Triangular silver nanoprism-based chemosensor for recognition of hyaluronic acid in human biofluids: a new platform for monitoring osteoarthritis treatment using smartphone-assisted digital image analysis.
Zahra SabriFarnaz BahavarniaMohammad HasanzadehNasrin ShadjouPublished in: RSC advances (2024)
Hyaluronic acid (HA) possesses unique viscoelastic properties and low immunogenicity, making it suitable for various biomedical purposes such as viscosupplementation in osteoarthritis treatment, assistance in eye surgery, and wound regeneration. The need for its quantification in human biofluids is crucial in clinical studies. This research work presents a novel approach using paper-based and parafilm-based photochemical techniques, employing triangular silver nanoprisms (TA-AgNPrs) as optical nanoprobes for HA detection in human biofluids. The interaction between HA and TA-AgNPrs leads to a notable change in the absorption spectrum, facilitating rapid and reliable measurement with a detection limit of less than 0.5 μM to 30 mM. The developed colorimetric setups, along with the single-drop parafilm colorimetric substrate, enable fast and on-site HA analysis. This research marks the maiden use of TA-AgNPrs for direct, rapid and sensitive HA detection in real samples, without the need for sample pre-preparation. The use of a digital image analysis strategy enhances the simplicity, affordability, and portability of this sensor, presenting promising potential for monitoring HA levels. This new technique is poised to enable early diagnosis of diseases associated with abnormal HA levels in human biofluids, thanks to its high sensitivity and selectivity in detecting HA.
Keyphrases
- hyaluronic acid
- endothelial cells
- gold nanoparticles
- loop mediated isothermal amplification
- pluripotent stem cells
- minimally invasive
- hydrogen peroxide
- sensitive detection
- living cells
- knee osteoarthritis
- fluorescent probe
- case report
- human health
- atomic force microscopy
- replacement therapy
- liquid chromatography
- single molecule
- data analysis