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Coupling Capillary-Driven Microfluidics with Lateral Flow Immunoassay for Signal Enhancement.

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Summary
This summary is machine-generated.

This study introduces a 3D-printed microfluidic device that enhances lateral flow immunoassay sensitivity for salivary cortisol detection. The automated washing step significantly reduces background noise, improving stress response analysis.

Keywords:
3D-printingcapillary valvecapillary-driven microfluidicscortisolfluorescence spectroscopylateral flow assay

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Area of Science:

  • Microfluidics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Microfluidics enhances analytical techniques, particularly lateral flow immunoassays (LFIA) requiring higher sensitivity for low-concentration analytes.
  • Salivary cortisol measurement is crucial for assessing physiological stress, but low concentrations necessitate sensitive detection methods.
  • Existing LFIA methods often lack automated steps, limiting sensitivity and increasing background noise.

Purpose of the Study:

  • To develop and validate a capillary-driven microfluidic device integrated with LFIA for enhanced salivary cortisol detection.
  • To implement an automated washing step within the microfluidic device to reduce background noise and improve assay sensitivity.
  • To quantitatively measure cortisol levels in saliva using the novel microfluidic-enhanced LFIA system.

Main Methods:

  • A multilevel microfluidic chip was fabricated using 3D printing with photocurable black resin and sealed with an optically clear adhesive.
  • The microfluidic chip was coupled to a lateral flow strip for a competitive immunoassay protocol.
  • An automated washing step was integrated to remove unbound quantum-dot-labeled antibodies, followed by fluorescence spectroscopy for detection.

Main Results:

  • The microfluidic device successfully quantified clinically relevant salivary cortisol concentrations in a buffer.
  • The automated washing step effectively reduced background noise by removing unbound labeled antibodies from the nitrocellulose membrane.
  • The 3D-printed valve design prevented reagent cross-contamination, ensuring assay integrity.

Conclusions:

  • The developed microfluidic device significantly improves the sensitivity of LFIA for salivary cortisol detection.
  • The automated washing step is key to reducing background noise and enhancing quantitative analysis of stress biomarkers.
  • This cost-effective, self-powered, and robust device is suitable for non-expert users in point-of-care stress monitoring.