies within the ease of which samples are collected, as well as patient comfort throughout the collection process [2]. Yacoubian Jr., Wish, and Perez (2001) found that the benefits in the ease of saliva collection were multifaceted. These benefits include the uncomplicated nature of collection, which, coupled with a low risk of direct contact and contamination, makes salivary diagnostics an attractive alternative to biological sample collection where contamination may be more challenging to avoid, such as with blood or urine analyses. For these reasons, saliva-based testing has become an increasingly popular choice in the creation of novel forms of diagnostic testing. With this, it has become increasingly important to delineate the characteristics of saliva viscosity due to its effects on the movement and interactions of the substances and molecules found within it. In the context of this study, viscosity refers to internal friction of a fluid, which is marked by the resistance of a fluid to flow [3].
While viscosity can affect the interactions and molecules within saliva is important to note in developing diagnostic tests, salivary viscosity itself can also be seen as an important factor in maintaining oral and overall health. A study by Katsuhiro Kitada and Takahiko Oho (2011) found that an increase in saliva viscosity decreases the bacterial co-aggregation between Streptococcus oralis and Actinomyces naeslundii [4]. Under normal circumstances, co-aggregation can prevent bacterial infection in the oral cavity, as co-aggregated bacteria may be swallowed before forming attachments within the oral cavity. The study indicated that increasing saliva viscosity decreased formation of these co-aggregated bacteria, which may allow for further health problems, such as pneumonia or other infections that may be brought on by the aspiration of oral bacteria or microorganisms [4]. The demonstrated health implications surrounding salivary viscosity further suggests the importance of developing protocols to accurately measure salivary viscosity following saliva collection.
The characteristics of salivary viscosity, namely the presence of aggregates, variations in temperature, sample handling, and time elapsed between sample collection and testing, serve as points of interest in the creation of laboratory protocols for salivary-based rapid diagnostic testing. Understanding how external factors affect saliva viscosity are important in generating guidelines for proper sample handling in saliva testing to ensure consistent and reliable results.
Multiple studies demonstrated in the literature reflect the variability of saliva viscosity. The 1998 Rantonen and Meurman study concluded that salivary viscosity can be dependent on the method of its production. Particularly, whether secreted by the submandibular, sublingual, or palatal glands [5]. Although the study demonstrated that the quantity of mucin within each saliva sample of differing origin did not change, the species of mucin did. Particularly, it was demonstrated that the saliva stemming from the sublingual glands demonstrated more elasticity than those of the submandibular and palatal glands, which would affect the viscosity of the saliva. In addition, the 2016 study by Antoon Ligtenberg, Erwin Liem, Henk Brand, and Enno Veerman found that acute exercise correlated with a significant increase of saliva viscosity when collected shortly thereafter [6]. These findings were parallel with the Rodica Murineanu, Corina Stefanescu, Agripina Zaharia, Carolina Davidescu, and Sorin Popsor (2011) study that found medication, general illness, and acrylic dentures to all correlate with a change in saliva viscosity [7]. This study suggested medication and disease state may affect saliva viscosity. For example, complete acrylic dentures were specifically found to correlate with an increase in salivary viscosity. It is also interesting to note the apparent correlation between salivary viscosity and dental cavities. A 2014 study by Animireddy et al found that in a sample of 75 school children, the cavity-free children had on average higher salivary viscosity than their counterparts [8]. These findings delineate some of the known variability to saliva viscosity discussed in the literature, which further demonstrate the necessity of qualifying the properties and behavior of saliva viscosity.
Beyond the variability of salivary viscosity, the level of normal viscosity is very different from that of other commonly used human biofluids in diagnostic testing. This is an important factor to note in the development of such tests, especially when considering technologies previously developed for other biofluids. The viscosity of normal cerebrospinal fluid, for example, is remarkably close to that of water, which is 1.00 cSt at 20°C [9, 10] Similarly, the kinematic viscosity of urine is 1.07 cSt at the same temperature [11]. These examples are lower than the kinematic viscosity of normal blood, which is around 3.65 cSt at 21.2°C [12]. While there is variability within the viscosities of these human biofluids, they are far lower than what we expect of human saliva, an important challenge to overcome in developing diagnostic testing.
Due to the interest in point-of-care saliva-based diagnostic testing, and based on the current literature demonstrating potential variabilities in saliva viscosity and associated causes, it is rather surprising that the literature on salivary viscosity characterization for protocol creation is rather sparse. This study hopes to address some of the gaps in the literature pertaining to salivary properties by exploring how viscosity changes upon freezing and subsequent thawing, and how it changes over time with consecutive trials, using the Cannon-Fenske experimental protocol, with the goal of aiding in the development of laboratory protocols pertaining to salivary-based diagnostic testing.
Based on the previous literature at hand, the research questions of this study are as follows:
How does the viscosity of collected saliva change over time with subsequent trials? How does the viscosity of collected saliva change after freezing and subsequent thawing?
In the absence of detailed information in the research literature, this study seeks to better understand the specific properties of saliva viscosity, and how saliva viscosity reacted to factors that are integral in the creation of lab protocols; specifically, how the samples are stored. It is not uncommon for biological samples to be frozen or cooled, and this makes sense with respect to slowing down bacterial contamination and maintaining biological molecules of interest. It is well understood that many human proteins, enzymes, vitamins, degrade over time [9] and that the degradation over time can be diminished by freezing or cooling samples beyond given temperature degradation thresholds, to allow for long-term storage.
Changes within the aggregates commonly found in human saliva, such as mucins, may also be affected by temperature and shear forces. Enzymes, such as salivary alpha-amylase, and hormones, such as cortisol, will degrade over time unless this process is inhibited, typically by freezing samples to −20 degrees Celsius, or below [10]. However, it is important to determine the effects of sub zero temperatures on viscosity itself, as the viscosity of the saliva may be impactful in how the aggregates are measured via point-of-care salivary biosensors. This was reflected in the 2013 Robles et al. study which found that the most consistent and reliable salivary alpha-amylase biosensor data was obtained from frozen and centrifuged passive saliva samples, rather than samples that were collected as fresh, passive, drool [11]. The authors hypothesize this discrepancy to be due to various factors of the saliva itself (such as mucin molecules), which may interfere with the device in question by preventing close binding to the sensor surface, as it attempts to detect quantities of salivary alpha-amylase. Also, this hypothesis reflects the prediction that salivary viscosity is an important factor in molecular measurements. While the aggregation effect was disadvantageous when in reference to the assessing quantities of the enzyme salivary alpha-amylase, it may actually be preferable when measuring quantities of different gases within saliva samples. This further delineates the importance of taking a closer look at physical salivary properties, in order to approach proposed handling methodologies appropriately, depending on the given purpose of the saliva sampling.
As previously mentioned, we hoped to better understand the properties of saliva viscosity in regards to different methodologies in sample preservation or usage. For this reason, two cycles of laboratory trials were conducted, with the aim of determining how saliva viscosity was affected. The first study aim is to determine how time alone affects saliva samples. The second trial aims at determining whether freezing and subsequent thawing affect the viscosity of the sample, as well. Both phases of data collection were done at a Biomedical Engineering laboratory, at Arizona State University, Tempe, and human saliva samples were collected within this department.
Participants were instructed to not eat within an hour of sample collection, and were then asked to drink approximately 100 mL of water immediately prior to saliva collection. This was to prevent short-term dehydration effects from confounding our variables. In addition, this aided in the ease of saliva collection. Participants were then asked to collect approximately 15 mL of passive saliva over a span of 25 minutes, into a plastic vial. The goal for saliva collection included diminishing the amount of air bubbles trapped within the saliva, by collecting the saliva very carefully, slowly, and with as little movement as possible. Foam-like saliva that is saturated with small air bubbles was not included in the overall 15 mL amount of passive saliva collected.
The viscosity of the collected saliva sample was measured using a Size 350 Cannon-Fenske apparatus, with a capillary radius of 0.045 cm, a shear rate of 2.08 1/s at 10cSt, and a viscometer constant of 0.5 cSt/s, which was cleaned and dried prior to commencing the viscosity protocol. The saliva sample was then poured into the apparatus, and allowed to flow through, while efflux time was measured concomitantly, indicating the time required for the meniscus of the viscous fluid to flow between the designated markings. This viscometer procedure was replicated 10 times consecutively for each collected sample, after which each sample was frozen and subsequently thawed the following day, at which point the viscometer procedure was performed again. A series of viscosity measurements of a 50% glycerol/water control solution was tested in the same manner to act as a control variable.
The kinematic viscosity of each trial was calculated using the efflux time and viscosity constant in the following relationship:
This procedure reflected the first half of the research questions, as to how saliva viscosity changes with time [12]. By comparing the time elapsed for the viscosity of human saliva with the glycerol/water solution, we are able to visualize how viscosity properties may be affected by the presence of the aggregates in unprocessed human saliva, which are not present in the glycerol/water mixture.
Data was collected for each measurement of salivary viscosity. The initial graph (Figure 1) represents the methodology behind the first research question; how does salivary viscosity behave over time? Figure 2 represents the viscosity of saliva (as average kinematic viscosity), with freshly collected samples of saliva, compared to with samples that were frozen, and subsequently, thawed for analysis.
This figure evaluates how salivary viscosity changes with time over subsequent trials following collection. This arbitrary time is demonstrated in sequential trials, as trials were completed one after the other following collection. This saliva kinematic viscosity is contrasted with that of a 50% glycerol water solution, acting as a control. In the passive saliva trials, we see a stark decrease in kinematic viscosity with each subsequent trial, however, the glycerol/water solution shows a slight variation within an expected range given the experimental apparatus and simple laboratory control of room temperature (22 C).
This figure demonstrates the statistically significant discrepancy between fresh and thawed passive saliva samples at 22 C. each bar represents an average of 10 trials. One outlier was removed from the thawed trial group as it was handled outside the guidelines of the lab protocol.
There are several points of interest with regards to the conclusions drawn from the experimental design that reflect the difference in how fresh saliva behaves, compared to fresh saliva, and compared to saliva that had been frozen and re-thawed. Such comparisons are reflected in the literature by the 2019 study by Johannsen et al. which found that salivary viscosity measurements varied depending on whether the saliva was untreated or subject to magnet-beating prior to viscosity measurements at low shear rates [13].
One aspect that is of interest is whether there is a methodology that can guide how fresh saliva can be processed in order to have a consistent flow property or ensure that the major aggregates, presumably due to entangled mucin chains, can be minimized quickly so that a rapid test can be performed (Figure 3).
This figure demonstrates the cross equation fit, using the limits of η∞ and η0. The independent variable is demonstrated to be 1/(1 + aT).
Bansil et al. [14] provide a molecular interpretation on how the structure of mucin leads to entanglements among the biopolymers in solution and create a range of viscosity effects depending upon concentration and mucin type. They suggest that a dilute solution of mucin generally has viscoelastic behavior that depends upon shear rate. A more general approach to the viscosity behavior of biopolymer solutions is given by Picout and Ross-Murphy [15] who provide experimental verification of the Cross equation:
with lambda being a time constant and gamma the shear rate.
Comparing the Cross equation to the data shown in this study in Figure 2 suggests that repeated shearing with a Cannon-Fenske viscometer is a cumulative effect, so one may conjecture the following modified forms of the Cross equation could explain the apparent viscosity change in saliva after repeated shearing due to the capillary flow within the viscometer.
or
where n is an integer and k is the number of repeat measurements in the first equation and T is the total elapsed time of shear for the combined repeat measurements. It is not as important to determine which equation may be better at predicting data than to understand how to use the concept to prepare saliva samples in a variety of ways rather than relying solely on freezing or centrifugation which can be cumbersome and time consuming.
The potential utility of these modified Cross equations is in determining a way to rapidly shear saliva samples using a simple microfluidic device or mixer, rather than subjecting the saliva to freezing or flowing the saliva through a longer tube to simulate the cumulative shear thinning shown in Figure 2. The shear rate experienced in the Cannon Fenske viscometer used for these experiments is on the order of 2 s−1, so based on the Figure 2 data of approximately a total time of 80 seconds of shearing is needed in order to reach a stable and minimum kinematic viscosity a device capable of deliver a shear rate of 200 s−1 which is well within the reach of portable and low cost homogenizers [16, 17, 18, 19].
Salivary viscosity can be an important parameter to consider when designing diagnostic devices for rapid testing. Consideration should be given to the fact that not only is saliva viscoelastic, but its apparent viscosity can change by mild shearing over a period of time. Shearing at rates as low as 2 s−1 can decrease its kinematic viscosity by more than half, which could change some kinetics of enzyme action, sensor signal development, or diffusive transport. After approximately a total time of 80 seconds of shearing at 2 s−1 can lead to a stable and minimum kinematic viscosity. The concept of biopolymer viscosity behavior being modeled by the Cross equation suggests that a device capable of delivering shear rates of 200 s−1 and above may be able to modify the mucin superstructure sufficiently to provide saliva samples with consistent apparent viscosities. Microfluidic devices or low cost handheld homogenizers could very quickly deliver the needed shearing action in order to provide a more consistent saliva sample, in terms of its viscous properties.
The institutions involved in the findings for this paper are Arizona State University, Tempe, and New Mexico State University. The data was collected at a Arizona State University Biomedical Engineering laboratory in Tempe, Arizona. This project was done under the supervision of Professor Antonio A. Garcia, who is now Associate Dean of Engineering at New Mexico State University in Las Cruces, New Mexico.
The authors declare no conflict of interest.
Special thanks to Professor Antonio A. Garcia, Associate Dean of Engineering at New Mexico State University. His mentorship is invaluable.
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