Filtered Saliva for Rapid and Accurate Analyte Detection for POC Diagnostics

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Point-of-Care Diagnostics

Background:

• Saliva is a promising, non-invasive medium for point-of-care (POC) diagnostics.
• Interfering components in saliva can limit the accuracy of biosensors.
• Standardized sample pretreatment is crucial for reliable saliva-based detection.

Purpose of the Study:

• To develop and standardize saliva sample treatment procedures for POC diagnostic devices.
• To eliminate interfering molecules and bubbles while preserving target biomarkers like spike (S) protein and glucose.
• To assess the compatibility and effectiveness of pretreatment systems with existing SARS-CoV-2 and glucose biosensing platforms.

Main Methods:

• Development of standardized filtration procedures using nylon wool (NW) and cotton wool (CW) filters.
• Testing filter compatibility with biosensing systems for S protein and glucose detection in saliva.
• Evaluation of filter effectiveness in viscosity reduction, protein content reduction, and biomarker preservation.
• Assessment of improvements in limit of detection (LOD), relative standard deviation (RSD), linearity, and sensitivity.

Main Results:

• A 20 mg NW filter optimized S protein detection, reducing viscosity by 80% with minimal protein loss (6%).
• A 30 mg CW filter optimized glucose detection, reducing viscosity by 90% and protein by 60.7% with only 4.3% glucose reduction.
• NW filtration improved S protein LOD fivefold (0.5 nM to 0.1 nM) and halved RSD.
• CW filtration enhanced glucose detection linearity (R²=0.99) and sensitivity (36.6 μA/mM·cm²).

Conclusions:

• Standardized saliva pretreatment using NW and CW filters effectively removes interfering substances for POC diagnostics.
• These filtration methods significantly enhance biomarker sensitivity, accuracy, and reliability.
• The developed filtration process is adaptable for various biomarkers and POC diagnostic systems, improving their market viability.