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Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

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Breathomics-Guided Solid-State Sensor for Noninvasive Point-of-Care Diabetes Screening.

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This study introduces a novel breath analysis method for diabetes mellitus (DM) screening. The developed point-of-care device accurately detects diabetes and diabetic ketoacidosis using volatile organic compounds.

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Biomedical Engineering

Background:

  • Diabetes mellitus (DM) screening is a global health challenge, with many cases undiagnosed.
  • Current noninvasive breath analysis methods for DM lack diagnostic specificity, often focusing only on acetone.
  • There is a need for accurate, scalable, and accessible DM screening tools, especially in resource-limited settings.

Purpose of the Study:

  • To develop and validate an integrated diagnostic strategy for DM screening using breath analysis.
  • To identify novel volatile organic compound (VOC) biomarkers for DM detection.
  • To create a portable, point-of-care (POC) device for rapid DM screening.

Main Methods:

  • GC-MS profiling of breath samples from 130 DM patients and 122 healthy controls to identify VOC biomarkers.
  • Development of a random forest (RF) model for DM classification.
  • Utilizing portable solid electrolyte gas sensors (SEGS) for rapid VOC detection.
  • Cellular-level metabolic investigation to understand the biological basis of breath VOCs.

Main Results:

  • Identified nine discriminative VOCs with a random forest model achieving a cross-validated AUC of 0.93.
  • The SEGS analyzer detected target VOCs at ppb levels within 30 seconds.
  • Clinical validation showed 100% accuracy for diabetic ketoacidosis (DKA) and 83.3% accuracy for DM.
  • Established a link between breath VOCs and nonvolatile metabolite (NVM) pathways in insulin-resistant cell models.

Conclusions:

  • An integrated diagnostic platform combining metabolomics, SEGS, and cellular analysis enables biologically interpretable and clinically validated DM screening.
  • The developed platform is field-deployable, offering a scalable and low-cost solution for DM screening.
  • This approach advances noninvasive diagnostic capabilities for diabetes management in diverse settings.