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Immunosensor with fluid control mechanism for salivary cortisol analysis.
Masaki Yamaguchi1, Yohei Matsuda, Shohei Sasaki
1Biomedical Engineering & Robotics Laboratory, Graduate School of Engineering, Iwate University, 4-3-5 Ueda, Morioka 020-8551, Japan. masakiy@iwate-u.ac.jp
This study presents a novel cortisol immunosensor for easy, on-site salivary cortisol analysis. The biosensor accurately measures cortisol levels noninvasively, correlating well with ELISA methods.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Biosensor Technology
Background:
- Cortisol analysis is crucial for diagnosing stress-related disorders.
- Current methods for salivary cortisol measurement can be invasive or time-consuming.
- There is a need for rapid, noninvasive, and point-of-care cortisol detection methods.
Purpose of the Study:
- To develop and validate a novel cortisol immunosensor for quantitative, noninvasive analysis of salivary cortisol.
- To integrate a fluid control mechanism for efficient vertical and lateral flow within the immunosensor.
- To assess the performance and reliability of the developed immunosensor for point-of-care applications.
Main Methods:
- A competitive immunoassay format was employed using a glucose oxidase (GOD)-labeled cortisol conjugate.
- Detected current was measured, showing an inverse relationship with sample cortisol concentration.
- The immunosensor incorporated a unique fluid control system for optimized sample handling.
Main Results:
- The cortisol immunosensor demonstrated high accuracy with R(2)=0.98 and CV=14% for salivary cortisol concentrations (0.1-10 ng/ml).
- Measurements were completed within 35 minutes, and the sensor showed reusability.
- Results closely correlated with the commercial ELISA method (R(2)=0.92).
Conclusions:
- The developed cortisol immunosensor offers a promising solution for noninvasive, on-site, and quantitative measurement of salivary cortisol.
- The biosensor's ease of use and accuracy support its potential for point-of-care diagnostics.
- This technology could significantly improve the monitoring of human salivary cortisol levels in clinical settings.
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