Nanosensor-Based Pattern-Generating Probe Accelerates Sepsis Diagnosis.

Area of Science:

• Biomimetic optical sensor arrays
• Nanotechnology in biosensing
• Fluorescence resonance energy transfer (FRET)

Background:

• Developing standalone pattern-generating sensors for complex clinical biofluid analysis is challenging.
• Existing methods may require complicated synthesis or lack speed.
• Need for rapid, accurate diagnostic tools for sepsis detection.

Purpose of the Study:

• To introduce a nanosensor-inspired sensor array (NanoSA) for multianalyte identification in clinical biofluids.
• To demonstrate the NanoSA's capability for rapid and accurate bacterial identification and infection discrimination.
• To evaluate the performance of machine learning algorithms in optimizing the NanoSA's diagnostic potential.

Main Methods:

• Fabrication of a nanoassembly with three sensors exhibiting multiple intermolecular FRET effects.
• Development of a six-channel pattern-generating sensor array for parallel sensing.
• Application of nine machine learning algorithms, including multilayer perceptron (MLP), for data analysis and optimization.

Main Results:

• The NanoSA identified 24 septic bacteria within 30 seconds with 96.9% accuracy.
• The sensor array successfully discriminated between multiplex bacterial infections, varying bacterial concentrations, and different biofluids (serum, urine).
• The MLP model achieved 97.6% test accuracy in differentiating clinical sepsis samples infected by five distinct bacteria within 30 seconds.

Conclusions:

• The NanoSA offers a promising platform for rapid, label-free, and accurate detection of bacterial infections in clinical settings.
• This biomimetic optical sensor array demonstrates significant potential for improving sepsis diagnosis turnaround time and accuracy.
• The integration of machine learning further enhances the diagnostic capabilities of the NanoSA for clinical applications.