Electrochemical Sensing toward Noninvasive Evaluation of High-Starch Food Digestion via Point-of-Use Monitoring Glucose Level in Saliva
Abstract
To provide individuals with healthier and more reliable dietary recommendations for diabetic patients, a pragmatic electrochemical sensing toward in situ monitoring of glucose in saliva was designed for noninvasive evaluation of high-starch food digestion. The proposed sensor was constructed by exploiting a carbon-based nanostructure for electrical conductivity and Ni-based nanozyme toward direct catalytic oxidation of glucose for signal output. Particularly, the catalytic host-guest interaction between nanozyme and glucose was investigated to be a hydrogen bond via molecular docking, and the C11 and O5 sites in the glucose molecule were attacked during host-guest catalytic reaction through density functional theory (DFT) research. With merits of simplicity, sensitivity, and accuracy, the electrochemical sensor exhibited good performance for monitoring glucose with a detection limit of 10 μM (corresponding to 1.8 μg mL-1). Moreover, it was well qualified to point-of-use trace glucose levels in saliva, offering a promising tool for noninvasive evaluation of high-starch food digestion (e.g., Wheat bread, Steamed bun, and Shao-mai) at different times after meals and eventually yielding benefits to dietary recommendations for diabetic patients.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.