PEGDA hydrogel-based biosensor for continuous salivary cortisol monitoring without pre-treatmentopen access
- Authors
- Lee, Suyoung; Cho, Eunseo; Song, Jaeyoon; Ramakrishnan, Neethu; Shin, Dong-Sik; Kwon, Youngeun; Kim, Jinsik
- Issue Date
- Jul-2026
- Publisher
- Elsevier B.V.
- Keywords
- Biosensor; Continuous monitoring; Cortisol; Hydrogel; Multivariate time series analysis
- Citation
- Talanta, v.304, pp 1 - 10
- Pages
- 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- Talanta
- Volume
- 304
- Start Page
- 1
- End Page
- 10
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/63754
- DOI
- 10.1016/j.talanta.2026.129518
- ISSN
- 0039-9140
1873-3573
- Abstract
- Cortisol is a steroid hormone that has wide-ranging effects on the body, including roles in mental health, immune response, and metabolic regulation, and it is directly linked to various disorders. However, its use in point-of-care settings has been limited, mainly because cortisol levels fluctuate constantly in the body and traditional biofluids like blood or saliva usually need extensive pre-treatment before analysis. Here, we introduce a poly(ethylene glycol) diacrylate (PEGDA) hydrogel-based biosensor to address these challenges. PEGDA hydrogels form polymer networks with mesh structures, and by adjusting the mesh size, interfering substances in samples, such as mucin in saliva, can be automatically filtered by size. To enable detection in the nanomolar range, we established a signal amplification system by incorporating cortisol antibodies and cortisol-HRP into the hydrogel and ensuring their stable retention. This allows for quantitative detection of cortisol in both artificial and real saliva across physiologically relevant concentration ranges. Additionally, continuous monitoring of salivary cortisol was achieved, and by applying time-series multidimensional analysis, the biosensor demonstrated the ability to accurately track dynamic cortisol levels in the body. Through the development of this hydrogel-based biosensor, we anticipate advances in disease diagnosis, long-term monitoring, and personalized health management systems. © 2026 Elsevier B.V.
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Collections - College of Life Science and Biotechnology > Department of Biomedical Engineering > 1. Journal Articles

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