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Review
. 2022 Nov 15;12(11):1020.
doi: 10.3390/bios12111020.

Recent Advances in Biosensor Technologies for Point-of-Care Urinalysis

Chuljin Hwang  1 , Won-June Lee  2 , Su Dong Kim  3 , Sungjun Park  1   4 , Joo Hee Kim  5
Affiliations
Review

Recent Advances in Biosensor Technologies for Point-of-Care Urinalysis

Chuljin Hwang et al. Biosensors (Basel). .

Abstract

Human urine samples are non-invasive, readily available, and contain several components that can provide useful indicators of the health status of patients. Hence, urine is a desirable and important template to aid in the diagnosis of common clinical conditions. Conventional methods such as dipstick tests, urine culture, and urine microscopy are commonly used for urinalysis. Among them, the dipstick test is undoubtedly the most popular owing to its ease of use, low cost, and quick response. Despite these advantages, the dipstick test has limitations in terms of sensitivity, selectivity, reusability, and quantitative evaluation of diseases. Various biosensor technologies give it the potential for being developed into point-of-care (POC) applications by overcoming these limitations of the dipstick test. Here, we present a review of the biosensor technologies available to identify urine-based biomarkers that are typically detected by the dipstick test and discuss the present limitations and challenges that future development for their translation into POC applications for urinalysis.

Keywords: biosensors; clinical application for urinalysis; dipstick test; point-of-care urinalysis; urine sensor.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Comparison of point-of-care urinalysis with the dipstick test and biosensor. (a) Schematic representation of how to test urine using the dipstick test. (b) Schematic representation of the advantages of using a biosensor for urinalysis, enabling improved sensitivity, selectivity, and reusability compared with those of the dipstick test.
Figure 2
Figure 2
Selective detection of glucose in biological samples using GFET biosensor (a) Illustration of the experimental setup to perform poly(3-amino-benzylamine-co-aniline) (PABA) electropolymerization on the graphene field-effect transistor (GFET). Source and drain electrodes are used as working electrodes of a three-electrode cell. The platinum wire and Ag/AgCl are used as counter and reference electrodes, respectively. (b) Overview of glucose oxidase (GOx) immobilization in PABA-GFETs and the glucose oxidation reaction. (c) Real-time electrical current measurement of the sensor after injection of spiked urine samples (VG = −0.2 V, VDS = 0.1 V) and (d) curve fitting using the linear response of sensitivity of −13 ± 2 μA per decade of glucose concentration (R2 = 0.9462, n = 2) (reprinted with permission from [30]; copyright 2021 RSC). (e) The binding energy of 3-acrylamidophenylboronic acid (AAPBA)-functionalized graphene with carbamide, creatinine, l-cysteine, and glucose, respectively. The negative binding energy (−0.194 eV) indicates strong chemical adsorption toward glucose. (f) Schematic illustration of the recycling detection of the polymer functionalized GFET with a simple solution process. The covalent bond between glucose and the polymer can be broken under an acid environment. (Reprinted with permission from Ref. [31]; copyright 2022 Elsevier.)
Figure 3
Figure 3
Selective detection of hydrogen ion (a) Schematic diagram of the 3D-printed electrode geometry and redox reaction of quinone groups on the electrode surface. (b) The relationship between pH and the reduction peak potential of three independent electrodes for the pH range from 2.02 to 11.22 with linearity (R2 ≥ 0.99 for all sensors). (c) Bar graph comparing the pH detection results obtained by the graphene/polylactic acid (G/PLA) sensor and a conventional glass pH probe in unadulterated real samples, including vinegar, cola, urine, serum, and antacid (reprinted with permission from [32]; copyright 2017 ACS). (d) Schematic the indium–gallium–zinc–oxide electrolyte-gated thin-film transistors (IGZO-EGTFTs) structure. (e) Transfer characteristics of the IGZO-EGTFTs with varying values of pH. As the pH decreased from 7 to 3, the threshold-voltage (VTH) shifted in a negative direction. (f) Sensitivity analysis based on the VTH shift of IGZO-EGTFTs, including a slope comparison with the Nernst limit and other technologies. Inset: transfer curves at pH 7 before and after exposure to the pH 3 solution (VD = 0.5 V) (reprinted with permission from [33]; copyright 2021 IEEE Xplore). (g) Response of VTH shifts of IGZO-EGFETs with and without amino-silanization exposed to varying pH levels (reprinted with permission from Ref. [34]; copyright 2018 Elsevier).
Figure 4
Figure 4
Selective detection of human serum albumin (HSA) in biological samples (a) Schematic illustration representing the operational mechanism for bromocresol green (BCG)–HSA binding. (b) Measurement of the conductance of the single-walled carbon nanotube (SWCNT)-field emission transistor (FET) in healthy human urine samples (reprinted with permission from Ref. [35]; copyright 2016 Springer). (c) Schematic illustration representing the protocol of the molecularly imprinted sensor. (Reprinted with permission from Ref. [36]; copyright 2019 Elsevier.) (d) Schematic diagram and electrical connections of the HSA detection mechanism with modified Ab/APTES. (e) Transfer characteristics with different HSA concentrations from 10 ng/mL to 100 µg/mL (VD = 0.2 V). (f) Storing stability test of the ZnO NRs-FET sensors for up to 360 days for the detection of 100 µg/mL HAS (VG = 0.5 and VD = 0.2 V) (reprinted with permission from Ref. [37]; copyright 2021 Springer).
Figure 5
Figure 5
Selective detection of ketone bodies in clinical samples using multi-layer enzyme biosensors (a) Schematic diagram and layout of the electrode (working, counter, reference) structure. A Cr/Au layer with 50/100 nm thickness was fabricated by the conventional semiconducting process. (b) Comparable CV measurement during 10 repeated tests using 50 mg/dL of AcAc solution. The degradation of signal intensity is less than 5%. (c) Measured current plot comparing the electrochemical sensor and dipstick acquired from ketone levels (+++: up to 100 mg/dL; ++: up to 50 mg/dL; +: up to 10 mg/dL; trace 5: 10 mg/mL, and – is normal level) in 20 patients, (reprinted with permission from Ref. [38]; copyright 2021, MDPI).
Figure 6
Figure 6
Selective detection of hemoglobin (Hb) in clinical samples (a) Proposed protocol of multi-walled carbon nanotube (MWCNT)-molecularly imprinted polymer (MIP) sensors for Hb detection. (b) The calibration curves reflect the response of the MWCNT-MIP sensors. (c) Evaluation of the selectivity of the MWCNT-MIP sensor toward myoglobin, human serum albumin, cytochrome C, and Hb. The potentiometric response showed no significant effects compared to Hb detection using the MWCNT-MIP sensor (reprinted with permission from Ref. [39]; copyright 2017, Elsevier). (d) Illustration representing fabrication and test environment setup for Hb detection by differential pulse voltammetry (DPV). (e) DPV response of the Fc-ECG/AuNPs/SPE electrochemical sensor against the concentration of Hb in the range of 0 to 1000 µg/mL. The inset shows a linear calibration curve for different concentrations of Hb. (Reprinted with permission from Ref. [40]; copyright 2019, Elsevier.)
Figure 7
Figure 7
Selective detection of nitrite in biological samples (a) Illustration representing the structure and electrical connections of the graphene electrochemical transistor (GECT) sensor. (b) Calibration curve of changes of channel current (ΔIDS) versus concentration of nitrite from 0.1 nM to 7 µM (VD = 0.2 V). Top-left inset shows the sensing principle of GECT toward nitrite. (c) Interference test with various ions such as K+, Li+, Ca2+, Mg2+, NH43+, Cl−, NO3−, SO42−, PO43−, CH3COO−, SO32−, I−, and glucose against 0.1 mM nitrite (reprinted with permission from Ref. [41]; copyright 2019, Elsevier). (d) Calibration curve of different concentrations of nitrite from 0.5 to 250 μM. The limit of detection (LOD) is 0.03 μM with linearity (R2 ≥ 0.9986). (e) Amperometric measurement operated at an applied potential of 1.0 V (50 ms each) with triplicate injections of nitrite in uric acid solution (a′–j′: 0.5–250 μM) (reprinted with permission from Ref. [42]; copyright 2020, Elsevier).
Figure 8
Figure 8
Selective detection of bilirubin (BR) in clinical samples using electrochemical biosensors (a) Schematic diagram representing a proposed procedure for multi-walled carbon nanotubes (MWCNTs) screen-printed carbon electrodes (SPEs) and electrochemically reduced graphene oxide (Er-GO) SPE bilirubin sensors for detecting BR. (b) Calibration curve of MWCNT-SPEs and Er-GO-SPEs showing the current response at various concentrations of BR (reprinted with permission from Ref. [43]; copyright 2018, MDPI). (c) Illustration of the sensing flow to detect BR on a modified glassy carbon electrode (GCE). Top-right inset shows the I–V response with (red dots; 1 µM, 25 µL) and without (blue dots) BR from aqueous solutions. A significantly low current response was observed due to the reduction reaction of BR on the IAO NRs/GCE surface. (d) I–V response curve of different aqueous BR concentrations from 0.01 nM to 0.01 M in 5.0 mL of PBS with various potentials from 0.0 to 1.4 V at −0.2 V steps. The current response slowly decreased as the concentrations of BR increased. The inset shows the magnified view of the current variations for +0.6 to +1.0 V (reprinted with permission from Ref. [44]; copyright 2019, RSC).
Figure 9
Figure 9
Selective detection of leukocyte esterase (LE) in clinical samples using electrochemical biosensors (a) Schematic diagram illustrating the experimental design with a chemiresistive method for the quantitative detection of LE in urine. (b) Relative resistivity calibration curve of LE-PAD toward different LE concentrations. The limit of detection was 1.91 (×5.1 U mg−1mL−1; 20 WBC per mL). All resistivity responses are presented as mean values ± standard deviation (SD), n = 3, and the corresponding points are overlaid. The correlation of the resistivity response was 99% (reprinted with permission from Ref. [45]; copyright 2020, RSC).
Figure 10
Figure 10
Selective detection of biomarkers for diagnosis of bladder cancer in clinical samples (a) Illustration representing the electrochemical mechanism for detecting NMP-22 relying on NH2-SAPO-34-Pd/Co-Ab2 (b) Linear responses of the immunosensor with NMP-22 concentrations ranging from 0.001 to 20 ng/mL; LOD and correlation coefficients are 0.33 pg/mL and 0.998, respectively. (c) Evaluation of the selectivity of the immunosensor toward NMP-22 (5 ng/mL) (1) solutions containing 500 ng/mL of interfering substances such as bovine serum albumin (2), vitamin C (3), trioxypurine (4), and glucose (5). Error bar = RSD (n = 5). (Reprinted with permission from [46]; copyright 2016 NATURE.) (d) For bladder cancer detection, schematic diagram of the IGZO-FET biosensor array functionalized by anti-NMP-22 (e) Real-time response of IGZO-FET toward different concentrations of NMP-22 from 0.0001 to 10 pg/mL. The drain current (ID) was measured at VDS = 1 V and VGS = 0.5 V (f) Evaluation of sensing performance toward different concentrations of NMP-22 in response to clinical urine samples from bladder cancer patients and healthy donors (reprinted with permission from Ref. [47]; copyright 2020, Elsevier).
Figure 11
Figure 11
Selective detection of biomarkers for diagnosis of prostate cancer in clinical samples (a) Schematic illustration of the fabrication and experimental process for electrochemical sensor based on Au-graphene oxide (GO) electrode for detecting prostate-specific antigen (PSA). (b) For electrochemical impedance spectroscopy analysis, Nyquist plots of (a) Pt, (b) Pt-Au (c) Pt-Au-GO, and (d) Pt-Au-GO-monoclonal anti-PSA antibody showing the real (Ζ′) and imaginary (Ζ′′) parts of impedance for the Pt-Au-Go-monoclonal anti-PSA antibody in PBS (100 mM, pH 7.4). (c) Calibration plots showing current change toward various concentrations of PSA from 0.001 fg/mL to 0.02 μg/mL. LOD for PSA was found to be 5.4 fg/mL (inset: the higher magnification of the DPV curve). (Reprinted with permission from Ref. [49]; copyright 2020, Elsevier.) (d) Schematic illustration of the operational mechanism of the electrochemical sensor to detect target miRNA by absorption on the gold electrode surface. (e) Corresponding DPV signal change on the concentration of miR-107 in the range from 10 fM to 10 pM. (f) Evaluation of the selectivity towards miR-200c, miR-429, and miR-107. The DPV response indicated no significant effects compared to miR-107 detection. The error bars represented the RSD of three independent experiments (inset: the DPV response). (g) Measured miR-107 level in three prostate cancer patients using the electrochemical sensor. (Reprinted with permission from Ref. [50]; copyright 2016, ACS.)
Figure 12
Figure 12
Selective detection of sodium ions in clinical samples (a) Schematic diagram of the experimental setup and (b) biosensor components of a two-dimensional structure composed of G-ISFET-ISM and FG-RE for detecting sodium ions on the PCB designed as an SD card. (c) Real-time response of G-ISFET with FG-RE toward different concentrations of sodium ions from 0.1 mM to 1 M. The gate voltage (VGS) was measured at VDS = 0.05 V and IDS = 180 µA. (d) Corresponding transfer characteristics are dependent on the concentration of sodium ions in real human patient samples. The top-right corner inset shows the linear relation between the shift at the Dirac point (VDirac) and the concentration of sodium ions with a sensitivity of −0.29 mV/mM. (Reprinted with permission from Ref. [51]; copyright 2021, MDPI.)
Figure 13
Figure 13
Selective detection of chloride ions in clinical samples (a) Schematic diagram showing the electrochemical mechanism for detecting chloride ions relying on the chloride complexation at the electropolished screen-printed platinum electrode (SPPtE) surface. (b) Linear responses of the SPPtE sensor with chloride ion concentrations ranging from 0 to 150 mM, registered for seven distinct SPPtEs; the relative standard deviation (RSD) and sensitivity are 5.8% (n = 7) and −24.147 µA/mM, respectively. (Reprinted with permission from Ref. [53]; copyright 2019, Elsevier.)
Figure 14
Figure 14
Selective detection of iodide ions in clinical samples (a) Schematic diagram of detection principle for iodide ions sensors. (b) Changes in the current of the biosensor with increasing iodide concentration under optimal conditions. Standard calibration curves of iodide were plotted with different iodide ion concentrations in the range from 1 to 40 µM (reprinted with permission from ref. [54]; copyright 2020, Springer). (c) Schematic diagram of the device structure and electrical connections. Ag/AgCl reference and Au electrodes are used as the gate and source/drain electrodes, respectively. (d) Transfer curve of IGZO-EGTFT with different iodide ion concentrations in the range from 10 to 104 µM in PBS solution. The drain current was measured at VG = −0.3 V and VD = 0.5 V. (e) Energy band diagram of Au/IGZO-electrolyte-Ag-AgCl showing the hole and electron transport path by the redox reaction of iodide ions. (f) Real-time source-drain current response dynamic switching every 5 s for the iodide ions status in samples from school children based on the World Health Organization guideline. (Reprinted with permission from ref. [55]; copyright 2022, Elsevier.)
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