Conductometric Biosensor for Quick Detection of Urinary Biomarkers in Chronic Kidney Disease Management

Chronic kidney disease (CKD) is a noncommunicable disease that brings long-term morbidity among people and is identified as a hidden epidemic. As the disease-causing pathophysiology process is different and complex for various diseases of CKD, specific biomarkers in urine should be monitored in aiding therapy and management of CKD. β-2 Microglobulin (β2MG), epidermal growth factor (EGF), and CD163 are three of such common biomarkers used in CKD management. Though different detection methods such as electrochemical sensors, electrochemiluminescence, and sandwich ELISA have been reported for these three biomarkers, they are associated with shortcomings such as complex device fabrication, longer detection time, and poor sensitivity. To overcome these disadvantages, the current study introduces high-resistivity-silicon (HR-Si)-based conductometric biosensors for rapid and sensitive detection of these three biomarkers. These conductometric biosensors successfully detected β2MG, EGF, and CD163 in urine with detection limits of 100 fM, 5 pM, and 10 fM, respectively. Other advantages of conductometric biosensors include relatively simple sensor fabrication, which is also low-cost, short sample incubation time (<15 min), fast detection time (<2 min), high selectivity, and the capability of miniaturization and compatibility with common electronics circuitry (ability to use as a portable device). These key features of the conductometric biosensors hold as a futuristic point-of-need tool in aiding CKD management and therapy.

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Publication Details

Journal
ACS Applied Bio Materials
Published
2025-10-22
DOI
https://doi.org/10.1021/acsabm.5c01024
Citations
1
Primary Topic
Genetic and Kidney Cyst Diseases
Type
article
Field-Weighted Citation Impact
1.82
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article

Conductometric Biosensor for Quick Detection of Urinary Biomarkers in Chronic Kidney Disease Management

Suchitra Chandrasekaran, Sharath Sriram, Ganganath S. Perera, Shanmuga Sundar Dhanabalan et al.
1 citations
ACS Applied Bio Materials
Genetic and Kidney Cyst Diseases
1.82
article

Conductometric Biosensor for Quick Detection of Urinary Biomarkers in Chronic Kidney Disease Management

Suchitra Chandrasekaran, Sharath Sriram, Ganganath S. Perera, Shanmuga Sundar Dhanabalan, Vipul Bansal, Sanje Mahasivam, Madhu Bhaskaran, Mingjie Yang, S P
article en
1 citations

Abstract

Chronic kidney disease (CKD) is a noncommunicable disease that brings long-term morbidity among people and is identified as a hidden epidemic. As the disease-causing pathophysiology process is different and complex for various diseases of CKD, specific biomarkers in urine should be monitored in aiding therapy and management of CKD. β-2 Microglobulin (β2MG), epidermal growth factor (EGF), and CD163 are three of such common biomarkers used in CKD management. Though different detection methods such as electrochemical sensors, electrochemiluminescence, and sandwich ELISA have been reported for these three biomarkers, they are associated with shortcomings such as complex device fabrication, longer detection time, and poor sensitivity. To overcome these disadvantages, the current study introduces high-resistivity-silicon (HR-Si)-based conductometric biosensors for rapid and sensitive detection of these three biomarkers. These conductometric biosensors successfully detected β2MG, EGF, and CD163 in urine with detection limits of 100 fM, 5 pM, and 10 fM, respectively. Other advantages of conductometric biosensors include relatively simple sensor fabrication, which is also low-cost, short sample incubation time (<15 min), fast detection time (<2 min), high selectivity, and the capability of miniaturization and compatibility with common electronics circuitry (ability to use as a portable device). These key features of the conductometric biosensors hold as a futuristic point-of-need tool in aiding CKD management and therapy.

ACS Applied Bio MaterialsVol. 8(11)
La Trobe University (AU), MIT University (MK), Department of Mathematical Sciences (RU), Micro & Nano Research Institute (CN), Nanobiosensorics Laboratory (HU), RMIT University (AU)
Openalex Percentile: Top 12%
Genetic and Kidney Cyst Diseases
1.82
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