A Point-of-Care Aptasensor for the Real-Time Detection of Sepsis Biomarker

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Nanotechnology

Background:

• Rising demand for noninvasive, portable point-of-care (POC) sensors for real-time diagnostics.
• Lipopolysaccharide (LPS) is a critical biomarker for sepsis, necessitating timely bedside detection to prevent fatal outcomes like septic shock.
• Current diagnostic methods may lack the speed and portability required for immediate clinical and food safety applications.

Purpose of the Study:

• To develop a highly sensitive and selective electrochemical sensor chip for the on-site detection of LPS.
• To create a sensor compatible with a portable analyzer for real-time diagnostic capabilities.
• To evaluate the sensor's performance in various sample matrices, including food and clinical samples.

Main Methods:

• Fabrication of an electrochemical sensor using functionalized carbon nanotubes (fCNT) and copper(I) oxide nanoparticles (Cu2O).
• Functionalization of the sensor surface with LPS-specific aptamers to ensure selective binding.
• Testing the sensor's sensitivity, selectivity, and detection range using standard LPS solutions.
• Validation of the sensor's performance in complex matrices such as mayonnaise, fruit juices, and whole blood.

Main Results:

• The sensor achieved a highly sensitive limit of detection (LOD) of 10 ag mL⁻¹ for LPS.
• A linear detection range was established from 10 ag mL⁻¹ to 10 ng mL⁻¹.
• The sensor demonstrated remarkable selectivity and sensitivity towards LPS in diverse samples.
• Successful detection of LPS in food samples (mayonnaise, fruit juices) and a clinical sample (whole blood) was achieved.

Conclusions:

• The developed electrochemical sensor offers a promising platform for the selective and sensitive on-site detection of LPS.
• Its compatibility with portable analyzers and ability to detect LPS in various samples highlight its potential for practical applications in food quality assurance and clinical diagnostics.
• This technology supports the trend towards personalized healthcare through real-time diagnostic capabilities.