Elsevier

Sensors and Actuators B: Chemical

Volume 269, 15 September 2018, Pages 346-353
Sensors and Actuators B: Chemical

Smartphone based optical biosensor for the detection of urea in saliva

https://doi.org/10.1016/j.snb.2018.04.108Get rights and content

Highlights

  • We developed a noninvasive smartphone based biosensor for urea using saliva as sample, which is first such report.

  • The sensor was fabricated by co-immobilization of urease & pH indicator on filter paper strip which changed color with reaction to salivary urea.

  • This color change can be used to deduce urea concentration using smartphone based application by reading RGB levels.

  • Clinical validation carried out on spiked or clinical saliva samples show great possibility of using the biosensor for diagnosis of uremia and CKD.

Abstract

In the present study, we have developed a smartphone based handheld optical biosensor for determination of urea in saliva. A simple strategy was adopted by immobilization of urease enzyme along with a pH indicator on a filter paper based strip. The strip changed color upon the reaction with urea present in saliva and the color change can be estimated using our smartphone based application based on RGB profiling. Calibration of the biosensor was carried out using spiked saliva samples and an exponentially decreasing calibration curve has been obtained for green pixel intensity in the broad range (10–1000 mgdL−1) with a linear detection range of 10–260 mgdL−1 and a response time of 20 s. The sensitivity reported for the biosensor in the clinically significant range was −0.005 average pixels sec−1/mgdL−1 with a LOD of 10.4 mgdL−1. Studies carried out on spiked saliva samples showed a good correlation between salivary urea estimated using our biosensor against phenol-hypochlorite based spectroscopic procedure. Development of a smartphone based biosensor for urea estimation eliminates the need for procuring a dedicated instrument as well as trained technician for daily monitoring and saves time as compared to traditional laboratory methods of analysis.

Introduction

Urea is the major end product of nitrogen metabolism in humans and is eliminated from the body mainly by the kidneys through urine but is also secreted in body fluids such as blood and saliva. Its level in urine ranges from 7 to 20 mgdL−1 which drastically rises under pathophysiological conditions thus providing key information of renal function and in the diagnosis of various kidney and liver disorders [1]. Increase in urea levels in blood, also referred to as azotemia or uremia is referred to as Chronic Kidney Disease (CKD) or End Stage Renal Disease (ESRD) and is generally caused due to the progressive loss of kidney function. Normal glomerular filtration rate (GFR) lies between 100–120 mL/min. which begins to fall below 70 mL/min with the onset of azotemia or uremia. For subjects with kidney failure, GFR reaches around or even less than 15 mL/minute [2]. Apart from CKD, several other conditions such as heart failure, hypovolemic shock, gastrointestinal bleeding, severe infections also leads to a rise in urea levels beyond normal [1]. Diabetes and hypertension has been reported as the major risk factors for CKD in both developing as well as developed countries followed by glomerulonephritis and cardiovascular disease [3,4].According to National Kidney Foundation, CKD affects around 10% of world’s population [5] and was ranked 18th among the various causes of deaths worldwide in 2010 with an annual death rate of 16.3 per 100000 [6] or over 1 million in total [3]. Therefore, diagnosis of kidney disease at an early stage is important in order to prevent the development of drastic consequences.

Kidney Function tests play an important role in the diagnosis of renal disorders at early stages. Several tests such as urinalysis, urine protein, creatinine clearance, serum creatinine, Blood Urea Nitrogen (BUN), Glomerular Filtration Rate (GFR) etc. involving either urine or blood samples are commonly grouped under Kidney Function tests [7]. Most important of these tests are Blood Urea Nitrogen (BUN) and serum creatinine which are frequently used in every diagnostic laboratory for estimation of renal function. These tests also form an essential part of radiological screening procedures such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) prior to the administration of radiological contrast agents such as iodinated contrast or gadolinium based contrast agents so as to prevent complications such as contrast medium induced nephropathy and nephrogenic systemic fibrosis [8].

Amongst several methods for the estimation of urea in body fluids, most are based on colorimetric procedures. Some of these are nesslerization [9], phenol-hypochlorite or Berthelot method [10] and diacetyl monoxime method [11,12]. Blood Urea Nitrogen test (BUN) is most commonly used test for assessment of blood urea levels and the test is frequently combined along with a serum creatinine test for the differential diagnosis of pre-renal, renal and post-renal hyperuremia. BUN measures the amount of nitrogen present in a subject’s blood. The main drawback of this procedure is that it is time-taking as well as involves blood extraction which is painful and inconvenient to the subject undergoing the procedure; therefore use of alternate body fluids for urea estimation is of great importance. Among non-invasive body fluids such as urine, saliva, sweat and tears, saliva is most easily accessible to the user due to ease in collection and its availability. Several research groups have also established a good correlation between salivary and blood urea levels using conventional methods [[13], [14], [15], [16], [17]]. However, development of a biosensor for measuring urea levels in saliva samples is an immediate necessity.

The first urea biosensor was reported by Guilbault and Montalvo by using a potentiometric urease enzyme electrode [18]. Since then various biosensors have been reported till date for the estimation of urea in biological fluids employing both electrochemical [[19], [20], [21]] as well as optical methods [[22], [23], [24], [25]] of detection. Various organic as well as inorganic matrices have been used for immobilization such as latex polymers [26], conducting polymers such as polypyrrole [27], metal nanoparticles [28], metal oxides [29] and so on. Among non-invasive category, several reverse iontophoresis based biosensors have been developed for the determination of blood urea concentration using potentiometric techniques [30,31]. Chen et al. developed a conductivity cell with 61 MHz surface acoustic wave resonator based measurement circuit for determination of urea in urine samples with a LOD of 30 ngml−1 [32].A piezoelectric biosensor feasible for detecting urea in urine samples has been developed by Yang et al. by immobilizing urease enzyme on nanoporous alumina membrane [33]. Likewise, an amperometric urea biosensor capable of detecting urea in aqueous solutions as well as urine has been developed by covalent immobilization of urease enzyme on N2 incorporated diamond nanowire film [34]. Another voltammetric sensor based on single walled carbon nanotubes has been developed by Chen et al. which can sense urea in urine samples [35].

However, all these biosensors developed for urea estimation suffers from several drawbacks such as use of complicated as well as costly methods in fabrication, requires trained personnel to gather results and most of them are not portable in nature, and hence can’t be used for in situ monitoring at hospitals or homes. One such portable clinical analyzer has been developed by Abott with commercial name ‘iStat portable clinical analyzer’ which can measure several parameters such as urea and creatinine using few drops of blood through its disposable cartridges but the device is costly as well as invasive in nature, therefore is not preferred by common people for routine monitoring [36,37]. One recent breakthrough in portable urea sensing was development of saliva urea nitrogen dipstick test strips by Evans et al. which was used to detect kidney disease in Malawi but had several limitations such as being qualitative and lacking accuracy, as the change in urease based pH strip’s color was compared using naked eye [38].

In this respect, for addressing all the drawbacks of these reported sensors, we have developed a smartphone based optical biosensor for the determination of urea in saliva samples using a simple methodology by immobilization of urease enzyme along with pH responsive dye on a filter paper based strip. The strip changed color due to the increase in pH upon the formation of ammonia as a result of urease enzyme reaction with urea present in saliva sample. The enzymatic reaction of urea with urease enzyme can be described as Eq. (1)CO(NH2)2+H2OUrease2NH3+CO2

The higher the concentration of urea present in the sample, the more ammonia was liberated and hence color change was more profound. This color change in the paper strip was then screened through RGB profiling with the use of a smartphone based application developed in-house. The urea concentration was deduced using calibration curve equation fed into the smartphone application itself. In today’s digital era, smartphones have become ubiquitous and contains several features such as high resolution camera, computational ability, and capability of integrating to several devices via in built Bluetooth and GPS systems, enabling researchers to use smartphone as a diagnostic tool for the detection of various diseases, metabolites, biomarkers, pathogens etc. [[39], [40], [41], [42], [43]]. Following this trend, we have also used smartphone based platform to make the device more user-friendly eliminating the need for procuring a dedicated instrument for analysis. Moreover, slope based calculation of test results was followed to enhance the sensitivity of detection and reduce ambient light interferences. In our previous studies, we had developed a smartphone based salivary glucose biosensor using a test strip and android app working on slope based calculation method and validated it on real samples for mass diagnosis of diabetes [44]. The algorithm for detection was altered in this app for sensing salivary urea level. For example, instead of cumulative calculation of sensor response in terms of Slope (R + G + B), in the present work we employed slope of green pixel intensity. The test strip preparation was also altered to suit urea detection.

Few other groups have also demonstrated smartphone based detection of biomolecules [45,46]. However their strategy involves more complex methods of fabrication of sensor or they have used open source image processing softwares to deduce analyte concentration in mostly offline mode compared to our online sensing method. In this respect, our present work involves a simple fabrication method where the strips are paper-based and hence biodegradable. Moreover, the analysis is done onsite with the help of developed smartphone app and hence can be used at such places where healthcare facilities are not easily available.

Section snippets

Materials

Smartphone (Samsung Galaxy SIII) was of Samsung India Limited make; laminator (Model ECO 12) was from Excelam™. Filter paper (Whatman number 1), polyvinyl alcohol (Cat. No.563900), urease enzyme (Cat. No. U4002-20 KU), sodium phosphate dibasic (Cat. No.V800397), sodium phosphate monobasic (Cat. No. V800376), phenol (Cat. No. P4161), sodium hydroxide pellets (Cat. No. 221465) were procured from Sigma Aldrich India; urea (crystalline, extrapure) was from Merck India Ltd.; phenol red indicator,

Immobilization and characterization of urease on the strips

Schematic of strip preparation and immobilization along with color change in the strips with respect to increasing urea concentration is depicted in Fig. 1(B&C respectively). The immobilized strips were stored desiccated at 4 °C until use. Enzyme activity and protein content of the strips were estimated in triplicates. The enzyme activity per strip calculated as per the phenol-hypochlorite or Berthelot assay was about 4.6 U (indicating ∼46% yield of immobilization) whereas the protein content

Conclusion

In the present study we have developed an optical urea biosensor using saliva sample and a smartphone. The urease-pH indicator immobilized strips changed color with respect to urea concentration in saliva sample and the color changes were detected using smartphone based app using RGB profiling and slope based calculation method. Calibration curve with green pixel intensity (Slope G) was found to be most sensitive with a sensitivity of −0.005 average pixels sec−1/mgdL−1 within the linear range

Acknowledgements

The author is obliged for the intramural financial support provided by Indian Institute of Technology Delhi. The author Anuradha Soni thanks Indian Council of Medical Research, New Delhi for research fellowship. A part of this work has been filed for Indian Patent (1587/DEL/2015 dated 1/6/2016).

Ms. Anuradha Soni Anuradha Soni is a Ph.D. research scholar at Centre for Biomedical Engineering (CBME), Indian Institute of Technology Delhi and has been working on development of clinical biosensors, especially those which are non-invasive in nature. She has authored 2 manuscripts and has developed a smartphone based non-invasive biosensor for salivary glucose detection.

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    Ms. Anuradha Soni Anuradha Soni is a Ph.D. research scholar at Centre for Biomedical Engineering (CBME), Indian Institute of Technology Delhi and has been working on development of clinical biosensors, especially those which are non-invasive in nature. She has authored 2 manuscripts and has developed a smartphone based non-invasive biosensor for salivary glucose detection.

    Mr. Rajat Kumar Surana is former B.Tech. student at Department of Textile Technology at Indian Institute of Technology Delhi and has interest in Android programming.

    Dr. Sandeep K.Jha is a joint faculty at the Centre for Biomedical Engineering (CBME), Indian Institute of Technology Delhi and All India Institute of Medical Sciences New Delhi. His areas of interest include Lab-on-a-chip and Microfluidics devices for biomedical applications; electrochemical and optical chemical and biosensors; bioinstrumentation & nanomaterials, conducting and synthetic polymers based immobilization techniques. Previously he served under various capacities at Banasthali University, India; Korea University, South Korea; KIIT University, Bhubaneswar, India; Myongji University, South Korea; Indian Institute of Technology, Mumbai, India and Bhabha Atomic Research Center, Mumbai, India.

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