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Colorimetric nano-biosensor for low-resource settings: insulin as a model biomarker
Zia Ul Quasim Syed1, Sathya Samaraweera1, Zhuo Wang1
1Department of Chemistry, Oklahoma State University, Stillwater, Oklahoma 74078, USA.
Sensors & Diagnostics
|August 22, 2025
Summary
This study presents a novel colorimetric immunosensor for detecting insulin biomarker at picomolar levels. This equipment-free method offers a promising solution for point-of-need disease diagnosis in resource-limited settings.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Biosensing Technology
Background:
- Biomarker detection at ultra-low concentrations is crucial for disease diagnosis but challenging in resource-limited settings.
- Existing methods often require sophisticated instrumentation, hindering point-of-need applications.
- Insulin is a key biomarker for glucose metabolism and diabetes management.
Purpose of the Study:
- To develop an equipment-free, colorimetric immunosensor for ultra-sensitive insulin detection.
- To evaluate the sensor's performance in various biological matrices like buffer, serum, and saliva.
- To demonstrate the potential application of the sensor for diabetes diagnostics.
Main Methods:
- A two-antibody sandwich immunoassay format was employed.
- Citrate-functionalized magnetic nanoparticles were used for target capture and isolation.
- Horseradish peroxidase (HRP) labeling enabled colorimetric detection of insulin.
- Comparative analysis was performed in buffer, diluted serum, and diluted artificial saliva samples.
Main Results:
- The developed immunosensor achieved a detection limit of 10 picomolar (pM) for insulin.
- Dynamic ranges varied across matrices: 10 pM–1 nM (buffer), 10 pM–10 nM (serum), and 50 pM–1 nM (saliva).
- Successful application was demonstrated in type 1 diabetes and healthy human serum samples.
Conclusions:
- The study successfully developed a sensitive, equipment-free colorimetric immunosensor for insulin detection.
- This technology enhances biomarker analysis in biofluids, suitable for point-of-need applications.
- Further optimization is needed to improve detection limits for human saliva analysis.

